ࡱ> ^`YZ[\][ bjbj ޚΐΐCsE**mmp ! `T-_$_$_$RRRRRRR$VX\Re_$I#@#_$_$RmmdT111_$m8R1_$R11ROwJQpʾHC+P{R0T0`T1PX 0X<QQLX'RT_$_$1_$_$_$_$_$RR1_$_$_$`T_$_$_$_$X_$_$_$_$_$_$_$_$_$* 3: SD Module- Data Science with R Program Assignment No. 1 Title: Getting Started with R installation, R objects and basic statistics. Objectives: Understand the basics of R Object, R Function, R Statistics essential for Data Science and Data Analytics Purpose. Problem Statements: Develop R-Script with the help of R Studio to Perform various R statistical operation include R object, Function etc. & Demonstrate R Studio Installation. Outcomes: 1. Students will be able to demonstrate Installation of R Studio. 2. Students will be able to demonstrate different types of R Objects. 3. Students will be able to demonstrate different Statistical Operation using R. Hardware Requirement: Any CPU with Pentium Processor or similar, 256 MB RAM or more,1 GB Hard Disk or more Software Requirements: 32/64 bit Linux/Windows Operating System, R Studio Theory: What is Data Science? Data science is a multidisciplinary blend of data inference, algorithmm development, and technology in order to solve analytically complex problems. At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value: Data science discovery of data insight This aspect of data science is all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, trends, and inferences. It's about surfacing hidden insight that can help enable companies to make smarter business decisions. For example:  Netflix data mines movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Netflix original series to produce.  Target identifies what are major customer segments within it's base and the unique shopping behaviors within those segments, which helps to guide messaging to different market audiences.  Proctor & Gamble utilizes time series models to more clearly understand future demand, which help plan for production levels more optimally. How do data scientists mine out insights? It starts with data exploration. When given a challenging question, data scientists become detectives. They investigate leads and try to understand pattern or characteristics within the data. This requires a big dose of analytical creativity. Then as needed, data scientists may apply quantitative technique in order to get a level deeper e.g. inferential models, segmentation analysis, time series forecasting, synthetic control experiments, etc. The intent is to scientifically piece together a forensic view of what the data is really saying. This data-driven insight is central to providing strategic guidance. In this sense, data scientists act as consultants, guiding business stakeholders on how to act on findings. Data science development of data product A "data product" is a technical asset that: (1) utilizes data as input, and (2) processes that data to return algorithmically-generated results. The classic example of a data product is a recommendation engine, which ingests user data, and makes personalized recommendations based on that data. Here are some examples of data products:  Amazon's recommendation engines suggest items for you to buy, determined by their algorithms. Netflix recommends movies to you. Spotify recommends music to you.  Gmail's spam filter is data product an algorithm behind the scenes processes incoming mail and determines if a message is junk or not.  Computer vision used for self-driving cars is also data product machine learning algorithms are able to recognize traffic lights, other cars on the road, pedestrians, etc. This is different from the "data insights" section above, where the outcome to that is to perhaps provide advice to an executive to make a smarter business decision. In contrast, a data product is technical functionality that encapsulates an algorithm, and is designed to integrate directly into core applications. Respective examples of applications that incorporate data product behind the scenes: Amazon's homepage, Gmail's inbox, and autonomous driving software. Data scientists play a central role in developing data product. This involves building out algorithms, as well as testing, refinement, and technical deployment into production systems. In this sense, data scientists serve as technical developers, building assets that can be leveraged at wide scale. Mathematics Expertise At the heart of mining data insight and building data product is the ability to view the data through a quantitative lens. There are textures, dimensions, and correlations in data that can be expressed mathematically. Finding solutions utilizing data becomes a brain teaser of heuristics and quantitative technique. Solutions to many business problems involve building analytic models grounded in the hard math, where being able to understand the underlying mechanics of those models is key to success in building them. Also, a misconception is that data science all about statistics. While statistics is important, it is not the only type of math utilized. First, there are two branches of statistics HYPERLINK "http://en.wikipedia.org/wiki/Classical_statistics" \hclassical statistics and HYPERLINK "http://en.wikipedia.org/wiki/Bayesian_statistics" \hBayesian HYPERLINK "http://en.wikipedia.org/wiki/Bayesian_statistics" \hstatistics. When most people refer to stats they are generally referring to classical stats, but knowledge of both types is helpful. Furthermore, many inferential techniques and machine learning algorithms lean on knowledge of HYPERLINK "http://en.wikipedia.org/wiki/Linear_algebra" \hlinear algebra. For example, a popular method to discover hidden characteristics in a data set is HYPERLINK "http://en.wikipedia.org/wiki/Singular_value_decomposition" \hSVD, which is grounded in matrix math and has much less to do with classical stats. Overall, it is helpful for data scientists to have breadth and depth in their knowledge of mathematics. Technology and Hacking First, let's clarify on that we are not talking about hacking as in breaking into computers. We're referring to the tech programmer subculture meaning of HYPERLINK "http://en.wikipedia.org/wiki/Hacker_(programmer_subculture)" \l "Definition" \hhacking  i.e., creativity and ingenuity in using technical skills to build things and find clever solutions to problems. Why is hacking ability important? Because data scientists utilize technology in order to wrangle enormous data sets and work with complex algorithms, and it requires tools far more sophisticated than Excel. Data scientists need to be able to code prototype quick solutions, as well as integrate with complex data systems. Core languages associated with data science include SQL, Python, R, and SAS. On the periphery are Java, Scala, Julia, and others. But it is not just knowing language fundamentals. A hacker is a technical ninja, able to creatively navigate their way through technical challenges in order to make their code work. Along these lines, a data science hacker is a solid HYPERLINK "http://rwxweb.wordpress.com/2012/01/31/teaching-algorithmic-thinking/" \halgorithmic thinker, having the ability to break down messy problems and recompose them in ways that are solvable. This is critical because data scientists operate within a lot of algorithmic complexity. They need to have a strong mental comprehension of high-dimensional data and tricky data control flows. Full clarity on how all the pieces come together to form a cohesive solution. Strong Business Acumen It is important for a data scientist to be a tactical business consultant. Working so closely with data, data scientists are positioned to learn from data in ways no one else can. That creates the responsibility to translate observations to shared knowledge, and contribute to strategy on how to solve core business problems. This means a core competency of data science is using data to cogently tell a story. No data- puking rather, present a cohesive narrative of problem and solution, using data insights as supporting pillars, that lead to guidance. Having this business acumen is just as important as having acumen for tech and algorithms. There needs to be clear alignment between data science projects and business goals. Ultimately, the value doesn't come from data, math, and tech itself. It comes from leveraging all of the above to build valuable capabilities and have strong business influence. What is Analytics? Analytics has risen quickly in popular business lingo over the past several years; the term is used loosely, but generally meant to describe critical thinking that is quantitative in nature. Technically, analytics is the "science of analysis" put another way, the practice of analyzing information to make decisions. Is "analytics" the same thing as data science? Depends on context. Sometimes it is synonymous with the definition of data science that we have described, and sometimes it represents something else. A data scientist using raw data to build a predictive algorithm falls into the scope of analytics. At the same time, a non-technical business user interpreting pre-built dashboard reports (e.g. HYPERLINK "http://www.google.com/analytics/" \hGA) is also in the realm of analytics, but does not cross into the skill set needed in data science. Analytics has come to have fairly broad meaning. At the end of the day, as long as you understand beyond the buzzword level, the exact semantics don't matter much. What is the difference between an analyst and a data scientist? "Analyst" is somewhat of an ambiguous job title that can represent many different types of roles (data analyst, marketing analyst, operations analyst, financial analyst, etc). What does this mean in comparison to data scientist?  Data Scientist: Specialty role with abilities in math, technology, and business acumen. Data scientists work at the raw database level to derive insights and build data product.  Analyst: This can mean a lot of things. Common thread is that analysts look at data to try to gain insights. Analysts may interact with data at both the database level or the summarized report level. Thus, "analyst" and "data scientist" is not exactly synonymous, but also not mutually exclusive. Here is our interpretation of how these job titles map to skills and scope of responsibilities:  What is R Program? R is a language and environment for statistical computing and graphics. It is a HYPERLINK "http://www.gnu.org/" \hGNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time- series analysis, classification, clustering, ) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of Rs strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control. R is available as Free Software under the terms of the HYPERLINK "http://www.gnu.org/" \hFree Software FoundationsHYPERLINK "https://www.r-project.org/COPYING" \hGNU General Public HYPERLINK "https://www.r-project.org/COPYING" \hLicense in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS. Evolution of R R was initially written by Ross Ihaka and Robert Gentleman at the Department of Statistics of the University of Auckland in Auckland, New Zealand. R made its first appearance in 1993.  A large group of individuals has contributed to R by sending code and bug reports.  Since mid-1997 there has been a core group (the "R Core Team") who can modify the R source code archive. Features of R As stated earlier, R is a programming language and software environment for statistical analysis, graphics representation and reporting. The following are the important features of R "  R is a well-developed, simple and effective programming language which includes conditionals, loops, user defined recursive functions and input and output facilities.  R has an effective data handling and storage facility,  R provides a suite of operators for calculations on arrays, lists, vectors and matrices.  R provides a large, coherent and integrated collection of tools for data analysis.  R provides graphical facilities for data analysis and display either directly at the computer or printing at the papers. As a conclusion, R is worlds most widely used statistics programming language. It's the # 1 choice of data scientists and supported by a vibrant and talented community of contributors. R is taught in universities and deployed in mission critical business applications. This tutorial will teach you R programming along with suitable examples in simple and easy steps. How to install R / R Studio ? You could download and install the HYPERLINK "http://ftp.heanet.ie/mirrors/cran.r-project.org/" \hold version of R. But, Id insist you to start with RStudio. It provides much better coding experience. For Windows users, R Studio is available for Windows Vista and above versions. Follow the steps below for installing R Studio: Go to https:/HYPERLINK "http://www.rstudio.com/products/rstudio/download/" \h/www.HYPERLINK "http://www.rstudio.com/products/rstudio/download/" \hrstudio.com/products/rstudio/download/ In  Installers for Supported Platforms section, choose and click the R Studio installer based on your operating system. The download should begin as soon as you click. Click Next..Next..Finish. Download Complete. To Start R Studio, click on its desktop icon or use  search windows to access the program. It looks like this:  Let s quickly understand the interface of R Studio: R Console: This area shows the output of code you run. Also, you can directly write codes in console. Code entered directly in R console cannot be traced later. This is where R script comes to use. R Script: As the name suggest, here you get space to write codes. To run those codes, simply select the line(s) of code and press Ctrl + Enter. Alternatively, you can click on little  Run button location at top right corner of R Script. R environment: This space displays the set of external elements added. This includes data set, variables, vectors, functions etc. To check if data has been loaded properly in R, always look at this area. Graphical Output: This space display the graphs created during exploratory data analysis. Not just graphs, you could select packages, seek help with embedded Rs official documentation How to install R Packages ? The sheer power of R lies in its incredible packages. In R, most data handling tasks can be performed in 2 ways: Using R packages and R base functions. In this tutorial, Ill also introduce you with the most handy and powerful R packages. To install a package, simply type: install.packages("package name") Note: You can type this either in console directly and press  Enter or in R script and click  Run . Basic Computations in R Let s begin with basics. To get familiar with R coding environment, start with some basic calculations. R console can be used as an interactive calculator too. Type the following in your console: > 2 + 3 > 5 > 6 / 3 > 2 > (3*8)/(2*3) > 4 > log(12) > 1.07 > sqrt (121) > 11 Similarly, you can experiment various combinations of calculations and get the results. In case, you want to obtain the previous calculation, this can be done in two ways. First, click in R console, and press  Up / Down Arrow key on your keyboard. This will activate the previously executed commands. Press Enter. But, what if you have done too many calculations ? It would be too painful to scroll through every command and find it out. In such situations, creating variable is a helpful way. In R, you can create a variable using <- or = sign. Lets say I want to create a variable x to compute the sum of 7 and 8. Ill write it as: > x <- 8 + 7 x > 15 Once we create a variable, you no longer get the output directly (like calculator), unless you call the variable in the next line. Remember, variables can be alphabets, alphanumeric but not numeric. You cant create numeric variables. Essentials of R Programming Understand and practice this section thoroughly. This is the building block of your R programming knowledge. If you get this right, you would face less trouble in debugging. R has five basic or  atomic classes of objects. Wait, what is an object ? Everything you see or create in R is an object. A vector, matrix, data frame, even a variable is an object. R treats it that way. So, R has 5 basic classes of objects. This includes: Character Numeric (Real Numbers) Integer (Whole Numbers) Complex Logical (True / False) Since these classes are self-explanatory by names, I wouldn t elaborate on that. These classes have attributes. Think of attributes as their  identifier , a name or number which aptly identifies them. An object can have following attributes: names, dimension names dimensions class length Attributes of an object can be accessed using attributes() function. More on this coming in following section. Lets understand the concept of object and attributes practically. The most basic object in R is known as vector. You can create an empty vector using vector(). Remember, a vector contains object of same class. For example: Lets create vectors of different classes. We can create vector usingc() or concatenate command also. > a <- c(1.8, 4.5) #numeric > b <- c(1 + 2i, 3 - 6i) #complex > d <- c(23, 44) #integer e <- vector("logical", length = 5) Similarly, you can create vector of various classes. Data Types in R R has various type of  data types which includes vector (numeric, integer etc), matrices, data frames and list. Lets understand them one by one. Vector: As mentioned above, a vector contains object of same class. But, you can mix objects of different classes too. When objects of different classes are mixed in a list, coercion occurs. This effect causes the objects of different types to  convert into one class. For example: qt <- c("Time", 24, "October", TRUE, 3.33) #character ab <- c(TRUE, 24) #numeric cd <- c(2.5, "May") #character To check the class of any object, use class( vector name ) function. class(qt) "character" To convert the class of a vector, you can use as. command. bar <- 0:5 class(bar) "integer" as.numeric(bar) class(bar) "numeric" as.character(bar) class(bar) "character" Similarly, you can change the class of any vector. But, you should pay attention here. If you try to convert a  character vector to  numeric , NAs will be introduced. Hence, you should be careful to use this command. List: A list is a special type of vector which contain elements of different data types. For example: my_list <- list(22, "ab", TRUE, 1 + 2i) my_list [[1]] [1] 22 [[2]] [1] "ab" [[3]] TRUE [[4]] [1] 1+2i As you can see, the output of a list is different from a vector. This is because, all the objects are of different types. The double bracket [[1]] shows the index of first element and so on. Hence, you can easily extract the element of lists depending on their index. Like this: my_list[[3]] [1] TRUE You can use [] single bracket too. But, that would return the list element with its index number, instead of the result above. Like this: my_list[3] [[1]] TRUE Matrices: When a vector is introduced with row and column i.e. a dimension attribute, it becomes a matrix. A matrix is represented by set of rows and columns. It is a 2 dimensional data structure. It consist of elements of same class. Lets create a matrix of 3 rows and 2 columns: my_matrix <- matrix(1:6, nrow=3, ncol=2) my_matrix [,1] [,2] [1,] 1 4 [2,] 2 5 [3,] 3 6 dim(my_matrix) [1] 3 2 attributes(my_matrix) $dim [1] 3 2 As you can see, the dimensions of a matrix can be obtained using either dim() orattributes() command. To extract a particular element from a matrix, simply use the index shown above. For example(try this at your end): my_matrix[,2] #extracts second column my_matrix[,1] #extracts first column my_matrix[2,] #extracts second row my_matrix[1,] #extracts first row As an interesting fact, you can also create a matrix from a vector. All you need to do is, assign dimension dim() later. Like this: age <- c(23, 44, 15, 12, 31, 16) age [1] 23 44 15 12 31 16 dim(age) <- c(2,3) age [,1] [,2] [,3] [1,] 23 15 31 [2,] 44 12 16 class(age) [1] "matrix" You can also join two vectors using cbind() and rbind() functions. But, make sure that both vectors have same number of elements. If not, it will return NA values. x <- c(1, 2, 3, 4, 5, 6) y <- c(20, 30, 40, 50, 60) cbind(x, y) cbind(x, y) x y [1,] 1 20 [2,] 2 30 [3,] 3 40 [4,] 4 50 [5,] 5 60 [6,] 6 70 class(cbind(x, y)) [1]  matrix Data Frame: This is the most commonly used member of data types family. It is used to store tabular data. It is different from matrix. In a matrix, every element must have same class. But, in a data frame, you can put list of vectors containing different classes. This means, every column of a data frame acts like a list. Every time you will read data in R, it will be stored in the form of a data frame. Hence, it is important to understand the majorly used commands on data frame: df <- data.frame(name = c("ash","jane","paul","mark"), score = c(67,56,87,91)) df name score 1 ash 67 jane 56 paul 87 mark 91 dim(df) [1] 4 2 str(df) 'data.frame': 4 obs. of 2 variables: $ name : Factor w/ 4 levels "ash","jane","mark",..: 1 2 4 3 $ score: num 67 56 87 91 nrow(df) [1] 4 ncol(df) [1] 2 Lets understand the code above. df is the name of data frame. dim() returns the dimension of data frame as 4 rows and 2 columns. str() returns the structure of a data frame i.e. the list of variables stored in the data frame. nrow() and ncol() return the number of rows and number of columns in a data set respectively. Here you see  name is a factor variable and  score is numeric. In data science, a variable can be categorized into two types: Continuous and Categorical. Continuous variables are those which can take any form such as 1, 2, 3.5, 4.66 etc.Categorical variables are those which takes only discrete values such as 2, 5, 11, 15 etc. In R, categorical values are represented by factors. In df, name is a factor variable having 4 unique levels. Factor or categorical variable are specially treated in a data set. For more explanation, click HYPERLINK "https://www.analyticsvidhya.com/blog/2015/11/easy-methods-deal-categorical-variables-predictive-modeling/" \hhere. Similarly, you can find techniques to deal with continuous variables HYPERLINK "https://www.analyticsvidhya.com/blog/2015/11/8-ways-deal-continuous-variables-predictive-modeling/" \hhere. Lets now understand the concept of missing values in R. This is one of the most painful yet crucial part of predictive modeling. You must be aware of all techniques to deal with them. The complete explanation on such techniques is provided HYPERLINK "https://www.analyticsvidhya.com/blog/2015/02/7-steps-data-exploration-preparation-building-model-part-2/" \hhere. Missing values in R are represented by NA and NaN. Now well check if a data set has missing values (using the same data frame df). df[1:2,2] <- NA #injecting NA at 1st, 2nd row and 2nd column of df df name score 1 ash NA jane NA paul 87 mark 91 is.na(df) #checks the entire data set for NAs and return logical output name score [1,] FALSE TRUE [2,] FALSE TRUE [3,] FALSE FALSE [4,] FALSE FALSE table(is.na(df)) #returns a table of logical output FALSE TRUE 6 2 df[!complete.cases(df),] #returns the list of rows having missing values name score 1 ash NA 2 jane NA Missing values hinder normal calculations in a data set. For example, lets say, we want to compute the mean of score. Since there are two missing values, it cant be done directly. Lets see: mean(df$score) [1] NA mean(df$score, na.rm = TRUE) [1] 89 The use of na.rm = TRUE parameter tells R to ignore the NAs and compute the mean of remaining values in the selected column (score). To remove rows with NA values in a data frame, you can use na.omit: new_df <- na.omit(df) new_df name score 3 paul &'(89:;ABC  ! " * 3 < A D E M m ýԈ|ԹԹԹнtot hzm5hzmhzm5 hzm@ hzm@ h} @hzm h-^5h? h-^5h? h} 5 h? 5 h? CJ h? 5CJh? h-^CJ h} CJ h-^5CJh-^h} h-^5CJ h-^5CJjh-^h-^U h-^5CJ h} 5CJ +'9:;BC ! "  ^]  ^gd? ^gd? gd? ^gd?  gd?  ^o^oZ^^^ $ea$gda  T U   ^ _ i j  $d]a$^^^  ^gdm^ gdJ gdzmgdzm  & F `     * + . ] ^ _ a f h i j    " # $ L M hjknoſѹ͵͵͵͵zrll h} @h} @CJ h} CJ(jhB\hB\EHU_HmHnHu h-^CJ h-^5CJ h-^5CJ h} 5CJ h-^5h-^ hm@ h-^CJhzmh-^5h} h} @ hzmhzmhzmhzm5 hm5 hzm5hzm hzm@ hzm@) # $ M ija~`awo^ $d]a$ $d]a$ $d]a$d]^`d]^`dP]^`$da$ $d*]a$$a$^ $d]a$ JQUV`a}~_`a)*>? h} @(jThB\hB\EHU_HmHnHu h-^CJ h} @(j8hB\hB\EHU_HmHnHu h} @ h} @ h} @h} @CJ h} CJ(jhB\hB\EHU_HmHnHuh-^h} h} @.a?u $d]a$^^ $d]a$ $d]a$$d]^`a$$d)]^`a$ $)^a$ $id^ia$ $d)]a$$a$   @C|OQ78@Aɴٮ٨ɢɛٓɓ~ٓɓh} B*phh} >*B*phwhjh-^U h-^5CJ h-^CJ h} @ h} @(j hB\hB\EHU_HmHnHuh-^ h} @ h} @h} h} @CJ h} CJ(jphB\hB\EHU_HmHnHu1PQ $ !$&5&b())))|^$a$^ $d]a$ $d)]a$$da$ $d]a$ $d]a$ $d-]a$$da$ $dP]a$$a$$a$ ABCghOPSUV # $ H L !!!!"!#!!!!!!!!/"2"y"|""""[#_#b#e#i#n######### $ $$$:$=$ڽڷ h} @ h} @ h} @ h} @ h} @ h} 6h} h} >*B*phwhh-^jh-^Uh} B*phH=$E$F$$$$$$$$$$%%r%u%z%&&&&&&-&.&4&5&a(b(~((((((s)v))))))))))))++d+j+++++,,@,C,G,L,,,,, h} @ h-^5CJ h-^CJ h} @ h} @ h} @ h} @ h} @ h} @ h} @h} >*B*phwhh-^jh-^Uh} A)+X-Y---..//0G1I1\1|u$a$^ $d]a$$d]^`a$$d]^`a$ $d]a$$a$ ^ $dP]a$$da$ $d]a$ $d]a$ ,,,,,,,,,,,,---- - ---------$-%-'-(-,---5-6-?-@-C-D-H-I-K-L-P-Q-W-Y----...//߻ߵ߯߻ߩǣߙߙߙߙ h-^5CJ h-^CJh-^ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @h} h} @jh-^Uh} >*@B*phwh5/////5/8/t/w//////////////004080K0N0~000F1G1H1I1[1\111111113344ԾξԾԾԍxԾԾh} B*phh} >*B*phwhjh-^U h-^CJjhB\UmHnHu(jhB\hB\EHU_HmHnHuh-^ h} @ h} @ h} @h} h} 6h} @CJ h} CJ(j hB\hB\EHU_HmHnHu/\13455(7)787977I8888~ ^d)]^`id^id]^ ^ $dP]a$$da$ $d]a$ $d]a$ $d%]a$ 45555556666D6E6W6X6Y6Z666666'7(7)777879777777(8+8?8@8H8I8J8M8N888888888::(jhB\hB\EHU_HmHnHu h} @ h} @h} @CJ h} CJ(jhB\hB\EHU_HmHnHu h-^5CJ h-^CJh} B*phh} >*B*phwhjh-^Uh-^h} 388:;/<<b=>>>>>@@{  & F  $d]a$^^ $d]a$d]^`Vid(]V^iid^id]^`d]^ ::::::;&;;;;;;;;$<%<.</<0<3<4<~<<<<<<<<<<<<<î٨ÓٍهrlW(jhB\hB\EHU_HmHnHu h} @(jlhB\hB\EHU_HmHnHu h} @ h} @(jPhB\hB\EHU_HmHnHu h} @(j4hB\hB\EHU_HmHnHuh-^ h} @ h} @ h} @h} h} @CJ h} CJ(jhB\hB\EHU_HmHnHu"<<<Y=Z=a=b=>>>>>>??U?V?a?b?c?=@>@C@D@K@L@@@@@@@@@@@@@BB>C@CLCNCXCZCCCCCCCCº䴬ϴϴϴϦh} @CJh} @ CJh} @CJ h-^CJh} @CJ h} CJh} B*phh} >*B*phwhjh-^U h-^5CJ h-^CJh-^ h} @h} h} @CJ h} CJ4@NCCCDDDDDF0Hze$ & F d]a$$ & F d]a$$da$^d & F d]^` & F ^` & F d^` & F d(]^` CC2D8DDDDDDDDEEEFFDFJFtFzFFFFFFGG'H(H/H0H?HDHHHHHHHIIIIIIIIIJJ(Jÿ䦚h} @B*ph h} B*ph  h-^5CJh} 5B*CJph333 h-^CJh} @CJ h} 5CJh-^h} h-^CJ(jhB\hB\CJU_HmHnHu h-^CJ h-^CJh} @CJ h} CJh} @CJ20HHIIIJJKKLLLL=NENdd]^T<^^$d]^a$ $^a$^$ & F d]a$$ & F d]a$(J,J/J9JVAVVV^VzV}VVVVVVVVVVVVVVWW W WWW#W$W*WXYY Z ZZZZZ!Z"ZPZ]ZZZ([0[[[[[[[(\*\\\^\\ӽӵ h} 6h} @CJh} @CJh} @CJ h-^CJ h} CJ h} @ h} @ h} @ h-^5CJh-^h} h-^CJBFTUVVVV WW+WYY ZZs & F (` & F P`$da$ $d)]a$  & F ` & F (` & F *` & F d` d-]d d] ZZ"ZZ[[*\^\\]]0]2],^-^` $d]a$^ $d]a$^^( & F RQ:^Q`:) $d]a$ $d)]a$  & F `\\\\ ]]].]0]2]+^,^-^5^8^^^|`~```````a a0a2aLaXaZanapaaaaabbbAbEbMbNbTbUbXbYbcbdbmbnbh} @CJ h} 6h} @CJ h} 6CJh} @CJh} @CJh} @CJh} @CJ h} @ h} @ h} 5 h-^CJ h-^5CJ h-^CJ h-^h} h-^CJ h} CJh} @ CJ4``2apaabNbYbdbnb~bbbbbbd $d)]a$ & F RQ:-^Q`:d & F R%d]%`)^ & F RQ:^Q`: & F RQ:^Q`:nb}b~bbbbbbbbbbbbbccc!c"c#c%c&c'c(c)c*cddddd ddddddddd d"d$d&d*d,d.d0d2d4d6d8d:ddز̬زؒ h} @ h} @h} @RH, h} @ h} @ h} RH h} @h} @RH, h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @h} h-^CJ h} CJ7>d@dBdDdFdHdJdLdNdPdTdVdXdZd\d`dbdddfdjdndrdtdvdxdzd~ddddddddddddddddddddddddب꺐 h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} @ h} h} @ 1dddddddddeeeeeeeff fffffff$f%f*f/f0f8f9f^fafzffffffg"gIgJgOgPg\g]g`gagegfgggggû˵ޭ˵˵ˍ˵˵h} @CJ h} @ h} @ h} @ h} @h} @CJ h-^CJh} @CJh} @CJ h} CJh} @CJ h} 5h-^ h} @h} h} @RHc h} RHc h} @7ddee fff%f0f9fPg]gl$ & F VU6d^U`6a$ $d"]a$$da$$ & F &d]&`a$ &d(]&*d & F V&d]&^` & F VU6(^U`6P$da$ ]gfggghhh!iJi^igihcd & F Vv%d]v%^`$ & F VU6d^U`6a$ $d]a$^ $ & F Ya$ & F RQ:^Q`: & F VU6d^U`6 d.]$ & F RQ:^Q`:a$ ghhhhhh2h6h:hAh i!iAiBiIiJiTiXiYi]i^idieifigiminioipiviwixiyiiiiiiiiiiiiijjjjjjjjjjjjjܶh} @CJ h} @ h} @ h} @h} @CJ h} @h} @CJh} @CJh} @ CJh-^ h} 6h} h} 5 h-^CJ h-^CJ h} CJ:gipiyiiiijjjjkkzi & F VU6^U`6 & F VU6.^U`6 & F VU6(^U`6$ & F VU6d^U`6a$ $d]a$ 'd)]' & F RQ:^Q`: & F Rh#d]h#^`) jjjjjjjjkkkkk~kkkkkkkkkkkkkkkkkkkkklllll l*l+l7l8l\ldlhlplllllll m mmmmmm̹h} @CJh} @CJh} @CJh} @CJ h-^CJh} @CJ h} 6h-^h} h} @ CJh} @CJh} @ CJ h-^CJ h} CJh} @ CJ<kkkkkkkkkll l+l8lll & F RQ:d^Q`: d)]- & F RQ:^Q`:^) & F RQ:^Q`: & F RQ:(^Q`:Plmm,m6m@mJmTm^mhm{mnnp $d]a$^ & F RQ:^Q`:.)*(d & F R}%d]}%^` & F RQ:.^Q`: & F VU6^U`6 mm%m(m+m,m2m3m5m6m*B*phwhjh-^U h} 5h-^ h} @ h} @ h} @ h} @h} h} @ h} @ h} @=Nzzzzzzzz{${4{E{V{~()d & F Vd]^`^  & F C & F Cd $&d)]&a$$ & F RQ:^Q`:a$$ & F RQ:d^Q`:a$ zzzzzzzzz{{{{{{#{${.{/{3{4{>{?{D{E{O{P{U{V{{{{{{{{{{{{{{{{{{{{{{{||||||ҜҖҐ h} @ h} @; h} @h} @ CJh} @CJh} @CJh} @CJ h} @h-^ h} @h} h} @CJ h-^CJh} @CJ h-^CJ h} CJh} @CJ8V{{{{{{|||||}}j$ & F RQ:d^Q`:a$ $d]a$$ & F Vd.]^`a$) d(]d %dP]% & F RQ:^Q`: qd & F Rd]^` |||||||||}}}&})}M}P}a}d}}}}}}}}}}}}}}}}}}JL 8> ):֡ h} 5 h} @ h-^CJUh} @CJh} @CJ h} @ h} @ h} @ h} 6 h} @ h-^CJh} @CJh} @CJ h} CJh-^h} <87 4 mark 91 Control Structures in R As the name suggest, a control structure  controls the flow of code / commands written inside a function. A function is a set of multiple commands written to automate a repetitive coding task. For example: You have 10 data sets. You want to find the mean of  Age column present in every data set. This can be done in 2 ways: either you write the code to compute mean 10 times or you simply create a function and pass the data set to it. Let s understand the control structures in R with simple examples: if, else This structure is used to test a condition. Below is the syntax: if (){ ##do something } else { ##do something } Example #initialize a variable N <- 10 #check if this variable * 5 is > 40 if (N * 5 > 40){ print("This is easy!") } else { print ("It's not easy!") } "This is easy!" for This structure is used when a loop is to be executed fixed number of times. It is commonly used for iterating over the elements of an object (list, vector). Below is the syntax: for (){ #do something } Example #initialize a vector y <- c(99,45,34,65,76,23) #print the first 4 numbers of this vector for(i in 1:4){ print (y[i]) } [1] 99 [1] 45 [1] 34 [1] 65 while It begins by testing a condition, and executes only if the condition is found to be true. Once the loop is executed, the condition is tested again. Hence, its necessary to alter the condition such that the loop doesnt go infinity. Below is the syntax: #initialize a condition Age <- 12 #check if age is less than 17 while(Age < 17){ print(Age) Age <- Age + 1 #Once the loop is executed, this code breaks the loop } [1] 12 [1] 13 [1] 14 [1] 15 [1] 16 There are other control structures as well but are less frequently used than explained above. Those structures are: repeat It executes an infinite loop break It breaks the execution of a loop next It allows to skip an iteration in a loop return It help to exit a function Note: If you find the section  control structures difficult to understand, not to worry. R is supported by various packages to compliment the work done by control structures. Useful R Packages Out of ~7800 packages listed on HYPERLINK "https://cran.r-project.org/" \hCRAN, Ive listed some of the most powerful and commonly used packages in predictive modeling in this article. Since, Ive already explained the method of installing packages, you can go ahead and install them now. Sooner or later youll need them. Importing Data: R offers wide range of packages for importing data available in any format such as .txt, .csv, .json, .sql etc. To import large files of data quickly, it is advisable to install and use data.table, readr, RMySQL, sqldf, jsonlite. Data Visualization: R has in built plotting commands as well. They are good to create simple graphs. But, becomes complex when it comes to creating advanced graphics. Hence, you should install ggplot2. Data Manipulation: R has a fantastic collection of packages for data manipulation. These packages allows you to do basic & advanced computations quickly. Conclusion/Analysis: Hence we are able to study basic concepts of R Installation, Objects, Function & Statistics. Assignment Question What is the Difference between Data Science and Data Analytics? Compare R and Python Programming language. What are disadvantage of R programming? What are advantages of R programming? Which command is used install a package in R? List the different types of R Objects with Syntax? What is Mean, Mode, and Median with R Syntax? What do you mean Normal and Binomial distribution? Write 4 in-built functions for Normal & Binomial distribution? Oral Question 1.What are the different data objects in R? 2.What makes a valid variable name in R? 3.What is the main difference between an Array and a matrix? 4.Which data object in R is used to store and process categorical data? 5.How can you load and use csv file in R? References:- HYPERLINK "https://www.analyticsvidhya.com/blog/2016/02/complete-tutorial-learn-data-science-scratch/" \hhttps://www.analyticsvidhya.com/blog/2016/02/complete-tutorial-learn-data-science-scratch/ HYPERLINK "https://intellipaat.com/interview-question/r-interview-questions/" \h}L ~.) $)^a$$)a$ $d^a$ $d)]a$$a$ $d]a$ $d$]a$$a$^ & F R&d']&^` :=]`}~"#;<=>FGMNOS/0129:NOhi$%/0t h} @ h} 5 h-^CJ h-^CJ h} CJh} @CJh} @CJh-^h} h} @N#<>NO02:Oi*$-dbdP]b^`dd^  & F `)2)^2!2d^2Xd]X "d$]"i%0uw~."^d^Xd]X"d]" $d]a$^)*(d^ ld)]ltuvwz{}~ ./34XY]^rwRTVxz ƶީjh-^U h-^CJ h} 5h} @CJh} @CJh} @!CJh} @CJh} @CJh} @CJ h-^CJh} @ CJ h} CJ h} @h} h-^;4^TVzmz d]d(]^P$da$ $d)]a$$a$^ d)]  & F ` & F .` & F d` d)] lm ,|}~ ;<FGgعس㭤؉ hmCJh} @CJ h} @ hm5hmhm5hm h} 6 h-^CJ h-^CJh} @CJ h} 6CJh} @CJh} @CJ h} CJ h} 5h-^jh-^Uh} h} >*B*phwh2}~Gr!O'ld]l^ ^  & F  `  & F  $`  gdmd] ^ghqrw!#$&'.15:x{} h-^CJ h} @ h} @ h} @ h} @ h} @ h-^5CJh-^ h} @h} h-^CJh} @ CJh} @CJ hmCJh} @CJh} @CJ h-^CJ h} CJh} @ CJ4'   d^gd-^&-D1$7$8$9DM ^&gd_ ed)]e^d]NOABCD   "$(*.а h} 5CJ h 5CJ h} CJjh-^h} Uh~Yjh~YUh} -h} h} B*CJOJQJ^J_HaJph"""hiVTh_0Jh_jh_UUh} B*phh} >*B*phwhh-^jh-^U2https://intellipaat.com/interview-question/r-interview-questions/ HYPERLINK "https://www.tutorialspoint.com/r/r_interview_questions.htm" \hhttps://www.tutorialspoint.com/r/r_interview_questions.htm  HYPERLINK "https://mitu.co.in" https://mitu.co.in        W (5)C (5)D (5)V(5) T (5)Total Marks with Sign Skill Development Laboratory Third Year Computer Engineering SNJBS K.B.J. COLLEGE OF ENGINEERING, CHANDWAD  PAGE 1 SNJBS K.B.J. COLLEGE OF ENGINEERING, CHANDWAD 1 $*059>Vt$d$If^a$gdua d$If^ d$If^69d$If]^6`9d$If]^`4d$If]^`44d$If]^`4.039<>UVW]a}~    ļĮhJB*mHnHphuh} B*phjh} U h} 6CJh} 6B*CJph h} CJh} huah} 5 h 5CJ h} 5CJVWXYZ[\;55555$Ifkd-$$If'ֈ^/ /k t04ayt"\]^_`a5/--^kd$$If(ֈ^/ /k t04ayt"$Ifa~   ed)]e ^( ^( ^> 00P:pzm/ =!"h#4$l%j Dp(/ =!"@#4$l%j (/ =!"@#4$l%j (/ =!"@#4$l%j (/ =!"@#4$l%j (/ =!"@#4$l%j (/ =!"@#4$l%j (/ =!"@#4$l%j (/ =!"@#4$l%j (/ =!"@#4$l%j (/ =!"@#4$l%j (/ =!"@#4$l%j (/ =!"@#4$l%j .:p-^/ =!"@#4$l%j FnzցO !آ;JFIF``C     C   7" }!1AQa"q2#BR$3br %&'()*456789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz w!1AQaq"2B #3Rbr $4%&'()*56789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz ?S(((('uŚCOu, O%MωS/~mר!z}gOfi$CP-YWyI{o?m7⽆7MWK gWM1UV(bOw~5ĝw㖻k5m.dL3DtꝸtYWn|~+݌eB-/khӸ1'iG`~п>_/EB-{r> x:77fg-Y*DFe]%H(>y#'i?|п_\߀nO ŞеE:xuJռSv]D~׵>{jźأB)o7m.|/#s C~"Z??|п_^ c f?qhn MΥKv_a{]Rywf_e//D}i$V#B-'k>++U׊]^x^ՙ'{Ko~ _IU׿o_[P}[9}п_G C~"Zo[ _'Z"NW^Ӌ?wmy_{{_ xFoVtF? GM C~"Z_GhO4/"סY>|'iq3, : zբW~Ef_u[k.[{e,)n"Qno_܏ޗe?B߈GhO4/"wƏךhv<ܳhdZ p/+vIj+/ ^c6FEh_/E?7ZH熭oMOXIvL}ޯ(~/rcVzk?hf,nFu[xHH(>G C~"Z??|п_[;x <ź^s8n4{{O>:o]Ukv?.5; 2>P򅝬:ջs輤w7so>(>G C~"Z??hO4/"mrsl<}@Kug_eUtW?*YRZ^"&6pK.%Ϲk*3GAoK(`~п>_/EB-z].𭏋4Kڣ=Ɖo-[~蕼?Um>3Q`m|qᫍny OW%6eR%mۗvGC_/h}п_^E_Gxw C~"Z??h_t/"׸GG}^?/_85WfםmJۢ۵e%? , srH1b^\H:RwQ@Q@Q@Q@Q@Q@Q@Q@Q@>_j9/@4kLXo/5ۇנ~@~ص,TޞWw`x*T?'WQrOo:<^mZkAo{|Q$˱XeTM\i^kO 5<1 {(ţI>xy]dʕd=u~'Ou+xYY~D35:_e5ϊOٯ,m%-:Xim{X|Rn)lYJ &-"Σ-śo74/OxXѵ)u[6KWr"}Ͳ-k? Tx[[⣯Gt5:ʷ o6fe_h~oH}[" -od+[[euN|Uo^,|I{I6~g6m=䷍/(v@GYyK~_MYijxu.o6T,Q ûU_^!Oj~]jEUe{ytk?@wU}sQ3k'%坾m,-R͊X = [ҼGI r +L\Eu%,K+ȖVM῀*}ß5Xg\Z杣Xx MnDDKkjmY𮣮i6l,o'nl6b]."f_)V'~uݶj 'Th)lȵ(Y7A,KOıE7>;/Zd)5^+У,;&+GCi|W{m~,Ogvx6uyO ȼ=w@ՄNA;2ybO^/j}2_k:ݶ/_]7}n_mxvh4 |>ԭ4x~GX6V[xM>U-iwV/qNk;(EeX*|G,,Jʎ۾Zk/ "[:և^Z}-%u+viYE&}f+_jOER.Ρ$no{6ٽh_&|#^/'|kh9-Eqqx[o%eD{߈kML1tK청灠{-JX%ogsKUKGῈ>,|;4W:U[M-[߻X%RKȋ.ݬT q}}/^tPuQVUTY"v~[ۯaY[CV$z<*?Z~}GU޳EF@kEYlutD˷sE>}_~Lj"n&~[F{)ⷊxhYbXehvm+}]غG =u!kmV{[xGu|ؓ2-}j@G}'Eko~&OŐj$q`cukҼzYU|[ 3Woz:_6/it߽߷6ݍ<| K Kէu2m-QK,EReeIWWe'RZIyo$v.l≢Ws[b=L=Um6\7ʱE-w[bk+V&F{|u72io xKs,V=3N{E'[Y]6ڟ]UN-bHM/)QkVh?m`X *F>w</ʾtc~(?˄e"QҖt<(((((((((o/5ۇנ~@~ص,UK?g ? ?Z?*Ozx;<_ }*{^lqGočGKMKK-ux./lZ' wᤛB$xVy|Frʓ޼MbvJYb/cOx^_{1-.!M:X=5ؿRvmXw?ߦ|gy~lx7QnƣjWZ,-Ǚ$WIRGOQb mS$^hz:[O ۮo3Qfg8XmRJ#ĭ[[0Фkr:TnEgkpҺK,]˻ٷ۹})oȌ_z vJlgkO/6"S2_dxfo}./FIմӼ:^Z+DҶ[s˵|ZӮ{ĺ't__ϨZ! Ŵޫ[(%}O\ηBxAF%΁sM/[K3_yoX"Wd.eZ|Rx> Q,4kWOIel}[*E{-YJ\^3Ou[J_'D[gw%^C{_s5/h٭NIdV(K_o%}ndܞje~,h%#ΈV`Q.Kb`vuK[bGdoRI  5Oi:=q,۬[fZ@yBɞ5:AO5{jWQ5O,T Rzun'-3O]+}ŝkqo+?N,Q7W||_$4xVúne'G2˹j웯ZX|H{iWVk[_԰i +7oٻwk>(Y0>&VmkWOp߼fC~OEIOŃ&{ C? 6cq闷sս-qtQ<[L Xai+˲'iVYueUnݽO~ZՏKmjOj:yھiiw;4-RQnVoo6.qV4n4?nʋFQn^Y^&_7kg̀'@l-F[o"LKuRD||/ '}sceT#Ώ}5?em^k$m%to"-Ž"}v훾mw/ޮHca,Ršvqg(Qe-֙EaKq+nﳦU|ߛz{Z-jv#MBW/)Yehŗny?c8t]b]S66kijx6 .QӡfiVykE]ʊ1K|Weym|5 sVnu-.VXO\ʩ+5D4Uuk?CK#Pp%sa.L%ݶfPNhU",=c vn5o,- )<,Q7ozboHZu/ųk{Zw6>׼)u-;Lnа+~{DbN̚t_}s{αGQ3YRyOqnʠ/nYl/U==ZTHUe~}DWʯx"O jQ_RʋIk jMqnky,h< yrʕ/#}./Xà躎?@4tӬ 8{)V(%Eڭi~m/ڗTZTV>EվZkKqg,QKmxA`Wf>au n?{5 oﶫ-b-^=nXt)k=(((((((((MC?-iKy&pzHO%;޾j?e_JWw`x*_?<_~׉5MZnQhe<-ԬͮiM]\4[WjvB <~дՊ'/iE(u]^(ي%xUӛO_mJXZ75N|寖2m6Le+}CSϸou-[핷m_x>1_~ DŽnۼqA,_ڢUoScJk}߈uh04)uH/總ҢL*)yLo_ڴ jMmAY]\h[|+e7mfV3Q%j1HլNm2uִۈ㕧ͷy[Z_5bWU~~>𿏬,|M}/hS٫KkDHJoh}Uf%@}9s>⭻Oh|@L-Bm>'D7m͛mu ֚VZhUPX-Kʼn~Ug#G_!5xrJOKt6ۣoVLbжzW$iL[Gҥ~}h%YD?mVx>/wG|j1]$RTIQUs޳sm^DPL.bx<7;yR,Z:gn(3QԴhV],K[EOͿeT < p4h{g LZ%fb;#,M/bYUV>hm=> RCh=bUeܪʍYjٿïK{ wWVqYj3juQ}ϵ2ċ>Ƶ4?֧ Z[xvHn[=uk5ܪ.> xo _eK[MvR~i_-ShOK3sh~lpIQKK+2LD~U۟w%]ElbuXX37̺K  / jm4kXѭhv[&|R4[e}j_~\iV˨]X}Yu(>oq+<ؕ5+vh>3 >ʋZ>ǢiiZ[}Ķo-b;>Pe+I si,wQ5|NO&ݿvWUi._&]~^)kkO]OC_WWZB$(NV_UYYW͕[Oy?Ūk{{8MJ%iv|ϱo`}7oᮃ'֋ Ml4/hE[ʍjmٻvؕ~?-ݍĞOekA xe(oM*ʻ~|ٗWG$:|8q)4+ sq#i>})w~iUSo'i{oǛV&u -4߃>&i:ƍP\KuD2"'oh_~?5|+v7gۻZJѯ?ڮ? lu7IԵHio&td5ߴ K{[|;:>} 񮛤|𻿟 (ye"ouJOŸhYNݒ([Z[Xi:m}0y,Oǧ,7'lѳҀ!!Y|&Yk6wk{Eeg_L9gǞٵ}K.oob{k7JVRO1Ogohgokm.߁?q S?|@w[?|@w[[4?q `{Z_j>Z?Ŀ|@w[?|@w[[5?q `Ŀ/9j?Ogohgokn.[5P?cx~,66Z: lf_d_X\O=YӵW9߇lA.˧Mw\|w[WuaG:J(;(((((sFFJ:UUՕDʿmՕqZjx7@mUW}2"]\oۻU(>x/\ HퟴKF;m OƑs/ f][AeqbEX!}l2VP,'+] #guoJ5đ4 +y.Fݯ~  \9vȉꍹ%k329-CzM=nm>)/mXey/ʭ-[E| VK'r/Tn"w{J"¿k׌ީY^]%].{'':>{''Q/=??ο^ο^z5}bKcOby3r;G3r;^EXXs ףQG+4 4':>{'':>{''Q/=??ο^ο^z5}bKcOby3r;G3r;^EXXs ףQG+4 4':>{'':>{''Q/=??ο^ο^z5}bH=?7KnAk }Pw}o3WDuWY:԰,_6Wwm|/Y9\*ĬN_gĚy|K߅|Y aN%ӥ߷knݳơv:)4_U yQEQEQEQEQEQEQEQEQE|yɨx?޽{ŭ3Ib?զ\jzVymykA M5^O_ؽĈ(˿rDj;|wſ>C$+igZZwZytegu};"ȗ[UW$_$O<1O^9.xBf障WĪ~djnj"xO?- uF T4ٯZƍYwi.k m2 *6Do.nܱ}Uz,G%jÏ__hieETX+7˲K9}W~[/PaE-QKnp3Xk~}Ɨo>𽝴iA5ƵI-%w|5ooo]oN)tqaW#|ʩI[ow'Կi ZMwƺ;|'utZ{泗gV'V~C>ҹˠ[|Qׁno5iokw͹6V_c#[Rt_hƫou,_c+>z[_vDIԴz/"WPq/]K+YE,v)UevwZ>Y{X%^-%Z}x͗sowj|$O:|7ӯ> Zm>cjⷊFi_ڪkM|5/4KoOx.Wٵ~6W>s{(,B/kSV7~KZ+_>!me:y^n_7vݻ঻o++7ܥě-~A ktXx"X74,[* PM?]vr۽y/v/+pn_7/V,>.xT,g X\6A/*oޯOW>4Qv>v{Դ[Uit#D[WصoľE?7H//u;RW-*D7/9?A<9 xR4}vLb"5%fܻ%X}k~V;~??sm O)lt[ٵW5qEUQ@QLG|0l𯙾UXnĜo"O?_3[%}_O?_3[%}^yQ@Q@Q@Q@Q@Q@Q@Q@Q@>_j9/@4kLXo/5ۇנ~@~ص,T;~N_ EZשf3h()QE_;xwz1W`/- -2$yĬ7յQƥmib'e(;mvzt)W OhzK ӥp&{{>%hED ||d%~{iwR^irNJX͕w*nXKӔJox@4e58eMʯ/Ҿ[Yxiᛨ/v,*&o2oݵYE?5r> 5/<2UOyKp;|ח7ÏVGiZƫ[Otm%VXD+'ޕbEt69>So^GdNaxmp-Ҷ*YwIw2.=EsG/O 3*.6_>ܟߛ}Ɵu#k|5+ĺݟĻKkK{͸Oڟj|0}yY Wn}gwjTWG4:?E]OOjOj`$_NVʻ'f/cShpku\E-<mwJݽ6Ɵ&_x;>'OZGYmK]껷} E o mM9Sh7b_\XDS+Wv(Sj+-Y떾8:'Fmwr/jx~5iA,"K~lD{Go>-i|EjJ--խyE?zһV_j?d m/P])+.2vo}ҫo}WDe!reZmֵxy,k}OZ)S|L߽u6Gem+hxς<M_K[7յ8|~xM/|]77GK?6z~.K*ũ%o-+*ޯ;PsXw|AUb>ei|_ޢܪi$Oy!i?Lֻ7U{Z^WZʹGVu֨>@he}4ƯwItɠ_~˧;~/#ſOej2o7> qXcƩ.͉*3JΨ/~U/|{ODZ}}_imKDeG~tߍ^?HE۲oE&I]͋~跪y|_ |-S)L_Mi)_EeV[%/ݓ33&Z'i[ڧG[jzW؟QwIQ:v/De b}?EVAEPEP|/lҾf_'sW'e70|<oU |}<oU yQEQEQEQEQEQEQEQEQE|yɨx?޽{ŭ3IbG׉f?XΖ]OAE(EP ((?^t藺FgZ\.ky{=KP/ /cH..{-ۼye}k2.WwZJ(EP (((>U_3|/lbN?য়@/য়@/<((((((((($fF]vWȞ&a7* ι2oQJ ō[Gԗ⮇hYqϝl:7v6;VĨңn}FS޼k=_'u mNU Q2ܫ: -vaeR3y Ti18oG*G*<@{AhW5W>#|:?􌏶G*G*<@{AhW5>#|􌏶G*G*<@{AhW5>#|􌏶G*G*<@{AhW5>#|􌏶G*G*<@{AhW5>#|􌏶G*G*<@{AhW5>#|􌏶G*G*<@{AhW5>#|􌏶G*G*<@{AhW5>#|􌏶G*G*<@{AhW5>#|􌏶G*G*<@{AhW5>#|􌏶G*G*<@{AhW5>#|􌏶G*G*<@{AhW5>#|􌏶G*G*<@{AhW5>#|􌏶G*G*<@AhO5>#|?>ǖ5xFHn}nݮb*GyO?* B>}.*v-zRU?{Y1}s!Oh_3[%}_Oh_3[%}\`QEQEQEQEQEQEQEQEQEQEƾE׿|i^Z}_$xNƿEZׯS'G]EWrX(EP (QE(,QEQEQE`( Q@X(EP (7N_Zz WwZ)ڼ(GnrHc5]W5Hc5]W5垠QEQEQEQEQEQEQEQEQEQEƾHo5G?|k`Uzo?<uQ^QEjMO-5iY\K2_$ݵc]nޭ}oþ:5x#ŗ>"k_^]jWZv#t[4[l_7]/Z¯E._{'OwCMuw]]1)qtmw6iwd|#,|Y#itv ?'m+g++V~[?xs^:UY[>n^i2[|1,Jq%W?ut^%h^Ԭt_ڶo]گߖTܪ}SiTxG۵$4IFM x6*麱_ڬ-$YZT/Mv*ިXe Ǧ[k^0Ú{]ywR7|ֿ_P#@ַuq=yRҿz{>o:4ǷgRգ{uei|uezeoȟ5v~xi]juZ,PK<o?rL/|G?4🊼'[=WIsIwlgh?kkFxZ,ZnӭwkSs2no/" xjR^÷2/%%Xn%kͪwܯ9q~^/o֛^5]RNMtw+EW]J|{J4/v'ѭc:O?^Ok^*YN-֥A+UQM_=|%i$?%{[];JӖ[Dm剥VUo˻5y?tmݷw˻jY%#7w3ŗ^u;3P,#Ӯ4!/7!_5j,ڿ?ީ/gox\g_EtJ;M?H;8D|?3϶Oߊ|T~/ϧucI4KqE=l_.7kBLj&7~]W>"M>y|"ho-/m~5/}CFݫ|:jEn<[[-'E-\g[ϗ>`~eތU;0zCۿ*Dy[|ۗv/8Ľcf_"{Nev%7/j\ܾ^Not((x5}Rot^*?k_Tj۱#?)괟 u_^ꬭZ;?ZDžmznl._q|W|(%[ ~7:2%Yh[{ȓY_o]{0k~>6{/мqh=Ic~hJq]>TOv>׭5ؠ _-wz/f3x%炿hyx]֯4 M2ĒʛWzZwe|FO>#MK/NV~ .Ka>٢kxyn6[gʊXƖ5L_h5X"{v)llj<;oλWm^~Gן _=Ox+Mԥ n%O7lv.V&ɵUWkc[ws>:GKvhٽwm~c]a@G> ֭;VoOMxZ\A ڻDJ_1߷,gM zF?U [E, }9xº\i5:ČLͷ_5yPUG¿ٺ}\y6\\U36-Oit}ݽ֙k_k>6~z&';V~ w;"{/aqk?Ow+nUSCqj:D^ 4[ [H] uě<*|v~nw xZ~x:=tصX%ӧ$woi <[7߇t.<3u2oqu/,HV~|z~?|Iss-Υh>UlK.twrx-6CleU+FUbvs ]x5 T 亃fo*z;~׾u_H{Zs>wf_|kb4mĞ584X[Wmh͋wɻi-~>k{YZi2=h K4W>jʲ[Z%fgU?OW> x[4oiaxfTYw24+]O'R/leWrwhoG^ݤMYZ/+m**lW)N~,[[Ү)kͻ33oUv׾j_x)JyD%TMl۷6Z##Do_ ǥx;{['̭/eeoe>~f<<'_ƙcNroajzZbŻt^jù~tm>~˞)v{%yy3?+dnx[O桤u]BX)eeXu7W:9 ix)M>=_h6v|Iͭk7?F?mSR}͗u¬y퉶f;o+K%r$smeU/YA;:Fk nFRl*Yݽׂ~x>:'aT b ZU?{mO^>%x1skv:[ \x^%tN{e[x֗˽vDz>kYXe?m'HPRWD{ݷm+~_$=bpH"E-w/U|ܭE}OEPEPEPEPEPC#u5t_*־xȺo?/k^[?<Q^K(]QE(,AEQ` (QEX.(p(]QE(, (QEX.(tsU`־N:#k?3/KG_S zIu_`S zIu_`זzEPEPEPEPEPEPEPEPEPEPyȯO~(ީgyt*>UV~/|\iZ׆t+t[h6fmޖpTիj8Sy?Q E ƫf|hTCƫa?Oϣ?ሼA?^ jgƏl5G,ύ8jf?x} E ƨ"OCU>4cp?f|hT}S0a?^ jb/_?5^g 3G6?gƏl5G3=} E ƨ"OCU>4cp?f|hT}S0a?^ jb/_?5^g 3G6?gƏl5G3=} E ƨ"OCU>4cp?f|hT}S0a?^ jb/_?5^g 3G6?gƏl5G3=} E ƨ"OCU>4cp?f|hT}S0a?^ jb/_?5^g 3G6?gƏl5G3=} E ƨ"OCU>4cp?f|hT}S0a?^ jb/_?5^g 3G6?gƏl5G3=} E ƨ"OCU>4cp?f|hT}S0a?^ jb/_?5^g 3G6?gƏl5G3=}ymnU.u)l,{]Zg|<|S>$jN J@mo6/T_ᖾgZ1P .iTOQH&c5]W5H&c5]W5wQ@Q@Q@Q@Q@Q@Q@Q@Q@!KHzP}⿉s^0X?޾¾+'5I?/?BEQEQEQEQEQEQEQEQEQEQEQEQEQEl~_p$3v=>?'O7o_\J=_7Hc5]W5Hc5]W5䞨QEQEQEQEQEQEQEQEQERG|W7NkڟWa^={G6g8Z(+珊Z.OBľ-*xsS{WyVok:9"oJW+cPֵx_*yo+DlT]Kğ%xOdt_dZunlYW͉]l>]k1K}E|7ڝ/u6K85=,_]/ʵRۙkh[ ˨Eo}_5lގ[ta%诘QF|Awៈ(Ҽ8-վgqyA<nVezo_~_~WſIti}>MrK[5ʉWoZ]7g?=Ǐ,OA,b>>S+7#Y]'IRš[oQ*ϵv6_şi|1XmOk.H'Y*[oYҜ1?. ;5Y"}fpK,Z%X_W韵޹2T-t-K1x@Ku->)wKXͷڄ1dDhUyϫ񧇴^ thڮYҮ3'Yݵ~f_Vg9xG>'i'48? ivq͢]^EK*ndeT_Sk|2EVGtW8H־ +×:/,˙ko.Og?߃|a.C[ZXY[k t,I(W]beMbGK>GmO_kG&Zm-ѻxV^$ewKo>/ſVx[KԮnK/7l^T<}]Wr,C9y{~:|A47]KEkwWCn\:yenk;6_Uk_ÿ> sίm <[8./UUWy_+m&>/}[KZn &vX&gkկL34L%IrQLcj'C{~7'O7o_\+s_Ok.|<oU |}<oU yQEQEQEQEQEQEQEQEQE-!@_Mz_9׬Oo^QiG?(<0L/+_-[gUVrgY ?n|oow+? +'_MYxC][ڜZZnՕ{mfmk(cuzj?i[*Y:/+),|ɷ{{|Muiv?o[9gHt3sXaKQ`<[WxSz&xW+@/Ng<e%ګ?_ޟo-#nj߇5kx_}oܻoTQ)r򇵪y.xB^4۽k2u_}mooݩUI?d l|7i,5Yu{NbO]w{U{ _ޯ-m(xFxyojn_6)vuwl7W"kS_]5|@37l[o{aaOCTc/'4JROKŞks^qܕems'D;@TMV^ާr> 'h*N?UE}bp?PP4¡/  hT—*Н&T>;@TMQRPC_A 'h*>S{ _| Bv??PP4QG*waKCN?UG*Н&(N)(¡/  hC_AEX=/T>;@TM| Bv?(; 'h*N?UE}bp?PP4¡/  hT—*Н&T>;@TMQRPC_A 'h*>S{ _| Bv??PP4QG*waKM yM%(YV}[ZMvÚM泱Tj3XW˻QZͷ-(XQEQEQEQEQEDdtl  HA?image4.pngb\ܪ!S6&w`8D|n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl  HA?image4.pngb\ܪ!S6&w`8`|n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl  HA?image4.pngb\ܪ!S6&w`8||n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl  HA?image4.pngb\ܪ!S6&w`8|n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl  HA?image4.pngb\ܪ!S6&w`8|n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl  HA?image4.pngb\ܪ!S6&w`8 |n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl  HA?image4.pngb\ܪ!S6&w`8 |n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl  HA?image4.pngb\ܪ!S6&w`8|n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl   HA?image4.pngb\ܪ!S6&w`8$|n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl   HA?image4.png b\ܪ!S6&w`8@|n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl   HA?image4.png b\ܪ!S6&w`8\|n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl   HA?image4.png b\ܪ!S6&w`8x|n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl   HA?image4.png b\ܪ!S6&w`8|n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl  HA?image4.png b\ܪ!S6&w`8|n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`Ddtl  HA?image4.pngb\ܪ!S6&w`8|n0ܪ!S6&w`PNG  IHDRLfaw{bKGD pHYs+IDATxA @99yK"`2Z{\,,I1hrzp5Ѻ4>;v9gM ~97_Ox顜oaЌdH`Op8K;,$B ,$B^BvXH`!wBd!H1[Anȯ!?0W,(InIENDB`xDdH±_*[|+?8)oR/XV?(c=ZEm~.ElYG*gԿtZ_,YG*q>{tD -]u.{?8T>OQ J*}[_]W?徭k.mQ}v,;ñ_M9SZ6]rT?+g J%xR[dqc=ҏXv?kwާK_vɬ_} {Wjڇ 1_ڇ 5aXtJ+Z 3Z 5?aXtJ#Z guhɬ_} {Wguiguhɬ_} {Wj?gujx{S 5aYuZi<1]K_v=5/]?kG z-|9=5/]o"ڟԿmuhɬ_} {WRKzj5d+Ϻ?aXtJSWC_vޫ?kv±G,;#گ]o#?kG_ {~~_ê/޾~o[?3㵴2\L,ʄ?XV?(c=ҿ5?0 ~w_vđ@+c=ҏXV?+K; ?guiabR[ {ttiy_ZJ'?({~v&+]C_vP ڔOQ Jؗzj3lK=5?a@ {t/禡;;GĿP ڔOQ Jؗzj3lK=5?a@ {t/禡;;GĿP ڔOQ Jؗzj3?管k;G,?(Xd'?muhؓz6abXd'?muhؓz6ab_ ~}c=OWğPmC_v,Xk?OQ J۟ nsP|1' J??_sPn5abC_ ~}c=OWoP_^? /,XkяOQ {~q?' 1OsXQ {~mUyg%޻|1Q {W&޼ ^/[ ~±G,+ m[]OKo? ?7,__zw_vKn};;Ul1K {W%W?_vJ O,?,_\~_vJ}k;U|1G {lW%sKg$[?1a?H`t J3I3`b[ ~G, <w_vI3`b_ ~±G,+ <w_vI3`bc ~±G,+ <w_vI3`b?H?`t Jz =?|1A {lW$_w_vI[?1a?H?`t J6㴿\zw_v Xka/XV?(c=ҿ7- \[]ڟ Xk/X6?(c=ҿ7*6Q?Ň {t/Ig6*x{k;Sz'?({~uE/?mui=5]?1eka?OS?aXE/kԩ]C_v,XG4~ñG,;5?muj^+]K_vڔOQ z-| R S_vi@J?`t4K;WXq?u޽!>xM!:}ZRM9J±G,+b|1mC]T4ۻE7,"kWl~>c=ҏX6?+ɼ {in"w}lH|VHxwdx?Z }MG֮oT!t_e?XV?hc=7Mγ.eqAc|ۿQ"6%pFgL/zG?.־4Čiw}bZ.kzlk,rj2mo6~[Q }aAW~(:cvkh:Mm6iCk~aŶ(;l7ւ|)3R+W"?-} u*P'OV=){_X}R캮j)~o,ھO*WHf_e|B|[j1Y[04oWIgfbEݥ}z]ZՏеߨx])NR9 Xjv~ ҿ|5u+}Sm"_+xo?\^{:%Ōٿobеߨ1еߨE8GO*?Խv.6ETnחVZ,U߇ koO~UQ*n$vD]u>_5o?˺ERp7Ef#Ҭ=WX-SZ-Էi?bۯdwϨkvvn/wۭī+m^̹1ڨ̹11?Ho|FҴLɂ/*-3*+>JŰb-=ӥEk=ގ~XG?^y^A,bh]N/^9w6}{h"Zڗ1mowl"[lUW"rIa_vb웭-n5ʛ?/Ͽ^#п1ڋď?ZU7/ũϣĊymLؖn$x_7 ef[X}2|?V]6˸Sv_]>%п1tEЏ)[M}'Ugq,oϸYQ6(k=.-e\g6Ql[T}g*o?W?KǏun糒O{8۽}W؏Tڭ7oJ?Y`k;OyRIYmSs*uz/*o/U3^iڇϣέ<_/{y,S/yqyI-~]+k_OSaj,MwK MG7UB?TC]>+..4KGU؟Ixºm}W;evK?|v >(o.j!t^Jy=φKox%,oq7~WKa=msy=ʱ~޿oĭ7.k'Cz%Y7|K\'bM; ߽ftw~b?|_6 n>O=.4+oH窢luo?\|H~µB|^ TSG|6}?ҳǛͿxg!:ĺ[}[mӶ˨z}rĿOV__?$KR?d[=om3R)R_e_|_ 6R񶱪E=֕rw'ߋg:o}^ Wo8c/@sGi_ޮE?RDvr^!E_m{{A?ěm7uXj_SͯBz3/])?~ пw|]x_1<~O Jy_><~ǟ/1ti>_O5+ǟ/1t_4/ǟ/^1t;x/.,k!ݕ<zǟ/^1ti>_*xjJg?п{|?S>Z}z3?g@??O_Ez3O^bi _]X??zK?C?Q^ ?|BG~~'O)^ ?|BG~~'O'W/]+Q SʨTf T^b<^ ? O.,/5zfoпw|ӿᚼ}B]/WSQI6?3e/.C ?G*y~/gOп{|<}BG<&Rǟ/1tg/п{|20(+)CyZvו;x?_b?ߏSe/.;Vϝkqt'RnTdYki4_dՠXPb㵡'u/sו;Ƽ>xCuo:z[7vϿ|UY˘ʼ6zWt |j{i/_vB>9er}_jw>[iRW؍+>;]-ʋsɻ5{5okOWȢ}3~'Ǎ_5t9Ro+=žۇʸOowN h75CUE}B}ڿ;?x۾by_2l~sE.,V^JZOW{G޶ގjc>yԿhjIuhtċ,{ɶ&)/7O֥6&+rߑ?m}G=**EH3.(t?PӰw]D>뾼ׅewjznUFokڷ[m=}so_{[oQ>{ODWtO gwpAky[6Ki^|6~}Vq~-o޲Rc~*A}(۾?="{EԀn|Q}*{E?߶ǿ9 n|Q}*=(ƹEԃ}(۾?="{EԆA}(۾?="{EԀn|Q}*{E?߶ǿ9n|T?߶ǿ5(.Rn|U{mQyo9)Wn|Q}*{E?߶ǿ9)n|T?߶ǿ?msRn|T?߶ǿ5(.Rn|U{mQyo9)Wn|Q}*{E?sRRn|T?߶ǿ?msRn|T?߶ǿ?msRGm{mQH w_V?m}Lǹ=QH?￾w_ǹ=Qso{m}?￾Oso{G5 ۾m}G="j@A}(۾kc~o"j@U۾m}sog="jB ۾m}G="jC ۾m}G="j@A}(۾?="{EԀn|Q}*5(ǼEԅU۾m}yog="jC ۾m}G="j@A}(۾?="{EԀn|Q}*=)?߶ǿ9n|U{mQyo9 }(۾k{Eԃn|Q}*W)VКT&jh_n|Q}*{E?߶ǿjaA}(۾kc\~"jAU۾m}so?m}?G5 *m}?￾Oso{G5!m}?￾Oso{G5 ۾m}OƹEԀ}(۾k{Eԅm}?￾Oso{G5 ?uOj="{Eԃ ?k~3_y?Z{Eyi k;]mk*~V;U??coȒo~HuP)ǪC<_v[vE gXq)-"4;N+7W5j~*MGº>XP&g/)_ սkwD?f #IKi[g|jxɨc[Ku'qy漭}կ@ n jlGGo i3,~3jq4qY%TKFT)G&ƫ__U'Vekϧh5cJԧԭt˛>}*6[V{voUX؈'+g^cRuh/r%/QDrMY7^24~ٽualjWf&Fd*YW-+}h[EO~uٳw kyBQom_ K7dS~KOSn)iWsy_w_."kϨgaRc'͹+\=4[;Em?迶>mO.4K݈ss|FנP7}۫Ws^ݍ5DW?dDE[mCFީD[S]V`Y~|`ufT/';SW{%=Ыo??O|PTt֚u-Czow5 zထW敾<ݕϧ/Yj ?'Z ֧L5l;njgyUl/\x~ KۼCA@λ/\1R?%lk_??_=C[]ߵ3< ʭ}ؿݯ?I_ޤ _oޢb jFPV&HC6<<}] o Pz}] ::{|/GCcΣά Py?C}]lyLY_otQ{|/GCczP?tP?tQ{|/GCc}otm>_.6<<}] ::{|/GCcΣά S 뺍P?{|/@Xot:{|/GCc}ucm}]lo}cm>_. <}] ::{|/GCcΣ}cm>_. ǝGXot|o S yՏP?t:{|/GCc}ڬu] ::{|/GCc}otm>_.67z:{|/GCcΣά Pzo])S ;*<}] ]o Pޣ}cm>_. O?}^i+7x":g>}*K ?K˘/Uwu[MGmEߛ=UC֌iuLoE]M?.;˷^ߢ!ió/1>./p}ěݶȳl\>3\:46,5k +[OkONעm7<,hD[: <;N@<"]u/W]lim(-?|k_k(Ib\Y_oq%߹+g_k-l,EI$U|EADUżRȝc'ͳ͋s{z'bQGZ^ltYco]E\LdtAg[~Zx.!xеqm]hU.,ɲ5tئQMwcdGv]tvEĹI|Iwyh9| w DWR//7G6Vemۿl?h|Ws/Ix-Q67t9[{G dOT_'?Ag_.yLԾq,/O//5Α ^\,S6[IVt}׭Csm+C6}ݐE6ŢΉ_??WŭWMO:}_7KWSM]5{M/VKqgGr~>i_v{\6K購w-QooXnߠu_jv-k]KRHmů?<yZ?}]x[k:''guo'q#?T?wZŸ Ok#zW?/zyWѭM|Rȇלr~>eQW2?~z]#f^2訫 /_ n.{ma^fe:WJԾ#Km?5JI-˳?fǮ%֥y潋I>EfyQQSة^ DQ~ԭF4{;M߂߂_uyaiŕՎ#ĒޤWH؟3ſr_1|QҴYo~ëAgO+yerC?.xfkm,Iln%t'"Hܗi>7ÐYig-ItԢKw/W/S6]~ 6]~ M7}(4f'lRϸENvU%~٨\YʖK/ۿu}*P?ނ߂A;۶ǥx~VO*woEunxW+ῈKYneuîA}XXFڑ$_$6]~ 6]~ ?,E/@W6TvW!tZYY֒^}ǔ|lGOlO,~/\d}L?e֟i]ɵ?\ժ{*r~֤bvI4/KxWFGoc}cD-B#OWtYeHT;^2ZΥaEuYžUX_6UѩcHU)˔ ߂߂s<״%京ڟ}Z_7.۾N|uy]:uh6z/7{tm#G..xO~"’V}n%j~m[uKWGWl-f slrJgǛE{T}K}Y{..pO꺭YKUD]'wWUJU}Byg سI|dc'F?{JW_ ξ[VkmL\=%Sy?my7yv112)cշcշ< 6nt Zmo[eZMzTڃS{^{^|Fդ:}vź]o7_~*+{scK?\4:yEk*""T{=[/G=[/^ |~AiJFO>Kj?ƟV^V[.{l;=E:>͟J =s~m_KǼCO7tjاS*K߹]?>8w}V= IJ~u}۶-{=K´O=QbVْ"7ؼwn W{H勥%y`O>eDlOA{=[/G%[v "kn/OOALKVUs4]YCXVHgiS~nsQ6Ѵپs~~-?i_ x e7j6XB]}Mߺo5='㵬힞lZm[(/YbuCd_ůcͷ=_6h/ u;-B}ϲ쉾{*Z)/bgOѿѧԭ(.%WM\Kd?ɷ{ȿŷՐ}'y?6WmW)n^N,>}WgvXYҳh>mΣw^566'ږ(o{;?=[oV~˿\ Y.5! b>,jWTݗ߂߂I~i?{Ƿtا|~*qoY7[)Zq.kexK{/%&M+}}WS&{Sv+{[˪_2k~~ehE~]Swoo+kk= ˖([]kހ&w(w*WeeTP]߂߂Q@v]~ 6]~ EUw(w*WeeTP]߂߂Q@v]~ 6]~ EUw(w*WeeTP]߂߂Q@v]~ 6]~ EUw(w*WeeTP]߂߂Q@v]~ 6]~ \a}jIgc.W+V?(w)0"o4e[9e⾊~Ru ʛӥs-^l?د6_D;o]'?k|my/ umxK7G~J' v #;r^l__fu鯛j_@]9 |+}-?_} 5|_}+g[W!džJu|kK~G5,֖o/{x6oRxmh?΍_˩\\}Slϛޟ^iٳu(͟{W]7ͽ~z|+Kv|L f=#&~GX/C+gU<\__+桨7`$K|?_9o/v1K=ky0JϾ~w2[;/?F[Egwo]"^7b%'_/_fo?}͢˩AbuY|O)䭏|KZO_cun;$_$eD}_ڊqjVϑsny&}q> 5WI"m*K}l*wƞҒ h/S>Ynl{IX-+="OhO:\E:zKoW/z͟g_z.u [?,-u Z]>;U,>gfU>K4~ο7G۠Gu`_k/zo:j{6eym u*}9JQOl͓{Pf<ܪ"ש1M3z y]/^aVݗ<i+Z?t?V+/dz<0W+&rʗ{Y)bS}|>_ᨼ{OKA<ٮ~he}&ܠDvI=b\oCVtyYdQ$"=^ —sٶ~cυ%whYKh~x,bdi[|+޴& Z?Zo+򨗻c=s*_RukYD>Yrj팖-j ;A|>J4?)x*[y[oy9N*_rX񯌖kb(V첽K"<_Vܿܪ|t񍝵r`iAWc/oԝcTcXxojlUnu#y'6ket,vuweISM).٥TC?y^Z/{_76Tnǿ񖏠]ĪRl/|7ZznIZ;5e."O͊-bޖee?oP͢y˷o^)i%:A*>}}?_ 1VӬ%m,W{i|Wr|Ꞃ?O~,cNQ6g?ڴoՊ]n~*k8/_tnn~#ɦi:_>ub}K?yok\Zx6?n6Ko[g%q.z/@_ݿݿjޏW?7oG?7oZeMM֯}/ecvtcvu_z>@_ݿݿjޓ@ݿݿj}~@_ݿݿj}~@_ݿݿjޏW?7oG?7oZeɠ =SD h{?TxW'߳oe G-3?@,5 Ŵ_6V_=$^ı&ٹ[oBGмU>Xx[_ODۿwAgi7MR_4OѶ?_CݧU7lԯ!-?mw`9Q'G_vJ导' v #;r޾r\U~9mڗD=w+篜j_@o+h663YlKn?opo./|/yE"nf ^"Э5H+GZ9%ޓw}k-e%o}<mFVdoh㵛Yt=ORgfiͮ$Eq3[ΐ,u7˩{?]'u/i~n⎋,vzǩ{?]SYej7v3=JSu;Y=5\\5Y.=?T}Rϛ\_2JXX57#KN44N-!-?t}R/%>X寕xHt BT ey-bmoP:$[߆&jymt =G|A5-/fktKo?g -.`b"o=bݟ"h-"MӴV̨d|0%.ͅ]6){kԑOўtU,6@`MxÖ?lmiVǹ(sFFny,}խN4$[ /6yѧg/-7ߚt/x,}X"vV[@FKe6m,'/7nw{<K [4ײoD?iu&kyiXo9k%Y,tdԿ7\tyy~JZ-Xǩ{?]kQ@?c罟o.7ZPO?g~욖֢2~ǩ{?]c罟o. 7G?g~j('z MKoO]kQ@?dԶO=O{߆Z=t}S֢2~ɩn_i~z ֵ=O{߆&}. 7G?g~j(Լ:-t_า&c2i`ؿtPO?g~z ֵz6?UY8<ӮŮЫ~Ǥi~|?uEsXoaOS[h2vvЫ2~ǩ{?]d罧o* ϳ7U/m_6o]oV_?ZMKP.Dy^VݱO[zWokPbFuDWlkto@XJͷآ]ؕ k.ޏyqdz_tBC b] ޻?'ebkvJFKoŲݿOMզs5+ "b/,[oRA\=3w~$}xw^]>e|'jqL-丹"HJ̱'ܑZW>?h?k_5!.5Yl[T2Zd(-_樂[C̾cGޣaIF V>}_;~S/^W-[;XIF V<<+y};Ձ_̾y}/W/!%;uj%_BJv S}Qw}? (/!%X;w'?տW}XΚ-v/]wfBh՞m%.//MuX淺E.:- %٭u3fUR?gγUQ@k۫qttP9Z*c K/}5t Z˻hx O6/eMR34OѶ9|Ҽ3zoմѶ9K'G_vJ导+ v #;R^r\U~3mܯj_@W?/zyT-{/ QW_?7g9Ռ**o^3s ^ KxStjH||^Y|:׼[{9uV#>w ^W|ᄐ̼Ҽ6]Hm^;o>O6oʿ6DonbVƚ-oQ{bRĞWQEWοE^Q~X1ei A"jWeBKJtcmVK.Dw? &huMmk>*Tڟ\O x\w5{G4]R<yNuTtR:D&MYKSé,RWUe](LJu-6M!]Anf}A%Goo2lgRnU7w>({nAD> HW>VmD\w3'..⹂Ihm/, o*!Yef/a{ 'BӾy,RZK̉>f:m](y{K]wxχf)ԵˏKy}c,Oƺo^6KmV ڇ?>ZvxQ!,u;k+fwXTcASLt1K=O'~^W4N崿>O;-;"wn5W5-W:i76x7t/{Ό|oIi|5ss O+\]*.O4WMm.kP-J,_-xN>_<'9twY ۆը]9~_mwdÿ xIК-n8mGo{W̍wޫ,bzmMq*sQ|H&4V;{K?`}gٿ{/lj|xVMBj|Ki>o|<[v>V˽Aqxoz~DA1A,Q:ۨ,m+znj]F,9%?w"Lۯf=;So[Y~'ڻysw5u(ot-R}T= &SWiփa=Jy|%k=̨~|ehXjjk K,Ooحf{kXV_ǾOx'f״?CJO(=-߿5/ٷv~ӵ +"ʕhدvm'ޠ/A\] ,꪿TZ:i{ݯdԽc4VLH7}ygIbw~m_-_?<|1iG ŦG얟ru%O*_լIa"O6BSՉ/tH|'q7 {h~U^ՠ՚*[%o*+]≛fvkO X&Vye\D>ybٷ~ިz|Vη G%71>}死X[ʸYMfVIEE:.K_+-WĖ:5~-EAZq"<XHZ*L~KE1DGW]@QPٹwvq$F}7nZ7->f>Z}7M@#LT)ܴ)oP~}wjZ)@eA5uO՛[B {?f?@J>ohԞ}/"O?d- rW|)e߸-^Vt˶UxqyWgޟo==vSǺfhGAmV]BY~q/*^Rkʯ7@[ 7QU[ 7QU[ 7K 7W|Q'Iֹ\+ylVe7Us]K 7\QUZWXb`coiD7>Fw^7ռ_jqYN|b(UeeM[P}[ 7QU>=>O[it1ZqE}-/Ů_#f+|~[ V1XOyqo|[qAFqT'5KFkHV8eZ'V'ĿO- YYC˧[Gu?Pvh-Gn?(*/|LOG+;wy~nM6ݎWB<-{y6Kom3}-Gn?(*{8 mfo[]m;j_V[ߥgky[z-Gn?(*CIաӭﬧVK8H%']Z'j!Ğ7_6m兵ĺ︞ L_:_"7 ݠoe߸?hk/Toimn'hv|"̑/+σ~1Zߎ-}+Qn:+U$Oy_w{m{w.?(*>qAFqWEWEw nĚk5M<߲ۿE䊻]HjͨK/$^-tkʯ?@[ 7QU6PWn?(*>qAFqVh@_e߸Oֶ6@?E? /"_?_Et(sE?EQ_?_Et?٣g4=G#Qlf?_?_EE?EWA1ph!Y`tY.6o)"e+}wYO1uMɻ?ۭ Ğm{,Rw]7ֲ/?y]2|Wzxd߭umC74\ߤīqw/7*I^FN9Ñw/oZŸ Ok=xúK_xSAq\~90 M"6^r{}_7Կ!/P,{:_'?x٫x1KWxg_ޮWqv?^ږ9>u -ݭnO6X/˽b x^WO4+]*$QJfݻ+;K[PU[$u_'~e-{;}^/|x¿h5 oH-uo,Egݻ#fu`yǘM r ls<=212-x-ľ&ѿU]oihaҒU~pλImG×l"t|֬_ b]Ҭ/Rx[v_vįCekvvOqvO}ye]6y_mw;Ǿ:L kjT.v[t+cPuwLDR;ͽK_般MV~9SƉ<Iu=ˋW~YW?~'|NOxDX']hH tRk]F 4VykM";/D7@kկ~ӺK-)[K&g(/Uʟm;zōdšKXjo3}!)V_gʿ!#ʕGO| ycx]¤R<^U+ʐ/x-*-cP.ٞm_bybRSUV_?k^~[Y~]E,ݯDo!գҖI.-wdk-Ց6|U? IϩjN[떺tǧn[!ݕQn>%մi\i6.\}JH]ߏx ZjVveIevZV?Nm߉ckGʉw[m/# 4%ub#fu[߰Ds7_ER=7zt oˣVgZ.{V&?h8τѥ[ǿow4 hMKPtͅqSD-?Ϗ:5 ^7q:| "Gʟm/A%/~=ן:.iu6K^4~:mU?uv_j"μ4&V|إ![H:-XxRu[Y-$x_6T_z|pڟ]ԉtX+)者ψ Wm}ߖ-g.YxP?ci+wQU~MiMxF]C-WU~J k^?Ե`5f$>Eݡsޯ;:~aO>ΧcK{qtJ$w}w_@u]U|ږvnn훿خߍw5ʂ{J[H_m^Qh6<ַ?~Fʀ>wŮGuB-|DvJW{}ڧ E;5GojcNQ3-um})hVڧ=#6zy7f{_*2-Ej gYSZ(4Ȣ{Y`E }G|77;_ ?Iq-Ηy7,?bn߭T֩iWf,_c,D{}{J>^U[]SHk #[ݨ[ڴ{Y?mS_9uVϗ^|CuרC poتsm^<[ƟwEڱ&~Xk4մSJl]boX>{vQnߺ_ ]t?SoWҮ4MN]e}Okg_sxGK5!6T6J5Ǖ+mx͋~}`x^nF-,+Y}e?e@i6fo3|~#|P46=j-tm%m3ͷ^lSĉoZ_Uu?Z|9-Ea}oEۥ,+,Q//-}ch@,x>=敩PkQYܥسyٱo?uyv|Jįu}&KE3Z=㯚!]t鍔lx#N[ԴZٰouK/O߽o=u>}PPՁռOxFQVeVRĎ]G-ω5iziں֝^ׯe>'ˑUѿ3ւ"e'Z>okMmxdw卶:4ZNؤ7(3"-p_y.cO%g_ʟ>}WWVORʶu;172=­w9notHʿ/)Ch[mh__=jC/iFX?oO֍w,D.WlڲyN"۵eM޻?~ kqiisvmm+yivvk wگ|=VnimF)"ڿö,+(##Wf>,k6<;}wP[W}۲m5ZZ1A3Oߴ4QDT+WGK_hK*Icf߽VxoG֎a6U򼏷[EǕvo~U.OZݷ"fəu'ػXv@j o5_ i6?Q%w. hZh{}bMڕo^|iI4\a*vo|iv᳣sqi tZb|ؠ_Tq[4EU7Y?[hW?"oj}W> i2ԞnR.Ym-bȉ7#z//'5ߊN?Zlw7p$Q+[J/"tmHQi_ k|)ON=7t~K&[եN/\$l毓;4^WmiZw|J5뚓iũŲ'K?|ՠ/5bet?_i:wCJ|Ooyk˨[ʎ QD|Տ>h- e{[X%4e(%%Yc///.lE ~NesXtIN[?bFU/o4ˆķھI{I6ߗ"KzK?Z^ZZyjS^l_}|nh0j֫-֦8S3ﻏu[K\K+h\6]\l5"Dwj~]xPMEJk.ϟo̮7nz,aΑ.ubi%xWGo{YPAilw}Kek+X1y_"_SkҼ}if[7Son}Ϳ ~ist.U[ȶDT_ꨟ:_t>4-2Kə J*ohDXЇo4EZhRk2ogԴ=K>W=<n~t}F{/tŻ2V~[xDtW[Gio{F]r4i|5r!,nvܿy}asVW%×ZNƗS,S7u]'ö2TxthQ3u_6U%YU[N46A2LۥU?̿5$|I/Gi_SbwkxR]ן>( o]mw;H_UR|+?x:kkƲWin.-_ox:sc{')I (<%~i?k_iRE+}~O>ܟ$e-kZyg'JZO=jDYߊ-QG_ t/Y}JX!YR^TowP۷nqZp|05fMZ]k3RڽmNvOkaOh4-R[kgO"ݝ*ޕi4oX kWTVvƮD嬏|_)-W÷\i؏bgSOn'Q$h.uTNo wOWڊ@6O|;/m+Ժ]"O>XeDv>_>$ӵKHome}$_mi~廷_]Gᶑx_[("k;w٭lɫRCW/tkq-W},h?)>&.G wn6Z6K]SM?h?n̕IK}/|S;;;[+EfؿwZϑW/dHBKK|ޣ7Wp> |)𾱯qjri e۲'-S-|akZz5e~qy[U⸉s~w.3.wU6 gl,b]]Eu.<_VSP֥u+mF[*+ۦ_ږ{M6D>J⹳/?L?>~βYԭ2_7US(etF/)e|hZm۞G&o,.^G} bʹnߕ󫶗P_"M ەK-QEQEQIږ (O((@(((((-3}>((7Y~ o h!>ya7ѵ  J5Ƒo VozzF[-ݝ1ʲ{D_2^oNԡF}U#%uo?m 44dZŅ]eX"cmuʌ}8#VROw;kG񮑯k:V:o-O ww 7R|9i/mYW,tnϚU݋^.(ԯFXi}-ӺwjW u`|S.;jLK n:UOUSڏ<`w˦i,ZԱ"+՗|6}oRٙt)jtk>&8w%;\tPHm*K*Y/o,:]Zb%Ty~:-@ý~&_ھvHJ>Wh_kyVu=.;,XIwNʴSYCP6{jFmš-/,5-AZ]Nq5^o[xt+6MS.[-;+KKLM@DĞ;#kiu"EGf?|,uhi>֣N/?4^Z濛tfVE7_/EO_J~_wvPF?yGO xzmYOkf/y,^R*VjOF?MECOF?MECOF?MECOF?MECOF?MECOC<~U5C??ݠA?}ճa ZRzE*gvvszw۱uw}r"V3+y64k٫uj0Þ%ҷӭ^)h{[57Q*n[#15 NJkˋmK]o-I[7+',t[p/nu_ZؖLխ>oE,~>Mț3Րp_i~:֞|E\'ݮ3:/;xDm7HӾמvHk}Ҽ?ʗwmu G4}7QDd^V<;\>o܊/ 3':ޕ7G STezɻuF?4}*-'R򧷋Ny{ⵕe_5y_X h:^x_I}FMU["NgY~j?^xųjZVŤi6l^UntȢϷ_?mN--U-ns}w^T^oevmr( ^*ttY,mn>F:]A,QNQ׺d[+KwyV4,|/gjvInXA|Ro5_!?t*_lBkE{Ey[&/? VYΕ-u`x"ur%!?ß^BY+ E^_#>{{ (hBo]hs3mSjW|_+nƓNx-Moao.ۄI^_UiY#tlܭқޛ/6v; Ne&yC'?=wI׳ͪAk̷ëʑ}w۶|R~_u h2hz%{A^|vߟw}߻V,U[KkLGi.GXGN^?9-2+fݬo>ϭ4L濛_7ڥ~Etmr}g+%hb,?k?T=|-\NUy4kfia-lUv];iJmA-/%I|ض:EoڈWtѼQNEK}Kڿy65ܯQ&xR UݾOux|u /K{uu ^Yխ&y!^Ynܫ}-C?S[k+זgCE{*l>egWWD}m/5}#UG]OSmK+G/uk`x_Ėx7R=ebVɾ%(?\t?;EjSgq-Oot~6d? xz /Q~rqq~O// E|F:eݵK}(zJ_t_7]^%).gbK|Jlo_6xkߌ$KWhm$_tXfGakU}(ouu+ԛ_{}A=lQ\KnۥO]~ nH&WYES((lE2}S6S;PvS袀ONQ@ Of}i(l@ e>-2Eֵ+/ d%pZ ~&6Ԯ A^7FyXS[i-5/ ϭך$xWCC5lk[ڏ>kᏀ-kM9|V/]rOsF^=//ZѾ*kS|5\("Oww-hh^:!Vz<[&}+{gw>OWޮCAۮ?vEۘm;iixboq}x7ܮU%C69mt[iM,hK+͖/5_}?8xMI_ំm<9̻7wf<]Bv}B-ʿ3zߋ:hqkRkm?/y_ x{#؃UYSr^>˻S7WCicDPEVΟx/ѶRʫ>z6?oO#~_V  }/+h<Gի`~7//m~?=oo4{彚Tbyn?߄|X3kF}uwnIKM~j[o ׋KnslYUw>Ԏj[>?ɆſnJž)Hr;Glվ++-,/,V}n+&[aUͻtx2 >sq=x],+?ϧ?[~Q/? }&]Oc쵖_6dwm?&OI/ۢ?ϳ_Z.{=wHPOqA6e򿊾[a㿀55=Gǃ@E}/S-Һ~ԵkZo AG,^4wk&a7x|?ؠiK'Wo%_dDnコT+Oq㫟 K1XhG{7{6>%Zߴe𧂮n%/5[mPE.6}+;߯D)UվrW̾؞~M][n>lJ&o_ Y|t fx+y"j-w[)^&hFg?+u,q5KhGի??Ds__??Ds__-Qj?X?K [G,F%l[z[V /+h /+hGի??Ds__??Ds__-QjOO>O>=oԞTѫ?Ds__??Ds__-RVl1TTfM_ke冇of ]DH!'R|/'״xJ"̺M7yURb?o]x:~ִbsb*{*ru%__ԝ*Z¿ Z¿Oky&tRMK"^ryK"^rh_ 넿)+ '%B_/Jz z{/"α,3/R$I~D:W/M;Ix\$숋rŹ׆zo>:[ mN]6{~'XT/hZ]$ocQ>{˭r8.$[s/.\|H͉^g VֱE췊%Hr7KopxV[Z| ԭC|PkNt ol62]"mR|H|#K_ i/Pϩ[ʗ|ĿWz6ssXGkYfwK(Vh{>Htu}?G6]xfCZ:+wݨhO xytmb /lmoYek,3~㶁xFТwy}506 Y`t|MUMZvj0Kq%O UԷJ?_[ kxMZ};Z5Igyv,_,_/Ad! ][k[.7>%d]mj,i+-'u~.tv_]vezƤ\}zB}Vh^{huF)[I^_Jػ_%\HNMO\M\\ -o/-e|k{öz..k7`Gwm?>] Frx{mJKԹođK[uKMþqͨ^Z^UIV')[lP\L5jqfg,?{5'.?>$5ic.gU߃S '/no}ļo浿_+Z~&= D!&%^Wi9ru+?ji#[6Hɉy~ zU;y_w|߻^si4x#O4MNoH+L;"/[뻽4O Y^į%7Րp%ŽFvٮw4X7g[r?e7O5ͨ Ktl;jo-Җ)?r՛Tv~ ҤI.|W[48I(|-Ae?4hpeRY]^&qM̫T!xxkFҭ._Aӷ-Ļ7;l+ݵOĞ{ *SKeym|JlƻWAhŇK=*yJ+&ߟvߗ/^<&|K}es6/R67ɻM'kV-|sC2] Y\hoX y_:u/0π4{F<5-ܱXY ^?>_nmܑor}ůY+:|,KsuR eBc[ Xmle. Mѭ 8x6;gZvw/̫{vȟ/Ÿ^i0+8.Rd\|kyQE|Sĭ|;-TQ24{; J +d6<>yvJnV]xúVa=ެm4 'o+|N?u "K {;(~x>m/?5GPԮ)$wo˫_ |:֝oy]%&MoZDZ3__CYXM- _h[7eJe5Qv[mb[7}]Q}-^XgvnOkti#N֭(o%l^Uݤ_#ζ7fTK{K[UDZ}io ;'Uz7n4@2E2})P(@2(E2Ewjm> NԴPEP)}QE2}}P6SPE>N)kZ7@)\aѵޥpZ|?_FS_?[]#O[R7nwAjx^.t ֬hHø|[yiY/jlWq.V> '[Mr-tw8𜧇>8X_~mFqxO-Wt1oٻv_oAn߇k'MB򺼾o~X#KI<קGu}:QBu-G@׭g76|>ߵςz/oxU-9j8⯇Dq;@+tmnZl:}oi%Ybe@4(e>O@M@Q@-O$w3W|~:޳jJ=~v57ܹ{uQ@Q@2EQE}QES)P(OE>e(f}23-EbS+*ڼ-e`d{~Joj7Ec-ihĮ\x3_`lxwJoiv70pݝ~+W.R#I{l_5+oڻN\'Ѷo ˶U|U\zrۮjJ<% ݯۮjJ<+ vGU/o_8Կ!/_G_8~Կ!/YocĿgO* RW~OJ_߆O;3my^e[M?jU2kʒ^W2ᄑBuWĚU0_7zEma|xC|ּCgNvޯupWR6O,H**t:&gH"XZ~xK϶#Z%ŻP]Epȼ-{ƕ>ݷB?Ϟ9_ ~'~f]\W~Y{wi9~c╼G7yN[ut_L4^WK}cQVO}two̵/m48'|dtmF?\ZSpxwmcJG_7 /lkЉ Y\u-{^xo"_Vv{殐~?ٞ3|A[ Jh.+WkD."|TԼ]-2_{#L~EbMC/[?|iA_XJ\R?t*En+~|ߕ~Z,ٷ-u+SEŖؓvX"2y[~y[~w7.4u4n ?'taH~Eܶ?4E_6KRx7ռ;p]O}/r/n%Fw<{S]׾(5 Zeoo-Z4Rۛr/Ve_Ꮩ}yiOSlIV>hW~:>ϊ֯Z\OcEl[hYVeW_'{c7WVnţ%LĻg]_tRv /w^o𭮩ǩ]6p3=yK͌|7}*ZW)+Gov/`K7k?\P=ofto/oU{ⸯ]SƘSŽb[uvO|7Z|DG([HÖ:vyq)ّ[%./}X_+Pk֥-/Y]dIQVh/Mf;an<7rx=j=rlߥčDoi|}?оxſ|3x~I,R#nXUOs+|›k]_G}s6嵞kn߶M-þ&i:[2XZ^ng "屮[IGWMN].}S'yV)7?>2Kף]#OM?{.e[~ZoǍ/tYn;A"o}%xƚ|0, c[۽3y6~xm"kԼ_^~.o iZo'DV_t[}&f!%]#AM:mWKUhyKJW\ۨ?/izMqy=WnvoJ_}ϭO&5Kuk+t?uϸ x=ЗGբ%o/t?7E/32~~_ d`tJɼU.E˩5<}n"(]O߁UoAgeЭ4pKʻ̭ܮ'_?^7NjV7P\hgT\dOKWM%ޢxWT.lxmnJv~oD<0xWO\hzk\<^+[vğ?>WxsF𭵤vɥKwn̬3$^Vͻ~ yōB?$ԮehL'o6_TM?VV֓-eҥOTXʷMqHʏG׼eៅ 5[o4WRXl͏[jKީo5ondմۍ;JW6J$oI|~Y]wUn`"&"]:̯^[hPs\Mujͩ-Kmʟhd+E]ݯ)SǾƫ/."Kn4/'h[`W__D>SƑmb>uMu4K{X,ٻ)dM3?=hEQ@Q@Q@Q@Q@Q@Q@Q@Q@Q@Q@Q@Q@Q@Q@Q@Rv7Y~ o h!>p|?_F|}kϴaѴZi_uLW5OJujY[ꚅk$}zo<x(KFryn*>o[o#ؕ y(߻|]rOriҗyijhֶiܼx$+k'1.ų]y煼epѳͼikgpd-ϓbU;[7U#eeos񏄴mG{hVWO#y˙]~nޮC:!n_ i^:|:޻h&ewj_^ e!Odc-AmM#_,g}FӼ?/%_6K,|O~8 }ɩh1KnKuk/_79]/?MrD桲(}-z?GS|sY^-LS\Ye:O-43e>|a;/xOY,4IogHM'ğ^ C_~)+!5WXM'¿4]OVeēn || ?14Αg|,tuVWs>_fc| '<<o >Eʬ }h-Z|52uC^j ʻ}wxfa]ru2]"_;T~S_ ~6|>G7|5kڕ׈ogP?үwW'ixwGv};~vZnrFko^O$J|K }zf O>[-U漣Y73l;v>;5!~zpۿWS^ԼM?Ķ6i]]V+0Ļ/Rm/_ugeyaO2m[w|?Qϋ &uMv!Yn!y~/=>k7='V2^-%*<4KZ Յjl_3|%U?wo+)[tHmZs7R]P迌0+4]ľ(tM[[-/X"mO\/¿|AtdYf]uW'TԴO bgͶ\@? ?_ o>,6:n&*:߿W}ۨ%k?,v^/۳7o(IxVED|06w4xW\KD(|Lo {%_ JJ' Nյ Y[U]V/ }%~kxCZoNt[oݫ.?^"ԭ}w]ɲ(vܳ~5'#“2l jy$-_7ʸn_yH~(םh{oݮmZ[H.:9|MQN_4wR YG.ˇ_c}0~ҿ |;޽9fӼ9ghXWZMwpX?QO߲t9$eV=ү ;/;@_^{oqkƃ]Ufۨ^'k][kWO[QRd^’zMSQφ:]ݾRv2K@~ x@%zy0_|@GŸ 4&ּ] A}PbV~Wo#gzí sRuK*]A,|#U_a?m/ i;x:%?Z/*4.?yRxWoMio.{}ZoπzWIWn%$Ȑblcl+s"Ѭi;PB%s;*4k߇m 'ÆQ(o14~ԟ ~2~<ѵ@e.>֯`oIߎZ_z,66W7>WOMĪvJ~>I-.ǁCIe].fD&"WE &ܿPpz>{z6ͳPO*V?d_fxo{}vWޣq3ƺ'/v-w5_do)/ih| }:ay;woǫv? bjWb>跫m"T[+?b^c+*ڼ'-++ҩh}kYDdԙ7|$_[kRޤWw Ž첯R>zơ _‰#Yfs_i߽⫔LOUQ>O?W~wAzU%)UٻuO޼S mzYwNLwdyu _TDžm5Rl_ _W/zyW+ڗD=w+:4{+:Po/J~u_džO;3u끗>%͕iJyĖ"~ofw^[sfk7>[,;|>xGK5W.|eޞlN?7}xoh:!s᥋B&vXD?z&߹=^ssx3~tOkÞ)𦃨ia,|y-b۷xXtZ7`{MV~Q\he|3P8GD64N|VO]rK.&E5~SK_h,n5/K/-kzz "\Z nϬD*߳ZñD5'O"4J٢h&$wkʿXڇ?kkd:R=5oR2"++:zeĚ_[:Yu~,ڜ[Iյ{-1k77TT:~kze=ΙyKyaWct+U[)߉=̪<5%Կ&v*_ፖ4 8aeTg̑/_H]._gV}ٿofTi RRlum[gFiv-tW7Ml%7w*/\SwwZCn>sA'/D?uig[_Shzm̐Zmawkur"-W]\ׂhnxNWZaqctӫiv?;v!>N:5߉%ewJg;msxXht,eUk|Eu}@z/,[#VmMJ̖}DkW_?YJ6Ph|)~Ȕh|)~ȕ(@# ~ ?YJ?OG?^ݲ +% +%{~6Ph|)~ȕkTxb:^lD,YWs^kE~Ϸ;+Ցwד^w֖]X EcƮM]*M_Z袊(((((((((+/ jV_?ZO\&"6h-huYmKZjImS#ϻdsItow}U+X6i]߻^{=n]qeҝK?vhʏ8BQ9|AgmXҢ)^[ |JT#'T5[=^ ি j[]K"#*.'_5} ?^ڍ yy~kyvnݯ>?k6v_|h?P_'+P_m iy(y<}Z5I|/ռ/YB2ڏ ?O5 xuyo4%?Kk+?߷nm{{/ۡuv]7ϕ'j.t~?5/y{mR_v-zu2dY۷z5~0)'SB(9Y^'y}a'L2ψ[]K\?a?_[6gsj?og=ϻ/:ǎ>32^e{\?Koٻl_JRӥYiR[/?O/HX/"|%~1@. <@mwJLWܿ"}A`@#l?T;>}WSqsi,Q̄W_Lٗ_HGկe;Ȯ<ĉ3~=76ݖ핪y־gۏGy|/E"X +m;O|#|#x⦚wzZ:57O2ىnȢ@Fi鶚m)mcgA)cWj~`|vzJ.6 q~g|V|qťZr[gWq\}R[fӝSP^zo(G7[}oZ4:hM-PypkTɯ|aȇiRҥqbZ #.W,k?ͧGxF}:Dz)# h'oAlE}ug,?hwMenƟ#JsJǢz :OC5wogk㟲#w 6ƯkŨKnQ[%Bۥu_*ʿkO?* !?~_<5Ɛ74۸Fn2_LokGۨQ4ZB}ɼ{~oR_2o?kW1)~j?[-zg_2[{T--dd_/CXO |5XGK5N.%t@,oA[a;}f:Vj,^ڿ܉u*m<GiwRC-VBeuk}/:޳oͻiIa}n2JW*P%6W05 MQ+xr|7(z|E&טA߱ cUQ QþOQVTYX5u AWko?2/>|gφ:?7563j2,4Ȫy7F_i?~|1е |:]`ZuxT<&ٷU'Xtho"|ߺw>}'aɨ|aSTik#/c]__5?5Xt >b<諺S #C^9WI5+40"3/jy_Xχl*ķozR9 1|.<[ERXVJdh\F-z역~KώZ^xn=;ʰ1[1?z|eor}Ҽ'-?e72ҩkݫRfGon^[;Hj|ևX!]nU>_*/:_[˖vCwjm m"bŸqkYHǐ̷;&WOT?o_Hxf_[Ro?o]gNLgdyu _TDžm5Rr\5#mܯj_@W?/zyTSocg_* \%I_\xo_#οTK߾z|O;3i4 5O-/o~_)e_oOD^Is᫝Wľ#Լ+e/W3o웾S$OKcšD5Ȗ/*j<ߛezW^xG_!xi;sVMldb_hM"@_~4]U_}:'\>j(7KwZ;ʐ^h%JW~{.QUŸV|to|/XOklukOHA7^F?rW]R̗1}^uo>Vϓo]d6IaE^6d^+͋!'^u5x0|W7..FSGQK,R>O)vv=k:jc[X/.Ep <_U-@;~~㩼Oi[˫~>Dv'ieOz_ᴺDŽ5mj}KS`cK/ʟwV ͤNXC,`-*়|E\džxE.Lݼiir^w_[~zb>!n\jC..EK:C>3H?})~mzrה?k_MZQ7x[襭7~uZߦE+;x7=㿊&4f5bekMΛkӾ0d_ Bо754^]JERK/ KN]Gc]6=U_O]]ZeڠLPnZMĴ%g/n|ah6ZiwuecybdI;@M/ #xQgmxWdVWyO?zzE3zyhߺEx?W^;l,NkXa쪟0RU,|]>f墀E2)PQ@7|(nZ7QY~ o kF}+7ῆ%,!I- FO5YR$Zy5G> AKچ ;]>Ϩ-b?zah?|nkjW1-le(Skn>.[kz.g߳͟"|ѵ/GuѴ]RX=>?<%Hmq+ÿᴾǨ==_c?Կ=Ɗm/_1Oj_MCC GECi|"P{Rz+ÿᴾǨ==_c?Կ=ƙ^# =C K߶]/.[boе=|ׄmG E6o5-{͘ao@Ex/7'o-Go/o緍[Wހ>|඿kb\̓'Ej-?Io#zE~&ހ>׀[~/aO4WhU4D~7gݳMw<(zex3;xY|ZfIXx%"~UzexMn'EO^-QezS Z5GsWi|"P{Rz?ᴾǨ=={2GK zڗ# =C Ki?_c?Կm/_1Oj_@OECi|"P{RzmO?1j  =C Ki-x6/'/GK zڗ#^ =C Kڟb?A={2GK zڗ#>ŨQz:6jo_U-ni|"P{RziX.-S .W}˨6jMlK-H+ͯ( 4U%TFv^(Uka}`q~$^9Ѽ3%֑ SUM>[owʕϕy{rWu/ |M( [>_\*ۻyL~έxwG ouKT]2ʗ~U-h?˦}of*vyʊ+-y^FxRy]xzR,*IKDgm/+٠է%𭬚ͭI}~ٵbkMo9 >6In崳[NUmYn}k& 4u<6+XʺJȯ-[[mpZu/x JG-W[|`ῃ5_k:SN_gyw Y;eRIa.{X"[GhN߽Eog?YZswI.k~&ԓlњU{v t)W|2PZ?o[}`ʾnvk2^gnzFyw-ZXDo+SnPYᏎ>'& 0h,K-y]yn#Kϛީ#Xuw=λIK.}+bx+>oh>%Do+6}͸M&mS54{4WfO|۪oUP\VڣjVD_aV>|7ʩVץ|[xKLoZrE=z][.ev-vO?]?%qi^gī-y߈?h{-jIQl Uu .>lPTw1^Y>]UߏCXPT]Zy%ڗoؗoO]J[1`wdm{;L^vjF}GUֹR ]xi*nFetJq7n|(K?!'>O{9tSĚԓOvKmگ̿goǯ~֜Zu%Hj7h {0ܖ-Wa@ih ԠXI|Kn?g]MRwӥyVP75n~wVzӼkW:5 ?½?"k2`|)3 ~*x$b-k,eW毉>7?j_P#;].,jHS츕_cW;S75ڏx+407}h}/?+WE5R-J]4uYSǍWZg59es¤=3_߰Qr>j JKgtW¾?࠿fb_9m/ #KG"A+~y,D QT>|I?gi>91֭J̷Sb~_ޫP'wʐƲϬ_<>ߛ/"D|G_> j&m ukǸxբw }^5mOZkCu/-o%V6IZDO&/kOۥwQ~:dگo֡x}Ǹeb<IOOuF__5f |٢TQ|k|d}Y>I6.Zo|2B 3$>ę~'^M|_)Qg[߻\q+&C÷K-n[-Unv_U2qWU'_^wU5}gCkr:U(_?~wX~Z \[^%κՕ}37w ]V\]i)g":I.v7듲j>0ZdI-˽?F'^bQʯM2ݵw<1/xI񿉴 hwɫvf_]YjWz'*ҧ"-iXx69-.W֭l[{Ymbm|Uw|)"8?N4_Jg/'#_.|#g[+?-j!l̿x^=]EW3n:||_s[:lUJ6|8HR)HMWj:x? OU٧s|fZ"CO->պ]4^Rm۵v%vF$w_j}c<ol_xemft>3H2:/׹GQkKN?dʕw}^ݻWoqQ%1Ŏ4O91_ ]kZt[Tg{ NJ H< ݪ_~$x啖,t|>}ImIyJ*3*Ur˗=yyδ#uOE%TV=ƯpΙ=Ϋt!}Q*E.e_oVg.aC4_^ΧytݯcH'QgYUkW1!}yQ9귛*H5_fK{hdj۔ȷt3RܺɬKho,EyO깏~<>I5}.=bn#Eqo/ vrhrkQjm[rWXك2W%*Yxu'ּ]qj)dze^_L>W=kW=ݥֶzƗ-T;phvڛ~+}կ$۶5Mw>h~Oxz]t"첪KK+" ~F^>x7xߨ>{T}ԗokcbETUD_hblOEH|$J<kEXMۼ/IJĿ_k?uI]Z}0w—j|H_[kf`_sWZBNQF1(3Sp o9kM7;ycq,Rn*}eȵז~Z}F_MiVT^n)EƇS:^_]Y^Kڄ?*gfzdпVoDׯ]At֣ZSMykQ?ڿ-K;Q*ÝWc֧'庺So>1E+ٻY[j_9hO(_<k|k__<k|k-!\k:Ɣ4ۮ追n׉_#P#l/_oVA]!&*'˵*yRkY#d.ݻjtW_jR>"{Wh5WfYV$VӮ]Z]^+ۃK6*|l۵Xzޟ<4mm$XmV/+mlW|ֺ4-SR5ugZq}ު͍j@7|%26ja_n؉})oZ =F<+v6G|Og]KOl% VJ^"%3lڪ@{SG滣jZMť=>U-r2o~fVʓhȧe_(/njwI⛝>Dg?kESDҼY7t o3MuI["&W6->Uۛ.hu 2>[7̸w7~o/mSYzVK[r|/_MEC/_r|P>r|/_MEC/_r|P>r|/_MEC/_r|P>r|/_MEC/_rSQ@YWݭƿr9SRXjW? \zcXjW \z28?6ҩk 9?ݯۮjJ<+ ؿݮőK7ܯ?j_@W?/zyTSocg/*m\%I_]xkm/%No/׆^mO6޼!x/J5+ 6QKbH};'} F|N\ţM,Ok*y:_|^)ϣ2Ή6M᫥DDY^-TmMglk> 뚯%cs.c.Oq}pQFM={--meo̱D?ߗ(x |wc?ZmIP<[R[T+k;!m 򬣾o*m<-vͺ|xAi[kq=<|d4٧VDu.+q/r)~_6o-~x:-rRI(+x|G/1<3_``SI<,/xKK|Wa^&O;D?ǎ=vOZI+<St~h]/ި º^]CjZee^(>3W0WI>yys;m5,u2|{wIσ|jdm;Muw KY]gxWPF?h>'[;9O_~ЯݟI)͇x3zt{qDn%s_+Uj?xQ-~]BNdh u7e՝>'о8xP+q OJ4[u;n/ܫcZ$_蚵nVuK4?oƙUu/>%/?Zv..5nhWD_ q_.u_iEuuuuĶ~kKK߲楦7ѵ->,|5紊D~-s@m-lY-2-:IR[n2ET<Ct˙d6w ^ phMuZmooqO?z~ܮ[cM⫍QUX(,eqjߺD&x[^+moj| .éD̚}gھʨv-=u~Ixr¶pZ@U۳,-~W~˺堰fO %V_'R/ڿ{u~пVGwmj>,_aӼ{I4ۺ7Wx{jjWڗt\J-u~M*=]iw:حn>O___+/sKxu{[h44LjY`sKm|ڀ9?O~'~[L;h*C7xFma%$%ߛOokXEE׊/"Bo;|ϭC&/q?\k |HҪIڍm$jCԖ'_r[vżw+Pg:Gt_;o\>xHykA*eWwoدύk/&)^MdmyVE,In+ZOGhZo}?x@ֳ9v2yk ZS c?a^^e ?€=~}QE(QLJ}Q@Q@Q@Q@(S6Q@(ֵ+/ eZtaѵݧ߮A"6=ox6>2Z$[͙UUu1zߊl+&÷rYq,DCE*ʿ+@|auM7LןQB4ZXb|s歿G>ie]Y;Erb[koj4j nFl$_w/֏j+mZ{/$[v/[C[zKVO>Z_%Qj>Lޠoy-=Z'Lޠoy-=Z>[zq)u6}(- Rl]cyO^;Ю-Isc߮E/*߽jλ&>[C,E&7z~ZWB΁6[[P<+έ\go\][KmkP<-,^SgoYfRRE¤^Wlhgͺv&k}?ϕU?>}6PmVm3Ku:[YW|;,_~UeDUo8"|^߳{\/~+KxsKmK潒]Mwj %^GWWW[X xXWIeWv1+-\u:7PYws~Y~q'MkߦA E[o{iU%7ٮy^V]kO_|A_r h{;y2yqVI>υ4gEw?HWKvV<9Axsv$iL{$;+3'˽Wuc[~熥MgP)%4q3+ʞSm2ŷjM3dժ5ơOend4.Ē 3@oCσ7YM/⻶7W{R˛iD*Y{_^RE~MΊf1w]gQhPY!YV)RTu]])]=W[YOie{TVm_6k!粺2q -]gի_JYᱥi̒-y{Lޠoy-=Z>[zKVDhDh|GըLޣ_y-=Z%Qiy-=Z%Qiy-=Z%QGcKVo}?Q@ը[z'#+G#+@ jʼG{ZiՕ~v9SRXjW_ \z_/?kBz28?6ҩk 6/k?6҉k 9?ݮőK6W?/zyW+ڗD=w*)h⿳)_+ zEK^ _]o^gO6} `&iVޟ:<{ןW6ec&YZڳyk,Ik>&}mٷ^TMmfc&EZk$w?WlM*+5{;;i Z-T%Sd[7?ͷ^g2\:J5Y _kKeq>c˯+?+Ӽ_XU:RO/k^K~Tپ/rKO־C_icKTem*?\LCY_A-BWPKE.ަܴx@\ڒ]??y{{_6_[gΛ|?بtFݧĿKk*:(:K}>)xq[ߑ~_~2k:;x  _e["to7b;~&E;(m {ˉoݭn]~~oj"L<W=mf%_>6kj+vYm؞-?g , |=N֭|,t?>E׽lmQ1?~*7kz ו[.[)v+}K_Zizk3P}1~ֿeimbOTU?*Hi<\*|~UEz7>(6g α-ՓVO$}쌫R񭟍.OxZծr^S7_vߑ_\灼YWwmgwENFͬ|/:'O|lkMN)|=erLg|X_o7 OiD6Lz|Q;\AueoXb !&xYkMW3FO+:/ۄ56|23[vn|=텪%ƍtW>t[W_,^%Z<c4 ]/~h*=F][?@Hi_4 E<=qZ6}scɿt|p|xu}+YgzVWD6:ڢ|VhO[rƷiP\X;yKU_hgj87^7Ox @7 ŷwv=w&h_o}v:]nl-5 |s#Xѷkm:|=z_tYn:]b_h]+_v@}L4x+6iΨϨK\/LYUuOe[>U_k>$ki>mP~:_VG?LqsO˧_5[T|eF۱lK/kkmmhRPZooh߶}E|oKOȭ-Vk{6ߺykվ{CMu?YO[W}ϴ~r_[z,znbHZ*ySI/;x xK8[x_ Kԥ{O?q,O?7K~}Lxk×^775J,,~!jțgDo?՟gxGc|x֟zM'YɵH[|PNe&krԬ,[O+jWcY񮑩:uݷ,-fX)%_*_.T(-"~(>WbTgv߽_kվO>#QlSo̾TvtO <E[\i>2^fKrVwſ)xR5&G5vnUS4@gA<\xj=cwngE+y^nOu?*Gi??^kG1%T{??^kG1%P׆Bß}9}?9[䪉cχ/=#Z]Awk6)VX镶|ݠw_)i)QEQES)PLiPEPEPEPEPEPEPY~ o kRA B}4kEmw4DYU_=}yGĺge1xK+{yfOD{f/V'Fn IN ]>XMv*=|7::ޥ^ijJ}3x[Wtt_Zw4=K}T·>>.뫣1}͊__}? _^9c﷦bijh|׌aޖ3}T{?c+LTeio=f^1}?@^)vJt^ };vO[v7Reio4=>T|Rutmymw޺xM{smqxOᬮ}J]bTV5nlE)WFc?z}Qo˻#;/x]Oxlu}6dkʍ+/+z|O/V♵o>,7^}QY?9@x_ %L{{6'E,_o)˻ξ:4K,::|vq*yfdb+OktfOPu~,֛Gn7v-gsVnO?zGn=d+|M.|CTp\IIW#bU_7tRuz'OŪ.|M?>ɩ%ڙ7,e߷b*Y|գ7OU^{^1}?PY/4nZ3}Q}>_k?GV-/5Zg+LT/4o_-xV?{?c+LTeio=f?Zgg٣fc+LTeio=f^1}?@F׌eio3}PrV;o>k?6-hd^m}摒Jo^A徹ԥYbqw/|Zп6޽DIwE^[Ǿ\.?mzgNgdp?j5Rr_[K_|xWAp,_|Rȇל}rqC_Ƌ{+7Re޽}+OٿJ/߆O;3j7^ln|=fiE}XcGeik&]FW{meb7Y{e#Joc}caeA*-$qwHm^_莝yHZgg#vٟ~ϑ߽ᮉwo7_YkWlAoo&y"U=oi敭o<4, *O,_C!'Ëi|Q?m-ZAZռ5hK}n5[ mg'$=ndmm߽~oX74BכxHxLuDKq76-lg]VO]Ǐm风/Kg&?$oۉ;'+ݻ|jm7~$j/ܢGv%K"clo4 xJ_Q;;=W̏eԿmb/P׾&4Y$ֵʗV5iY{?D Z WL#YmxLeXzvmgK+kPԢayu< -h_}~~͖^ԥI*~[tr$"hxk^K>EL->ճhbvmU4-SහSҗAҼ߲*[Rו+**߻i;c?o.aHҮaW{/\٪ %qoi~ds7I|{Ţi0ޗUo^gxˤCM.w^W<~k-i$ҤWӢ }WR,dՊiLS@<_`: /'¨D &]?N{k>uK/=z!oT5 ('g𮇤x%{xWI&_ t\%.7N [f]̿~X8αR]M3AG%ۿ_hCρ~Jvͯ^RYrqՏσ޹hSKuk#Bȴ|0cmL<}*T?foo-GX]]4hVW~ NPB!TQ:[wڑ-ۻ;5>|'7u+ * :ZӴ)h>g߷k'*?f/}:ouVuwB77b|EwoeNDrKK/oPYk%om?ZhKoK_2ݻoּfU6?Sb$[?ٶ徫//5+ZgMA2AkX6;n ܰýG~ewJnkoM쯶7T~߈tmCM94oJ۞l|_Wh&-vE31\}w-A'ZOƻJnAmo[E[?~oj )?u&<7 ζ["W؟|wlgO_3GS 6mD+2X~m]I +[]~ ^T/[>bV'H']>&E÷7|un汧/UԼŸs|sƖc̓u~_!?3IznuM8fUlyVVH_ :4]^u V+hShP3zs*yR;&+K啾+ׯ| o h:|n,>Щ[7?-tW_h_|;]KEbokޒcVn^-3,TռAM\]\P/UTDRi&>$?A$*7-y/>0md_emH[y"waτլu( YờګmV׈yIʉʉbwlEƟDǩA6,!KX 24Q|_~Ehti0_;nkyjONsV6ڢ"7^D+G_᭪})(SQO (E>e(SOE>e(SQOOe(7\.<1Ev@S7/j_Sڻ-ܺ*ηv~}rLոeVi/Go4 L%ZKEk-$R}+5 +mOJ}SpM$>\w kw[|~ _|=;^4mJ+aխVLͿרhdSDox{W}WN~ͩ}ʕ[k!dK6&M?k W2R-7Rieof&M!Oi|%cYkxÓZ?;qO|[m}[V\!l/U)R/x˽k|=xFDwھu-*eiZ]jnO^~XY4ϴ _ʊ'YQ)j?b}淲+vKYWcV߉e$OÍ/?dq:l쫵mo _~oKl\iÓ˦O&E2ZDvޑ*;7}Xb+9Q"eډU_ odچǰ[Wԩ?'".MŧKz)} /˳[' ?#EԬ[`Dȸ%]ZO7}[lxݑ-YQm#{kt3φZ拤N=s+<QCoJ~2 3jWWxs:j 쫫wMSsfx+[%4{U%"}3nu}MM5Q/]?>S>*#mKxCf}.Ϻ+mij [(Ց>3TmZVV6q^%V{Z_!oQՠд9Q͵WO r/~?zF-M^m\uk=ܶ}Л6?۴9l4Ki%l7/~tͿhME|:Ese}Y=O;Ļ9;۝BӼJ-ctQM+xM^5ÝY]rꚪ뛾lT]?'r|Oguzڦb{  OxNԭ}ʷBoY~J>z/wVkf5+1׫%|}oX^0'dJ&4S\p-)~emwwɣwʩu7(<][|=eo&}UeimkĪu#?5=kS1A;6'yv5 ?܊-Qk^tYmXm$>˻km_qZ;>%qOi)bh޷oo.u[|yy,;;׻ixOc~GƯw\KocuJң}bl'oT<5>(T"h%RıEK/:g|UUm;esu,Q}7zGW<}5'Q{ȥhbU+~@x4ֵ /P5[ />oQ:ʒmDj E?ny,b)be[uZc6+OzLv2}M;M?݌ʹҞ) n⾖e-KEu/ͺ-W@dž|"_ 46kKk}^η m^PxHNoV O)Ob X6n-ŌN/{qk:lso OH.viwg&ڲlo y?/""kfZ_YAqOrVy_p۳z6㪞>3뺮e-oʊ2}_AQmǩ>xG:jM}"FV6rԫUVSO?xXdEבqieTX-I_d_%/i _ m] DQ, %i^#5Yo'[\mn>| K֡{=|r_3Z_u-oIx4Vsp^g]\E_|$$>#խIX"lVgGq+ի/ÏG&.4[X4vmiT~]ҧH<)Exoc&T3Kk2_A?> /ZƋei6 {_5I~X D9 ~$jv~%ճІ-ԱE[e^b\OuinU{|v7'?G5te;'/E/ԿǽKnE{obRbؾƫ ↂZm]uXSPR?6]mk\|&v_>ZĚVڎ[AEbϮ2w\5>mݻ cgԯO{ཞ+-/ۢ6嫾*^[kV."}>XvV>xNY4G3[?p,mW>&K떾Q6KiߗOX4x,J_>Gb-}/vh5w4 FtWK(Q,{mo dk~|v_hn~EԶ\?E}V{{ĝ:}ŧEsQES)QEQEQE2EPEPEPE2@Q@Q@S(QEQEQEx<,#kVg_ѫ@ x_ڿlj.ŕ7m}ĉ]':>}*? EfXţʻ(\֛`q-[}>iv{]6ggXc2٥ykiw1^jKw_E?˺D[NT}㎧w>j6?}i=. -eMg̞TWЧ~Z猢t˘ssJsD[kO-ZdOyi*coJ_Tcy$WۮtZ/WGF'<4[\K-֢QD|uU"9%ة||9x-o^t[Aq R6Rңh[K9{%d5oU*\ܼSYK>%eImbWTKo/㖋CF]YRKiS6%t{ⵓf{gڈkji~ 懫Zjv:eݭio/./}|LZu3,WiĒw-s|sx:j]=5<ݿ/^ ͵IVw0ğu嶟>ya5ݣIś2:Y߉F#)ǘ_Gok^Եcgz흚Ry| .e}%'o? //cM?GyYbХFfoeZ4M;.{}Ҋq,TZmWRGylU߱>wj1$]COZúwHWSvV]G4xUyIwa:KmS:_ZkȷoM1wf6)}fX29\omk,Vog=,Rϕ?~YhjR}YoޟtCY{Yn$uubnlu_gRG[{~_`}m_na!͢{ץTeO).^(>_O+vMR?wG"FʓϽ/ۢ[je?٩~+?^Ӿ+.,f6S+^f<.>+{?٣kTox]+QWle]K~ =w^ZW =}e%YuO>>Oouk=Y"'3$Rn]_~}~^Ssе9|C,Vi-;I~Og5~Z'jkkwJ.?S2#+ߕ/_~T,h ji fIo~=QE/+;|G8Ӽ0:hizC?ݿu*mYmQවmnth۬Ib^y)ewv7ͺ*o7:yl,5} (m m,nE~ K|tu?ӭmo}_NmաV"N^ŦE]i6|Q*yn7T|o׃&4Ǚ,IbRbIc/)]h(Ҿ)kگd-+Rku7q"K\ '"-uu_=l鮿{w>߿Wwmoڿthgb,[ڸ-K_ <=hꚴz=6j6}$tV6KH5?w+O縖 ,[~MEu}=PKy/r4fח sibl{8Jr߽&[}5]06{xKĬZ"W$AN F.J+qviF]MPukѢŶqƏ f˻{oިI;6&Ś~kt3@[vlݼo)W_xZNJhnxquiety|W&M75ׁ>jQljnmZ4gݻoO|-x@T)g{W߱ٿ_HW>gougqA~$GS:OíIEgMAsp=KW#"GF4RvvVK*eWݕ?Nωu}?N欑ipE)f$)bIb2*>|>&gv̬+Oz?E/mQT_έiCzUXb7Tgi_,4RPM5t;XK?FWhtZv*;jw2칖-boZσ<%]wqyg;7bVIoY:V̖Zs[c,ɷs7d_i\Vi m7<S5]vU~)6a7[U[z/n촔RT]FTw[??6ZZρ)jO}V"{k||pڏď|EҵKZ[ib [ok}wo_|TOt2GnmVmۚeoi6 4Xo܉n+|˶Vo $REs^Sg]'V<_<#x&cM'◅1|>ftEE܍7-q>-gt R+W e[KpQncU׆|?nE ͎iIA jWW|z$w>)״:~/6yas\nLepKy&m{-b:,uk|W7~4b5MKm]?σ"iƮ4ҧ}JS,ʑEQE}2E2@Q@Q@Q@ L@((l_ZEy4R#|n_?Z6~~7X>TԼk]}'_&6=z)͔(%>@2b~6lOďO𭷌R–WdCD:2>E>?UѵoY^[ݷڞm_o}ʉ D0^~(E,=/X>$=QxzOXFKm}r9|x* úҧa?|w-/4)O]_'Ψ6߈}?I`w,qYQ¾E;xRXn?[7P4,VW@T7GC'@躼_BW[4[w~yeIbؿ.ڗ㦱~_Ե ;`Y<w̵ }FSS@X(]F nVqjwV6)XJʲZ'v-rگ?XѾ cRu_OEՌ]KZuܟ_U̒}֧OÏo|GOxO_-ʊuF*'O|Pԯ>1&h^0bҖ~ͷO@}Ryn ӟs k:\h@X"Ieo+{VWŸ'w7w _ u+A -|xSSNoK+k}j9>G֞ӵP⺔.,?B]5~qk<}ǚKj7|W-峗gߕ]Q^F6W7R3L'n"k|G}_#"Ծ!韰 ˬxmu[hB_)||ǎAkqz,x+-͒}(2Ou|>#ecy귿)uhވ릧bF 3JUR!(ּC)?[֣hjl~o+'1Ot nC'VJ=ǚT*1_ys/c:Zk̯*tYeuGeJۼ~j=zZV6m%Ojޢ9sK⯉-wv~YӢXXϺ7%c|!+no >;+]?@KS9G|\Ե".i>!&k2J[wo.W/Se埁#mDž_!iaL$qu2? ϽOͿIK(9yRKXSZ߹ѿ]g/V:s^VֱZYFכ紫w@xW_|a ’jKHƣ{ijʺ$y7_|__ĶV)3_[۴JD2|j"oB6޾"/cB6޽L'32<Z¿Ok?6ҩk 9?ݮőK6jo@jo@gUe+__x{_ T?넿WW ࿆m=yc˭Utwԥ[}_X-&m5zx㽰_u??%Yٷ#yw➉6oyw-[O=|鐦ȑwԴ'4x4kRMog[Ymґd%hw|'O4zVe5wjIb=/;+fNHR!5fc9|*K>v|UxcG%Mڮ?_M/ŏxq/c!\i]ZjO/k^K~U۽>ڈu5)5`lzf |'V2y-:| Et=m$ԡxf0[],_#aٳoUD7}uGq+]X5Ķ/߳W!՟ĿO/E}9%w>^Uɾ-W:t  %Ӵ7[}IYftV?_5Eύax\YOXMguu?}jʷytۻ~vR>O͕[ÚtͨjzL>}_I˻#U(~xB}mKUep?+nO+o|r'/7ោ/,.u VPb+A[_zևzĞO6Jʞo-gl |;M֭|,t6on_)>fq[ٓPCKnn"{=̍/ڶ|E|U?˚Z9}V۫y^j,E>[R\kjLstm4?^RW2ۿCkDeK>hk_k2'K:خ3hv . 'r|:5x_l5>ڭZZiV[VڞRZmOq{ܯkWV->+])9y sKכ>>'x]bUї61+}fzþ0XZrhKerE_eZ>-Gğ-_gZ`:5om)fT]l //. Yhy}b}αwur`ԬuS]u-Vhk߿T?u/b!5 9o4y;JY_xW_jLr߈]>%G|)k"o7sB|C6ZuIm?5"+tH+Du7[[NM4-]Eoo۳6o\W~+:$QRWA,[/yR< {-3>% ϋu & BHK'_EVإUPY?sUҵ{9KqPMiyek/)w}⮶z(-Х.nݺ-sk<[9wf>㋑u/jvm[{-1e\Wj|_71vLa.Yew76|.'|<%֟EZv3%VwB*;>5>|? G_uksk>i{-먵[+M'%v["*sj \EE)gu,[bn?{"Y4_ozt]5^`_)]{{#Ox;_4= j$+[+6$b2_lxZa敩[cyYymS[Fm> zƷIMySy_e+y[~q#잟p=Vr/ۺR)jl?4亀QG/dlH৕5vMTMQnjg+֪۶񅧎9oEs ʈQyO;'ԇrG{'Uͬn] J~ o~k}/Lb{Yo]K/*U{ePյkyoni}yM+Ju+ִA[WLI V1K>WojE*g:}YԼV/湳M2Z 2][DDɶ'?|'A6^Ci 7yjk6_1YB UeTH+۶_o좴i:Vw7쫮1u׽"OiOZǿ|Ew\<3}ce}-QEQEQEQEQEQEQEQEQEQEQEQEֵ+/ d'W]q>_q7Ѵ4QEQEQEx?Exl<|ƶ<7;=|^]rxT:OWmeyOf_~>#66ʲK|ݶ-mz71Cm՜vdEr㉼G}? ӯx%*/jf_/-.t Eg5ŔUv4Am WZ?VjR_@>k'B֯gso?ކ=MfJNR¶-ۺ_{zWMH-/*__Ch̼"h?~=>5uhii4Rj+]f?? Ѡ<guk;4ʷ2nk_^E7!ȴ¬$? #o<;⹼KYiOqW/QmV칦hmo=hZllUUd9PW«?$?Uz"zOxvZ_&l&!`l[~j| 4|2sjz<#e^j5|'DߺOSmk¬$?Uz"Ձg |)#M7;,w٬dkV{c,>o_U" WZfo{c}xe_RT޸GV܇Ugde`G '! տUz" XZ3?/h1i,ln*ttݮg_>/m?95{Q ~HwJYKH-5)~/E /Oxsx~+UWzO%E}swooT|725𗌮UJ^i]#߲&kEmobVjR_G*__Ch|s/xr_ņMvfy2wfgZz-}Mu5Ie/v~KM>)$Z𿇥uZw\YqrXW_]f^'ӵK0}U_6+].ú}NO[ HW#nfo_Vo*coChWMH-TҿghB@tMNWӱ,SeŽ75ŏlK,qbzU;VYKH-)/E^KOxS?,׿"K*GڭVG6>$i.>7IYv#}Wuj¬$?MsSWJ֥bY|"E4jGgÚNs o%6~L,^kJjwW{oK-GLҬbX--lPƽkf>%ȧq]"ѩ_.it/?%ȧq]"ѩ_.~ԿYmzgNgdyO _TDž_?mZ¿OkduR M"6^rk]9 n/ٿJp@J~o/p@Z~s?8_6gmvu=5{[6uM6_V{wU/+fx]Ҥ+h_q:}fv׆z%Kƾ1|ko v2YwSMOs75п2u߾<5uᤆmk-bo|Qď/ 6).-kY|EܿV~ x^|Cml&o-iUgmڤSKn8-5MF4 IQ^NG[7];|){|?fHĖhQG%ӵ /Qde>NL#>6؏IS; t:WNtXm/`;kɣO"}d>uܫL[Ic }#$Qbʻk>ok5y}9@=ik/~;x{LԮἎO7}/6XݽSzt]lj;?V/7[g_ի(Wegͯ4=JU$_#.5C0||"č'tU_X|%ixhxMK]o*mAK)Y}]/tX?=|D)F'TiDYU WRqگx-Yee;T1wtZyѶ{j9'[L o~*SNv}wG55ͥ~n۩YJɵޣ!R_3Sĺ%΢ڎwkY$ڈ2'm_Zz%.P;:+{0ltJ.V|ӻ??iW]4iiڭa(Q%K,M5>Zv6Z\p@5ej\汯i--q2lt?P3K\&~![˱_6WU7_C+/$s>ёUz|7THD3?t} H9E-ٖ'Y5ZF~DKM7oU'iLHRڋkxѮa_7&2m?~4V1\ߊ֥HNs>yJ-?Z|Rt7$k=5vXsD9vW [Ŧn}]T>oݷoV>'kRёu4y.m<߳*o_hkD]7JǚLwZm&Xv>߻PU$?:f~G0{HjWousg.4zo>\K\D%XT~ZGn#{o7DR7 O@Qk(~2ʬ}hDz-KҖRhYvL\n?vGGYkOѹi~DG4Ebw45Gs3Y=kv@yLT|ԚwO"ѭu$r#o+eܭ}֯D~'l 6~l 9)b;^5o=K2kgX՞_4FCR Ӳ}+\n4d[X1g 5s=as*ycn( J1;2k◅oYVwg{n.xVmYwc-f~_i{HD6O\鲮Y#g[]¬~VPSy.,eeXq*]/N2efU-Z[=:ݯ%6dO#vpMioizԲ\<gsw\}Ŀij[M$RW{v>m-? dMuX:J<7^>o棚";\sHMqzZkZ;kIT&kGÚGtXl쭻rs22)ܚ[vcA,ⶏP漿;UEouqL/E*. DB&'#_-:Z?k\xWZ&>epeknyC;.s)>]}'_&6^J}'jm>e=f#_)u NOi~gXUWh|G6xmJ}/l_$[UUmcgE']ەy? [K?KĺKJymၭVX_+z]֥ibgWmSJ sxH:?5MQEQE'jZ(()(P袙@O((eNPEPᦟ.X'WWuutf-q~ .Jڏo|ʻQokۻWM>_}*{ 5W Xڊe( ߅c4Kk]c4Kk@EP)+E;J/zzWw/M0_Ѷe8q?o5Rl_?o5Rr\5#n5K"^rs]9 n[/ٳJp@Z~lq\%߻zO;3iSè3?ƗQ:Op4U?ۯK^XV(WIVox{Ɵ-xgQ< ˟hKs6ʊQDD[WX[EaeomX}xsOү[_{%ߔy݊;;^^t R[)%ʛ7W ?I<}x. YRUr2qf4J@.h^HM7Wv: ,O,_go}j4JBAv"Du-ǚyZ mzNK 6~ԩ+yr_׾'!]6} spmf$۾קU ʟh_4OiũO4y5ʻ-e}|E.|=oyKDpt׳cq#/ix=iZͽޣwsLw!iVRN kĻS5\#2Y]'?"9y_ -4Z+F{?if|VV$U_"5%xݳg9{8{'i,,l en^X;*漿> zʹ`;yvO{1~Hӌ>:M*\ +`]3aYѾTPhxTNስ{=>%%]_ 5He7#Y[."UFWup}j}NyQ/xקgond[$Wo·>GY{ʗ‘D,oo?֎}i{ SZ^[(FvHĽ?JGW^tHk G,_]&=)⯖?Jc.c𮙭iZƦWZcE,_e>c4πivzݯ#x [|b7nMOK{WXgv,yV8NcLG5.,.ҭp:ѧhZ4ֶ7^[3""%H|s}kN0+Dk&i>׵1je3݈7تV+cbOrO*ݶˣXvWI&NwKD{oν*hq1JouMcaitj%E$@J7j}!5h&T>-~@SO~h*{*fcH]T]/bcjb]6IZF="_*1F1V4e{m67d:ǔ%j4|Fm8[Ij/P/'7q| n/K: (yHҠ/hlm![DEzymSJ ( (P?hwZjwseEv((((((((#ZɴKK;Y׮}lSGW#PQEQE=U> ?aS[3Ze^+}>nqEPEP^{CfImЫ~W-zQ@ğԯjo׭>$ȧ?tF|Sn?mzoNgdyO _TDž_?oZ¿ YTm|Rȇל}rqC_[K~/➑>0=+>#[Oyplyv::oU+ go/ؑuy>6PIVz*մ[ {?OJ*"AXǔUf?xMB&WZ~ ^%2EuhhrWLԋm%O迂}wFV6ʿ;=N\ ];XX, HUO#oo~&t\M5Μb/;8KL | |ͭV|W+?iw~}kH~_/'MEq:JmXAn_g|kZ޷Z%.ܬ-t4d<-Z1}[7wmsģ}jFZAkw'^'Q\Ƕ&X)Wr= ,py_p'̯E[ikgʫ"ʻWw"[m_(/qKfui λ|5\6t<1VXs|f9l\ .&OU#&wo{Q S66W)n%S4"wfm[>!mEŊʛ+֬mo.i2J뵷"&niwW Z<v>o֋ Yk7)ku*/M3\ZD^#O w1<.77 Vψv}emcn"7'oEp6,óW7u\}~O7ڟ?{I4ygyv;$ _wPOzRV7 rI vgpzܝ"OUK[\oh.RNpz-?Om/nV)7SmǕ Sm_MKkC(mܿD֢(,(In}QES)QEQES)PL@}}w>ԴSo}2 }PL@S7'? kVOZOkFl}k^7g=-J#}+K#4Zt o}Wv=SJY-/1Kw$[Ǩ? Yċ{놷yU$3+˷u]_M]?Ku_jĖ櫼Oݵײ7Ju:.O*}2{#w<;kz6ZֻKPxM 6]J~&_.gi?oiVU&~n+tu紸[ZKZ:}ϼVnze?vS⯈:o.!YyWz6^ Q>ڲ _ڧ=zyw}R=oÚ1\ Ԝ%z7FŠ(EeL%NU_i[M@?6;;MΗsw-IR[o.ޯ~UWגYZeu=W-yE-B~Zev][}W~-4hږ ( ( (EQEQL.f5툻E3Fik̮~?ZI U/Ktju|S 8lW|_/u_Zm [{N(&iY붮ݾM^K_X>՗Ὲh^$ҵtiNʿ]MV o8Faj'j>&n0gJoϖW7:l_ :D:gȖvvS(EW*/[z*߅_c4Ko@E$ȧ?tF|Sn?m}A'E9_5+ڛ`qm˿"p?#>߯jJ<+ v#~a*YTu鯛j_@W?/zyV+rLj~͟Un?넿 W?zlq\%߻zO?3b4\JѩEqqyI~o~:{? xmoU?oX%/^.|k:,^MijmbvnUOkm$ Vߗg^CrJe_Ny>oYWg^X.XZı+)T?j{_kϲ|*V~Ezӗz>Z,O6x܋ծG'i&$N5o>إMk`Л5߆.-*ݱq[;úB+otY+oUݻI4R+/2ݝk-y߁iZԠҢҬxew5͛U/ |i-.-).n}O첺Zﺂ=N>ֵk[.AHVTeڕ}ρmNҤ[9;w}ŷ7\ߌ~EZ/ĚUvC}T_\΍2}Z5o j6zoH"tmoaմ{7-Ԯb[gn[^%|+[[NHv S%uo|ՉšO÷z"iDHvv|7ί ֖,5YfmZ[/~oXa,uawos[jh{i#{," n's752/OYWodQKLmlht#ﵭ[Lm쥺h/5Koo^2b{}ׇ4EI"Wnѫ xKE!ѤY^ |Ӽ?˧Yd/Mtʳ+|;|gѴGn[WY|ض?ʭ}᪑:xfv]E)xٷ|Bn":n_~RDek _W|%>Gc2IU+WY?k=k{}j&]D"%9`tGoZ ]yP?z;6]-msD_|9͠驨2],R4*lڿ}_v%K1_JׅwEw\e9N]f%DSgђU܈A^'mh^Sn3z%c=w[MWfVYW݌ni0˯k4f>SW=U N?ze?6o=ڠ[<~?xfmf)niʌ[9'R4 "ZlY%M/s7dl4SmݕWo_9b{_Io}%!_XՂ'E}ISXMlQkmSR-[y_._+3Ӽ{MwK]?Ү-]ܭ>YS|ݪtW5Q:Č-Zݻujk:- u[o&;t~1е}@ڵ9etM->>ڿV_]g_G#$tmCN bh?aiPWL@ru}.Q O` oqvȿj]OmҢҷޯ;_C5 kx} ټ??闕D"x(vR (,Nܯ>nz% 'PH"[˟+Vm:V~.a._W oiC+%uoпgxW{,Z^2Fy' |8n[[=L>"x"Dg>XNi>74/.uk"ܮY?l4A}m|\no `-Ha׵k KMmpz/G4Jk;9~pg(/@J'zos:A;]%ؠ_ͷwɾ-WBҴKoue+leW*نE]-.ϴ%/۝?OG?9b+{(O򥉶n/Q"{l2K,Jy˹U~b{V&K}DL}|=#WZM%^yU'?uzJx֬qCć,底t4k-R-Jl_:ϗwqMf_k^k6=.} t6'VvW_c.^닠MkeyˤKVڣljnq*4ݯHՉP̿ާVIuۻ|׆zoxHtt[ۋ[KHOc{\Dz퇈".`geXG:yU`}ku O'ܭY+,^Yn"]Ec.k'4M?:-vHu/}PYQ aW"uIe> T{5H}{գBѭ// K{{E l bnUK)m~iw+AҮgl60+\*3;D_s檐Dkmw+y ԞTb4?!c WwۻE(|?iW:~diInɷ[\.;i5ƼڔMyK<,^W2z|K7F𽖛{!NoD1,oeiִ#"іmvj~ݬב?N|9<3w;xZ%żSy?[> ~+k}_2'͗-'h-&bnmxMʞ)f]-|~ڬzhgc\nwG'lWCٿ V}.U}N[#{PYEjooYxNVW(4Pq{jRgfy;S5-CVfoeXͦFՖXo[q*QnejFthZ4]{{[$ey?ď l,4F)Sg&嵉U_|w>uAsi?mSڍpT_TGP>2tkKۭC\m2\ƋZ:kͤʿ$6joقPҵm:GF.orfYRՓg>p1t;Xe''h񖙨ꚭnKUkHwrggPJp\/U}^m⿂x{ 9Qi`g1|m.x`!ImaxmlUR/.ƀ=#ž*g sTvM>vES&esZ[+b|TAs [^Ggqj ,y?gx~4Iou.At>43qa2=ǚYc_Mŵm|^zު2+5+wUY7}ߕko~^!gӥY`_*hՊǕP~UiZl`c:W4|}ck߇RReK6$J,MkuOY$ Zy n.$؋Z|[5g=M[<^12yVjE;| O xX|g[^xNe ,Q|?˱_a%xG x_EƁucq"tm[T8>w_zuXG}{ 'ú`t«_i4Oĵy]B/mJKGOlz+m/ݯAE>=9 ͪCWWf?ШO/ xk/7y_nbvuy^[Zi5Яn$'iQW˦ghWas}](L?E6{nUW e{Zﶫvy[=w@WxxWU^i.k 2[h袊((|UK?wR֯}_)w6ՠ +_麎6aW۬pT(((?l7$?1+@C[\x4&oLeh?*h[ڟ#šڭ}/;;kK.O햸M?ɳȼMvPlrxɤK_Okw hO{RެrF>y/m;րxE 6+[?"6W@cMf.е?^xO[{IWYw/OWm+=VL-PKo"7-rt$ȧ?tF|Sn?m}A'E9_5+ڛ`qm˿"p?#>߯jJ<+ ؿݯ߯jJ<+ ؿݮ*őK6j_@_j_@Vg~Ω⃷:=ޯg)k^訫/ 4{]7k?_6_WiyfmVMat-?oE?xKI}BfFg]:ƸC,Kk,VgKeybSj^)yE:x^Yto$-,o??ʒ>z]km5̚#ʃzy;5}_c§/?. QX߈ G=.}.MwcT3[|9Im"ĭrI;{sk=@U j̰k;"W3:E}*mcJ-/-^lP4lw~ڏc~"Sźj1aZoؼݿN_k/徾!KR(+ wHC6[UZğ6ϞUw΋?Z7G){X߈UwƋ=U4_c~"W΅ҵDR֯){|+Ҵ{ "l.~5ve B G_ĭB6_?q)>kG6_͗{*,_JOG͗{*?e 3%B6_?q)>kG6_͗{*4ݥ,_J͗{*?e 3h4ݭB6_;vG͗{*?e 3h4ݭB6_;vG͗{*?e 3ve B G>kG6_͗{*4ݥ,_J͗{*?e 3ĭB6_?q(,_J͗{*?e 3h4ݭB6_;vG͗{*?e 3ve B G_ĭB6_;vG͗{*?e 3h4ݭB6_;vG͗{*?e 3i~?e B G>kG6_%O+y feY:tOsw+g(t{J4uin嶋I_mwmWZizVeIw2}7U;?_誛ajP2{z ;y"֥\=Ksc^+l5ҥ7hV@|_ GCA=ָ6kX_[ojtt=diem|8%ޟxo%) VEk+WVȻ]˶ ޼[;ok Ŧ,[e0w{5w|ؾTizm3.Sg{EO(o|ݮM*%75,Of{v>>-ܟ*y3)ek[O72E-ɻem??ˢyۿdD; ͪCסזK;!^{Z+[wuzrmQ37ڇ>*ϻDt=2.͟jۿwڥߝ(OH>8׍Ť1i_g*|~O4&x&S{;Z:'|h;o"'|=ͫ+Է/wu{Eq)|W 2ߧ.7_:,?K>+mVVKOQ e> (\oxdiQ1ۧWDUP_ so-t̩ͥ o}T˷{q !t?"L7[mkS袀 (8=~(@JV.}=kOxfїɵ_|eUxui mo"/t-OW/(4_9Q/_MxZ6-57/T +?i;T>hеOIT:}Vԭ7[_$.O;\gi㿅V0"xKؼb-^&W_Ҁ<o#l?i_:66;vP񏂼cxoAgEeb}׿=y?t9/ ѣͶ~{/}+"uv>u/W_@=U!_3KW>!/QE|HNZ/zz{Gsxj?7F׫DG|_K_|xWA|FWU-}_p,_ |Rȇל} |Rȇלb._ <{/JVGmf[<[JB׬)+m6[)"}js<̏xKfqRu]mwW3qm{8Y`67ٞ(|w]`?yqsYx~KIk}]Z} WWg _J~~?:g }<-q3tYjU֝'jS<}>W5[GjlWusiWA~?E}W(Jy}='cPoiyR^TR{l)hu#b}EE}?]1jZ5[;[׊_=^e_}:LRkKyF-"[}g/^i/i/":_*[W_{iw?ꄶ٣(ώ~ƕ>:_*>s>t}}'4li_߅ώ~ϳ\ϴ_fh4ԿTҿR Vgٮ/\ϴ_4ԿTҿR Vgٮ/\ϴ_4ԿTҿR Vgٮ/\ϴ_4ԿTҿR Vgٮ/\ϴ_4ԿTҿR Vgٮ/\ϴ_4ԿTҿR Vgٮ/\ϴ_M?[W_?li_߅3?Gٮ/?[W_?li_߅3?Gٮ/}Oƕ>:_* [J~~?*5E}k^ [J~~?*-+|u/Ub[iW0˻j-X5E} cJK/G-+|u/Uf}}5E} cJK/G-+|u/Uf}}5E}?tώ~ƕ>:_*>s>t>ThҿR Q cJK/Yfh>s>t4ԿTҿR Vgٮ/\ϴ_M?[W_?li_߅3?Gٮ/M?[W_?li_߅3?Gٮ/M?[W_?li_߅?sϴ3?@{-+|u/U4ԿUk?@{-+|u/U4ԿUk?@{-+|u/U4ԿUk?@{-+|u/U4ԿUk?@{-+|u/U4ԿUk?@{-+|u/U4ԿUk?@Oƕ>:_*\Q&_,uW?Gٮ/߅)luhux /,NyU|@J嵸e$/*%ʒ_/]Oٮ/\ϴ_Xu}bAkKk۽tt /}eʟEe{|[fhw٧h Hj-[K9_ίyk~CG&uHjjw6j/~𯈼wX>9o&ԐC=\K[WE__GJ~[\zo44Zd_ݻE[b6^ B&~Y/2 RwY7қ@+ᇂ.sisjoS>'75{":;MVr]CpWyKᧃ5/x'L;6Y(?zu|xGDhDѴKZTӬJms;^ywiVY_KK@BV;kۋa⤺%Kʩw7V~C>5f[jW6_~$.x _J\R t Þ.ծ/>Ѯ[]6vo}a\ѿ*_?2?_]q^+?x-fҚG7}_?TEQP+bScij1KKyUWۿk]-Xٛlۥm@S?^Kix~)uFj+aWz,cʀ2u_>u-{&we,%j|Nus .{+Wwg|q`;-]h<1צWk'Ѽ#CZޭ7(]B G;Mw5v~Ac?LΡ%WW=) cmk?t>?8FkxWZz[ɾ9֠ oO]񪣢|Cz:w*]]muU|)x%u=&n֩Oٚ>j/ xJɧ}TKTX455\}~4{=SKs⫛ Rx%t#nUk k $SK@=~]/Qx}+5l[~uW˷ΙNNx7]Jd3FF_\;}?^^w) HD=U!_3KU ?+iր= (KGsxj?7F?SVa ^6޽\'32<Z¿Ok?7ҩk 9?ݮőK6۽|Rȇל} |Rȇלb*_ <{/JhJB׬)+ ϶U+? oz'us?^j|I/(.?/>^$ۿ~Y]~lu.iuI|լ~udT^Rݻ?:: MU/b_=x׏5-r^6LIt%t_|WO>}|^f*SxO)«.u/n%{]ovKQyuev~#t8?D[a]j-p.%4ޤUvo}/㦏q/Kۋ{_$H|z7-7nPچhV_q͊(mW%}?eK=jGCpWZ2j7WQKgjo%7r=湥iz5ݓ}ʟ{|eK=jʅݿǺZ|К</TeO=hʟ{|:*T/*@(RxO_SeO=i4J?ʗ{|**T/_SeO=hʟ{|:*T/_SeK=i?4N/ʟ{|:*T?/ TURxE]ʗ{|/ TURxE]ʗ{|/ TURxE]ʗ{|/ TURxE]ʗ{|/ TURxE]ʗ{|/ TURxE]ʗ{|/ TU'T/)W4eK=hw*_GT?)QW4eK=hRU4o |b]+q+k*_P\?lʟ'-@',S¾"UMxJkڄzEV7RGuc>:>o Ay/<3KxcA_ ׶0_jROmRyʟڵxb+w`پ&MmzT7;|w6j=ڂϑx Ğѭu x]e_+>бJIv׋ҾM5}C&bV-[~˽*ݢH}ϟO ֱc./kVwD]^$[S4}|},|~Y}빬d6:ދ[ |}ˈmg]~ ?ݧkTg٭Yu{k><=&y.*ko%eߺVWY|-u>(uxQmO͉vu[4b*ȉs<p>-|IcD+gfjI Sho.{)Y-y\]DTYj8#.R/ve^ UKOZ70neb?T?3WWO?f*-}/^I?׬/xƿ"tq5겵L^ǯi.o9 <zu|UqYcj+>֋wŏ}j4.u5}?WxÞ!N%tm/]ER4Mmk>"Эl.N)yXWOZqkxeidK;?uh7_cl7{_c[x #So*ѬmhӼm|Ng5~Y|'se-AҼZnbo:~&~u>)ofeռElZ6<i@z;.{L;[6Ŀ-uб,ʫb lo]-Rvu]G&/_c=֫ZIXD_ zkⷾ,?%C,IWw_%vD `K_߄_?&._5xG7vo}w'-m,_V^{) ?H@?O!_3Ku +iր=E>9/)]ѫ_0[o_O|HNZ/zzr8O?7ҩk 9?ݯ߯jJ<+ vjGU/_8Կ!/_G=|Rȇלb y%)?_}kT|?JOEE_ZxY.jl_k? g5i5; Ask=X/]>[m4ʟOgk[:: g:} E-_X$V:͌WyI΍,yӜweM׮m*"'Uݪ]é#y1\'wo^#8RotV*ʪJ?z&yhihWInn]h* 9-o\;lo&]u=O-7U ԫ:$K-[TD=?¿|I)tht FXظ|V[t[fZֿtɴo-/IMڄ TKV>(O0CwT*|mR{ȼȴ_e|H;lmckƃ}5RmWD?O$NvvnYSv]_Pr^'SxȰ{]l)Cj{K!yJʟ?L|_F4e<85 -enm W*o>>͗K+Q|rvt,+4eY]y/V,o)^t%Q?;lZ|GU"HԞoD+5m)T,mw>Wd[ro~?ݭW^t4՟YS/Z /<_ZX,QmYmr-!Ңӥ~keٮMntM>w_?߬\;M'u*]^]mWyYwxS񖷯ixz=2Gm"gGYʊT/-SVe)}GC {=JX5>D2MSV\ʳKTؕX^\Ѿ]U%Z[PhenpҭښT*beL_ no Tݱ6<[?[+Ksm{yܮZ,["[ +K*/5{v˼gյ}KIJ[׏Kf/5QvV*_}R\iU&V԰Yy^]gyvlfr!|@r\Gy}3KYehu,H!ԥ?rjXHʋs'V4 Tp8tJ1RU>|}u4"nU6tx5 dgbohߟk?i3đ]6KRx%MO+I-~_{k<C~MQ-i{I{E+*kakPҤy@\t.$lqHȾOr-i{+xehފ}Ͻʡ_1x-4Gxo6]yRR+2|>Q?/ Ɠu#;.^n^P7$l'kCkyȪbo+OY籉&)Dɻmw?#mt}&Ҥ;Gۗw+^_%B\MmyomR<_"? ̼} &?6ߺotu爯o] [e:+/7m>㕫mض,^jJѫ:ۥVTpe/v!hK}LjP*\l__r՟xTO4F!ϙXO\AԿf筟o*j7YEԿf筟o*j7@y/G/ٵ?g~ڟMP^KKmOzߦ6=lTy/RS>ͩ[?UEKmOzߦ6=lTG/ٵ?g~ڟMP^MMKmOzߦ6=lTy/RS>ͩ[?UEKԿf筟o*j7@y/GRS>ͩ[?UEMKmOzߦ6=lTG/ٵ?g~ڟMP^KԿf筟o*j7@y/G/ٵ?g~ڟMP^KԿf筟o*j7@y/G/ٵ?g~XAP<&6_-C?+uVA뽫\F%&h"ɵ6.~f2[]>8xoSO"FSJ YS~dG, /߇uI +O]->*V+X{kT#Ŷ$ρSBi/M-ujlht۷WԩG}!k6=B46~eWBz |OC> K=sznvɽaT}@|4i˥Ou-ͳ͓/C״U+y7֍p?O(jZ_ڧ=z&>MMkfҠ7W[bmRWk\π?jXf<vmle>Eqۿď*iMmrvnڿjtv>TgOտ{ut 'D_E_jjlR%{3o:S^<4ӦRh[/˳o;p?<}o477ʛ%]N鑗ms?<]xKYD~&GI?bwPN_xڼ^V+[VWW_4CWQySۿdj-_?_z?ѩ^^Al_> Mޣ ~}}Zw->j_?SC<ŝ/J |?DBcZM}W }_痕]>x{w7:="ExW]SΏWo-y3~4U ǩjes\=}]ߺDt^/e>ZS]jlmp~dڏ]xET *LjU|%eIh.͹Wݮ#DW@[ TG{6g_ιN ,Y/u)P\ù53'jQϹeh6|Q}O2[uoJۣӣq/lR9WҬ|w2N;?)V=@讻Zgpؼ'ƃ6ע>g*i2J?nfZ)[.$WHe_<[Ԛ x^MM=>W_){6]+OO/i=mH,k7"eE)-s.V.fvʬ +&}߽${ReoԾxSS6m 5Mo),Mm-֕t;z͜/ouJ9~]m]VhmQs<OvFi-wly{ͻΰ/ OS/;%nn͞VݢoجSܫ^iS!)J23=KȤFi|5*mۗl8$ͦamB\<lWo}.ȯ.5+qXc{e]V 6g+# ZfR#'OcA,(tX4MTsW?'lhУ!k<3$|?9^\)]9d9o=u\W1x shÏx sj){IK)~oQ,{k>ûݷwʿ6*烼)i H1bysז72\o1Ïx sk)NR,YzK^Z]DM 6\;FԴ2FMB=JDi弖-WY_cZx si? .LV?>#և}6{By&Xw} -ń=} y71Ï_um? 秌?2@zGS[H)[Lok5K}>4_yK.'zMFߺ5FS! qzx sh8o=Owm/1/.M=|cֹɴ8Z&S>Owm 秌?6=ʙ^! qzx si?>Owm{ 秌?6_ |%1}j: pM?k+.kKtۢZS((e>(e> ( ( ( (PNK@eA5Jk@.Vƚ?n,Xў%K{t_g{Ff}kaQOSR@ zw^!Ҿ&O\ZG-"_+Jwk,|?}} o>*>kMbk1q].+ŋ(S-GxOPkin俛VUKtxzRU\5u^#?/ǤCjR\鉧K>Y^-j14߳Ue V+`M;g VߗmzDE6ڵڇ'š׈VeelPOɥ?b?n7ooo5W9+sO-5ڋoMq+];}͛_c|qOzχFWח7/ڥ߽cZ/wY˹SNٛ?Jco"ZWۢ,f}:M-͋|@Wa5|M3ƹzN6scrҷW[s8T-sz_Ï 藑^/c1DUh&UWG]T2^kb?Ggo+y^o ewWڕm&qRW_ C)XUEh\~ik^gv:~ ,()V?'-څB 5o\vX>v{.[[宺MG/Y55w}Ef+mo^lKǍ/.fehy C_%C;8*U;iq\wx֏#6ZyK.?8>*xVouGX"}GH{uv_U0rB}E|oDkIkqvC#v[毠}E|-L//DsL(}S(QLה~_k?MZzMoK@ǁKR`xD-Rwٻ7W,,!_>yw,E'rǨz+;V mOFcr^ۢuxVmz}Z((()(QE2^gIk7&]Ԗ:ukvL:oO|JW^Լ]Kz$+V%X}EP^e ?½ʼ#e5̒2'8a*"Q_/OkRk0 |yiq>>^%o*ɵg¿|WxNе >+ʯ/."tBz9eGD"m+||oN X[Mes K6,H-ו7wQ|Ȧf}QEQEQLLEPEPY~ o kRA Bq>_q7ѵ'W@_~, &Yk_tom~u/;INGM^@~u/;GIԿ{W'?_ R#? R+ +y.5 ;Y8݊BTR\;>9A.u-:Ŵ[|?ÿ kgL.[h-|>Z_/cK+5 ŝ%Ne7y[k}]<9fOut(>>Kٰy/ͷUNTh^1~%xs\յ #Cϭ(5v^om-OK?>|oZ-RPڌuqߓv~Mw+Et{SNڋi"^ |6oGNi_KQ~L.4 7dM{UX۵+*1+tu i֚m[-On_.ݧ '᝟u{/PӢ ,Qk||ɿzmkg}+K+I/TG+qUb۷n?jS y_E |fN y:gVYb;Og,+IoݣkbG?a;vxNɢ{{7Rk_/+ߓWY|(実6zi2tX#}ښH~SE7-Zx'Mk2? XgWs~F][Aq<MT{e~*~KR?j(iiBEmbj vomsmt?Au&^iXgq:3q>SKR4_> ahsh:ֿVsM@{{'u_?U:,XNngj$[R:&{cg[[[."f bz_+Y|/F- kE\Km,ië%19uVo PRkVZ^ucyv/E~fٷ۫Oj.|}]컾Oܧ9MG 9C֞-'K3oqњ-²+?k/l~Nd:T_罞O+u?ף:lJkZڣ]At<_hM*۵?l:>(u Ѽ/|_4~}S{ρLпE-~]_rP "^^)kw oM/Ayipi>"vyocabUo|'άҋ?X67y6jOwGឩk_h"ߩã겆/fbn}CҾM>mk֛uk9[_2_-¨myMh*gO?ⷊ?x .]_Po%c 9sω~9I ֎+{刕Y)m_-5[x"ISk{MnW}EEgMKپgE}'_^*~Hiiw7,?Z2|}C < Q]17-|!mV ||OY|]yK&g{V7wY?c^"}򼨢ѯ_>i~w$ ia,k畿fƀ>A'C[>/xᏉg-?*5}s ai-וn[m|q|5״al үֲ+loIWҟ|ewwE+K{{|J߉ZW VQqq6n"-u!OssT |ExO÷Y.n6ew{zͯo}Gu]6+ +~oD,vf}W4Ɵ?R^˦-n"]w_}i>==otN;IӤZk]ΧKxgE.e,u/ث;o'.寋~Dw{-CTw[AXY^)bo,<[߀?^Ҿ|W5xWգk M<߲buھ?G^p7<1F\o* =ѿ_ a⤷Z_T ulFgsy:Lbw+ c,RȖEGwUG|=;#L-m_k =F}j{.6-oG]&w_Hl&Imkݾ#x)%Aw7I.ɲxƹր=|)+i֓xzAl+ݱ|߿JA4Y=͝\Em'|/x5=kJuz| p=o(٫Ex /}Ŝv>?֬1+.op2|~<-mweFk8.#ާտul!s>P*Ep-`]ʿ w:=vVxnտE.y0W[]_4KK*z5Kg%,[hQ6&}SO((KEiNԴQ@Rs@ O+/ hw?ZO'_&6d |5oh7_ZRμ3_ZR΀^aqn|u?Ait63=īIk>0 k7ݒOP W*oKpi3}#m2y_.ԉvQZgX\R/OOtP/?zhM k8>~L[m1+{wWi-Sx~ILKvݱ#\gsZJo#n"Ykm-ԱDu;WƯ_&.ato,/}Ϳx^B5qžx/ hJ+eg3|/. r}/Ve/7 J/Z٭e{Y<6sҀ=./o[Vv:eH̿*i.oMKJUf_5h?~x6vQj7Z%ڣM#nUUƿO|楤hWWwii+H}T??fR~{|nOSD|q[/ض~-,آ_4P*C u~j/C?buϲ =F[&odeUW?jf]|9?j/kSU[&?;O޵e|(6OŒNMr-pY]E}|W|Ox3FNGҬ|QD;t + ?-mRiWWѾ kMwG]^KQ_>|V|ҼK$բ%X#mR|!> o0Vg,ۏ _/jfß5KYwǫuE'>}'j/?:ĺ?wi.eD)g '_ZGqi6/{SK皋@@S((YL|8{^4zֹ3h-_gQU+/Zm<=jm>fq"f Dž/xKhtgSܕ4ڢ?o_>;|&|o] mԏ]Swȕ/+6om],،K/S@Ey?h_?1-=gnmlOp?o?4xjѶ&{~?塉Sݠ(8G4 5x_"ܒ,NGO3okklkin7JͷdHG|/=3~ҵ&zVkꌢXMwpiMN߉cЯue{9+FP7'F|?YKG cFu R/?i_RKcݟh~>߾Eq_ )_-|S]5!'yV+@3hWh xH-Azt+G?zkпW.xÌ Z¿Ok?6ҩk 9?ݮőK7ݯ?j_@W?/zyVt4[ω_?WukٽSK%}o?'fo<xbkUv<,[,?}\\U?"|YWoޮ;8t(PERb@u7}>))QH(@ E)W~_k?MZzMoK@ρLпE-~]_rP "^^)k*¿V?xweeXșMW@jvs·]-~KW~P%x|/[O9F>_+Wοb%h;4Eqܬo/?z>j_>7 cX.bhz6->?$4~0P[UQIm[Po&/E+wv&^@7~? ~xioy 6DEџO$WХHe##up >kW/:5?QF@K{}φ>$P=<\Q]yMo kٟ_ǿ.) HRӤU}:~!ޛE--*&=KYk> XJ]$W-Ie/*ُMw쟠kZWuKmZ.T%mʛWnZ0(>6|8<iB+dZ _uCᛙR5eo%tĿ/XtIGQKR m8W:%u I[rϻ o ZQuAdmLW_|vx7য়4B(.$V~fO^.xō6٦Ϲe,r}{_X?ٛP}WÚ}ީ"Oֳ $% ^/|9xLt[+E* _7i&еhmdELe=_,?iO~ 6zKw-y-~(g{?V[;[W$l2Ŀ쯛5{|.]|)wޭ/u1PߍO|9no-=k?"'?k[ZwǞ xWTM6mKD-\4x[Y=|%>s]_>#js̈1%~v˪X~T4;_[Ԯ#y| oƿRl5ozJjV:%Ɵ*n[e~/߳gh CxB]E ;s ݫsUݯ̟ ~ w>Żw>ky4/>)xǚ?^ѵ%hF7B&ß|e?iwovfoe ?€=E ( ( ( ( ( ( ( ( ( ( kZր2q>_q7ѵ'W@?hڕhڕtna-ܩ,$J5>i߂~4T,"o~Ε?t-qu|YW˕-77iM'u%KlzD"uo5p~+a7~ zH-NQ.Q媏ze/(4k.![C+ŷtlR/??[ѫ@%I; wί1ixu&9Wr"n_nߟ_-?K-}KHπa@c?*ig>Fge 67o##njC4ᯃ/(}%?ٿ{7WÏ2hZK֩o/2EiRfދW|1htԟ(Wv_'js k_Yx}?h5o.m@[g&E៌~%=z/&ͬ_ʟ}ahxo ="[g %&gNGP͟TG<?4ko_ūGas[z,M3K>Ⱇ/kO*o 4ᥖ]gPܦgZ}O)e7])/ğ5Ь6mM*ʅIX*%YS7Ŀ e~|յ_쒦}wWgOxᗄ5ˮnM"_*u!>qb֛Wj{FO_h i R=NO*,,WUW;z쵦[!htZ\bh"}@?OKw??;i//4]n/Cm̿>bIb_xGO1xD'ʌۓoW?lo$|-9B$2'Oq侕RmZ(8_Z Wo t_{ߎ߳KJ-3r~g 67 +S|wrN7|5-4m^jQ#؟+w db]Gt{jboB_D~"ؽ<^|AUWM}Q*;&O Z>t;|ڛ&DO|oM'SIb | ¿/ il}{O_^0-<%Euee ǹvZ:M|A$ __i@U;MsT_Gt[K `[cG޿2}} _׃WKּ%xYӵv/K<ɻWP>jiiWW b5'goW^gqi: b&ܲ#r?"&*/<跒[TοW_Z?6 WIO ÚA7;h֗W_)s^`>XۮjJ+ vGE/okڗD=w+oC_[<7ls\%kPw~ef*_+/m>7q`C_:o 61=guݦbYݬj<#knMƓDWۏ{?ޮni 6Jw]+WOY[- B.>fD͉2ʇewO~*x@]?iVxM/*>26]]]O[襎"ڑ3靿+~2-1bKXcO0[SNU]w+gn*J}stOB<%ko3OfDn]?Ryyޮ< j^gͭj㷂X[73ᗇvld"ho;V)w.>V훱XRj =e͆`eq,Rv|+n2FQxn>4Co sIDYgIJ**yQoLŃyF[m6͕wkjUiBYReeYvUj}᪷ )|&<;ilt{F޲[xGߎK;/t՞X O˹WX:IUZ%NԼ0Y<Sz|?W±bOKEt׭$OofT_ZO^Zi*ZB(%7FO{Gwk-džu+]u7"IxXȍRjN[?y<_þb\Xŧ\J$MW\1UtM'Ԯ-~"7_Pgxz] +k앥_5o]]NNby%rq[9ۻᾇom[IE;m}~"U0ҧn^Y? t}gĚ79[{8.^U @a[6Pyk KgtvZRj{J<_,3+ĺpjn".7V5_^!H[(5+;HXGye_+~U[~3Z$r+4ʒʀWoW宪úeoU{disHY0uOy _<=yjwڛ][5/'ʺX6yqEUC/O̺Sj$S|ySErᇆ 8Hʳ/ J?+VT|4)뵗ϗg#n䮘Tߌ9}O _j^Z[xk2X>#xF%]&xmhnzVmnh7FǕk)ܻwWv~'xmviM)Hkw)d]6{Q0= "_oѴ+:@+5֣m_WK}>Vƫ(*+|K}>Vƨi/YZr7gj_j7O*No%> ?+RTy|,KPQ^_/5GIϧ Կ{o%> ?+RTy|,KPQ^_/5GIϧ Կ{o%> ?+RTy|,KPQ^_/5GIϧ Կ{o%> ?+RTy|,KPQ^_/5GIϧ Կ{o%> ?+RTy|,KPWBß}gj_jo;|VëM6L Zu7ŷ64KLOO(f}QIހ((((((KEVŦ4"3s~5`啂E廻𯚴^-Mך|zìң/mOE/:אM+o_" r?}hmJ:σr?}hmJ:(ZVS—,S.un6'%6kCiWKXV{m:PR"il[^>mzg?g׿mvO-eY'畾_}E|cT64ot[i4(c>]ޯ>h7>{k7LQ5@$~j.4mN-Z +o~e^5|&OEsǚ"e14R4_wh~FnFiT.VVnU.ͫԗ\Hï_u?vgta_ګq[쉢dQր<[?3E7AcLJbFXo~?O-~'ľ%.m4yL'bJsxoJU[ :eVJ]=y'OT>z)]_XlM-Ѿf = T2kחW>V-Y~fUikuZ(%_ֽc|>,3jZkܵn-H>c̟(k~-HrENY[܋jsp~Z_m['4?7N>|EƿfS)^(믕?> | g] mYn7lfk(G&xZ!\?m&DnTx/'/^>a?otˆG7n܉=ߢ\h xKvy5Yz2̿{Š?b/ڋYֵ4Rj,ۊThV 񖕫?-twwajR$QncL A1Uv:e okm HUڨM?"2 +uucq7nq,6WY:?uњ(K=ڍ%n84~'f?'?[Ὴ4?I{m2O/D$OהPǿP_ׇ<7[/s7#2mo`_k.qwZtmؤYd{E9V5/L𽗍?mq6ԥF5v+~?mǁIMg6ʍ5wuo#<7:jk}@~-_IG⟈RD,V_K+K#~j]Du}J^E~X|;?m_^ X|!4<4Pn4Ybybu)2-i^*GԤV]/m+/3o*NŠWB_GOt:.^(Tt}EyOψMXR'|ɦWJ̈"}*wz>@3hWh xH-\'ѶWh xH->\.?mzwNgdyO _TDžo5Rr\5#mܯj_@W?/zyV+rLj~͟Un?넿 ^ Mj~ſg[/'\y?l_ݵy?φ/{Bϳ|/tZ}뺔E*E& ?dj_ɟ¼ysJ\=(>'F@ŽV^} #R OaG,K+?AF@YkEy,K+?/,K?5Ur=\,}Oc _YkEy,K+?/,K?5Ur;}y,K+?/,K?5Q +Ͽdj__ ('SϿdj__ ('4WԿQ #R OaG+;RןԿQ #R OaG+h>'F@ŽV^} #R OaG,K+?AF@YW 9XLF@YW 9XEy,K+?5/r=YW ?dj__ (`z 5/ԿQ+Ͽdj__ ('4WԿ_',K+?AF@)?dj__ (`z 5/ԿQ+Ͽdj__ ('4WԿQ #R OaG+h>'F@ŽVv>'F@ŽV^} #R OaG,K+?AF@YW 9XEy,K+?5/r=YW ?dj__ (`z 5/ԿQ+Ͽdj__ ('4WԿQ #R OaG+h?& ?dj_ɯŽVV_?Zdj__ * y{>o(`W|mjs[k/;"KIWQO *٧~_hL4~+hhsxvk23XMaKQeQymkv/PGںM5/aK7|etc5?/YkF@)8lz! 2\:dj_ɯYkd5!c5?/YkF@(`q / Ck!5CF@(& 9Xw6O_O '6?_=s& ?c_ (`q / C|eu?Q P OaG+k!5C'ᯧ?YkF@(`q / Ck!5C>@XW 9Xo6?_=skv_?Qw_AK ?Bk],}C+?r8lz!d5!c P OaG,}C+?;'ᯧ?l1\:c_ ('d5!c5?/YkF@(`q? }@XW 9Xo6?_=s~xMs& ?dj_ɯ—(ſSj&ha4˫}O_Q0OѶ׉G=W_oڗ`??O.xÌ 5 #o?7Rr_|ҹk_ 6/kduR z/@T_⾃q><ŔZ|1?5׬lebة3̟:>ߺɲWZr//Y^9`..`[ieo޽4[.管2uKBO`O2__.O[\ _./G3D?*_.?{ O=WK^ _.h{0߉k.管2tf˯T}c >`z4~@?ĴGK^Y u=u f˯T}c >`z4~@?ĴGK^Y u=u f˯T}c >`z4@?ĴHkKז6]]C6\]Ce}1?oi s0߉?5~'.管2t6\]Ce}1??i-{0߉4@?Ŀ]yO3e?&_.f˟L]XϿ=z4@?Ŀ]z`u?͗?P?.管2t}c >`z`tIKה6\]Cel'ɃWKk/A~%Nk2__.Q sCe}+xr/-3Zr//^w sCKG0&a_~ WйZr//^u s=u lR'ɃWTk.'jsמ6O f}2'Ƀb ~%\q?ĵ?͓ze6O a?L?Eֿ\_-7Ziĵ_͗??*Z?.T}c >` K~%Ziĵ_͗??*Z?.T}c >` K~%Rk_./^o sCKG0&a_~ KиZ_jMkyk.T6\Ph}1?j}sȿĴj-oȿĿ]y3e ?&Z[ϯ='~ Eй[r//^o ?&Z?'G&aQk.E~%\Kכ6O f}2'ɃbZ ߉?ᨵ"u͓ze_L}o >`zG5B__.j-oȿĿ]y3d޾hl-[ϯ='~ Eй[r//^o ?&Z?'G&aQk.E~%\Kכ6O f}2'ɃbZ ߉?ᨵ"u͓ze_L}o >`zG5B__.j-oȿĿ]y3d޾hl-[ϯ='~ Eй[r//^o ?&Z?'G&aQk.E~%\Kכ6O f}2'ɃbZ ߉?ᨵ"u͓ze_L}o >`zG5B__.j-oȿĿ]y3d޾hl-/?L?H\Ku͓ze6\Ph}1?isĿ] ߉f˟-'3d?&Z>0{ O=Cg\A~% ߉k.Tf˟-Gp_=z4BZ?᥵ח6\PhlRg >b ߉?᥵u͗??*Zg3e?&_.a?L¿=O[\z/G4@?Ŀ]yg3e?&_.f˟L]XϿ=z4@?Ŀ]z`u?͗?P?.管2t}c >`z`tIkKה6\]Cel'ɃWIK -yO3e?&_. u=u a?L?S׿i {߉f˯T6]]CO5A~%?uA~.?f˟L]͗?P>0{ O=gC^_.i {߉of˟L]͗?P>0{ i {߉?5A~%ɿ.管2t6\]Ce}+5A~%׿&l s=u O?Y׿C^_.z/G3e?&_.a?L¿=gC^_.i {߉of˟L]͗?P>0{ oi {߉Oi{ߋ.z/G3e?&_.a?L?V׿K^ _.z/G3e?&_.a?L?S׿^K -н,l s=u O?᥵t ߉f˟-͗??*Z>0{ O=?s\z//L?\Kי6]]Cl2T}c >`_ ߉g5.B__.KI ?&Z>0{ j]sȿĿ] u͗??*Z?.T}c >`z'5^B߉?WKם6OC?.管2t}c >`z K~% )~%.管2t4CeOѿqĿ]9?j]i\Kל6\Pi?'2'a'9?߉>%H+`:o(gwvU ? mf?ʋs[츝O85)sBDYT._5յ No7WQo_e-òufx__;YٲN^\ǥ@QBUR.̛[\̵[ ?[P"ΏD,o= ?[@Y:?uP"ΏD,ou`!g<ByJByԏ@?Y:?u ?[( ?G"ηByn D,Y::?0t!g6666666666666666666666666666666666666666666666666hH6666666666666666666666666666666666666666666666666666666666666666662 0@P`p2( 0@P`p 0@P`p 0@P`p 0@P`p 0@P`p 0@P`p8XV~ OJPJQJ_HmH nH sH tH V`V -^Normal 1$7$8$$CJOJPJQJ_H aJmH sH tH d@d -^ Heading 1 @&^.5CJOJPJQJ\^J_H aJmH sH tH DA`D Default Paragraph FontRi@R 0 Table Normal4 l4a (k ( 0No List ZB@Z -^ Body Text^(CJOJPJQJ^J_H aJmH sH tH d@d -^List Paragraph)^Q`: OJPJQJ^J_H mH sH tH ZOZ -^Table Paragraph OJPJQJ^J_H mH sH tH H"H a0 Balloon TextCJOJQJ^JaJV1V a0Balloon Text CharCJOJPJQJ^J_H aJ4U@A4 } 0 Hyperlink >*ph0aQ0 } 0 HTML Cite6]PK![Content_Types].xmlj0Eжr(΢Iw},-j4 wP-t#bΙ{UTU^hd}㨫)*1P' ^W0)T9<l#$yi};~@(Hu* Dנz/0ǰ $ X3aZ,D0j~3߶b~i>3\`?/[G\!-Rk.sԻ..a濭?PK!֧6 _rels/.relsj0 }Q%v/C/}(h"O = C?hv=Ʌ%[xp{۵_Pѣ<1H0ORBdJE4b$q_6LR7`0̞O,En7Lib/SeеPK!kytheme/theme/themeManager.xml M @}w7c(EbˮCAǠҟ7՛K Y, e.|,H,lxɴIsQ}#Ր ֵ+!,^$j=GW)E+& 8PK!Ptheme/theme/theme1.xmlYOo6w toc'vuر-MniP@I}úama[إ4:lЯGRX^6؊>$ !)O^rC$y@/yH*񄴽)޵߻UDb`}"qۋJחX^)I`nEp)liV[]1M<OP6r=zgbIguSebORD۫qu gZo~ٺlAplxpT0+[}`jzAV2Fi@qv֬5\|ʜ̭NleXdsjcs7f W+Ն7`g ȘJj|h(KD- dXiJ؇(x$( :;˹! I_TS 1?E??ZBΪmU/?~xY'y5g&΋/ɋ>GMGeD3Vq%'#q$8K)fw9:ĵ x}rxwr:\TZaG*y8IjbRc|XŻǿI u3KGnD1NIBs RuK>V.EL+M2#'fi ~V vl{u8zH *:(W☕ ~JTe\O*tHGHY}KNP*ݾ˦TѼ9/#A7qZ$*c?qUnwN%Oi4 =3ڗP 1Pm \\9Mؓ2aD];Yt\[x]}Wr|]g- eW )6-rCSj id DЇAΜIqbJ#x꺃 6k#ASh&ʌt(Q%p%m&]caSl=X\P1Mh9MVdDAaVB[݈fJíP|8 քAV^f Hn- "d>znNJ ة>b&2vKyϼD:,AGm\nziÙ.uχYC6OMf3or$5NHT[XF64T,ќM0E)`#5XY`פ;%1U٥m;R>QD DcpU'&LE/pm%]8firS4d 7y\`JnίI R3U~7+׸#m qBiDi*L69mY&iHE=(K&N!V.KeLDĕ{D vEꦚdeNƟe(MN9ߜR6&3(a/DUz<{ˊYȳV)9Z[4^n5!J?Q3eBoCM m<.vpIYfZY_p[=al-Y}Nc͙ŋ4vfavl'SA8|*u{-ߟ0%M07%<ҍPK! ѐ'theme/theme/_rels/themeManager.xml.relsM 0wooӺ&݈Э5 6?$Q ,.aic21h:qm@RN;d`o7gK(M&$R(.1r'JЊT8V"AȻHu}|$b{P8g/]QAsم(#L[PK-![Content_Types].xmlPK-!֧6 +_rels/.relsPK-!kytheme/theme/themeManager.xmlPK-!Ptheme/theme/theme1.xmlPK-! ѐ' theme/theme/_rels/themeManager.xml.relsPK] DRtDG?o{Rt?o{jQY%-~8=eE LRVp_eHkRt@j<f 8   A=$,/4:<C(J T\nb>ddgjmnr,ttz|:t g.?BDFHIKLNPQSUWZ\]^acfgjklnp}  a)\18@0HENFTZ`d]ggiklpqNzV{}i'V\a@ACEGJMORTVXY[_`bdehimo|~7@BgOU"E$$$)))--..D.W.Y...N5556666 747Z[$[l[[[\O]U]!jLjRj(qqqq@rrrr s s,s?sRtXXXXXXXXXXXXXXXXXXXXXXXovx!    ,R$ցO !آ;vzh # AA@ `(    VA?"image5.jpeg3"?  0#" ? B S  ? (  \B  3 ( 3"?  0 3"?   0 3"?   0 3"?   0 3"?   0 3"?   0 3"? 'G)Rt*%.er 7x$p2 -k@$Vk@V+k@$^;<k@*^;*<k@$^;<k@)*^;*<k@jko*NR a h )AF''' '''''G)H)T+]+.H.!/&/Y/^////0I0J0N0b01111,2-21292h2i2m2y2222233"3635588=/=>>#G)GII J JJJKKpLwLLLLLLLMMNNOOOOOOOOP#P8PAPPP*Q3QPQYQuQ~QQQRRS SSSSS TT V VVV%V=VYV[VpVtVxV|VVVVVVVVVVVW!W"W$W-W1W2W4W]W_WWWX#X*X.XyYYZZ]]]]]]^^^ ^5^9^=^A^T^V^^^^^^^&_(_*_8_9_;___T`\`j`r`Ta[a]acaganaoaqasayaaa#f$f7f8fkkkkkk/l9l;l@lBlHlJlOlQlYl qq s@sCsCsEsEsFsFsHsIsKsLsNsOs\s]sbscshsisnsosssts|s}ssttPtStCx+|}NR "QUbuF#Y#Y%^%N(h(++11!5)5T7f77777I8T83:>:< <=0===>>>>>>??0@>@@AEA-C;^`;_H o(sH tH " 4 ;^4 `;_H o(sH tH " j ;^j `;_H o(sH tH " ;^`;_H o(sH tH " ;^`;_H o(sH tH "  ;^ `;_H o(sH tH " B;^B`;_H o(sH tH " x";^x"`;_H o(sH tH " +BI^B`ICJOJPJQJRHd^J_H aJo(sH tH jI^j`I_H o(sH tH "  I^ `I_H o(sH tH "  I^ `I_H o(sH tH " I^`I_H o(sH tH " I^`I_H o(sH tH " <I^<`I_H o(sH tH " fI^f`I_H o(sH tH " "I^"`I_H o(sH tH " 49O"(*b-5cu9.nLS6bV:R[Y8dio/GV `^0~_ԶwN%vsQnn_֤(p[H¦9s8t<^.|g|0:HB^x+$j elHA_SVu( >t/B&,jRzL4P?JnIw܎?8f9_\rVTl%+*d"qBЇ &FL ̓Fpv,Q0&&PPz$BG?G@GBGCrDrErGrHrIrJKLTUXYacDiDjArRtx@x:xx@xBxFx@xLx@xPx@xTx@xXx@x\x@x`x@xdx@xnx@xtx@xx@xx@x @UnknownG*Ax Times New Roman5Symbol3. *Cx Arial9=  @ Consolas7.@ Calibri5. .[`)TahomaA BCambria Math"1hww4b:>Ib;4n0 sLs2Q HP $P-^2!xx Assignment NoXXX Ghanshyam<         Oh+'0`   ( 4@HPXAssignment NoXXXNormal Ghanshyam5Microsoft Office Word@G@C@tBC4b՜.+,D՜.+,8 hp|  : s Assignment No Title\8*N _PID_HLINKSCreatedCreator LastSavedAq.Bhttps://mitu.co.in/|#<?;https://www.tutorialspoint.com/r/r_interview_questions.htm| B<Bhttps://intellipaat.com/interview-question/r-interview-questions/|a19[https://www.analyticsvidhya.com/blog/2016/02/complete-tutorial-learn-data-science-scratch/|fy6https://cran.r-project.org/|SF3ihttps://www.analyticsvidhya.com/blog/2015/02/7-steps-data-exploration-preparation-building-model-part-2/|'j0chttps://www.analyticsvidhya.com/blog/2015/11/8-ways-deal-continuous-variables-predictive-modeling/|_-jhttps://www.analyticsvidhya.com/blog/2015/11/easy-methods-deal-categorical-variables-predictive-modeling/|DS*2http://www.rstudio.com/products/rstudio/download/|DS'2http://www.rstudio.com/products/rstudio/download/|D$1http://ftp.heanet.ie/mirrors/cran.r-project.org/|N!"https://www.r-project.org/COPYING|N"https://www.r-project.org/COPYING|b1http://www.gnu.org/|b1http://www.gnu.org/|P!http://www.google.com/analytics/|RFhttp://rwxweb.wordpress.com/2012/01/31/teaching-algorithmic-thinking/|<http://en.wikipedia.org/wiki/Hacker_(programmer_subculture) Definition_ :http://en.wikipedia.org/wiki/Singular_value_decomposition|/X ,http://en.wikipedia.org/wiki/Linear_algebra|Z 1http://en.wikipedia.org/wiki/Bayesian_statistics|Z 1http://en.wikipedia.org/wiki/Bayesian_statistics|g 2http://en.wikipedia.org/wiki/Classical_statistics|@J;Microsoft Office Word 2007@VEC  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~      !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGIJKLMNOQRSTUVWX_Root Entry FpHCaData 1Table:YWordDocumentޚSummaryInformation(HDocumentSummaryInformation8PCompObjy  F'Microsoft Office Word 97-2003 Document MSWordDocWord.Document.89q