JS McMillan Fisheries Ltd. (JSM) is a Vancouver-based company with operations in nearly all levels of the commercial fishing industry, from supply through distribution. The heart of the operation is the processing facilities where freshly caught Pacific salmon are prepared for sale to end consumers and institutional buyers. As the operations of JSM evolved, the decision making for allocating a catch of salmon with varying characteristics amongst a set of final products has become too complex and time consuming. The focus of this study is to determine an effective and efficient method for JSM to allocate daily a fresh salmon harvest between the various products they produce on a daily basis. The goal is short-term production planning, to allocate the catch among the products in such a manner that the profit potential of the catch is maximized, i.e. prepare a production schedule that maximizes the total profit over the planning horizon. Additional goals of this project include: automation of the decision making process for the catch allocation, "what if" planning, decreasing expert dependency, reducing decision making time, and building a practical and innovative decision support tool. In order to solve this problem efficiently and effectively, optimization models were developed for allocating the catch to the end products. A corresponding decision support tool was built for the end-users at JSM.

In emergency medical transport, “time to definite care” is very important. Emergency medical services and transport medicine agencies have several possible vehicle options for interfacility transfers. Use of a land vehicle, helicopter, or fixed wing aircraft will be dependent on patient condition, distance between sending and receiving hospitals, crew configuration and capabilities, and other factors such as weather and road conditions. This thesis lays out the complex process of patient transfers and highlights the challenges in decision making under time pressure; it then describes the behaviour of human operators in estimating time to definite care. To support the operators in choosing a transportation mode, a decision support tool was built, which provides relevant time estimates for interfacility transfers based on historical dispatch and call data. The goal is to enable operators to make evidence-based decisions on vehicle allocation. A prototype interface was generated and was evaluated through a usability study.

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The 'Arco Seco' or 'Dry Arc' region of the Republic of Panama is considered to be the driest in the country, where many areas of this region experience severe water stress during the months of January through May. This study was conducted to develop a tool for the assessment of sustainable implementation of water harvesting through ponds for agricultural purposes in the region. A computer based Decision Support System (DSS) has been developed specifically for the Arco Seco region in order to facilitate pond storage capacity estimation. As part of the DSS, four computer programs have been designed for four different case scenarios; the first one is for sites that have high water demand and no topographical restrictions for pond size; the second is for fairly high water demand, no topographical restrictions for pond size, and for farmers who wish to have a backup of water to use mostly during drier years; the third is for low water demand, usage during the dry season only, and topographical restrictions for pond size, and finally the fourth is for constant water demand throughout the year, and for sites where runoff is the only water source.* / *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).

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Research has shown that data warehouses potentially offer great investment opportunities to business. To benefit from this, business needs to invest large sums of money. Such investments are very risky, as no guarantee of the success of these ventures can be given. Object-oriented development has proved successful for developing operational systems in industry. This study researches object-oriented techniques to discover whether these techniques could be used successfully in data warehousing. A literature study focuses on the definition of an information systems development methodology and defines the components of such methodology. A further literature study on four popular object-oriented methodologies determines the commonalities of these methodologies. In conclusion, a literature study on data warehouse methodologies is done to discover the phases and techniques used in developing data warehouses. Based on the literature, a method is proposed to build a data warehouse harnessing object-oriented phases and techniques. The proposed method is applied as an interpretive experiment, followed by an evaluation of the data warehouse implemented. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2009.

Current monitoring tools in the intensive care units focus on displaying physiologically monitored parameters (e.g. vital signs such as heart rate, respiratory rate and blood pressure) at the present moment. Added clinical utility can be found by analyzing how the conditions of a patient evolve with time, and automatically relating that dynamics to population trends. Variability analysis consists of monitoring patterns of variation over intervals in time of physiological signals such as heart rate and respiratory rate. Given that illness has been associated in multiple studies with altered variability, most commonly lack of variation, variability monitoring represents a tool whose contribution at the bedside still needs to be explored. With the long term objective of improving care, this thesis promotes the use of variability analysis through three distinct types of analysis: facing the technical challenges involved with the dimensionality of variability analysis, enhancing the physiological understanding of variability, and showing its utility in real world clinical applications. In particular, the contributions of this thesis include: the review and classification into domains of a large array of measures of variability; the design of system and methods to integrate multiple measures of variability into a unique score, called composite measure, bringing relevant information to specific clinical problems; the comparison of patterns of heart rate variability during exercise and sepsis development, showing the inability of single measures of variability to discriminate between the two kinds of stressors; the analysis of variability produced from a physiologically-based model of the cardiovascular system, showing that each single measure of variability is an unspecific sensor of the body, thereby promoting multivariate analysis to the only means of understanding the physiology underlying variability; the study of heart rate variability in a population at high risk of sepsis development, showing the ability of variability to predict the occurrence of sepsis more than 48 hours in advance respect to the time of diagnosis of the clinical team; the study of heart and respiratory rate variability in intubated intensive care unit patients, showing how variability can provide a better way of assessing extubation readiness respect to commonly used clinical parameters. Overall, it is hoped that these novel contributions will help promoting bedside applications of variability monitoring to improve patient care.

Research has shown that data warehouses potentially offer great investment opportunities to business. To benefit from this, business needs to invest large sums of money. Such investments are very risky, as no guarantee of the success of these ventures can be given. Object-oriented development has proved successful for developing operational systems in industry. This study researches object-oriented techniques to discover whether these techniques could be used successfully in data warehousing. A literature study focuses on the definition of an information systems development methodology and defines the components of such methodology. A further literature study on four popular object-oriented methodologies determines the commonalities of these methodologies. In conclusion, a literature study on data warehouse methodologies is done to discover the phases and techniques used in developing data warehouses. Based on the literature, a method is proposed to build a data warehouse harnessing object-oriented phases and techniques. The proposed method is applied as an interpretive experiment, followed by an evaluation of the data warehouse implemented. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2009.

This dissertation focuses on developing imaging informatics algorithms for clinical decision support systems (CDSSs) based on histopathological whole-slide images (WSIs). Currently, histopathological analysis is a common clinical procedure for diagnosing cancer presence, type, and progression. While diagnosing patients using biopsy slides, pathologists manually assess nuclear morphology. However, making decisions manually from a slide with millions of nuclei can be time-consuming and subjective. Researchers have proposed CDSSs that help in decision making but they have limited reproducibility. The development of robust CDSSs for WSIs faces several informatics challenges: (1) Lack of robust segmentation methods for histopathological images, (2) Semantic gap between quantitative information and pathologist’s knowledge, (3) Lack of batch-invariant imaging informatics methods, (4) Lack of knowledge models for capturing informative patterns in large WSIs, and (5) Lack of guidelines for optimizing and validating diagnostic models. I conducted advanced imaging informatics research to overcome these challenges and developed novel methods to extract information from WSIs, to model knowledge embedded in large histopathological datasets, such as The Cancer Genome Atlas (TCGA), and to assist decision making with biological and clinical validation. I validated my methods for two applications: (1) diagnosis of histopathology-based endpoints such as subtype and grade and (2) prediction of clinical endpoints such as metastasis, stage, lymphnode spread, and survival. The statistically emergent feature subsets in the diagnostic models for histopathology-based endpoints were concordant with pathologists’ knowledge.