The objective of this thesis is to develop and computationally test a new algorithm for the class of network models with generalized upper bound (GUB) side constraints. Various algorithms have been developed to solve the network with arbitrary side constraints problem; however, no algorithm that exploits the special structure of the GUB side constraints previously existed. The proposed algorithm solves the network with GUB side constraints problem using two sequences of problems. One sequence yields a lower bound on the optimal value for the problem by using a Lagrangean relaxation based on relaxing copies of some subset of the original variables. This is achieved by first solving a pure network subproblem and then solving a set of single constraint bounded variable linear programs. Because only the cost coefficients change from one pure network subproblem to another, the optimal solution for one subproblem is at least feasible, if not optimal, for the next pure network subproblem. The second sequence yields an upper bound on the optimal value by using a decomposition of the problem based on changes in the capacity vector. Solving for the decomposed problem corresponds to solving for pure network subproblems that have undergone changes in lower and/or upper bounds. Recently developed reoptimization techniques are incorporated in the algorithm to find an initial (artificial) feasible solution to the pure network subproblem. A program is developed for solving the network with GUB side constraints problem by using the relaxation and decomposition techniques. The algorithm has been tested on problems with up to 200 nodes, 2000 arcs and 100 GUB constraints. Computational experience indicates that the upper bound procedure seems to perform well; however, the lower bound procedure has a fairly slow convergence rate. It also indicates that the lower bound step size, the initial lower bound value, and the lower and upper bound iteration strategies have a significant effect on the convergence rate of the lower bound algorithm. / Graduation date: 1990

Pulmonary metastasis is the most frequent cause of osteosarcoma (OS) mortality. The aim of this study was to discover and characterize genetic networks differentially expressed in metastatic OS. Supervised network analysis of OS expression profiles was performed to discover genetic networks differentially activated or organized in metastatic OS. Broad trends among the profiles of metastatic tumours included aberrant activity of intracellular organization and translation networks, as well as disorganization of metabolic networks. The differentially activated PRKCε-RASGRP3-GNB2 network, which interacts with the disorganized DLG2 hub, was additionally found to be differentially expressed among in vitro models of human OS metastasis. PRKCε transcript was more abundant in some metastatic OS tumours; however the difference was not significant overall. In functional studies, PRKCε was not found to be involved in migration of M132 OS cells, but its protein expression was induced in M112 OS cells following IGF-1 stimulation.

Pulmonary metastasis is the most frequent cause of osteosarcoma (OS) mortality. The aim of this study was to discover and characterize genetic networks differentially expressed in metastatic OS. Supervised network analysis of OS expression profiles was performed to discover genetic networks differentially activated or organized in metastatic OS. Broad trends among the profiles of metastatic tumours included aberrant activity of intracellular organization and translation networks, as well as disorganization of metabolic networks. The differentially activated PRKCε-RASGRP3-GNB2 network, which interacts with the disorganized DLG2 hub, was additionally found to be differentially expressed among in vitro models of human OS metastasis. PRKCε transcript was more abundant in some metastatic OS tumours; however the difference was not significant overall. In functional studies, PRKCε was not found to be involved in migration of M132 OS cells, but its protein expression was induced in M112 OS cells following IGF-1 stimulation.

The project is a seven chapter laboratory manual-text covering the topic of D.C. network analysis. It is individualized in nature and it is based on the retrogressive chain. The chapters cover Kirchoff's law, Branch Current Analysis, Mesh Analysis, Nodal Analysis, Thevenin's Theorm, Norton's Theorm, and Superposition Theorm. Each chapter has five sections. They are: (1) a list of behavioral objectives, (2) a presentation of the pertinent information, (3) a number of typical problems with answers, (4) a list of additional resources and, (5) the laboratory procedure.

In recent years new ideas for nutrient management to control eutrophication in estuarine environments have been under consideration. One popular approach being considered in the Chesapeake Bay Program is called the “top down” approach based on the premise that restoring algal predators, such as oysters and menhaden, will limit excess phytoplankton production and possibly eliminate costly nutrient control programs. The approach is being considered to replace or use in conjunction with the “bottom up” approach of reducing nutrient loads. The ability to model higher trophic levels such as fish, as well as the eutrophication processes driving production of primary producers in an aquatic ecosystem is needed. CE-QUAL-ICM (ICM) and Ecopath were two models selected for this research. ICM is a time- and spatial-varying eutrophication model that uses nutrient loads to predict primary producers, while Ecopath is a static mass balance model representing an average time period (e.g., season or year) and uses values of primary producers and other groups to predict fish biomass. Linking the two models will provide the means of going up the food chain by trophic levels. The Chesapeake Bay was chosen as the study site since both models are in use there. Before coupling ICM and Ecopath, common links between the two models were found. Ten groups were identified with such variables as production rates, consumption rates, and unassimilated food/consumption. A post-processor/subroutine was developed for ICM to aggregate output data from 3-D to 0-D to be used in Ecopath. Two Ecopath runs were developed with data from ICM and the Chesapeake Bay (CB) Ecopath model to see how network interactions differed with data representing the same system. Four additional runs were made, creating perturbations (i.e., increased phytoplankton production) using the CB Ecopath model and replacing the primary producers with data from ICM. Final runs of ICM were conducted looking at adjusting three parameters to try to restore the Bay back to 1950 conditions. It was demonstrated that ICM data can be coupled with Ecopath to study management strategies in eutrophication. Because of model formulations there was no data exchange from Ecopath back to ICM.

Thesis--University of Texas at Austin. / Vita. Photocopy of typescript. Ann Arbor, Mich. : Xerox University Microfilms, 1978. -- 21 cm. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 153-158).

Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 230-240). Also available in electronic version. Access restricted to campus users.

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 61-65).

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published_or_final_version / abstract / toc / Mathematics / Master / Master of Philosophy