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Network models with generalized upper bound side constraintsBolouri, Maryam 27 July 1989 (has links)
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
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Protein Kinase C Epsilon and Genetic Networks in Osteosarcoma MetastasisGoudarzi, Atta 20 November 2012 (has links)
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.
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Protein Kinase C Epsilon and Genetic Networks in Osteosarcoma MetastasisGoudarzi, Atta 20 November 2012 (has links)
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.
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Development of a laboratory manual for the study of network analysis / Laboratory manual for the study of network analysisTermes, Thomas Bruce 03 June 2011 (has links)
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.
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Coupling of ecological and water quality models for improved water resource and fish managementTillman, Dorothy Hamlin 15 May 2009 (has links)
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.
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Algorithms and applications for generalized networksHultz, John Wesley, January 1976 (has links)
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).
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Optimal road pricing in transportation networks /Zhang, Xiaoning. January 2003 (has links)
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.
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Hierarchical distributed algorithm for optimization of flows and prices in logistics distribution networks /Brayman, Vladimir. January 2003 (has links)
Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 61-65).
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Stochastic network interdiction: models and methodsPan, Feng 28 August 2008 (has links)
Not available / text
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Algorithms for the minimum cost flow problemLam, Yan-yan, 林欣欣 January 2004 (has links)
published_or_final_version / abstract / toc / Mathematics / Master / Master of Philosophy
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