The goal of this work is to analyze the evolution of household consumption of the states in the EU. The consumption will be researched in the view of classification COICOP, which is the classification of individual consumption by purpose. After mapping of this evolution the estimation of future values will be done from known time series. This estimation will be performed by two different ways. First one will respect the composition of household consumption in sections of classification COICOP. The second one will only work with time series of average consumption for all sections together. To compare the states cluster analysis will be done. This analysis will be done by two ways again. First one will be aimed to analyze the current situation and the second one will be aimed to analyze the evolution of household consumption. Instead of Microsoft Excel STATGRAPHICS X64 CENTURION and SPSS will be used in this thesis. Household consumption prognosis is the main benefit of this thesis. This prognosis is made for all sections of COICOP. Analysis has shown, that the consumption should rise in future. There are few exceptions, mainly countries with not good economic situation as Greece.

This diploma thesis aims to identify collaboration factors within a joint NGO project called Reconstruction of the State. The goal is to find out the factors that led to the establishment of this joint project as perceived by representatives of the cooperating organizations. Another objective is to discover a possible pattern of collaboration factors within this project. The theoretical part introduces the main theories of interorganizational cooperation and the conceptualization of collaboration factors that have been described in these theories. The conceptualization of the collaboration factors forms the basis of a questionnaire issued to the NGOs' representatives. The results of the survey are used as input in a hierarchical cluster analysis that represents the key part of the empirical part of this thesis. The results of the cluster analysis are thus derived from the responses given by representatives of the 13 NGOs involved in the Reconstruction of the State project and they identify the key collaboration factors as well as their configuration. The final section presents and further interprets the findings resulting from the cluster analysis within the chosen theoretical framework. Key words: collaboration factors, organizational resources, NGOs, interorganizational collaboration, cluster analysis

The aim of the first part of this thesis is to explain the theoretical basis of transmission electron microscopy and to mention fundamental parts of transmission electron microscopes. The next part of this work is focused on possible methods of image segmentation, the use of neural networks in the detection of objects in an image and the subsequent clustering of results. The theoretical part of the thesis is concluded with an explanation of some already published methods of automatic detection of biological structures in microscopic images and theoretical design of the algorithm, which will be subsequently developed. The process of training neural networks in order to automatically detect biological structures in an image is described at the beginning of the practical part. This is followed by an evaluation of the results achieved by these networks. Subsequently, cluster analysis methods are applied to these results, the products of which are compared with each other and also with the results obtained by already published methods.

Hadoop is a popular software framework written in Java that performs data-intensive distributed computations on a cluster. It includes Hadoop MapReduce and the Hadoop Distributed File System (HDFS). HDFS has known scalability limitations due to its single NameNode which holds the entire file system namespace in RAM on one computer. Therefore, the NameNode can only store limited amounts of file names depending on the RAM capacity. The solution to furthering scalability is distributing the namespace similar to how file is data divided into chunks and stored across cluster nodes. Hadoop has an abstract file system API which is extended to integrate HDFS, but has also been extended for integrating file systems S3, CloudStore, Ceph and PVFS. File systems Ceph and PVFS already distribute the namespace, while others such as Lustre are making the conversion. Google previously announced in 2009 they have been implementing a Google File System distributed namespace to achieve greater scalability. The Generic Hadoop API is created from Hadoop's abstract file system API. It speaks a simple communication protocol that can integrate any file system which supports TCP sockets. By providing a file system agnostic API, future work with other file systems might provide ways for surpassing Hadoop 's current scalability limitations. Furthermore, the new API eliminates the need for customizing Hadoop's Java implementation, and instead moves the implementation to the file system itself. Thus, developers wishing to integrate their new file system with Hadoop are not responsible for understanding details ofHadoop's internal operation. The API is tested on a homogeneous, four-node cluster with OrangeFS. Initial OrangeFS I/0 throughputs compared to HDFS are 67% ofHDFS' write throughput and 74% percent of HDFS' read throughput. But, compared with an alternate method of integrating with OrangeFS (a POSIX kernel interface), write and read throughput is increased by 23% and 7%, respectively

This work deals with delineation of ECG signals. First we will become familiar with ECG and commonly used processing methods. Various transformation methods of ECG leads. Further we will describe methods of delineation ECG signals. Second part is dedicated to metod used in this work for delineation of ECG signals from CSE database. Particulary method using Dyadic Wavelet Transform. Work include, description of used program and results. Third part is dedicated to methods of ECG leads transformations and cluster analysis. In conclusion we will evaluate the results.

This term project deals with the classification of 1D gel electrophoresis samples. It describes the theoretical information about gel electrophoresis, various types of errors, processing of the image and its classification using the cluster analysis. One of the main goals is creation of images with the highest quality as possible. A realization of pre-processing and detection of the sample borders is made in the MATLAB environment. And finally, classification of samples is done with subsequent statistical analysis.

Adverse drug reactions are unwanted effects alongside the intended benefit of a drug and might be responsible for 3-7\% of hospitalizations. Finding such reactions is partly done by analysing individual case safety reports (ICSR) of adverse events. The reports consist of categorical terms that describe the event.Data-driven identification of suspected adverse drug reactions using this data typically considers single adverse event terms, one at a time. This single term approach narrows the identification of reports and information in the reports is ignored during the search. If one instead assumes that each report is connected to a topic, then by creating a cluster of the reports that are connected to the topic more reports would be identified. More context would also be provided by virtue of the topics. This thesis takes place at Uppsala Monitoring Centre which has implemented a probabilistic model of how an ICSR, and its topic, is assumed to be generated. The parameters of the model are estimated with expectation maximization (EM), which also assigns the reports to clusters. The clusters are improved with Consensus Clustering that identify groups of reports that tend to be grouped together by several runs of EM. Additionally, in order to not cluster outlying reports all clusters below a certain size are excluded. The objective of the thesis is to improve the algorithm in terms of computational efficiency and quality, as measured by stability and clinical coherence. The convergence of EM is improved using stepwise EM, which resulted in a speed up of at least 1.4, and a decrease of the computational complexity. With all the speed improvements the speed up factor of the entire algorithm can reach 2 but is constrained by the size of the data. In order to improve the clusters' quality, the community detection algorithm Leiden is used. It is able to improve the stability with the added benefit of increasing the number of clustered reports. The clinical coherence score performs worse with Leiden. There are good reasons to further investigate the benefits of Leiden as there were suggestions that community detection identified clusters with greater resolution that still appeared clinically coherent in a posthoc analysis.

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Existing studies of household overcrowding in Sweden are often descriptive and examine patterns at a large scale. Levels of overcrowding have increased since the mid-1980s and the highest shares are found in the largest cities among residents with a low income, a migration background, living in rental apartments, and often with children. The aim of this thesis is to increase the understanding of the measurements of household overcrowding, its development over time, its spatial patterns and its determinants at a small-scale neighborhood level with application to the City of Stockholm. It examines how the associations between overcrowding and other neighborhood characteristics can be understood in different neighborhood settings and what the implications are of using different scales and definitions of overcrowding. Cluster, correlation and regression analyses have been conducted using administrative data aggregated to key code areas and city districts. The results demonstrate that there are two types of overcrowding within the City of Stockholm, which are spatially separated and associated differently with socio-economic, demographic and housing characteristics of neighborhoods. It is suggested that explanatory segregation theories related to preference and economic and discriminatory structures are needed to understand the uneven spatial distribution of overcrowding in the City of Stockholm.

Analysis of EU divorce rates in socio-demographic perspective Abstract The aim of this diploma thesis is to analyze the basic trends in the development of the divorce rate in selected countries of the European Union, in the period of 1995 through 2015 and to find similar or different trends among them. The work is focused not only on the development of individual indicators of the divorce intensity, but also on the possible causes of changes in family behavior, both from a demographic and sociological point of view. Therefore, the second part of the work focuses on attitude analysis of the evaluators by particular country of the European Union, namely the specific attitudes of choosing a family, marriage and an opinion on divorce. The intensity of divorce rates increases during the observed period in all monitored countries, therefore the share of divorced persons in the European population increasing, too. The reasons are mainly the social and economic changes in society, which go along with the changes in family behavior. Divorce is tolerated by society and as behavior is justifiable for most people. On the other hand, family and marriage are still very important values for the people of European countries. Keywords: divorce rate, family, Europe, attitudes, binary logistic regression, cluster analysis