Indiana University-Purdue University Indianapolis (IUPUI) / Bayesian networks, one of the most widely used techniques to understand or predict the future by making use of current or previous data, have gained credence over the last decade for their ability to simulate large gene expression datasets to track and predict the reasons for changes in biological systems. In this work, we present a dynamic Bayesian model with gene annotation scores such as the gene characterization index (GCI) and the GenCards inferred functionality score (GIFtS) to understand and assess the prediction performance of the model by incorporating prior knowledge. Time-course breast cancer data including expression data about the genes in the breast cell-lines when treated with doxorubicin is considered for this study. Bayes server software was used for the simulations in a dynamic Bayesian environment with 8 and 19 genes on 12 different data combinations for each category of gene set to predict and understand the future time- course expression profiles when annotation scores are incorporated into the model. The 8-gene set predicted the next time course with r>0.95, and the 19-gene set yielded a value of r>0.8 in 92% cases of the simulation experiments. These results showed that incorporating prior knowledge into the dynamic Bayesian model for simulating the time- course expression data can improve the prediction performance when sufficient apriori parameters are provided.

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Document classification is used to sort and label documents. This gives users quicker access to relevant data. Users that work with large inflow of documents spend time filing and categorizing them to allow for easier procurement. The Automatic Classification and Document Filing (ACDF) system proposed here is designed to allow users working with files or documents to rely on the system to classify and store them with little manual attention. By using a system built on Hidden Markov Models, the documents in a smaller desktop environment are categorized with better results than the traditional Naive Bayes implementation of classification.

This thesis is concerned with characterizing the quality of Hidden Markov modeling when learning from limited data. It introduces a new perspective on different sources of errors to describe the impact of undermodeling. Our view is that modeling errors can be decomposed into two primary sources of errors: the approximation error and the estimation error. This thesis takes a first step towards exploring the approximation error of low order HMMs that best approximate the true system of a HMM. We introduce the notion minimality and show that best approximations of the true system with complexity greater or equal to the order of a minimal system are actually equivalent realizations. Understanding this further allows us to explore integer lumping and to present a new way named weighted lumping to find realizations. We also show that best approximations of order strictly less than that of a minimal realization are truly approximations; they are incapable of mimicking the true system exactly. Our work then proves that the resulting approximation error is non-decreasing as the model order decreases, verifying the intuitive idea that increasingly simplified models are less and less descriptive of the true system.

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In this paper, a cooperative underwater multi-channel MAC (CUMAC) protocol has been proposed with both delay mapping and channel allocation assessment in order to improve network performance and handle triple hidden terminal (THT) problems in underwater sensor networks. A novel channel allocation matrix (CAM) was developed for estimating propagation delay and increasing utilization of channel. In the proposed scheme, every node maintains a database for delay mapping, based on which the sender runs a scheduling algorithm prior to transmitting any data. This delay mapping database assists a node in predicting packet collision probability. The overall objectives are—first, to increase the rate of successful transmission through mitigation of THT problems in multi-channel underwater sensor networks; and second, to increase channel utilization leveraging the database of delay mapping and channel allocation assessment. Results from performance evaluation demonstrate the efficiency of the proposed CUMAC-CAM protocol in terms of packet delivery ratio, energy consumption, end-to-end delay, network throughput, collision probability, packet loss ratio and fairness index compared to the contemporary CUMAC protocol and RTS/CTS based multi-channel MAC protocols.

There are two themes in this dissertation, that is, the effect of universal demand law change on corporate risk-taking in the U.S. property & casualty insurance industry, and the application of hidden Markov model to auto telematics data. The first chapter presents my study in the first theme and the rest two chapters present the other theme. In Chapter 1, "Does Shareholder Litigation Affect Corporate Risk-Taking? Evidence from the Property-Casualty Insurance Industry", I explore whether shareholder litigation affects corporate risk-taking differently depending on distinct organizational structures. I use a law change, called Universal Demand (UD) Law, as an exogenous shock and develop three risk-taking measures that are unique in the U.S. property-casualty insurance industry: leverage risk, asset risk, and underwriting risk. The insurance industry provides an interesting opportunity for the study as shareholders in mutual insurers are an ambiguous concept in the legal world, as opposed to the common argument in the insurance literature. The results show that along with UD law adoption, insurers increase their risk-taking. After taking organizational structures into account, the impact of the law change differentiates. Stock insurers increase all three risk-taking measures while mutual insurers decrease their Leverage Risk and increase Asset Risk measures. For different time windows, stock insurers respond faster with respect to their Asset Risk compared to mutual insurers. In addition, I proceed to examine the main economic channel for the impact and find that the free cash flow argument is not the main channel. Chapters 2 and 3 present the study in auto telematics data using a proprietary data source. Both studies are based on the application of hidden Markov model (HMM). Specifically, Chapter 2, "Auto Insurance Pricing Using Telematics Data: Application of a Hidden Markov Model", develops an HMM-based clustering framework to predict auto insurance losses using driving characteristics extracted from telematics data. Through a simulation experiment based on a proprietary telematics data set, I show that HMM can effectively classify driving trips using model-implied hidden states, and HMM-based pricing methods provide better predictive power measured by both deviance statistics and mean squared error. Importantly, the proposed framework not only enables us to price usage-based insurances at a granular level, but it is also viable for estimating long-term insurance losses utilizing the limiting properties of HMM. Chapter 3, "Theoretical Framework of a 3-Layer Hidden Markov Model for Auto Insurance Pricing", is a theoretical extension of the second chapter to improve the framework at a more granular level. I develop a 3-layer HMM for risk classification, which links driving behavior characteristics with risk classes and loss estimation. The proposed model presents a direct structure among all variables and utilizes time series data without aggregation. Furthermore, this study provides a theoretical framework to estimate the 3-layer HMM using the Expectation-Maximization (EM) algorithm. The parameters of Bernoulli distributed loss count (per unit of time) and Gamma distributed loss severity can be solved at least numerically, and the negative definite Hessian matrix indicates that the solution of the first-order condition of the log-likelihood function achieves its local maximum. / Business Administration/Risk Management and Insurance

Bilinguals have been posited to have, compared to monolinguals, enhanced cognitive control, consequently exhibiting greater cognitive reserve, which is thought to subsequently delay the onset of clinical expression of dementia. Based on recent evidence suggesting that the more languages one manages the greater cognitive reserve, and that trilinguals undergo greater exercise in language control than bilinguals, this thesis investigated the effects of trilingualism and ageing on cognitive control, in young adults to older adults. As the thesis investigated the novel field of trilingualism and cognitive control, task complexity, the age of second and third language acquisition, language use, and physical and cognitive activity were also, importantly, assessed, as these are possible influencing factors in test performance. The participants completed several cognitive tasks; namely the Simon task, the Inhibition of return task, the Stroop task (inhibition) and the N-back task (working memory). The novel discovery of a trilingual (and bilingual) disadvantage was observed, which could explain some previous inconsistent findings in the bilingualism literature, where trilingualism may influence bilinguals’ test performance, as trilinguals and multilinguals are often mixed in with the bilingual group. Furthermore, the results suggest that second language acquisition and language use does not consistently predict performance in trilinguals (and bilinguals), nor does cognitive activity, although physical activity may modulate language group differences. Importantly, the results from this novel investigation of the effects of trilingualism and ageing on cognitive control suggest that trilingualism (and bilingualism) can, in some cases, be detrimental to cognitive control.

<p>The decision making processes in today's manufacturing systems represent very complex and challenging tasks. The desired flexibility in terms of the functionality of a machine adds more components to the machine. The real time monitoring and reporting generates large streams of data. However the intelligent and real time processing of this large collection of system data is at the core of the manufacturing decision support tools. </p> <p>This thesis outlines the use of Frequent Episodes in Event Sequences and Hidden Markov Modeling of throughput, quality and maintenance data to model the deterioration of performance in the components that make up the manufacturing system. The thesis also introduces the concept of decision points and outlines how to integrate the total cost function in a business model. </p> This thesis deals with the following three topics: <p>First, the component-based data structure of the manufacturing system is outlined especially throughput, quality and maintenance data. In this approach, the manufacturing system is considered as a group of components that interact with each other and with raw materials to produce the manufactured product. This interaction creates a considerable amount of data which can be associated with the relevant components of the system. The relations between the manufacturing components are established on a physical and logical basis. The components properties are clearly defined in database tables specifically created for this application. The thesis also discusses the web services in manufacturing systems and the portable technologies used in plant decision support tools. </p> <p>Second, the thesis presents a novel application of Frequent Episodes in Event Sequences to identify patterns in the deterioration of performance in a component using frequent episodes of operational failures, quality failures and maintenance activities. A Hidden Markov Model (HMM) is used to model each deterioration episode to estimate the states of performance and the transition rates between the states. The thesis compares the results generated by this model to other existing models of component performance deterioration while emphasizing the benefits ofthe proposed model through the use of the plant data.</p> <p>Finally the thesis presents a methodology usmg HMM probability distributions and Bayesian Decision theory framework to provide a set of decisions and recommendations under the condition of data uncertainty. The results of this analysis are then integrated in the plant maintenance business model.</p> <p>It is worthwhile mentioning that to develop the techniques and validate the results in this research; a Manufacturing Execution System (MES) was developed to operate in an automotive engine plant. All the data and results in this research are based on the plant data. The MES which was developed in this research provided significant benefits in the plant and was adapted by many other GM plants around the world.</p> / Thesis / Doctor of Philosophy (PhD)

In educational research, the concept of school climate has been linked to quality teaching, teacher-pupil relations, and explicit values education, among others. In Sweden, despite showing positive teacher-pupil relations, critiques towards classroom management and a lack of teacher authority, which affect its school climate, have been made. This research aims to compare different teacher-pupil authority relations and their influence on the transmission of the hidden curriculum in Sweden. The study is going to be framed within the perspectives, definition, and current research on the hidden curriculum transmission and Wrong’s types of authority applied to classroom contexts. Non-participatory observations were conducted in a municipal school, an independent school, and an international school located in Sweden to collect data for this research. These observations take place within two Grade 9 classrooms of grundskola, and a Grade 3 classroom of gymnasieskolan. Maribel Blasco’s operationalisation of the hidden curriculum is adapted to classroom contexts to conduct the observations. Findings were described and compared considering the author’s ontology and epistemology, the operationalisation of the hidden curriculum, and the theoretical framework that guided this research. The findings of this research relate to the implicit transmission of values through authority responses, the teacher’s classroom management strategies and teaching style, their consistency and coherence in the application of classroom rules, and the implicit transmission of messages through pedagogical strategies. The researcher outlines the implications that this study has for teacher training programmes in Sweden. The direction of further research is also delimited.