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Central Arizona Project (CAP) water was treated using the process of slowsand filtration, chemical pretreatment and RO membrane. Both bench scale plate and frame reactor and pilot scale tests suggested RO membrane fouling by clay and organic matter with minor scaling by CaCO3 and BaSO4. Several strategies were studied to reduce RO membrane fouling and scaling. The first is choosing optimized operation conditions through bench scale tests. The second is to modify the traditional concentration polarization model for a better fouling/scaling prediction. This modified model was also used to optimize concentrate spacer design, which leads to reduced concentration polarization index. The third is to develop a method for anti-scalant test and comparison, which can be used for anti-scalant selection and dose optimization.Additional to these strategies, pre-oxidation pretreatment for RO membrane in water reuse application was investigated at bench and pilot scale. In the MBR-Ozone-RO train study, ozone showed certain impact on RO membrane fouling, but no significant difference was made on membrane cleaning frequency. UV and UV/AOP impacts on RO membrane fouling tests were done on plate and frame reactor. UV did not show any competency to reduce membrane fouling, while UV/AOP tests showed promising results by reducing RO membrane fouling rate by 50%.

The effectiveness of multidimensional scaling (MDS) techniques in recovering the underlying dimensionality of dichotomously-scored data was examined for unidimensional and multidimensional data. Thirty-three data sets of varying numbers of dimensions with differing patterns of item discrimination were generated using a multidimensional latent trait model in a Monte Carlo simulation study. Margin-sensitive measures (agreement, phi, and kappa) and margin-free measures (Φ/ Φ(max), Yule's Q, and the tetrachoric correlation) were used as measures of similarity and the resulting matrices were scaled in one through five dimensions. Values of the stress coefficient, S₁, S₁ by dimensionality plots, and plot configurations were examined to determine the dimensionality of the item set. Principal components analyses (PCAs) of phi and tetrachoric matrices were carried out as a basis for comparison. In addition, MDS and PCA were used to examine a data set comprised of items obtained from the routing tests of the Head Start Measures Battery. Two effects of item discrimination on MDS results were especially noteworthy. First, factors tended to be located equally distant from each other in the MDS space. Items were located closest to the factor for which the primary factor loading occurred. Second, as item discrimination decreased, items tended to be more widely dispersed from their appropriate locations in space. Extra dimensions in the MDS representational space were required for margin-sensitive coefficients to accommodate difficulty effects. Margin-free coefficients generally eliminated difficulty-related dimensions, although occasional problems were noted with the tetrachoric correlation. Analysis of the HSMB revealed that the data were primarily unidimensional, although specific effects due to each subtest were clearly present in the analysis. MDS was found to be a useful technique and its use in conjunction with PCA or factor analysis is recommended.

There is a remarkable relationship between an organism's metabolic rate (resting power consumption) and the organism's mass. It may be a universal law of nature that an organism's resting metabolic rate is proportional to its mass to the power of 3/4. This relationship, known as Kleiber's Law, appears to be valid for both plants and animals. This law is important because it implies that larger organisms are more efficient than smaller organisms, and knowledge regarding metabolic rates are essential to a multitude of other fields in ecology and biology. This includes modeling the interactions of many species across multiple trophic levels, distributions of species abundances across large spatial landscapes, and even medical diagnostics for respiratory and cardiovascular pathologies. Previous models of vascular networks that seek to identify the origin of metabolic scaling have all been based on the unrealistic assumption of perfectly symmetric branching. In this dissertation I will present a theory of asymmetric branching in self-similar vascular networks (published by Brummer et al. in [9]). The theory shows that there can exist a suite of vascular forms that result in the often observed 3/4 metabolic scaling exponent of Kleiber's Law. Furthermore, the theory makes predictions regarding major morphological features related to vascular branching patterns and their relationships to metabolic scaling. These predictions are suggestive of evolutionary convergence in vascular branching. To test these predictions, I will present an analysis of real mammalian and plant vascular data that shows: (i) broad patterns in vascular networks across entire animal kingdoms and (ii) within these patterns, plant and mammalian vascular networks can be uniquely distinguished from one another (publication in preparation by Brummer et al.). I will also present results from a computational study in support of point (i). Namely, that asymmetric branching may be the optimal strategy to balance the simultaneous demands of maximizing the number of nutrient exchange sites (capillaries or leaves) versus hydraulic resistance to resource transport (publication in preparation by Brummer et al.). Finally, I report on improved methods of estimating whole organism metabolism based solely on measurements of vasculature.

This thesis argues that a multidimensional profile incorporating mastery goals, performance goals, social goals and extrinsic goals, as well as factors relating to self-perceptions, would provide a better understanding of achievement motivation in adolescents than a univariate or dichotomous framework. Additionally this thesis also explores whether the use of lower-order dimensions provides information that offers a more detailed analysis of achievement goals over and above that found by the higher-order factors alone. A newly developed multidimensional measure, the SMOSA (Self Motivational Orientation Scale for Adolescents) of achievement motivation was used to examine changes of different motivational pursuits and perceptions of self across grades and sex in an adolescent population. The information found provides a more detailed analysis than previous research, which relied on an evaluation of means to explain differences between samples. Therefore, educators will be provided with a comprehensive understanding of the patterns of change in achievement motivation during adolescence and such knowledge may equip them with a way of measuring students’ approaches to facilitative learning and the ability to explore students’ paths for optimal engagement. / Doctor of Philosophy (PhD)

This thesis argues that a multidimensional profile incorporating mastery goals, performance goals, social goals and extrinsic goals, as well as factors relating to self-perceptions, would provide a better understanding of achievement motivation in adolescents than a univariate or dichotomous framework. Additionally this thesis also explores whether the use of lower-order dimensions provides information that offers a more detailed analysis of achievement goals over and above that found by the higher-order factors alone. A newly developed multidimensional measure, the SMOSA (Self Motivational Orientation Scale for Adolescents) of achievement motivation was used to examine changes of different motivational pursuits and perceptions of self across grades and sex in an adolescent population. The information found provides a more detailed analysis than previous research, which relied on an evaluation of means to explain differences between samples. Therefore, educators will be provided with a comprehensive understanding of the patterns of change in achievement motivation during adolescence and such knowledge may equip them with a way of measuring students’ approaches to facilitative learning and the ability to explore students’ paths for optimal engagement. / Doctor of Philosophy (PhD)

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In finance, the implied volatility surface is plotted against strike price and time to maturity. The shape of this volatility surface can be identified by fitting the model to what is actually observed in the market. The metric that is used to measure the discrepancy between the model and the market is usually defined by a mean squares of error of the model prices to the market prices. A regularization term can be added to this error metric to make the solution possess some desired properties. The discrepancy that we want to minimize is usually a highly nonlinear function of a set of model parameters with the regularization term. Typically monotonic decreasing algorithm is adopted to solve this minimization problem. Steepest descent or Newton type algorithms are two iterative methods but they are local, i.e., they use derivative information around the current iterate to find the next iterate. In order to ensure convergence, line search and trust region methods are two widely used globalization techniques. Motivated by the simplicity of Barzilai-Borwein method and the convergence properties brought by globalization techniques, we propose a new Scaled Gradient (SG) method for minimizing a differentiable function plus an L1-norm. This non-monotone iterative method only requires gradient information and safeguarded Barzilai-Borwein steplength is used in each iteration. An adaptive line search with the Armijo-type condition check is performed in each iteration to ensure convergence. Coleman, Li and Wang proposed another trust region approach in solving the same problem. We give a theoretical proof of the convergence of their algorithm. The objective of this thesis is to numerically investigate the performance of the SG method and establish global and local convergence properties of Coleman, Li and Wang’s trust region method proposed in [26]. Some future research directions are also given at the end of this thesis.

Abstract The synaptic input received by neurons in cortical circuits is in constant flux. From both environmental sensory changes and learning mechanisms that modify synaptic strengths, the excitatory and inhibitory signals received by a post-synaptic cell vary on a continuum of time scales. These variable inputs inherent in different sensory environments, as well as inputs changed by Hebbian learning mechanisms (which have been shown to destabilize the activity of neural circuits) serve to limit the input ranges over which a neural network can effectively operate. To avoid circuit behavior which is either quiescent or epileptic, there are a variety of homeostatic mechanisms in place to maintain proper levels of circuit activity. This article provides a basic overview of the biological mechanisms, and consider the advantages and disadvantages of homeostasis on a theoretical level. / text

In finance, the implied volatility surface is plotted against strike price and time to maturity. The shape of this volatility surface can be identified by fitting the model to what is actually observed in the market. The metric that is used to measure the discrepancy between the model and the market is usually defined by a mean squares of error of the model prices to the market prices. A regularization term can be added to this error metric to make the solution possess some desired properties. The discrepancy that we want to minimize is usually a highly nonlinear function of a set of model parameters with the regularization term. Typically monotonic decreasing algorithm is adopted to solve this minimization problem. Steepest descent or Newton type algorithms are two iterative methods but they are local, i.e., they use derivative information around the current iterate to find the next iterate. In order to ensure convergence, line search and trust region methods are two widely used globalization techniques. Motivated by the simplicity of Barzilai-Borwein method and the convergence properties brought by globalization techniques, we propose a new Scaled Gradient (SG) method for minimizing a differentiable function plus an L1-norm. This non-monotone iterative method only requires gradient information and safeguarded Barzilai-Borwein steplength is used in each iteration. An adaptive line search with the Armijo-type condition check is performed in each iteration to ensure convergence. Coleman, Li and Wang proposed another trust region approach in solving the same problem. We give a theoretical proof of the convergence of their algorithm. The objective of this thesis is to numerically investigate the performance of the SG method and establish global and local convergence properties of Coleman, Li and Wang’s trust region method proposed in [26]. Some future research directions are also given at the end of this thesis.