This thesis is a qualitative study based on interviews conducted in early 2007 and aims at getting an understanding of the political environment from an opposition party’s point of view in Northern Sudan. The study is also trying to illustrate how good governance and democracy are building on each other and that good governance is a pre-requisite for democracy. In this perspective Sudan has a long way to go. The main assumption is that women are key to good governance and internal democracy and a democratic society. If the party excludes women, it can not be representative, participatory or equitable and inclusive. The thesis is therefore looking at the structures of one opposition party, the Umma Party, and women’s political participation in the party. This description is then analysed in light of Good Governance’s characteristics of participation, representativeness, equity and inclusiveness in the political society arena. The following questions were asked and answered: • What are the basic features of the political environment within which Sudanese opposition parties operate? • What are the leadership structures and decision making processes of the Umma Party in North Sudan? • How do women access these structures and processes? The main finding is that the Umma Party and Sudan do not fulfill the characteristics or the indicators to claim good governance or democracy – not within the party and not within the country.

The increase in the use of digital video/image has showed the need for modeling and querying the semantic content in them. Using manual annotation techniques for defining the semantic content is both costly in time and have limitations on querying capabilities. So, the need for content based information retrieval in multimedia domain is to extract the semantic content in an automatic way. The semantic content is usually defined with the objects in images/videos. In this thesis, a Genetic Algorithm based object extraction and classification mechanism is proposed for extracting the content of the videos and images. The object extraction is defined as a classification problem and a Genetic Algorithm based classifier is proposed for classification. Candidate objects are extracted from videos/images by using Normalized-cut segmentation and sent to the classifier for classification. Objects are defined with the Best Representative and Discriminative Feature (BRDF) model, where features are MPEG-7 descriptors. The decisions of the classifier are calculated by using these features and BRDF model. The classifier improves itself in time, with the genetic operations of GA. In addition to these, the system supports fuzziness by making multiple categorization and giving fuzzy decisions on the objects. Externally from the base model, a statistical feature importance determination method is proposed to generate BRDF model of the categories automatically. In the thesis, a platform independent application for the proposed system is also implemented.

Single Nucleotide Polymorphisms (SNPs) are the most frequent genomic variations and the main basis for genetic differences among individuals and many diseases. As genotyping millions of SNPs at once is now possible with the microarrays and advanced sequencing technologies, SNPs are becoming more popular as genomic biomarkers. Like other high-throughput research techniques, genome wide association studies (GWAS) of SNPs usually hit a bottleneck after statistical analysis of significantly associated SNPs, as there is no standardized approach to prioritize SNPs or to select representative SNPs that show association with the conditions under study. In this study, a java based integrated system that makes use of major public databases to prioritize SNPs according to their biological relevance and statistical significance has been constructed. The Analytic Hierarchy Process, has been utilized for objective prioritization of SNPs and a new emerging methodology for second-wave analysis of genes and pathways related to disease associated SNPs based on a combined p-value approach is applied into the prioritization scheme. Using the subset of SNPs that is most representative of all SNPs associated with the diseases reduces the required computational power for analysis and decreases cost of following association and biomarker discovery studies. In addition to the proposed prioritization system, we have developed a novel feature selection method based on Simulated Annealing (SA) for representative SNP selection. The validity and accuracy of developed model has been tested on real life case control data set and produced biologically meaningful results. The integrated desktop application developed in our study will facilitate reliable identification of SNPs that are involved in the etiology of complex diseases, ultimately supporting timely identification of genomic disease biomarkers, and development of personalized medicine approaches and targeted drug discoveries.

<p>This thesis is a qualitative study based on interviews conducted in early 2007 and aims at getting an understanding of the political environment from an opposition party’s point of view in Northern Sudan.</p><p>The study is also trying to illustrate how good governance and democracy are building on each other and that good governance is a pre-requisite for democracy. In this perspective</p><p>Sudan has a long way to go. The main assumption is that women are key to good governance and internal democracy and a democratic society. If the party excludes women, it can not be representative, participatory or equitable and inclusive.</p><p>The thesis is therefore looking at the structures of one opposition party, the Umma Party, and women’s political participation in the party. This description is then analysed in light of Good Governance’s characteristics of participation, representativeness, equity and inclusiveness in the political society arena. The following questions were asked and answered:</p><p>• What are the basic features of the political environment within which Sudanese opposition parties operate?</p><p>• What are the leadership structures and decision making processes of the Umma Party in North Sudan?</p><p>• How do women access these structures and processes?</p><p>The main finding is that the Umma Party and Sudan do not fulfill the characteristics or the indicators to claim good governance or democracy – not within the party and not within the country.</p>

Thesis (Ph. D.)--University of California, San Diego, 2002. / Vita. Includes bibliographical references (leaves 339-352).

Project (B.A.)--James Madison University, 2009. / Includes bibliographical references.

The aim of this paper is to examine how the press describes the public and private role of the Swedish monarch in texts that report about the biography Carl XVI Gustaf – den motvillige monarken (Sjöberg et al. 2010) when it is published in November 2010. The examined period ranges between the days just before the release and a fortnight after. The analysis is limited to standard Swedish papers: a daily, Svenska Dagbladet, and an evening paper, Expressen. Questions asked in the study are: In what ways do the analysed texts raise a discussion about the monarchy’s importance or function in the society of today, that is a public debate on the monarchy? To what extension are status symbols used in the constructing of royalty, that is what Jürgen Habermas (2003) describes as representative publicity? What similarities and differences are found when comparing the news articles in the daily and in the evening paper? The method used to answer the aim and questions is the critical discourse analysis, as Norman Fairclough (1995) describes it, and the theoretical perspective of the essay is Jürgen Habermas’ (2003) theory about the bourgeois public sphere. The result shows that the news articles in the daily unsurprisingly construct only a public who wants to debate on the monarchy. The evening paper instead addresses its readers both as cultural consumers, which the study sees as representative publicity, and as civilians who want to discuss the monarchy’s importance or function of today’s society. Important to notice is that when the evening paper is challenging the monarchy it’s always made in an implicit manner. The public role of the monarch is in both the daily and the evening paper said to be powerful and his public role is said to influence his private role in different ways. It is also obvious that the focus in Expressen is upon the most intimate sphere of the privacy of the monarch. The description of the private room is important here, in addition the spatial portraying uses status symbols when constructing royalty. Finally, the analysis shows that the monarch simultaneously portrays both as an ordinary human being and as a very special person in exclusive surroundings. Earlier research has proved that Swedish media wants to describe royalty like this.

In this study, I examine the factors that influence school districts’ commitment to implement ESL (English as a Second Language) education in compliance with the federal Bilingual Education Act of 1968. To explain variation in implementation effort, I focus on several features of the local implementation environment, including the role of Latino descriptive representation. Utilizing data on all public school districts in Texas, I employ a Heckman two-stage estimation procedure that accounts for factors that influence school districts’ decisions to implement bilingual education programs as well as factors that affect the amount of resources school districts are willing to allocate towards bilingual education. The results indicate that Latino school board and teacher representation play a positive and statistically significant role in determining: 1) whether school districts implement bilingual education programs; and 2) the level of expenditures and teacher positions allocated towards bilingual education. Thus, policy implementation outcomes translate into substantive representation.

Background: The WHOQOL-OLD is an instrument for the assessment of subjective quality of life in elderly people. It is based on the WHO definition of quality of life and is available in more than 20 languages. However, in most countries, the psychometric properties of the WHOQOL-OLD have been assessed only on the basis of small local samples and not in representative studies. In this study, the psychometric properties of the WHOQOL-OLD are evaluated based on a representative sample of Germany\'s elderly population. Methods: Face-to-face interviews with 1133 respondents from the German population aged 60 years and older were conducted. Quality of life was assessed by means of the WHOQOL-BREF, the WHOQOL-OLD and the SF12. Moreover, the GDS, the DemTect and the IADL were applied for the assessment of depressive symptoms, cognitive capacities and capacity for carrying out daily activities. Psychometric properties of the WHOQOL-OLD were evaluated by means of classical and probabilistic test theory, confirmatory factor analysis and multivariate regression model.Results: Cronbach\'s alpha was found to be above 0.85 for four and above .75 for two of the six facets of the WHOQOL-OLD. IRT analyses indicated that all items of the WHOQOL-OLD contribute considerably to the measurement of the associated facets. While the six-facet structure of the WHOQOL-OLD was well supported by the results of the confirmatory factor analysis, a common latent factor for the WHOQOL-OLD total scale could not be identified. Correlations with other quality of life measures and multivariate regression models with GDS, IADL and the DemTect indicate a good criterion validity of all six WHOQOL-OLD facets.Conclusions: Study results confirm that the good psychometric properties of the WHOQOL-OLD that have been found in international studies could be replicated in a representative study of the German population. These results suggest that the WHOQOL-OLD is an instrument that is well suited to identify the needs and the wishes of an aging population.

In recent years, the advance of information and communication technologies has allowed the storage and transfer of massive amounts of data. The availability of this overwhelming amount of data stimulates a growing need to develop fast and accurate algorithms to discover useful information hidden in the data. This need is even more acute for unsupervised data, which lacks information about the categories of different instances. This dissertation addresses a crucial problem in unsupervised data analysis, which is the selection of representative instances and/or features from the data. This problem can be generally defined as the selection of the most representative columns of a data matrix, which is formally known as the Column Subset Selection (CSS) problem. Algorithms for column subset selection can be directly used for data analysis or as a pre-processing step to enhance other data mining algorithms, such as clustering. The contributions of this dissertation can be summarized as outlined below. First, a fast and accurate algorithm is proposed to greedily select a subset of columns of a data matrix such that the reconstruction error of the matrix based on the subset of selected columns is minimized. The algorithm is based on a novel recursive formula for calculating the reconstruction error, which allows the development of time and memory-efficient algorithms for greedy column subset selection. Experiments on real data sets demonstrate the effectiveness and efficiency of the proposed algorithms in comparison to the state-of-the-art methods for column subset selection. Second, a kernel-based algorithm is presented for column subset selection. The algorithm greedily selects representative columns using information about their pairwise similarities. The algorithm can also calculate a Nyström approximation for a large kernel matrix based on the subset of selected columns. In comparison to different Nyström methods, the greedy Nyström method has been empirically shown to achieve significant improvements in approximating kernel matrices, with minimum overhead in run time. Third, two algorithms are proposed for fast approximate k-means and spectral clustering. These algorithms employ the greedy column subset selection method to embed all data points in the subspace of a few representative points, where the clustering is performed. The approximate algorithms run much faster than their exact counterparts while achieving comparable clustering performance. Fourth, a fast and accurate greedy algorithm for unsupervised feature selection is proposed. The algorithm is an application of the greedy column subset selection method presented in this dissertation. Similarly, the features are greedily selected such that the reconstruction error of the data matrix is minimized. Experiments on benchmark data sets show that the greedy algorithm outperforms state-of-the-art methods for unsupervised feature selection in the clustering task. Finally, the dissertation studies the connection between the column subset selection problem and other related problems in statistical data analysis, and it presents a unified framework which allows the use of the greedy algorithms presented in this dissertation to solve different related problems.