A Technical Due Diligence (TDD) investigation is an important step in the process of obtaining financing, or in mergers and acquisitions, for a wind power project. The investigation, the scope of which varies depending on the stage and nature of the project, involves reviewing important documentation relating to different aspects of the project, assessing potential risks in terms of the quality of the information available and suggesting mitigation or other risk management measures where required. A TDD assessment can greatly benefit from increased objectivity in terms of the reviewed aspects as it enables a sharper focus on the important risk elements and also provides a better appreciation of the investigated parameters. This master’s thesis has been an attempt to introduce more objectivity in the technical due diligence process followed at the host company. Thereafter, a points-based scoring system was devised to quantify the answered questions. The different aspects under investigation have a complex interrelationship and the resulting risks can be viewed as an outcome of a causal framework. To identify this causal framework the concept of Bayesian Belief Networks has been assessed. The resulting Bayesian Networks can be considered to provide a holistic framework for risk analysis within the TDD assessment process. The importance of accurate analysis of likelihood information for accurate analysis of Bayesian analysis has been identified. The statistical data set for the right framework needs to be generated to have the right correct setting for Bayesian analysis in the future studies. The objectiveness of the TDD process can be further enhanced by taking into consideration the capability of the investing body to handle the identified risks and also benchmarking risky aspects with industry standards or historical precedence.

China has continued the economic reform and open door policy over 30 years with many great achievements, such as the second largest GDP, the largest import and export economy with the largest infrastructural investment in the world. On the other hand, the conflicts and risks the firms especially for small and medium sized manufacturing enterprises (SMEs) have faced are extremely serious and more acute due to the economy growth and increasing social wealth, especially in Yangtze River Delta, in the general context of ever increasing cost such as labour, land and higher customers’ expectations such as the quality of product. These serious problems are challenges for the competitiveness of SMEs in Yangtze River Delta. This research aims to investigate and improve the competitiveness of SMEs by the main variables such as enterprise’s resources, product’s competitive issues and innovation activities related barriers. To achieve the aim, the research employed a mixed method of quantitative and qualitative approaches to build the competitiveness’s belief network model by Bayesian Belief Networks and analyze the factors of the most important variables by the SPSS software. Secondly, 36 entrepreneurs of small and medium sized manufacturing enterprises in Yangtze River Delta have been carefully selected to participate in the questionnaire survey and face to face interviews. All participants are entrepreneurs who have run enterprise for at least three years. Five kinds of resources, competitive issues and innovation have been identified as the variables of competitiveness. The findings of research are mainly related to the three aspects which are general view of variables; barriers to innovation activity and importance of variables for improving the competitiveness; and the factor analysis of quality management practices. Firstly, the general condition of financial resource is the worst in resource sector of SMEs; Dependability is the best performance in competitive issues of SMEs; Lack of finance is generally identified the biggest barrier to innovation of SMEs. Secondly, the Physical resource in resource sector and Quality in competitive issues sector are the most important variables for improving the competitiveness of SMEs after BBN assessment; Lack of technical experts is the most serious barrier when the SMEs are really focusing on the innovation according to the BBN assessments. Thirdly, the factor analyses have identified the key independent factors explaining the quality management practices in these SMEs. Finally, these findings can help the SMEs build variables’ impact tables based on the outputs from the conditional assessment of BBNs to make more efficient and effective decisions when they try to improve the enterprise competitiveness, with detailed recommendations. At the same time, the importance and factors of good quality management practices have also been argued to help the entrepreneurs improve the quality performance and their enterprise competitiveness.

Lean Six Sigma is a method used to improve the quality and efficiency of processes by reducing variation and eliminating wastes (non-value added activities) in an organisation. The concept of combining the principles and tools of Lean Enterprise and Six Sigma has been discussed in the literature. The majority of Lean Six Sigma applications in private industry have focused primarily on manufacturing applications. The literature has not provided a framework for implementing Lean Six Sigma programmes in non-manufacturing or transactional processes like those in the Entertainment Media industry. The Saudi Broadcasting Corporation (SBC), like many other industries in Saudi Arabia, has high occupational safety risks, such as electric, fire and fall hazards which often occur in the media workplace. These risks are considered very costly and affect productivity and employee morale in general. The main objective of this research is to provide a synergistic approach to integrating occupational health and safety programmes and Lean Six Sigma tools using the DMAIC (Define-Measure-Analyse-Improve-Control) problem-solving method to strengthen and assure the success of safety programmes in the Saudi Broadcasting Corporation (SBC). This research identifies the roadmap (i.e. activities, principles, tools, and important component factors) for applying Lean Six Sigma tools in the media industry. A case study addressing the safety issues that affect employees’ performance within the Saudi Broadcasting Corporation (SBC) TV studio is used to validate work outlined in this research. Furthermore, the Bayesian Belief Networks (BBN) method is used to understand the probability occurrence of safety hazards. The application of the Taguchi Experimental Design method and other Lean Six Sigma tools, such as Cause and Effect diagrams, Pareto principles, 5S, Value Stream map, and Poka-Yoke have been incorporated in to this research. The application of Lean Six Sigma DMAIC problem-solving tools resulted in significant improvement in safety within SBC. The average electrical hazard incident decreased from 2.08 to 0.33, the average fire hazard incident decreased from 1.25 to 0.08, and the average fall hazard incident decreased from 3.42 to 0.17. The research has important implications for the company and its employees, with positive outcomes and feedback reported by top management, the senior technicians, and experts. The research improved the safety by reducing electrical, fire and fall risks. The Safety training sessions are one of the most significant factors that improve their safety awareness. It is observed that Lean Six Sigma problem-solving tools and methods are effective in the Saudi Broadcasting Corporation (SBC).

Companies, across the globe are concerned with risks that impair their ability to produce quality products at a low cost and deliver them to customers on time. Risk assessment, comprising of both external and internal elements, prepares companies to identify and manage the risks affecting them. Although both external/supply chain and internal/production line risk assessments are necessary, internal risk assessment is often ignored. Internal risk assessment helps companies recognize vulnerable sections of production operations and provide opportunities for risk mitigation. In this research, a novel production line risk assessment methodology is proposed. Traditional simulation techniques fail to capture the complex relationship amongst risk events and the dynamic interaction between risks affecting a production line. Bayesian- integrated System Dynamics modelling can help resolve this limitation. Bayesian Belief Networks (BBN) effectively capture risk relationships and their likelihoods. Integrating BBN with System Dynamics (SD) for modelling production lines help capture the impact of risk events on a production line as well as the dynamic interaction between those risks and production line variables. The proposed methodology is applied to an industrial case study for validation and to discern research and practical implications.

Software practitioners lack a consistent approach to assessing and predicting quality within their products. This research proposes a software quality model that accounts for the influences of development team skill/experience, process maturity, and problem complexity throughout the software engineering life cycle. The model is structured using Bayesian Belief Networks and, unlike previous efforts, uses widely-accepted software engineering standards and in-use industry techniques to quantify the indicators and measures of software quality. Data from 28 software engineering projects was acquired for this study, and was used for validation and comparison of the presented software quality models. Three Bayesian model structures are explored and the structure with the highest performance in terms of accuracy of fit and predictive validity is reported. In addition, the Bayesian Belief Networks are compared to both Least Squares Regression and Neural Networks in order to identify the technique is best suited to modeling software product quality. The results indicate that Bayesian Belief Networks outperform both Least Squares Regression and Neural Networks in terms of producing modeled software quality variables that fit the distribution of actual software quality values, and in accurately forecasting 25 different indicators of software quality. Between the Bayesian model structures, the simplest structure, which relates software quality variables to their correlated causal factors, was found to be the most effective in modeling software quality. In addition, the results reveal that the collective skill and experience of the development team, over process maturity or problem complexity, has the most significant impact on the quality of software products.

This research explores the potential of improving the explainability of outliers using Bayesian Belief Networks as background knowledge. Outliers are deviations from the usual trends of data. Mining outliers may help discover potential anomalies and fraudulent activities. Meaningful outliers can be retrieved and analyzed by using domain knowledge. Domain knowledge (or background knowledge) is represented using probabilistic graphical models such as Bayesian belief networks. Bayesian networks are graph-based representation used to model and encode mutual relationships between entities. Due to their probabilistic graphical nature, Belief Networks are an ideal way to capture the sensitivity, causal inference, uncertainty and background knowledge in real world data sets. Bayesian Networks effectively present the causal relationships between different entities (nodes) using conditional probability. This probabilistic relationship shows the degree of belief between entities. A quantitative measure which computes changes in this degree of belief acts as a sensitivity measure . The first contribution of this research is enhancing the performance for measurement of sensitivity based on earlier research work, the Interestingness Filtering Engine Miner algorithm. The algorithm developed (IBOX - Interestingness based Bayesian outlier eXplainer) provides progressive improvement in the performance and sensitivity scoring of earlier works. Earlier approaches compute sensitivity by measuring divergence among conditional probability of training and test data, while using only couple of probabilistic interestingness measures such as Mutual information and Support to calculate belief sensitivity. With ingrained support from the literature as well as quantitative evidence, IBOX provides a framework to use multiple interestingness measures resulting in better performance and improved sensitivity analysis. The results provide improved performance, and therefore explainability of rare class entities. This research quantitatively validated probabilistic interestingness measures as an effective sensitivity analysis technique in rare class mining. This results in a novel, original, and progressive research contribution to the areas of probabilistic graphical models and outlier analysis.

<p>Estuaries interfacing with the land, atmosphere and open oceans can be influenced in a variety of ways by anthropogenic activities. Centuries of overexploitation, habitat transformation, and pollution have degraded estuarine ecological health. Key concerns of public and environmental managers of estuaries include water quality, particularly the enrichment of nutrients, increased chlorophyll a concentrations, increased hypoxia/anoxia, and increased Harmful Algal Blooms (HABs). One reason for the increased nitrogen loading over the past two decades is the proliferation of concentrated animal feeding operations (CAFOs) in coastal areas. This dissertation documents a study of estuarine eutrophication modeling, including modeling of major source of nitrogen in the watershed, the use of the Bayesian Networks (BNs) for modeling eutrophication dynamics in an estuary, a documentation of potential problems of using BNs, and a continuous BN model for addressing these problems.</p><p>Environmental models have emerged as great tools to transform data into useful information for managers and policy makers. Environmental models contain uncertainty due to natural ecosystems variability, current knowledge of environmental processes, modeling structure, computational restrictions, and problems with data/observations due to measurement error or missingness. Many methodologies capable of quantifying uncertainty have been developed in the scientic literature. Examples of such methods are BNs, which utilize conditional probability tables to describe the relationships among variables. This doctoral dissertation demonstrates how BNs, as probabilistic models, can be used to model eutrophication in estuarine ecosystems and to explore the effects of plausible future climatic and nutrient pollution management scenarios on water quality indicators. The results show interaction among various predictors and their impact on ecosystem health. The synergistic eftects between nutrient concentrations and climate variability caution future management actions.</p><p>BNs have several distinct strengths such as the ability to update knowledge based on Bayes' theorem, modularity, accommodation of various knowledge sources and data types, suitability to both data-rich and data-poor systems, and incorporation of uncertainty. Further, BNs' graphical representation facilitates communicating models and results with environmental managers and decision-makers. However, BNs have certain drawbacks as well. For example, they can only handle continuous variables under severe restrictions (1- Each continuous variable be assigned a (linear) conditional Normal distribution; 2- No discrete variable have continuous parents). The solution, thus far, to address this constraint has been discretizing variables. I designed an experiment to evaluate and compare the impact of common discretization methods on BNs. The results indicate that the choice of discretization method severely impacts the model results; however, I was unable to provide any criteria to select an optimal discretization method.</p><p>Finally, I propose a continuous variable Bayesian Network methodology and demonstrate its application for water quality modeling in estuarine ecosystems. The proposed method retains advantageous characteristics of BNs, while it avoids the drawbacks of discretization by specifying the relationships among the nodes using statistical and conditional probability models. The Bayesian nature of the proposed model enables prompt investigation of observed patterns, as new conditions unfold. The network structure presents the underlying ecological ecosystem processes and provides a basis for science communication. I demonstrate model development and temporal updating using the New River Estuary, NC data set and spatial updating using the Neuse River Estuary, NC data set.</p> / Dissertation

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Der Einfluss des Menschen verändert die Erdoberfläche in gravierendem Maße. Die Anwendung von Landnutzungsmodellen ist etabliert, um derartige Prozesse zu analysieren und um Handlungsempfehlungen für Entscheidungsträger zu geben. Landnutzungsmodelle stehen unter dem Einfluss von Unsicherheiten, welche beim Interpretieren der Ergebnisse berücksichtigt werden müssen. Dennoch gibt es wenige Ansätze, die unterschiedliche Unsicherheitsquellen mit ihren Interdependenzen untersuchen und ihre Auswirkungen auf die projizierte Änderung der Landschaft analysieren. Aus diesem Grund ist das erste Ziel dieser Arbeit einen systematischen Ansatz zu entwickeln, der wesentliche Unsicherheitsquellen analysiert und ihre Fortentwicklung zur resultierenden Änderungskarte untersucht. Eine andere Herausforderung in der Landnutzungsmodellierung ist es, die Eignung von Projektionen abzuschätzen wenn keine Referenzdaten vorliegen. Bayes’sche Netze wurden als eine vielseitige Methode identifiziert, um das erste Ziel zu erreichen. Darüber hinaus wurden die Modellierungsschritte „Definition der Modellstruktur“, „Auswahl der Eingangsdaten“ und „Weiterverarbeitung der Daten“ als wesentliche Unsicherheitsquellen identifiziert. Um das zweite Ziel zu adressieren wurde eine Auswahl an Maßzahlen entwickelt. Diese quantifizieren Unsicherheit mit Hilfe einer projizierten Änderungskarte und ohne den Vergleich mit Referenzdaten. Mit diesen Maßzahlen ist es zusätzlich möglich zwischen quantitativer und räumlicher Unsicherheit zu unterscheiden. Vor allem in räumlichen Anwendungen wie der Landnutzungsmodellierung ist diese Möglichkeit wertvoll. Dennoch kann auch ein absolut sicheres Modell gleichzeitig ein falsches und nutzloses Modell sein. Deswegen wird ein Ansatz empfohlen, der die Beziehung zwischen Unsicherheit und Genauigkeit in bekannten Zeitschritten schätzt. Die entwickelten Ansätze geben wichtige Informationen um die Eignung von modellierten Entwicklungspfaden der Landnutzung zu verstehen. / Human influence has led to substantial changes to the Earth’s surface. Land change models are widely applied to analyze land change processes and to give recommendations for decision-making. Land change models are affected by uncertainties which have to be taken into account when interpreting their results. However, approaches which examine different sources of uncertainty with regard to their interdependencies and their influence on projected land change are rarely applied. The first objective of this thesis is therefore to develop a systematic approach which identifies major sources of uncertainty and the propagation to the resulting land change map. Another challenge in land change modeling is the estimation of the reliability of land change predictions when no reference data are available. Bayesian Belief Networks were identified as a useful technique to reach the first objective. Moreover, the modeling steps of “model structure definition”, “data selection” and “data preprocessing” were detected as relevant sources of uncertainty. To address the second research objective, a set of measures based on probabilities were developed. They quantify uncertainty by means of a single predicted land change map without using a reference map. It is additionally possible to differentiate uncertainty into its spatial and quantitative components by means of these measures. This is especially useful in spatial applications such as land change modeling. However, even a certain model can be wrong and therefore useless. Therefore, an approach is suggested which estimates the relationship between disagreement and uncertainty in known time steps to use this relationship in future time steps. The approaches give important information for understanding the reliability of modeled future development paths of land change.

This thesis details the development of a model of driver behaviour at railway level crossings that allows the probability of an accident under different conditions and interventions to be calculated. A method for classifying different crossings according to their individual risk levels is also described.