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Stress is the most common reason for sick leave in Sweden today. The physical demands are less in today’s work life, but the psychological demands have increased, resulting in increased stress related ill-health. The aim with the current study was to gain an understanding in how individuals that has been or are on stress-related sick leave experience the social support received at the work place and where they experienced that the primary responsibility for the sick leave was. Nine participants from self-help groups for stress was interviewed with a qualitative hermeneutic approach. The interviews were transcribed and analyzed with van Manen’s (1990) “selective or highlighting approach”. The analysis was grounded in four research questions; causes of stress-related sick leave, perceived responsibility for the sick leave, social support, and facilitating factors for returning to work. The result showed that the participants experienced lack of rewards, high demands, low control, lack of social support, insufficient recovery and denial of symptoms of stress. The participants often blamed themselves and took on the primary responsibility. The self-help groups acted as substitute for the lacking social support as well as increased the self-awareness and motivation among the participants.

The aim of my diploma thesis is to find out and influence positively with the help of some compensative exercises posture, flexibility and muscular unbalance among swimmers from the Swimming club in Jindřichův Hradec. Experimental research was divided into two measurements between which half of the swimmers did the compensative excercises. The input and output measurements corresponded with their content and evaluated posture, flexibility and swimmers'muscular strength. Standard tests were used for testing and evaluation. Posture was evaluated with the help of plumb line and standing on two bathroom scales. Flexibility was evaluated by standardized tests of movability according to Měkota, Thomayer and Otta tests. Muscular shortening was tested according to Janda. Standing jump, pull-ups, or rather staying power in pull-up and press-up staying power in a kneeling position were used to compare muscular strength. Key words: compensative exercises, swimming, posture, muscular unbalance

Being non-renewable source of energy, oil maintains the largest contributor to the energy mix of all counties in the world. Consequently, oil-refining industry is a field of particular concern for the governments and society. This work focuses on oil-refining industry in the countries of Eastern and Central Europe. This topic is very sensitive for CEE counties due to continuously rising energy prices, vulnerability of the supply security and current EC regulations concerning emissions trade and common environment policy. This thesis aims to answer to question what will happen to the industry in CEE counties after implication of new EC regulations and development of the renewable sources of energy. For this purpose, SWOT analysis of the industry in different counties was performed, highlighting major strengths and potential threats. Further analysis describes major mergers and acquisitions in the industry, FDI and current problems of trade imbalance. Particular attention is given to the security of supply and dangerous dependency on the crude oil imports. The results show that some EC policies and regulations could be potentially perilous for the counties which have large crude reserves and developed oil-refining and petrochemical industry, influencing such factors as cost of production and competitiveness of the product in the market. However, further development of renewable sources is often the only option available for the counties with no fossil fuel reserves and poor developed refining infrastructure, aimed to eliminate ever-increasing energy dependency.

In binary Gaussian process classification the prior class membership probabilities are obtained by transforming a Gaussian process to the unit interval, typically either with the logistic likelihood function or the cumulative Gaussian likelihood function. Multiclass classification problems can be handled by any binary classifier by means of so-called binarization techniques, which reduces the multiclass problem into a number of binary problems. Other than introducing the mathematics behind the theory and methods behind Gaussian process classification, we compare the binarization techniques one-against-all and one-against-one in the context of Gaussian process classification, and we also compare the performance of the logistic likelihood and the cumulative Gaussian likelihood. This is done by means of two experiments: one general experiment where the methods are tested on several publicly available datasets, and one more specific experiment where the methods are compared with respect to class imbalance and class overlap on several artificially generated datasets. The results indicate that there is no significant difference in the choices of binarization technique and likelihood function for typical datasets, although the one-against-one technique showed slightly more consistent performance. However the second experiment revealed some differences in how the methods react to varying degrees of class imbalance and class overlap. Most notably the logistic likelihood was a dominant factor and the one-against-one technique performed better than one-against-all.

In 2016 the average industry downtime cost was estimated to $260.000 every hour, and with Swedish industries being an important part of the national economy it would be desirable to reduce the amount of unplanned downtime to a minimum. There are currently many different solutions for system supervision for monitoring system health but none which analyse data with machine learning in an industrial gateway.   The aim for this thesis is to test, compare and evaluate three different algorithms to find a classifier suitable for a gateway environment. The evaluated algorithms were Random Forest, K-Nearest Neighbour and Linear Discriminant Analysis. Load imbalance detection was used as a case study for evaluating these algorithms. The gateway received data from a Modbus ATV32 frequency converter, which measured specific features from an induction motor. The imbalance was created with loads that were attached on a fly-wheel at different angles to simulate different imbalances. The classifiers were compared on their accuracy, memory usage, CPU usage and execution time. The result was evaluated with tables, confusion matrices and AUC- ROC curves.  Although all algorithms performed well LDA was best based on the criteria set.

Este trabalho apresenta uma investigação sobre o alcance da aplicação da teoria da onerosidade excessiva, prevista nos artigos 478 a 480 do Novo Código Civil, aos acordos de acionistas, tipo contratual cada vez mais presente na realidade empresarial brasileira. Especial atenção é dada ao fato de que o acordo de acionistas está inserido em um contexto marcado não só por um ambiente negocial e mercadológico sujeito a acontecimentos imprevisíveis que podem desequilibrar as prestações de forma excessivamente onerosa para uma das partes, mas também marcado por um equilíbrio na composição do conteúdo contratual e na alocação de riscos correspondente combinados pelas partes. Para isso, serão analisadas as questões relativas ao alcance da aplicação do mecanismo de reequilíbrio contratual mencionado em acordo de acionistas, à identificação do objeto do conteúdo contratual que contém o programa de alocação de risco e ao elemento-guia utilizado pela jurisprudência nesse sentido, uma vez que foi o uso desenfreado do reequilíbrio contratual que fez com que este fosse quase expulso da sistemática contratual durante o período clássico. A pesquisa apontou como elemento-guia autorizador da aplicação da onerosidade excessiva pelos órgãos judiciais estudados a conjunção da identificação dos critérios legais com a identificação do fato de a onerosidade excessiva estar além daquele risco implícito e da álea normal da natureza do negócio jurídico celebrado. Tais resultados apontam para uma criteriosa possibilidade de correção de desequilíbrio contratual compatível com a dinâmica e o ambiente dos acordos de acionistas / This work presents an investigation concerning the scope of application of the excessive onerous theory, provided in articles 478 to 480 of the Brazilian Civil Code, in the sharesholders agreement matter, a contractual type increasingly present in the Brazilian business reality. Special attention is given to the fact the sharesholders agreement is inserted in a context characterized not only by the negotial and market environment subject to unpredictable events that may disrupt the provision in an excessive onerous way to a relevant party, but also characterized by a balance in the composition of the content and of the contractual allocation of risks combined by the relevant parties. For this, we analyze the issues related to the scope of application of the mentioned contractual rebalancing mechanism in the shareholders agreement matter, to the identification of the contractual content object that contains the risk allocation program and to the guide-element used in the jurisprudence in this sense, since it was the umlimited use of contractual rebalancing that caused this was almost kicked out of the contractual systematically during the classical period. The survey pointed out as guide-element to the application of excessive onerous by the legal courts studied the association of the legal criteria identification with the identication of the fact that the excessive onerous being beyond that inherent and normal risk concluded of the nature of the legal business. These results point to the possibility of a careful correction of contractual imbalance compatible with the shareholders agreements dynamic and environment.

A diabetes mellitus é uma das endocrinopatias mais frequente em cães e sabe-se que esses pacientes são mais propensos a desenvolver complicações anestésicas quando comparados aos não-diabéticos. Desta forma, objetivou-se avaliar, durante o período perioperatório, as possíveis alterações no equilíbrio ácido-base e complicações anestésicas em cães diabéticos submetidos a facoemulsificação. Foram incluídos 30 cães, sendo 15 diabéticos e 15 não portadores da afecção. Foram determinados o pH, bicarbonato e déficit de base, além de eletrólitos plasmáticos (sódio, cloro, potássio). Adicionalmente, foram avaliadas as variáveis cardiorrespiratórias no período trans-anestésico. Com relação aos distúrbios ácido-base, os pacientes não apresentaram alterações compatíveis com acidose metabólica, apenas discreta acidemia após 30 minutos de anestesia. Apesar dos pacientes diabéticos apresentarem valores de bicarbonato inferiores ao grupo controle, estes permaneceram dentro dos valores de referência. A distribuição dos valores de eletrólitos foi diferente entre os grupos, exceto os valores de cloro. Nos pacientes diabéticos, a hiponatremia ocorreu em seis animais ao término do procedimento cirúrgico e 73% dos cães apresentaram hipercalemia após a administração da medicação pré-anestésica. A complicação anestésica mais comum foi a hipotensão arterial, sendo que 80% dos animais diabéticos apresentaram pressão arterial média inferior a 60 mmHg após indução anestésica. Concluindo, houve variação discreta do equilíbrio ácido-base nos cães diabéticos. Tendo em vista que a acidemia foi verificada em ambos os grupos, sugere-se que a mesma esteja relacionada ao procedimento anestésico. Os pacientes diabéticos submetidos à anestesia geral são mais propensos à hipotensão arterial, sendo que essa alteração merece maiores investigações / Diabetes mellitus is one of the most common endocrinopathies in dogs and it is know that these patients are more likely to develop anesthetic complications when compared to non-diabetics. Thus, this study aimed to evaluate potential changes in acid-base balance and anesthetic complications in diabetic dogs undergoing phacoemulsification during the perioperative period. Thirty dogs were included, fifteen diabetic and fifteen non-carriers of the disease and were analyzed for pH, bicarbonate, blood gas and plasma electrolytes (sodium, chloride, potassium). Additionally, we evaluate the cardiorespiratory variables in the peri-anesthetic period. With respect to acid-base disturbances, patients showed no changes consistent with metabolic acidosis, but a mild acidemia after 30 minutes of anesthesia. Although diabetic patients showed values of bicarbonate below the control group, these were within the reference values. The distribution of the electrolyte was different between groups, except for the amounts of chlorine. In diabetic patients, hyponatremia occurred in six animals at the end of the surgical procedure and 73% of dogs showed hyperkalemia after administration of premedication. The most common anesthetic complication was hypotension, and 80% of diabetic animals showed mean arterial pressure below 60 mmHg after induction of anesthesia. In conclusion, there was a slight variation of the acid-base balance in diabetic dog. Given that acidemia was observed in both groups, it is suggested that it is related to the anesthetic procedure. Diabetic patients undergoing general anesthesia are more prone to hypotension, and this change deserves further investigation

Os recentes avanços da ciência e tecnologia viabilizaram o crescimento de dados em quantidade e disponibilidade. Junto com essa explosão de informações geradas, surge a necessidade de analisar dados para descobrir conhecimento novo e útil. Desse modo, áreas que visam extrair conhecimento e informações úteis de grandes conjuntos de dados se tornaram grandes oportunidades para o avanço de pesquisas, tal como o Aprendizado de Máquina (AM) e a Mineração de Dados (MD). Porém, existem algumas limitações que podem prejudicar a acurácia de alguns algoritmos tradicionais dessas áreas, por exemplo o desbalanceamento das amostras das classes de um conjunto de dados. Para mitigar tal problema, algumas alternativas têm sido alvos de pesquisas nos últimos anos, tal como o desenvolvimento de técnicas para o balanceamento artificial de dados, a modificação dos algoritmos e propostas de abordagens para dados desbalanceados. Uma área pouco explorada sob a visão do desbalanceamento de dados são os problemas de classificação hierárquica, em que as classes são organizadas em hierarquias, normalmente na forma de árvore ou DAG (Direct Acyclic Graph). O objetivo deste trabalho foi investigar as limitações e maneiras de minimizar os efeitos de dados desbalanceados em problemas de classificação hierárquica. Os experimentos realizados mostram que é necessário levar em consideração as características das classes hierárquicas para a aplicação (ou não) de técnicas para tratar problemas dados desbalanceados em classificação hierárquica. / Recent advances in science and technology have made possible the data growth in quantity and availability. Along with this explosion of generated information, there is a need to analyze data to discover new and useful knowledge. Thus, areas for extracting knowledge and useful information in large datasets have become great opportunities for the advancement of research, such as Machine Learning (ML) and Data Mining (DM). However, there are some limitations that may reduce the accuracy of some traditional algorithms of these areas, for example the imbalance of classes samples in a dataset. To mitigate this drawback, some solutions have been the target of research in recent years, such as the development of techniques for artificial balancing data, algorithm modification and new approaches for imbalanced data. An area little explored in the data imbalance vision are the problems of hierarchical classification, in which the classes are organized into hierarchies, commonly in the form of tree or DAG (Direct Acyclic Graph). The goal of this work aims at investigating the limitations and approaches to minimize the effects of imbalanced data with hierarchical classification problems. The experimental results show the need to take into account the features of hierarchical classes when deciding the application of techniques for imbalanced data in hierarchical classification.

Algoritmos de aprendizado de máquina são frequentemente os mais indicados em uma grande variedade de aplicações de mineração dados. Entretanto, a maioria das pesquisas em aprendizado de máquina refere-se ao problema bem definido de encontrar um modelo (geralmente de classificação) de um conjunto de dados pequeno, relativamente bem preparado para o aprendizado, no formato atributo-valor, no qual os atributos foram previamente selecionados para facilitar o aprendizado. Além disso, o objetivo a ser alcançado é simples e bem definido (modelos de classificação precisos, no caso de problemas de classificação). Mineração de dados propicia novas direções para pesquisas em aprendizado de máquina e impõe novas necessidades para outras. Com a mineração de dados, algoritmos de aprendizado estão quebrando as restrições descritas anteriormente. Dessa maneira, a grande contribuição da área de aprendizado de máquina para a mineração de dados é retribuída pelo efeito inovador que a mineração de dados provoca em aprendizado de máquina. Nesta tese, exploramos alguns desses problemas que surgiram (ou reaparecem) com o uso de algoritmos de aprendizado de máquina para mineração de dados. Mais especificamente, nos concentramos seguintes problemas: Novas abordagens para a geração de regras. Dentro dessa categoria, propomos dois novos métodos para o aprendizado de regras. No primeiro, propomos um novo método para gerar regras de exceção a partir de regras gerais. No segundo, propomos um algoritmo para a seleção de regras denominado Roccer. Esse algoritmo é baseado na análise ROC. Regras provêm de um grande conjunto externo de regras e o algoritmo proposto seleciona regras baseado na região convexa do gráfico ROC. Proporção de exemplos entre as classes. Investigamos vários aspectos relacionados a esse tópico. Primeiramente, realizamos uma série de experimentos em conjuntos de dados artificiais com o objetivo de testar nossa hipótese de que o grau de sobreposição entre as classes é um fator complicante em conjuntos de dados muito desbalanceados. Também executamos uma extensa análise experimental com vários métodos (alguns deles propostos neste trabalho) para balancear artificialmente conjuntos de dados desbalanceados. Finalmente, investigamos o relacionamento entre classes desbalanceadas e pequenos disjuntos, e a influência da proporção de classes no processo de rotulação de exemplos no algoritmo de aprendizado de máquina semi-supervisionado Co-training. Novo método para a combinação de rankings. Propomos um novo método, chamado BordaRank, para construir ensembles de rankings baseado no método de votação borda count. BordaRank pode ser aplicado em qualquer problema de ordenação binária no qual vários rankings estejam disponíveis. Resultados experimentais mostram uma melhora no desempenho com relação aos rankings individuais, alem de um desempenho comparável com algoritmos mais sofisticados que utilizam a predição numérica, e não rankings, para a criação de ensembles para o problema de ordenação binária. / Machine learning algorithms are often the most appropriate algorithms for a great variety of data mining applications. However, most machine learning research to date has mainly dealt with the well-circumscribed problem of finding a model (generally a classifier) given a single, small and relatively clean dataset in the attribute-value form, where the attributes have previously been chosen to facilitate learning. Furthermore, the end-goal is simple and well-defined, such as accurate classifiers in the classification problem. Data mining opens up new directions for machine learning research, and lends new urgency to others. With data mining, machine learning is now removing each one of these constraints. Therefore, machine learning's many valuable contributions to data mining are reciprocated by the latter's invigorating effect on it. In this thesis, we explore this interaction by proposing new solutions to some problems due to the application of machine learning algorithms to data mining applications. More specifically, we contribute to the following problems. New approaches to rule learning. In this category, we propose two new methods for rule learning. In the first one, we propose a new method for finding exceptions to general rules. The second one is a rule selection algorithm based on the ROC graph. Rules come from an external larger set of rules and the algorithm performs a selection step based on the current convex hull in the ROC graph. Proportion of examples among classes. We investigated several aspects related to this issue. Firstly, we carried out a series of experiments on artificial data sets in order to verify our hypothesis that overlapping among classes is a complicating factor in highly skewed data sets. We also carried out a broadly experimental analysis with several methods (some of them proposed by us) that artificially balance skewed datasets. Our experiments show that, in general, over-sampling methods perform better than under-sampling methods. Finally, we investigated the relationship between class imbalance and small disjuncts, as well as the influence of the proportion of examples among classes in the process of labelling unlabelled cases in the semi-supervised learning algorithm Co-training. New method for combining rankings. We propose a new method called BordaRanking to construct ensembles of rankings based on borda count voting, which could be applied whenever only the rankings are available. Results show an improvement upon the base-rankings constructed by taking into account the ordering given by classifiers which output continuous-valued scores, as well as a comparable performance with the fusion of such scores.