The primary purpose of this study was to determine what factors influence the integration of educational technology as perceived by Texas teachers. The secondary purpose was to examine the relationships between the determinant factors. This study answered the following questions: 1) Are there significant differences in teachers’ perception of school technology and readiness across grade level and subject area? 2) Are there significant differences in teacher-principal technology readiness congruence across school percentage of economically disadvantaged students? 3) How do teachers’ perceived levels of technology readiness predict student mastery of Technology Applications (TA) TEKS? To address the research questions quantitative procedures were followed to investigate whether significant relationships existed among dependent variables, school technology and readiness, and teacher-principal school technology and readiness congruence, and the independent variables (a) grade level, (b) subject area, and (c) percentage of economically disadvantaged students. Data analysis indicated significant differences in teacher school technology and readiness perceptions by grade level and subject area, and significant differences in teacher-principal school technology and readiness congruence by school percentage of economically disadvantaged students. Using path analysis a theorized Texas School Technology and Readiness Effects Model was validated. The findings were that (a) teachers in higher grade levels and more technical subject areas perceived their school technology and readiness at significantly higher levels; (b) as the percentage of economically disadvantaged student increased in a school, teachers perceived their school technology readiness at lower levels and were less congruent between their perceived school technology and readiness and their principals’ ratings of the teachers’ school technology and readiness; and (c) Leadership Administration and Instructional Support, followed by Infrastructure for Technology, Educator Preparation and Development, and Instruction Practice were the main drivers for student mastery of Technology Applications (TA) related Texas Essential Knowledge and Skills (TEKS). / text

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Medical computer vision is a novel research discipline based on the application of computer vision methods to data sets acquired via medical imaging techniques. This work focuses on magnetic resonance imaging (MRI) data sets, particularly in studies of schizophrenia and multiple sclerosis. Research on these diseases is challenged by the lack of appropriate morphometric tools to accurately quantify lesion growth, assess the effectiveness of a drug treatment, or investigate anatomical information believed to be evidence of schizophrenia. Thus, most hypotheses involving these conditions remain unproven. This thesis contributes towards the development of such morphometric techniques. A framework combining several tools is established, allowing for compensation of bias fields, boundary detection by modelling partial volume effects (PVE), and a combined statistical and geometrical segmentation method. Most importantly, it also allows for the computation of confidence bounds in the location of the object being segmented by bounding PVE voxels. Bounds obtained in such fashion encompass a significant percentage of the volume of the object (typically 20-60%). A statistical model of the intensities contained in PVE voxels is used to provide insight into the contents of PVE voxels and further narrow confidence bounds. This not only permits a reduction by an order of magnitude in the width of the confidence intervals, but also establishes a statistical mechanism to obtain probability distributions on shape descriptors (e.g. volume), instead of just a raw magnitude or a set of confidence bounds. A challenging clinical study is performed using these tools: to investigate differences in asymmetry of the temporal horns in schizophrenia. This study is of high clinical relevance. The results show that our tools are sufficiently accurate for studies of this kind, thus providing clinicians, for the first time, with the means to corroborate unproven hypotheses or reliably assess patient evolution.

Current studies of the highly complex pathobiology and molecular signatures of human disease require the analysis of large sets of high-throughput data, from clinical to genetic expression experiments, containing a wide range of information types. A number of computational techniques are used to analyse such high-dimensional bioinformatics data. In this thesis we focus on the development of a novel soft clustering technique, DifFUZZY, a fuzzy clustering algorithm applicable to a larger class of problems than other soft clustering approaches. This method is better at handling datasets that contain clusters that are curved, elongated or are of different dispersion. We show how DifFUZZY outperforms a number of frequently used clustering algorithms using a number of examples of synthetic and real datasets. Furthermore, a quality measure based on the diffusion distance developed for DifFUZZY is presented, which is employed to automate the choice of its main parameter. We later apply DifFUZZY and other techniques to data from a clinical study of children from The Gambia with different types of severe malaria. The first step was to identify the most informative features in the dataset which allowed us to separate the different groups of patients. This led to us reproducing the World Health Organisation classification for severe malaria syndromes and obtaining a reduced dataset for further analysis. In order to validate these features as relevant for malaria across the continent and not only in The Gambia, we used a larger dataset for children from different sites in Sub-Saharan Africa. With the use of a novel network visualisation algorithm, we identified pathobiological clusters from which we made and subsequently verified clinical hypotheses. We finish by presenting conclusions and future directions, including image segmentation and clustering time-series data. We also suggest how we could bridge data modelling with bioinformatics by embedding microarray data into cell models. Towards this end we take as a case study a multiscale model of the intestinal crypt using a cell-vertex model.

由於全球化營運的趨勢，許多企業皆邁向跨國的經營模式，組織的層級與規模隨業務需要逐漸擴大，導致對於整合與控制企業資源、企業流程和資訊系統的需求日益增強。不但必須了解國內外企業的所有資源與營運狀況，還需將所有資源加以整合，並整合上下游成為一個完整的供應鏈模式，以能即時提供對企業有利的資訊供決策之用。而企業經營面對的競爭也是全球性的，更快速地反應市場及客戶需求，或是更流暢地與上、下游廠商溝通，都是企業必備的條件，但這樣的理想便需要透過作好組織內部的企業資源規劃（Enterprise Resource Planning，ERP）來達成；亦即良好的企業資源規劃為供應鏈有效運作之基礎。 因此國內企業也逐漸體認企業資源規劃系統的重要性，尤其面對全球化競爭、組織扁平化、產業知識快速擴散與新科技對企業的衝擊，國內各企業無不全力投入提升企業競爭力的行列，對於企業資源規劃系統的需求也就日益增加。但目前國內尚無適合台灣企業本土化的導入方法，可作為企業資源規劃系統導入實施的步驟依據，故而提出一套能適合國內企業導入企業資源規劃系統的方法實為當務之急。 有鑑於此，本研究透過文獻探討蒐集彙整的方式，以瞭解成功導入企業資源規劃系統之方法；接著，利用模式推導方式，提出一適合國內企業導入企業資源規劃系統的參考模式及建議；再者，本研究將以半導體產業之IC製造業為例，對事先選定的個案公司透過深入訪談、實際觀察、文件及檔案資料收集等方式，輔以非結構化問卷進行個案研究；最後，藉由個案研究所得之結論，針對之前所提之參考模式加以修正，以供國內半導體產業之IC製造業的借鏡，並作為未來導入企業資源規劃系統之參考。 / For the trend towards global operations, the business models of many enterprises become in international style, and the organizational hierarchies and sizes of these enterprises are also becoming expanded. Therefore it extraordinarily needs to integrate and to control the business resources, processes and information systems of an enterprise to overcome these changes. To reach this goal, an enterprise must draw up the right enterprise resource planning (ERP). And also, a right enterprise resource plan is the basis to integrate its upstream and downstream as to be an efficient and effective supply chain. To face the worldwide competition and new information technologies striking, many enterprises in Taiwan are growing to realize the importance of implementing ERP systems to enhance their competition capabilities. Although the demand of implementing ERP systems is becoming great, it lacks a model of implementing ERP systems for the enterprises in Taiwan. Therefore, to provide a suitable model for the Taiwan’s enterprises is a task of the urgency at present. In this research, firstly we survey the existing models for the implementation of information technology applications and ERP systems. Secondly, we study the operating characteristics of the enterprises in Taiwan, and propose a preliminary model of implementing ERP systems for the enterprises in Taiwan. Finally we analyze the properties of IC manufacturing industry and visit an IC manufacturing enterprise as to be our real study case to get its implementation experience, and use this information to revise our preliminary model. The model proposed in this thesis could be used as a reference model for the IC manufacturing enterprises to implement their ERP systems.

Anxiety and depression are highly prevalent diseases with common heritable elements, but the particular genetic mechanisms and biological pathways underlying them are poorly understood. Part of the challenge in understanding the genetic basis of these disorders is that they are polygenic and often context-dependent. In my thesis, I apply a series of modern statistical tools to ascertain some of the myriad genetic and environmental factors that underlie fear-related behaviours in nearly two thousand heterogeneous stock mice, which serve as animal models of anxiety and depression. Using a Bayesian method called Sparse Partitioning and a frequentist method called Bagphenotype, I identify gene-by-sex interactions that contribute to variation in fear-related behaviours, such as those displayed in the elevated plus maze and the open field test, although I demonstrate that the contributions are generally small. Also using Bagphenotype, I identify hundreds of gene-by-environment interactions related to these traits. The interacting environmental covariates are diverse, ranging from experimenter to season of the year. With gene expression data from a brain structure associated with anxiety called the hippocampus, I generate modules of co-expressed genes and map them to the genome. Two of these modules were enriched for key nervous system components — one for dendritic spines, another for oligodendrocyte markers — but I was unable to find significant correlations between them and fear-related behaviours. Finally, I employed another Bayesian technique, Sparse Instrumental Variables, which takes advantage of conditional probabilities to identify hippocampus genes whose expression appears not just to be associated with variation in fear-related behaviours, but cause variation in those phenotypes.

This quantitative methods study explored the knowledge, attitudes and practices of PK-5 teachers on the use of technology applications in the classroom. The Texas State Board of Education has set in place standards for technology applications that require the use of technology applications across all grades. Likewise, the American Academy of Pediatrics (AAP) published new guidelines on the use of technology by children. However, it is the responsibility of teachers to implement and embed these standards, while also paying attention to the recommendations of the AAP. I developed a survey that provided demographic information, and included 12 items to measure the knowledge, attitudes and practices of teachers of technology applications in the classroom. Participants included 251 PK-5 teachers from three different districts in a South Texas region. Multiple regressions were conducted for each of the constructs produced by a factor analysis. Knowledge and attitudes presented no statistically significant results from individual teacher characteristics, but there were statistically significant differences on attitudes by districts. The regression analysis for practice reported a statistically significant difference between teachers that held a master's degree and those who did not. I conclude that technology applications implementation in the PK-5 classrooms is still developing and evolving, but there is still no consistency across districts, and further research in this matter is still needed.