This thesis describes a short study to examine the behaviour of submerged flow over a thin plate weir with differing upstream and downstream bed levels i.e. an unequal bed weir as opposed to an equal bed weir where the upstream and downstream beds are at the same level. As submerged weir flow is a function of downstream conditions, it was thought that a lower downstream bed would make submerged flow over the weir easier, This is turn suggested that; The shape of the upstream head (Hu) vs downstream head (Hd) graph would change, being initially more steep in the unequal bed case. The Froude Number of the approaching flow would be lower for the unequal bed weir than for an equal bed weir at the same submergence. Using one of the existing submerged flow formula would lead to an erroneous calculated value. A series of measurements was done on two model weirs of different sizes subject to successive levels of submergence. Analysis of the readings of upstream and downstream heads indicated that the difference in bed levels was significant and the three effects above were noted. The work also suggested a new form of equation to calculate flow over a submerged weir. This was looked at briefly.

This thesis considers likelihood inferences for generalized linear models with additional random effects. The likelihood function involved ordinarily cannot be evaluated in closed form and numerical integration is needed. The theme of the thesis is a closed-form approximation based on Laplace's method. We first consider a special yet important case of the above general setting -- the Mantel-Haenszel-type model with overdispersion. It is seen that the Laplace approximation is very accurate for likelihood inferences in that setting. The approach and results on accuracy apply directly to the more general setting involving multiple parameters and covariates. Attention is then given to how to maximize out nuisance parameters to obtain the profile likelihood function for parameters of interest. In evaluating the accuracy of the Laplace approximation, we utilized Gauss-Hermite quadrature. Although this is commonly used, it was found that in practice inadequate thought has been given to the implementation. A systematic method is proposed for transforming the variable of integration to ensure that the Gauss-Hermite quadrature is effective. We found that under this approach the Laplace approximation is a special case of the Gauss-Hermite quadrature. / Graduation date: 1994

Abstract Solving Quantified Boolean Formulas Horst Samulowitz Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2008 Many real-world problems do not have a simple algorithmic solution and casting these problems as search problems is often not only the simplest way of casting them, but also the most efficient way of solving them. In this thesis we will present several techniques to advance search-based algorithms in the context of solving quantified boolean formulas (QBF). QBF enables complex realworld problems including planning, two-player games and verification to be captured in a compact and quite natural fashion. We will discuss techniques ranging from straight forward pre-processing methods utilizing strong rules of inference to more sophisticated online approaches such as dynamic partitioning. Furthermore, we will show that all of the presented techniques achieve an essential improvement of the search process when solving QBF. At the same time the displayed empirical results also reveal the orthogonality of the different techniques with respect to performance. Generally speaking each approach performs well on a particular subset of benchmarks, but performs poorly on others. Consequently, an adaptive employment of the available techniques that maximizes the performance would result in further improvements. We will demonstrate that such an adaptive approach can pay off in practice, by presenting the results of using machine learning methods to dynamically select the best variable ordering heuristics during search.

Abstract Solving Quantified Boolean Formulas Horst Samulowitz Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2008 Many real-world problems do not have a simple algorithmic solution and casting these problems as search problems is often not only the simplest way of casting them, but also the most efficient way of solving them. In this thesis we will present several techniques to advance search-based algorithms in the context of solving quantified boolean formulas (QBF). QBF enables complex realworld problems including planning, two-player games and verification to be captured in a compact and quite natural fashion. We will discuss techniques ranging from straight forward pre-processing methods utilizing strong rules of inference to more sophisticated online approaches such as dynamic partitioning. Furthermore, we will show that all of the presented techniques achieve an essential improvement of the search process when solving QBF. At the same time the displayed empirical results also reveal the orthogonality of the different techniques with respect to performance. Generally speaking each approach performs well on a particular subset of benchmarks, but performs poorly on others. Consequently, an adaptive employment of the available techniques that maximizes the performance would result in further improvements. We will demonstrate that such an adaptive approach can pay off in practice, by presenting the results of using machine learning methods to dynamically select the best variable ordering heuristics during search.

Thesis (Ph. D.)--Oregon State University, 2003. / Typescript (photocopy). Includes bibliographical references (leaves 43-46). Also available on the World Wide Web.

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The aim of this research was to examine differences in general nutrition knowledge and prenatal sources of infant feeding information among women planning to feed breast milk, breast-milk substitutes, or a combination of breast milk and breast-milk substitutes. Subjects completed a demographic survey, a test of nutrition knowledge, and a questionnaire on sources of infant feeding information. There were no statistical differences in age, education, and nutrition knowledge scores among women in the three groups. A significant relationship was observed between education and nutrition knowledge; women who had attended college courses scored higher.003) on the nutrition knowledge test than those who had not attended. Health care providers (82%) and reading materials (82%) were the most frequently cited sources of infant feeding information. While general nutrition knowledge appears to be positively related to education, it does not appear to influence the infant feeding decision. / Department of Family and Consumer Sciences

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