In my thesis, I focused on the effects of volunteering on professional employment chances of graduates. The theoretical part is devoted to volunteering, its kinds, and its importance to the personal development of volunteers. The other half of the theoretical part of this thesis is devoted to jobs and their comparison with volunteering. My objective was to find out whether employers prefer, when selecting their employees, graduates who have experience and skills from volunteering, and how this experience is viewed by employers in the non-profit sector as compared with employers outside the sector. I used the method of questioning, the technique of questionnaires, and controlled interviews. The results were elaborated in graphical form with absolute figures and tables using the chi square statistical test. The results of this thesis have confirmed my initial hypotheses, namely that employers prefer graduates with experience from volunteering and that the experience from volunteering of graduates is evaluated as an important element in recruitment, especially by non-profit sector employers. A following step to improve understanding of volunteering by employers would be, I my opinion, to suggest changes to legislation that would help organizations make better use of volunteer assistance. I would like to suggest to volunteers themselves to record better their volunteering activities so that they can use such materials as an attachment to their CV{\crq}s when applying for a job.

The management of missing data is a major concern in classification model generation in all fields but poses a particular challenge in situations where there is only a small quantity of sparse data available. In the field of medicine, this is not an uncommon problem. While widely subscribed methodologies like logistic regression can, with minor modifications and potentially much labor, provide reasonable insights from the larger and less sparse datasets that are anticipated when analyzing diagnosis of common conditions, there are a multitude of rare conditions of interest. Primary angiitis of the central nervous system (PACNS) is a rare but devastating entity that given its range of presenting symptoms can be suspected in a variety of circumstances. It unfortunately continues to be a diagnosis that is hard to make. Aside from some general frameworks, there isn’t a rigorously defined diagnostic approach as is the case in other more common neuroinflammatory conditions like multiple sclerosis. Instead, clinicians currently rely on experience and clinical judgement to guide the reasonable exclusion of potential inciting entities and mimickers. In effect this results in a smaller quantity of heterogenous that may not optimally suited for more traditional classification methodology (e.g., logistic regression) without substantial contemplation and justification of appropriate data cleaning / preprocessing. It is therefore challenging to make and analyze systematic approaches that could direct clinicians in a way that standardizes patient care. In this thesis, a machine learning approach was presented to derive quantitatively justified insights into the factors that are most important to consider during the diagnostic process to identify conditions like PACNS. Modern categorization techniques (i.e., random forest and support vector machines) were used to generate diagnostic models identifying cases of PACNS from which key elements of diagnostic importance could be identified. A novel variant of a random forest (RF) approach was also demonstrated as a means of managing missing data in a small sample, a significant problem encountered when exploring data on rare conditions without clear diagnostic frameworks. A reduced need to hypothesize the reasons for missingness when generating and applying the novel variant was discussed. The application of such tools to diagnostic model generation of PACNS and other rare and / or emerging diseases and provide objective feedback was explored. This primarily centered around a structured assessment on how to prioritize testing to rapidly rule out conditions that require alternative management and could be used to support future guidelines to optimize the care of these patients. The material presented herein had three components. The first centered around the example of PACNS. It described, in detail, an example of a relevant medical condition and explores why the data is both rare and sparse. Furthermore, the reasons for the sparsity are heterogeneous or non-monotonic (i.e., not conducive to modelling with a singular model). This component concludes with a search for candidate variables to diagnose the condition by means of scoping review for subsequent comparative demonstration of the novel variant of random forest construction that was proposed. The second component discussed machine learning model development and simulates data with varying degrees and patterns of missingness to demonstrate how the models could be applied to data with properties like what would be expected of PACNS related data. Finally, described techniques were applied to separate a subset of patients with suspected PACNS from those with diagnosed PACNS using institutional data and proposes future study to expand upon and ultimately verify these insights. Further development of the novel random forest approach is also discussed.

In this study we explored some of the factors associated with biased processing of attitude-relevant information. We were particularly interested in the possibility that a self-affirmation, by reducing self-evaluative concerns, might increase participants' willingness to impartially evaluate information that conflicts with their current views. We examined students' reactions to arguments about increasing tuition as a function of four factors: attitude importance, argument strength, the congruence of arguments with existing attitudes, and our experimental manipulation of affirmation. We found that affirmation reduced biased evaluation only for participants who rated the issue as important. We also found that affirmation dramatically impacted the perception of argument strength. Stronger counterattitudinal arguments were rejected by non-affirmed participants, who did not distinguish them from weak arguments, presumably because of the esteem threat posed by a strong ideological challenge. Affirmed participants, though, evaluated strong counterattitudinal arguments more positively.

In this study, I investigate how the issue of immigration affects Latino voter turnout. I hypothesize that U.S. Latino citizens who view immigration as highly important and helpful to the United States will be more likely to turn out to vote in midterm and presidential elections. In addition to a contextual analysis on elections in Arizona and California, I perform a probit regression analysis on survey data from Pew Hispanic's 2004 National Survey of Latinos on Politics and Civic Participation. The results are mixed with respect to the initial expectations. While respondents who view immigration as important and helpful are more likely to turn out than those who view immigration as important and hurtful, the results suggest that respondents who find immigration as unimportant may not be less likely to turn out. Further, there are some differences between Latino subgroups, although these differences are minor. Ultimately, the hypotheses presented in this study find moderate support.

Name of thesis Flight equipment and its importance in efficiency categories of paragliding Abstract: The goals The goal of this thesis is to find out appropriate instruments and the dependence of their using on the performance category of pilot. Practical contribution of this thesis will be find out the most frequent usage of the instruments between pilots and how important they are for pilots. Methods The thesis is compiled in the form of quantitative research. For the collection of data we have chosen questionnaries, which were situated in a paragliding web. Results Detected facts represent that with increasing efficiency category, the count of respondents, for who instruments are important, is increased. The next fact represent, that with increasing efficiency category, the count of respondents, who have and use instruments, is increased. The most important instrument for all efficiency categories is variometr and then GPS. Key words Paragliding, instruments, efficiency category, importance.

Title : Analysis of motivational factors for sailors in the ship class Flying Dutchmen Introduction: This diploma thesis deals with posture problems of motivational factors for sailors in the ship class Flying Dutchmen. The aim of the work was to develop a preview of the motivational factors and at the end compare the identified facts in context with similar studies in other areas. The main experimental strategy we chose a qualitative approach. Methods: The research material was developed based on semistructured interview, narrative analysis, inquiry investigation and observation. Additionally quantitative approach was used, mainly in evaluating the inquiry investigation The processing of the results, we used statistical processing in Microsoft Excel. All the results were interpreted by means of comparison in Microsoft Office Word. Results: The results showed us that the principle factors of motivation are "Positive enjoyment ", "Cooperation" and "Beloging with natur". For most of our probands was the first motive to the beginnings of the yachting community boaters. Everyone devote most of their free time sailing, this means that sailing is important in their lives, we can described as their lifestyle is not for them mere sport or form of entertainment. Keywords: Sailing, Flying Dutchmen,...

ABSTRACT WOULD NOT LOAD ON DSpace.

Student number : 0314533R MSc Project Report School of Mining Engineering Faculty of Engineering and the Built Environment / Globalization has created investment opportunities for enterprises around the world. Attracting foreign investment into developing regions has been a key challenge in the strategies for economic growth and poverty reduction of developing countries. Overall, the results of foreign investment have been disappointing in some countries. Recent studies confirm that factors such as corruption, political instability, armed conflict, and other non-commercial risks have negatively impacted foreign direct investment inflows. However, there is renewed hope among government personnel that private investment can play an increasingly significant role in helping economic` growth. As host governments, international corporations, investment banks and multilateral insurance agencies learn from the mistakes of the past and commit themselves to improve the environment for business for mining industry, the prospect for the future brightens. Moreover, foreign investors are recognizing that if the host government can create a competitive environment, investments in developing countries have the potential to be highly profitable. It is hoped that this research effort will contribute in some way to better understand the inability of some countries to attract FDI. The current situation in developing countries is of particular relevance to the theme of this research project. The study compares non-commercial risk ratings for foreign direct investment inflows compiled for ten selected countries. The matrix provides a comparative assessment of noncommercial risk ratings, and highlights the importance of country risk and event risk as components of a composite risk rating.

Rare events have very low probability of occurrence but can have significant impact. Earthquakes, volcanoes, and stock market crashes can have devastating impact on those affected. In industry, engineers evaluate rare events to design better high-reliability systems. The objective of this work is to increase efficiency in design optimization for rare events using metamodeling and variance reduction techniques. Opportunity exists to increase deterministic optimization efficiency by leveraging Design of Experiments to build an accurate metamodel of the system which is less resource intensive to evaluate than the real system. For computationally expensive models, running many trials will impede fast design iteration. Accurate metamodels can be used in place of these expensive models to probabilistically optimize the system for efficient quantification of rare event risk. Monte Carlo is traditionally used for this risk quantification but variance reduction techniques such as importance sampling allow accurate quantification with fewer model evaluations. Metamodel techniques are the thread that tie together deterministic optimization using Design of Experiments and probabilistic optimization using Monte Carlo and variance reduction. This work will explore metamodeling theory and implementation, and outline a framework for efficient deterministic and probabilistic system optimization. The overall conclusion is that deterministic and probabilistic simulation can be combined through metamodeling and used to drive efficiency in design optimization. Applications are demonstrated on a gas turbine combustion autoignition application where user controllable independent variables are optimized in mean and variance to maximize system performance while observing a constraint on allowable probability of a rare autoignition event.

Dans cette thèse, nous explorons de nouvelles méthodes d’analyse d’images pour la détection précoce des changements métaboliques cérébraux causés par la maladie d’Alzheimer. Nous introduisons deux apports méthodologiques que nous appliquons à un ensemble de données réelles. Le premier est basé sur l’apprentissage automatique afin de créer une carte des informations pertinentes pour la classification d'un ensemble d’images. Pour cela nous échantillonnons des blocs de Voxels selon un algorithme de Monte-Carlo. La mise en œuvre d’une classification basée sur ces patchs 3d a pour conséquence la réduction significative du volume de patchs à traiter et l’extraction de caractéristiques dont l’importance est statistiquement quantifiable. Cette méthode s’applique à différentes caractéristiques et est adaptée à des types d’images variés. La résolution des cartes produites par cette méthode peut être affinée à volonté et leur contenu informatif est cohérent avec des résultats antérieurs obtenus dans la littérature. Le second apport méthodologique porte sur la conception d’un nouvel algorithme de décomposition de tenseur d’ordre important, adapté à notre application. Cet algorithme permet de réduire considérablement la consommation de mémoire et donc en évite la surcharge. Il autorise la décomposition rapide de tenseurs, y compris ceux de dimensions très déséquilibrées. Nous appliquons cet algorithme en tant que méthode d’extraction de caractéristiques dans une situation où le clinicien doit diagnostiquer des stades précoces de la maladie d'Alzheimer en utilisant la TEP-FDG seule. Les taux de classification obtenus sont souvent au-dessus des niveaux de l’état de l’art. / In this thesis we want to explore novel image analysis methods for the early detection of metabolic changes in the human brain caused by Alzheimer's disease (AD). We will present two methodological contributions and present their application to a real life data set. We present a machine learning based method to create a map of local distribution of classification relevant information in an image set. The presented method can be applied using different image characteristics which makes it possible to adapt the method to many kinds of images. The maps generated by this method are very localized and fully consistent with prior findings based on Voxel wise statistics. Further we preset an algorithm to draw a sample of patches according to a distribution presented by means of a map. Implementing a patch based classification procedure using the presented algorithm for data reduction we were able to significantly reduce the amount of patches that has to be analyzed in order to obtain good classification results. We present a novel non-negative tensor factorization (NTF) algorithm for the decomposition of large higher order tensors. This algorithm considerably reduces memory consumption and avoids memory overhead. This allows the fast decomposition even of tensors with very unbalanced dimensions. We apply this algorithm as feature extraction method in a computer-aided diagnosis (CAD) scheme, designed to recognize early-stage ad and mild cognitive impairment (MCI) using fluorodeoxyglucose (FDG) positron emission tomography (PET) scans only. We achieve state of the art classification rates.