<p>In the context of highway safety factors, road geometrics and pavement condition are of particular interest to highway managers as they fall within their direct control and therefore can be addressed through highway projects. In spite of the preponderance of econometric modeling in highway safety research, there still remain areas worthy of further investigation. These include 1) the lack of sufficient feedback to roadway preservation engineers regarding the impacts of road-surface condition on safety; 2) the inadequate feedback to roadway designers on optimal lane and shoulder width allocation; 3) the need for higher predictive capability and reliability of models that analyze roadway operations; and 4) the lack of realistic simulations to facilitate reliable safety impact studies regarding autonomous vehicles (AV). In an attempt to contribute to the existing knowledge in this domain and to throw more light on these issues, this dissertation proposes a novel framework for enhanced prediction of highway safety that incorporates machine learning and econometrics with optimization to evaluate and quantify the impacts of safety factors. In the traditional highway operating environment, the proposed framework is expected to help agencies improve their safety analysis. Using an Indiana crash dataset, this dissertation implements the framework, thereby 1) estimating the safety impacts of the road-surface condition with advanced econometric specifications, 2) optimizing space resource allocations across highway cross-sectional elements, and 3) predicting the fatality status of highway segments using machine learning algorithms. In addition, this dissertation discusses the opportunities and the expected safety impacts and benefits of AV in the emerging operating environment. The dissertation also presents a proposed deep learning-based autonomous driving simulation framework that addresses the limitations of AV testing and evaluation on in-service roads and test tracks.</p>

Background and purpose: The main goal was to examine which factors in the psychosocial work environment that would predict turnover intention among engineering employees in a larger global company within the oil and gas industry. The second goal was to examine if the predictors would differ for employees under the age of 40, compared to employees over the age of 40. Method: Data was collected using a self-reported electronic questionnaire designed by Mille Myhre and myself. The analyses included three control variables and seven independent variables, which also encompassed a new aspect of the psychosocial work environment research in relation to turnover intention, namely the personal resources optimism and selfefficacy. The questionnaire was distributed through an e-mail sent from the Vice President HSE, and a sample of 128 participants was used in the analyses conducted in SPSS. Key findings: The predictors were job satisfaction, leadership and sickness absenteeism, and were found to have different rank of importance for the employees in the two age groups, regarding the predictor’s beta value. Optimism and self-efficacy were not found as predictors of turnover intention in the current sample. Conclusion: To manage the employee’s turnover intention the leaders should focus on the employee’s satisfaction with their work, keeping a high qualitative transactional leadership and be observant to the employee’s sickness absenteeism, but control for the employee’s age if they were to initiate actions to control for turnover intention.

This study investigated the influences of personal value systems and risk propensities on managerial decision-making quality during value clashes. The post-globalisation business landscape is impacted by role players of vastly differing personal attributes, hypothesised to have varying influences on decision-making behaviour. A deeper understanding of how these attributes impact decision-making quality will therefore enrich the literature and arm practitioners with improved decision-making skills. A review of behavioural decision-making literature revealed three core approaches: the normative (prescriptive) perspective, focussed on decision analysis, the cognitive limitations perspective highlighting the boundaries of human cognition and the psychological (values/emotions/motivations) perspective allowing for ethical- or value-boundedness. The extant literature contributes little on the quality of decision-making exhibited by managers, or how to improve it. It also doesn’t consider variance in decision-making between groups defined by personal value and risk traits. This study therefore aimed to establish whether decision-making quality varied with variances in personal attributes, and whether an intervention would improve decision-making behaviour. The research, conducted on a sample of 460 South African managers, established the demographics and value- and risk orientations of the participating group. Three value clashing scenarios, incorporating social-relational framing interventions, where introduced to gauge the decision-making behaviour of the test subjects. Decision-making quality was assessed through the integrative complexity measure and qualitative assessments were conducted on the decision motivation texts. Decision-tree analyses, multiple regression analyses as well as T-tests comparing the decision-quality produced by individuals of opposing orientations, revealed a clear relationship between the value segments of self-enhancement and openness to change and higher quality decision-making. Social risk-taking was related to better decision-making and reframing the scenarios produced better decision-making quality responses, if the reframing was done harshly enough. The qualitative analysis supported these findings, but hinted at additional, context specific decision motivators. This study contributed an integrated view of decision-making literature, tested the application of integrative complexity as a measure of decision quality and introduced new perspectives on how value orientations, risk proclivities and scenario framing relate to decision-making quality. Practitioners can apply this to assess individuals in terms of their decision-making abilities, and can improve decision-making quality in managers through scenario re-framing. / Thesis (PhD)--University of Pretoria, 2017. / Gordon Institute of Business Science (GIBS) / PhD / Unrestricted

Big data is an enabling technology in digital transformation. It perfectly complements ordinary linear models and generalized linear models, as training well-performed ordinary linear models and generalized linear models require huge amounts of data. With the help of big data, ordinary and generalized linear models can be well-trained and thus offer better services to human beings. However, there are still many challenges to address for training ordinary linear models and generalized linear models in big data. One of the most prominent challenges is the computational challenges. Computational challenges refer to the memory inflation and training inefficiency issues occurred when processing data and training models. Hundreds of algorithms were proposed by the experts to alleviate/overcome the memory inflation issues. However, the solutions obtained are locally optimal solutions. Additionally, most of the proposed algorithms require loading the dataset to RAM many times when updating the model parameters. If multiple model hyper-parameters needed to be computed and compared, e.g. ridge regression, parallel computing techniques are applied in practice. Thus, multiple learning with sufficient statistics arrays are proposed to tackle the memory inflation and training inefficiency issues.

The presented thesis deals with the issue of statistical quality control of a specific production process. The thesis presents a range of statistical tools that can be used to identify the factors causing a high proportion of non-standard pieces. The diploma thesis practically introduces the application of these quality management tools to the production process characterized by an increased proportion of non-standard pieces, in which the factors causing their occurrence are unknown, and as following the thesis in detail introduces the approach how to detect these factors. The last part of the work summarizes the recommendations handed over to the company in order to verify the conclusions of the thesis.

The aim of thesis is to describe development of the GDP, its components and other corresponding indicators. The experimental part describes development of the indicators during 1993 - 2010, analyses of performance on convergence criteria, analyses of the Czech Republic' competitiveness and evaluating the benefits of EU accession. In the conclusion data were analyzed using regression analysis of two periods, before and after joining EU.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Along the Brazilian poultry production history, large volumes of data are generate by computational tools or manually collected on production environment. This data may be have implicit patterns, from which is possible to extract new answers to questions that can enhance the production scale. In this sense, this work was developed with a database from Languiru Cooperative of Teutônia, Rio Grande do Sul. Were evaluating lots of 112 associated producers in a five years period, from 2007 to 2012, a total of 2319 observations, and the broiler farms are located in different regions of cooperative scope. Data were tabulated and subsequently submitted to multiple regression analysis, comparison of means and data mining process. The results show that the environmental conditions and the altitude influence broiler production, changing the mortality and weight gain, it nas possible establish mortality and weight gain regression models, involving altitude and environmental variables. / Ao longo do histórico de produção avícola brasileira, grandes volumes de dados vêm sendo gerados por ferramentas computacionais ou de forma manual diretamente no ambiente de produção. Nestes dados, podem haver padrões implícitos, a partir dos quais é possível extrair novas respostas para questões que podem potencializar a produção em escala. Assim, foi desenvolvido o trabalho a partir de um banco de dados, oriundo de Cooperativa Languiru da cidade de Teutonia, no Rio Grande do Sul. Foram avaliados lotes de 112 associados da cooperativa, no período de 5 anos, compreendendo os anos de 2007 a 2012. As granjas localizam-se nas diferentes regiões que compreendem a abrangência da empresa, totalizando 2319 lotes. Os dados foram tabulados e posteriormente submetidos à análise de regressão múltipla, de comparação de médias e a mineração de dados. Os resultados demonstram que as variáveis ambientais, bem como a altitude influenciam no desempenho das aves, alterando a mortalidade e o ganho de peso, foi possível estabelecer modelos de predição de mortalidade e ganho de peso envolvendo a altitude e as variáveis ambientais.

<p>In the field of the construction industry, the research work has been widely focused on identifying key performance indicators and critical success factors without assessing the impact of conflict environment factors. This study focusses on the impact of post-conflict environment factors on local construction organization performance. This research presents a performance prediction model comprising the effect of post-conflict environment factors on construction organization performance. The proposed framework of this study has four stages: identify key performance indicators (KPIs), identify post-conflict environment impacting factors, determine critical success factors (CSFs), and formulate success strategy to improve performance. Analytical hierarchy process (AHP) and multiple linear regression (MLR) techniques are applied to analyze the data.</p> <p>The study finding indicates that there is a significant relationship between the post-conflict condition impacting factors and local construction organization performance, which is insufficiently studied in previous research work. Thus, the developed framework will benefit academic scholars and industry practitioners to analyze and evaluate challenges and opportunities caused by different external environment conditions in the post-conflict construction industry. </p>

From the Proceedings of the 1971 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 22-23, 1971, Tempe, Arizona / In order to construct a simulation model for ephemeral streamflow and to examine in depth the problem of the worth of data for that model, measurements of the ephemeral streamflow of Rillitto creek, Tucson, were analyzed for the period 1933-1965. The simulation model was based on several hypotheses: (1) flow durations and their succeeding dry periods (time when no flow is present) are independent; (2) the distribution of the lengths of the dry periods and flows is stationary over a certain period of the year (summer); (3) stationary probability distributions for flow durations and for dry period lengths can be derived. A related problem was how to derive a simulation model for the total amount of flow (in acre-ft) within 1 flow period. Three variables were considered: flow duration (minutes), peak intensity of flow (cu ft/sec) and antecedent dry period-minutes (ADP). Because the assumption of variance constancy does not hold, a multiplicative regression model was used. Using an analysis of variance, which is described in detail, the worth of the 3 kinds of data were examined in relation to total flow. It was concluded that there are at least 5 times during the year when the flow intervals differ significantly, and the ADP is not important in determining flow volume because of the poison flow arrival rate in summer. Events occur at random and are not clustered as in summer, indicating that channel moisture does not differ much between flow events.