The master thesis concentrates on the problematics of the regional development in connection with the educational structure of the Czech Republic. The main goal of this working paper is to evaluate, whether the level of the education reached has an impact on the regional development. For purpose of the analysis, NUTS 3 regions of the Czech Republic have been used. The regional development has been defined with five economic indicators, namely GDP per capita, average monthly gross salary, long term unemployment, foreign direct investments and employment in the primary sector. Nowadays education on the university degree is gaining importance and that is why university degree of the working population has been set as independent variable. For the proof of the dependence between university degree and dependent economic indicators of regional development, linear regression analysis has been used. In order to analyze the development of the dependence in time, for each economic indicators regression analysis of two years has been compared.

The 2010 National HIV/AIDS Strategy outlined three important goals for managing the current HIV pandemic in the U.S.: (1) reduce the number of people who become infected with HIV; (2) improve access to health care and health-related outcomes for people living with HIV/AIDS (PLH/A); and (3) reduce HIV-related health disparities. Each of these goals tacitly depends upon reducing HIV-related stigma, and this study examined how HIV+ individuals evaluate coping efforts to overcome stigma’s impact on quality of life (QOL). a structural regression model was developed to instantiate the reappraisal process described by Lazarus and Folkman’s transactional theory of stress and coping, and this model indicated that maladaptive coping fully mediated the relationship between dispositional forgiveness and perceived stigma, which supports the prediction that coping efficacy is related to stress reduction. Additionally, maladaptive coping fully mediated the relationship between dispositional forgiveness and QOL, supporting the contention that forgiveness is a critical aspect to the evaluative process that influences how PLH/A cope with stigma. Lastly, the model showed that when PLH/A engaged in maladaptive coping to mitigate stress-related stigma, these individuals experienced increased stigmatization and reported significantly lower levels of health-related QOL. in contrast, PLH/A that reported higher levels of dispositional forgiveness were significantly less likely to use maladaptive coping to overcome stigma. Therefore, dispositional forgiveness works through coping to alter perceptions regarding stigmatization, while indirectly influencing attitudes related to health distress, mental health, and cognitive and social functioning. the theoretical and clinical implications of these findings are discussed.

The goal of the thesis „Econometric model of flat prices in Brno“ is to create econometric model based on data from housing market. The theoretical part of the thesis defines variables, and use descriptive statistics. The practical part of the thesis deals with creation econometric model and interactive calculator.

The challenges posed by climate change are a threat to human well-being as well as to natural ecosystems, but researchers indicate that awareness of and concern about climate change varies considerably. In this thesis, we investigate the socio-demographic factors associated with the perception of climate change, analysing four data sets collected from four surveys on urban middle-income households in Mexico. Our empirical strategy relies on the estimation of logit regressions on a binary variable defining whether respondents consider climate change as an important issue. Results indicate that age, gender, education, employment status, household size, and having in the household a member with a respiratory illness are important predictors that shape Mexicans’ perception of how important climate change is. It is important to know the determinants that effect climate change perception in order to develop sustainable policies to mitigate the risks of climate change.

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Wind power becomes one of the most promising energy sources in the electricity generation sector in Texas over the past decade by declining levelized cost of wind energy. However, recent studies show that the wind farms in Texas are relatively less productive. Hence, this study aims to find out reasons of inefficiencies by constructing a two-stage performance assessment of wind farms in Texas. In the first stage of analysis, comprehensive input- and output-oriented Data Envelopment Analysis (DEA) models are applied to evaluate productive efficiencies of the 95 large utility-scale wind farms by using pre-determined three input and two output variables. The sensitivity analysis is provided for the robustness of the DEA models with different combinations of input and output variables of the original model. The slack analysis and projection data are obtained for inefficient wind farms to find out optimal input-output variables. Tobit regression models are conducted for the second stage of the analysis to investigate the reasons of inefficiencies. DEA results indicate that half of the wind farms were operated efficiently in Texas during 2016. 13 wind farms were performed at the most productive scale size, ten wind farms should reduce their operational size to improve production efficiency, and 72 wind farms have the notable potential to increase their production efficiency by expanding operational sizes with modern wind turbine technologies. The sensitivity analysis shows the importance of each input-output variables. Tobit regression models indicate that elevation of the site, rotor diameter, hub height, and brand of the turbine have significant contributions to the relative efficiency scores of the wind farms, and the age of turbine has a negative impact on the productive efficiency of the wind farms.

Regression analysis is one of the most extensively used statistical tools applied across different fields of science, with linear regression being its most well-known method. How- ever, the traditional procedure to obtain the linear model estimates, the least squares approach, is highly sensitive to even slight departures from the assumed modelling frame- work. This is especially pronounced when atypical values occur in the observed data. This lack of stability of the least squares approach is a serious problem in applications. Thus, the focus of this thesis lies in assessing the available robust alternatives to least squares estimation, which are not so easily affected by any outlying values. First, we introduce the linear regression model theory and derive the least squares method. Then, we char- acterise different types of unusual observations and outline some fundamental robustness measures. Next, we define and examine the robust alternatives to the classical estimation in the linear regression models. Finally, we conduct a comprehensive simulation study comparing the performance of robust methods under different scenarios. 1

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20688号 / 工博第4385号 / 新制||工||1681(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小池 克明, 教授 三ｹ田 均, 准教授 須崎 純一 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Traditional power plants only utilize about 30 percent of the primary energy that they consume, and the rest of the energy is usually wasted in the process of generating or transmitting electricity. On-site and near-site power generation has been considered by business, labor, and environmental groups to improve the efficiency and the reliability of power generation. Combined heat and power (CHP) systems are a promising alternative to traditional power plants because of the high efficiency and low CO2 emission achieved by recovering waste thermal energy produced during power generation. A CHP operational algorithm designed to optimize operational costs must be relatively simple to implement in practice such as to minimize the computational requirements from the hardware to be installed. This dissertation focuses on the following aspects pertaining the design of a practical CHP operational algorithm designed to minimize the operational costs: (a) real-time CHP operational strategy using a hierarchical optimization algorithm; (b) analytic solutions for cost-optimal power generation unit operation in CHP Systems; (c) modeling of reciprocating internal combustion engines for power generation and heat recovery; (d) an easy to implement, effective, and reliable hourly building load prediction algorithm.

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