Modern power networks are being driven ever closer to both their physical and operational limits. As a result, control systems are being increasingly relied on to assure satisfactory system performance. Power system stabilizers (PSSs) are one example of such controllers. Their purpose is to increase system damping and they are typically designed using a model of the network that is valid during nominal operating conditions. The limitation of this design approach is that during off-nominal operating conditions, such as those triggered by daily load fluctuations, performance of the controller can degrade. The research presented in this report attempts to evaluate the possibility of employing an adaptive PSS as a means of avoiding the performance degradation precipitated by off-nominal operation. Conceptually, an adaptive PSS would be capable of identifying changes in the network and then adjusting its parameters to ensure suitable damping of the identified network. This work begins with a detailed look at the identification algorithm employed followed by a similarly detailed examination of the control algorithm that was used. The results of these two investigations are then combined to allow for a preliminary assessment of the performance that could be expected from an adaptive PSS. The results of this research suggest that an adaptive PSS is a possibility but further work is needed to confirm this finding. Testing using more complex network models must be carried out, details pertaining to control parameter tuning must be resolved and closed-loop time domain simulations using the adaptive PSS design remain to be performed.

A software program was written that implements a finite element analysis (FEA) solution as the basis of an optimization function used for guiding the inverse design problem of aligning fibres, minimizing displacements in a fibre-reinforced polymer composite part in response to a given loading condition, for various part geometries. The FEA solution makes use of the superlinear RGNTet4 element, which includes 3 displacement and 3 rotational degrees of freedom at 4 nodes. Convergence testing verified the accuracy of the solver versus symbolic results for simple cases. Multivariate optimization over fibre orientations and volume fractions was carried out for a simple test case using the NLOpt nonlinear optimization library. Both derivative-free and gradient-based algorithms were tested. Low-Storage Broyden-Fletcher-Goldfarb-Shannon was the most effective algorithm. Four more complex cases were examined, and by varying fibre orientations, reductions of 48%, 66%, 58% and 32% were achieved in displacements at the loaded nodes.

Diese Arbeit liefert ein dreidimensionales numerisches Homogenisierungskonzept, welches beim Übergang von der Mikro- zur Makroskala einen Wechsel in der Kontinuumsbeschreibung beinhaltet. Während für die Beschreibung der Makroskala das verallgemeinerte Cosserat-Kontinuum verwendet wird, basiert die Mikroskala auf der klassischen Cauchy-Theorie. Um das homogene Cosserat-Ersatzmaterial im Rahmen numerischer Simulationen nutzen zu können, erfolgt die Implementierung geeigneter Finiter Elemente in das Programmsystem Abaqus und deren Verifikation. Neben der Diskussion der bei der Homogenisierung beobachteten Effekte werden anhand eines idealisierten Modells eines biaxialverstärkten Mehrlagengestrickes die Vorteile gegenüber der klassischen Herangehensweise aufgezeigt. / This contribution provides a threedimensional homogenization approach which includes the switch of the continuum theory during the scale transition. Whereas the microscopic scale is described in the framework of the classical Cauchy theory, the macroscopic scale is based on the generalized Cosserat continuum. In order to use the obtained homogeneous Cosserat material, suitable finite elements are implemented in the commercial program system Abaqus followed by an appropriate verification. Beside the discussion of the arising effects the advantages of this approach compared to the classical procedure are shown by means of an idealized model of a biaxial woven fabric.

The sharing of needles among injection drug users (IDUs) is a common route of Human Immunodeficiency Virus and Hepatitis C Virus transmission. Through the increased utilization of social network analysis, researchers have been able to examine how the interpersonal relationships of IDUs affect injection risk behaviour. This study involves a secondary analysis of data from a cross-sectional study of 156 IDUs from Winnipeg, Manitoba titled “Social Network Analysis of Injection Drug Users”. Multiple logistic regression analysis was used to assess the individual and the social network characteristics associated with needle sharing among the IDUs. Generalized Estimating Equations analysis was used to determine the injecting dyad characteristics which influence needle sharing behaviour between the IDUs and their injection drug using network members. The results revealed five key thematic findings that were significantly associated with needle sharing: (1) types of drug use, (2) socio-demographic status, (3) injecting in semi-public locations, (4) intimacy, and (5) social influence. The findings from this study suggest that comprehensive prevention approaches that target individuals and their network relationships may be necessary for sustainable reductions in needle sharing among IDUs.

Environmental factors affect fish distribution and fisher behavior. These factors are seldom included in stock assessment models, resulting in potentially inaccurate fish abundance estimates. This study determined the impact of these factors using the commercial catch rate of sauger (Sander canadensis) and walleye (Sander vitreus) in Lake Winnipeg by: (1) the use of satellite data to monitor turbidity and its impact on catch via simple linear regression and (2) the effect of environment on catch and effort using generalized linear models. No statistically significant relationship was found between catch and turbidity; a result which may be due to small sample sizes, the fish species' examined, and variable turbidity at depth. Decreased effort was correlated with harsh weather and decreased walleye catch. Increased walleye catch was correlated with low temperature and low Red River discharge. Increased sauger catch was correlated with high temperature, high cloud opacity, and average Red River discharge.

Modern power networks are being driven ever closer to both their physical and operational limits. As a result, control systems are being increasingly relied on to assure satisfactory system performance. Power system stabilizers (PSSs) are one example of such controllers. Their purpose is to increase system damping and they are typically designed using a model of the network that is valid during nominal operating conditions. The limitation of this design approach is that during off-nominal operating conditions, such as those triggered by daily load fluctuations, performance of the controller can degrade. The research presented in this report attempts to evaluate the possibility of employing an adaptive PSS as a means of avoiding the performance degradation precipitated by off-nominal operation. Conceptually, an adaptive PSS would be capable of identifying changes in the network and then adjusting its parameters to ensure suitable damping of the identified network. This work begins with a detailed look at the identification algorithm employed followed by a similarly detailed examination of the control algorithm that was used. The results of these two investigations are then combined to allow for a preliminary assessment of the performance that could be expected from an adaptive PSS. The results of this research suggest that an adaptive PSS is a possibility but further work is needed to confirm this finding. Testing using more complex network models must be carried out, details pertaining to control parameter tuning must be resolved and closed-loop time domain simulations using the adaptive PSS design remain to be performed.

One-way and two-way classification models in experimental design for both balanced and unbalanced cases are considered when the errors have Generalized Secant Hyperbolic distribution. Efficient and robust estimators for main and interaction effects are obtained by using the modified maximum likelihood estimation (MML) technique. The test statistics analogous to the normal-theory F statistics are defined to test main and interaction effects and a test statistic for testing linear contrasts is defined. It is shown that test statistics based on MML estimators are efficient and robust. The methodogy obtained is also generalized to situations where the error distributions from block to block are non-identical.

In this study, the most prominent goodness-of-fit tests for censored samples are reviewed. Power properties of goodness-of-fit statistics of the null hypothesis that a sample which is censored from right, left and both right and left which comes from uniform, normal and exponential distributions are investigated. Then, by a similar argument extreme value, student t with 6 degrees of freedom and generalized logistic distributions are discussed in detail through a comprehensive simulation study. A variety of real life applications are given. Suitable test statistics for testing the above distributions for censored samples are also suggested in the conclusion.

For well over 200 years, scientists and doctors have been poking and prodding brains in every which way in an effort to understand how they work. The earliest pokes were quite crude, often involving permanent forms of brain damage. Though neural injury continues to be an active area of research within neuroscience, technology has given neuroscientists a number of tools for stimulating and observing the brain in very subtle ways. Nonetheless, the basic experimental paradigm remains the same; poke the brain and see what happens. For example, neuroscientists studying the visual or auditory system can easily generate any image or sound they can imagine to see how an organism or neuron will respond. Since neuroscientists can now easily design more pokes then they could every deliver, a fundamental question is ``What pokes should they actually use?' The complexity of the brain means that only a small number of the pokes scientists can deliver will produce any information about the brain. One of the fundamental challenges of experimental neuroscience is finding the right stimulus parameters to produce an informative response in the system being studied. This thesis addresses this problem by developing algorithms to sequentially optimize neurophysiology experiments. Every experiment we conduct contains information about how the brain works. Before conducting the next experiment we should use what we have already learned to decide which experiment we should perform next. In particular, we should design an experiment which will reveal the most information about the brain. At a high level, neuroscientists already perform this type of sequential, optimal experimental design; for example crude experiments which knockout entire regions of the brain have given rise to modern experimental techniques which probe the responses of individual neurons using finely tuned stimuli. The goal of this thesis is to develop automated and rigorous methods for optimizing neurophysiology experiments efficiently and at a much finer time scale. In particular, we present methods for near instantaneous optimization of the stimulus being used to drive a neuron.

Southern flounder (Parlichthys lethostigma) is a recreationally and commercially important flatfish species found in the Gulf of Mexico, and recent analyses indicate that the northern Gulf of Mexico population is in decline. For proper management, knowledge of habitats used throughout the juvenile stage is needed. The aim of the current study is to examine habitat use of young-of-year (YOY) southern flounder in the Galveston Bay complex using habitat distribution models and acoustic telemetry. A set of habitat distribution models examined how habitat use changes during the first year of life. In addition, southern flounder were tagged with acoustic telemetry transmitters and monitored with a novel receiver array that allows for measurements of fine-scale movements. These movements were compared to habitat maps to examine habitat selection. Habitat distribution models determined that habitat requirements for southern flounder change with ontogeny and season. Newly settled southern flounder were most influenced by physicochemical parameters and the presence of seagrass beds. YOY southern flounder, however, showed increased occurrence at freshwater inlets during summer and fall months, and occurrence decreased at tidal inlets during the fall. Predictions of habitat suitability across the Galveston Bay complex indicate that the factors influencing occurrence of southern flounder change with season, ontogeny, and availability of suitable habitats. With acoustic telemetry, it was apparent that habitat use by southern flounder was nonrandom and influenced by benthic and other physicochemical conditions. Habitat analyses indicated that southern flounder used sand habitats more frequently than seagrass, oyster reef, or salt marsh habitats. Telemetry results also indicated that depth and water temperature were important determinants of habitat suitability for YOY southern flounder, with individuals preferring deeper and cooler regions of the water column in Christmas Bay. Both model and telemetry analyses indicate that habitat use by YOY southern flounder is dynamic across multiple spatial and temporal scales, with distributions and movements influenced strongly by ontogenetic changes in habitat associations, temporal and spatial variability in physicochemical conditions, and tidal cycles.