Thesis (Ph.D.)--University of Alberta, 2010. / A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Rehabilitation Science, Faculty of Rehabilitation Medicine. Title from pdf file main screen (viewed on January 31, 2010). Includes bibliographical references.

Thesis (Ph. D.)--Institute of Paper Chemistry, 1942. / Includes bibliographical references (p. 97-99).

In a distributed system many underlying nodes or processes work in tandem to come up with a solution to a given problem. In this report, we are concerned with distributed systems where each node is given the same problem, and the system uses the solutions provided by the nodes to formulate the answer. In our case, the problem is a simple question with two possible answers, and only one answer is correct. The system is asked the question at the beginning of a round. Once the system answers the question, the round is over, and the system is given the correct answer, then another round is started. To answer the question, the system uses the answers from each node, and based on the weight of the individual nodes, it decides on its answer. In this report, we experiment with multiple ways to update the weights of the underlying nodes, and aim to study the impact of certain limitations and parameters imposed on the system; such as the maximum accuracy of the underlying nodes and the number of underlying nodes. / text

The purpose of the simulation was to assess the impact of weight pattern assignment when using the multiple membership random effects model (MMREM). In contrast with most previous methodological research using the MMREM, mobility was not randomly assigned; rather the likelihood of student mobility was generated as a function of the student predictor. Two true weights patterns were used to generate the data (random equal and random unequal). For each set of generated data, the true correct weights and two incorrect fixed weight patterns (fixed equal and fixed unequal) that are similar to those used in practice by applied researchers were used to estimate the model. Several design factors were manipulated including the percent mobility, the ICC, and the true generating values of the level one and level two mobility predictors. To assess parameter recovery, relative parameter bias was calculated for the fixed effects and random effects variance components. Standard error (SE) bias was also calculated for the standard errors estimated for each fixed effect. Substantial relative parameter bias differences between weight patterns used were observed for the level two school mobility predictor across conditions as well as the level two random effects variance component, in some conditions. Substantial SE bias differences between weight patterns used were also found for the school mobility predictor in some conditions. Substantial SE and parameter bias was found for some parameters for which it was not anticipated. The results, discussion, future directions for research, and implications for applied researchers are discussed.

The majority of the research conducted on consumer choice phenomena focuses on how choices are made and the processes that lead up to those choices. While these are essential aspects within the breadth of choice knowledge that exists today, little research has been conducted on the options that are rejected during this process. Thus, the overarching goal of this dissertation is gain an understanding of consumer choice processes and outcomes through the lens of a nearly chosen alternative. Specifically, this dissertation investigates how the decision process can cause a close second option to be rejected when the chosen option is found to be unavailable.As a means of achieving these goals, I first demonstrate the phenomenon that consumers do not always select a close second option when the first choice option is unavailable, contrary to the prediction of economic rationality. Next, I propose that the decision process itself, specifically the use of a tie-breaking attribute to differentiate between close options, triggers a choice outcome that does not include the original second choice option, but rather an alternative that possesses this tie-breaking attribute. Finally, I examine the implications that the preference reversal phenomenon described above has for retailers and manufacturers.My original interest in this phenomenon stems from anecdotal evidence provided by a variety of informants. Although this evidence helped me to recognize the prevalence of rejected second choice options, experimental design is used to investigate this phenomenon and the boundary conditions that confine this effect. Consequently, my dissertation consists of 6 experiments. Experiment 1 and a pilot study establish the effect and investigate the theoretical process that account for my findings. Experiments 2 through 4 rule out alternative explanations and add support towards the existence and prevalence of the effect. Finally, Experiments 5 and 6 explore the impact of these results for improving the performance of marketing managers. It is my belief that incorporating the dynamic effects of the second-most preferred option may ultimately lead to more accurate and sophisticated prediction of buyer choices, more effective retailing and personal selling strategies, and more profitable management of product line portfolios.

Generalized estimating equations (GEEs) are commonly used in the modelling of correlated data. However, in the presence of informative cluster sizes, estimates obtained using GEEs may be biased. In order to correct for this bias a weighted GEE may be used. Previous research has extended the use of weighted GEEs to a longitudinal setting but requires that cluster sizes remain constant over time. In this thesis, two new weighting schemes are investigated to allow for valid parameter estimation in a longitudinal setting where cluster sizes are informative and may change over time. Specifically, this thesis considers weighting by the inverse of the time-dependent cluster size, and by the total number of observations for a given cluster. Through Monte Carlo simulation, the performance of traditional GEEs, GEEs under previously proposed weighting schemes, and these two new models are compared. Results of these studies show that weighting by the total number of observations results in unbiased parameter estimates with excellent coverage.

The primary grain in oat spikelets is larger than the secondary grain. Oat genotypes were grown to determine the effects of planting primary and secondary grains separately on vegetative and reproductive growth. Plants from primary seeds had higher yields than those from secondary seeds. The advantage was associated with earliness of heading. It was concluded that increased seed uniformity would be advantageous. / Reductions in panicle size by removal of all primary grains and some secondary grains indicated that secondary grains have more growth potential than observed in untreated panicles. However, secondary grains in the treated panicles did not achieve weights equalling those of primary grains in the untreated panicles. / Variation in primary:secondary seed weight ratios was found among experiments. The variability was attributed mainly to instability of secondary grain weight. / The inheritance of secondary grain weight and of the primary:secondary seed weight ratio was studied at two locations. For both traits, additive and non-additive genetic effects were evident. Dominant gene action was relatively unimportant, suggesting good potential for improvement through pure-line breeding.

The use of analytical techniques for fraud detection and the design of fraud detection systems have been topics of several research projects in the past and have seen varying degrees of success in their practical implementation. In particular, several authors regard the use of credit risk scorecards for fraud detection as a useful analytical detection tool. However, research on analytical fraud detection for the South African insurance industry is limited. Furthermore, real world restrictions like the availability and quality of data elements, highly unbalanced datasets, interpretability challenges with complex analytical techniques and the evolving nature of insurance fraud contribute to the on-going challenge of detecting fraud successfully. Insurance organisations face financial instability from a global recession, tighter regulatory requirements and consolidation of the industry, which implore the need for a practical and effective fraud strategy. Given the volumes of structured and unstructured data available in data warehouses of insurance organisations, it would be sensible for an effective fraud strategy to take into account data-driven methods and incorporate analytical techniques into an overall fraud risk assessment system. Having said that, the complexity of the analytical techniques, coupled with the effort required to prepare the data to support it, should be carefully considered as some studies found that less complex algorithms produce equal or better results. Furthermore, an over reliance on analytical models can underestimate the underlying risk, as observed with credit risk at financial institutions during the financial crisis. An attractive property of the structure of the probabilistic weights-of-evidence (WOE) formulation for risk scorecard construction is its ability to handle data issues like missing values, outliers and rare cases. It is also transparent and flexible in allowing the re-adjustment of the bins based on expert knowledge or other business considerations. The approach proposed in the study is to construct fraud risk scorecards at entity level that incorporate sets of intrinsic and relational risk factors to support a robust fraud risk assessment. The study investigates the application of an integrated Suspicious Activity Assessment System (SAAS) empirically using real-world South African insurance data. The first case study uses a data sample of short-term insurance claims data and the second a data sample of life insurance claims data. Both case studies show promising results. The contributions of the study are summarised as follows: The study identified several challenges with the use of an analytical approach to fraud detection within the context of the South African insurance industry. The study proposes the development of fraud risk scorecards based on WOE measures for diagnostic fraud detection, within the context of the South African insurance industry, and the consideration of alternative algorithms to determine split points. To improve the discriminatory performance of the fraud risk scorecards, the study evaluated the use of analytical techniques, such as text mining, to identify risk factors. In order to identify risk factors from large sets of data, the study suggests the careful consideration of both the types of information as well as the types of statistical techniques in a fraud detection system. The types of information refer to the categories of input data available for analysis, translated into risk factors, and the types of statistical techniques refer to the constraints and assumptions of the underlying statistical techniques. In addition, the study advocates the use of an entity-focused approach to fraud detection, given that fraudulent activity typically occurs at an entity or group of entities level. / PhD, Operational Research, North-West University, Vaal Triangle Campus, 2011

In the work of the Control Engineering research group of the Department of Computer Science, Electrical and Space Engineering, Signals and systems at Luleå University of Technology a need had arisen for a path planning algorithm. The ongoing research with Unmanned Aerial Vehicles(UAVs) had so far been done with any complicated paths being created manually with waypoints set by the uses. To remove this labourious part of the experimental process a path should be generated automatically by simply providing a program with the position of the UAV, the goal to which the user wants it to move, as well as information about the UAV's surroundings in the form of a 3D map.In addition to simply finding an available path through a  3D environment the path should also be adapted to the risks that the physical environment poses to a flying robot. This was achieved by adapting a previously developed algorithm, which did the simple path planning task well, by adding a penalty weight to areas near obstacles, pushing the generated path away from them.The planner was developed working with the OctoMap map system which represents the physical world by segmenting it into cubes of either open or occupied space. The open segments of these maps could then be used as vertices of a graph that the planning algorithm could traverse.The algorithm itself was written in C++ as a node of the Robot Operating System(ROS) software framework to allow it to smoothly interact with previously developed software used by the Control Engineering Robotics Group.The program was tested by simulations where the path planner ROS node was sent maps as well as UAV position and intended goal. These simulations provided valid paths, with the performance of the algorithm as well as the quality of the paths being evaluated for varying configurations of the planners parameters.The planner works well in simulation and is deemed ready for use in practical experiments.

The purpose of the present study was twofold: (1) to determine if X-irradiation can be considered a direct stress agent, and if so, to what extent it differs from other stressors; and (2) to further elucidate the role of the adrenal cortex in the radiation syndrome by determining the more immediate responses of this system to X-irradiation.