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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Constitutive modelling of elastomers using the finite element method

Hogan, John January 2000 (has links)
No description available.
2

Mathematical frameworks for the transmission dynamics of HIV on a concurrent partnership network

Parker, Christopher Gareth January 1996 (has links)
No description available.
3

Investigation and Modeling of Professional Interpersonal Networks: Transportation Planning and Modeling Community Case Study

Bustillos, Brenda January 2015 (has links)
The purpose of this research is to investigate, acquire knowledge, and better comprehend the transportation planning and modeling community. This task is accomplished through the investigation of existing social and professional networks within this community by constructing and analyzing an abstract network representation of this community. Specifically, this research explores the actors (i.e., professionals, agencies) and their relationships (i.e., ties, interactions, etc.) within the professional interpersonal (social) network where they conduct business on a regular basis. Actors and relationships are represented in terms of a nodes and links within the constructed network. The network is then analyzed in an effort to answer questions such as, who are the actors, where do interpersonal relationships exist, where are social structures found, what does the evolution of this community look like over time, and what can this evolution tell us. This study has collected information from transportation professionals directly associated with the decision-making, planning and/or modeling process within the transportation planning and modeling community. The data is collected through an in-house designed online survey disseminated to the identified target audience. The designed survey is structured to capture information required for the identification of actors and relationships (or entities and ties) within the transportation planning and modeling community. With the network constructed, analysis methods derived from mathematics, computer science and social network analysis fields are implemented to identify local and global patterns, "influential" actors, and collaborative structures as well as examine network dynamics, which transpire within the environment that these transportation professionals navigate, form bonds, and collect information on frequent basis.
4

Using mortars to upscale permeability in heterogeneous porous media from the pore to continuum scale

Bhagmane, Jaideep Shivaprasad 20 September 2010 (has links)
Pore-scale network modeling has become an effective method for accurate prediction and upscaling of macroscopic properties, such as permeability. Networks are either mapped directly from real media or stochastic methods are used that simulate their heterogeneous pore structure. Flow is then modeled by enforcing conservation of mass in each pore and approximations to the momentum equations are solved in the connecting throats. In many cases network modeling compares favorably to experimental measurements of permeability. However, computational and imaging restrictions generally limit the network size to the order of 1 mm3 (few thousand pores). For extremely heterogeneous media these models are not large enough in capturing the petrophysical properties of the entire heterogeneous media and inaccurate results can be obtained when upscaling to the continuum scale. Moreover, the boundary conditions imposed are artificial; a pressure gradient is imposed in one dimension so the influence of flow behavior in the surrounding media is not included. In this work we upscale permeability in large, heterogeneous media using physically-representative pore-scale network models (domain ~106 pores). High-performance computing is used to obtain accurate results in these models, but a more efficient, novel domain decomposition method is introduced for upscaling the permeability of pore-scale models. The medium is decomposed into hundreds of smaller networks (sub-domains) and then coupled with the surrounding models to determine accurate boundary conditions. Finite element mortars are used as a mathematical tool to ensure interfacial pressures and fluxes are matched at the interfaces of the networks boundaries. The results compare favorably to the more computationally intensive (and impractical) approach of upscaling the media as a single model. Moreover, the results are much more accurate than traditional hierarchal upscaling methods. This upscaling technique has important implications for using pore-scale models directly in reservoir simulators in a multiscale setting. The upscaling techniques introduced here on single phase flow can also be easily extended to other flow phenomena, such as multiphase and non-Newtonian behavior. / text
5

Estimating Network Features and Associated Measures of Uncertainty and Their Incorporation in Network Generation and Analysis

Goyal, Ravi 19 November 2012 (has links)
The efficacy of interventions to control HIV spread depends upon many features of the communities where they are implemented, including not only prevalence, incidence, and per contact risk of transmission, but also properties of the sexual or transmission network. For this reason, HIV epidemic models have to take into account network properties including degree distribution and mixing patterns. The use of sampled data to estimate properties of a network is a common practice; however, current network generation methods do not account for the uncertainty in the estimates due to sampling. In chapter 1, we present a framework for constructing collections of networks using sampled data collected from ego-centric surveys. The constructed networks not only target estimates for density, degree distributions and mixing frequencies, but also incorporate the uncertainty due to sampling. Our method is applied to the National Longitudinal Study of Adolescent Health and considers two sampling procedures. We demonstrate how a collection of constructed networks using the proposed methods are useful in investigating variation in unobserved network topology, and therefore also insightful for studying processes that operate on networks. In chapter 2, we focus on the degree to which impact of concurrency on HIV incidence in a community may be overshadowed by differences in unobserved, but local, network properties. Our results demonstrate that even after controlling for cumulative ego-centric properties, i.e. degree distribution and concurrency, other network properties, which include degree mixing and clustering, can be very influential on the size of the potential epidemic. In chapter 3, we demonstrate the need to incorporate information about degree mixing patterns in such modeling. We present a procedure to construct collections of bipartite networks, given point estimates for degree distribution, that either makes use of information on the degree mixing matrix or assumes that no such information is available. These methods permit a demonstration of the differences between these two network collections, even when degree sequence is fixed. Methods are also developed to estimate degree mixing patterns, given a point estimate for the degree distribution.
6

Sensitivity Analysis and Forecasting in Network Epidemiology Models

Nsoesie, Elaine O. 05 June 2012 (has links)
In recent years, several methods have been proposed for real-time modeling and forecasting of the epidemic curve. These methods range from simple compartmental models to complex agent-based models. In this dissertation, we present a model-based reasoning approach to forecasting the epidemic curve and estimating underlying disease model parameters. The method is based on building an epidemic library consisting of past and simulated influenza outbreaks. During an influenza epidemic, we use a combination of statistical, optimization and modeling techniques to either assign the epidemic to one of the cases in the library or propose parameters for modeling the epidemic. The method is presented in four steps. First, we discuss a sensitivity analysis study evaluating how minute changes in the disease model parameters influence the dynamics of simulated epidemics. Next, we present a supervised classification method for predicting the epidemic curve. The epidemic curve is forecasted by matching the partial surveillance curve to at least one of the epidemics in the library. We expand on the classification approach by presenting a method which identifies epidemics similar or different from those in the library. Lastly, we discuss a simulation optimization method for estimating model parameters to forecast the epidemic curve of an epidemic distinct from those in the library. / Ph. D.
7

Multimodal Networks in Biology

Sioson, Allan A. 14 December 2005 (has links)
A multimodal network (MMN) is a novel mathematical construct that captures the structure of biological networks, computational network models, and relationships from biological databases. An MMN subsumes the structure of graphs and hypergraphs, either undirected or directed. Formally, an MMN is a triple (V,E,M) where V is a set of vertices, E is a set of modal hyperedges, and M is a set of modes. A modal hyperedge e=(T,H,A,m) in E is an ordered 4-tuple, in which T,H,A are subsets of V and m is an element of M. The sets T, H, and A are the tail, head, and associate of e, while m is its mode. In the context of biology, each vertex is a biological entity, each hyperedge is a relationship, and each mode is a type of relationship (e.g., 'forms complex' and 'is a'). Within the space of multimodal networks, structural operations such as union, intersection, hyperedge contraction, subnetwork selection, and graph or hypergraph projections can be performed. A denotational semantics approach is used to specify the semantics of each hyperedge in MMN in terms of interaction among its vertices. This is done by mapping each hyperedge e to a hyperedge code algo:V(e), an algorithm that details how the vertices in V(e) get used and updated. A semantic MMN-based model is a function of a given schedule of evaluation of hyperedge codes and the current state of the model, a set of vertex-value pairs. An MMN-based computational system is implemented as a proof of concept to determine empirically the benefits of having it. This system consists of an MMN database populated by data from various biological databases, MMN operators implemented as database functions, graph operations implemented in C++ using LEDA, and mmnsh, a shell scripting language that provides a consistent interface to both data and operators. It is demonstrated that computational network models may enrich the MMN database and MMN data may be used as input to other computational tools and environments. A simulator is developed to compute from an initial state and a schedule of hyperedge codes the resulting state of a semantic MMN model. / Ph. D.
8

Continuum Models for the Spread of Alcohol Abuse

Teymuroglu, Zeynep 23 September 2008 (has links)
No description available.
9

Representation of Tones and Vowels in a Biophysically Detailed Model of Ventral Cochlear Nucleus

Yayli, Melih January 2019 (has links)
Biophysically detailed representations of neural network models provide substantial insight to underlying neural processing mechanisms in the auditory systems of the brain. For simple biological systems the behavior can be represented by simple equations or flow charts. But for complex systems, more detailed descriptions of individual neurons and their synaptic connectivity are typically required. Creating extensive network models allows us to test hypotheses, apply specific manipulations that cannot be done experimentally and provide supporting evidence for experimental results. Several studies have been made on establishing realistic models of the cochlear nucleus (Manis and Campagnola, 2018; Eager et al., 2004), the part of the brainstem where sound signals enter the brain, both on individual neuron and networked structure levels. These models are based on both in vitro and in vivo physiological data, and they successfully demonstrate certain aspects of the neural processing of sound signals. Even though these models have been tested with tone bursts and isolated phonemes, the representation of speech in the cochlear nucleus and how it may support robust speech intelligibility remains to be explored with these detailed biophysical models. In this study, the basis of creating a biophysically detailed model of microcircuits in the cochlear nucleus is formed following the approach of Manis and Campagnola (2018). The focus of this thesis is more on bushy cell microcircuits. We have updated Manis and Campagnola (2018) model to take inputs from the new phenomenological auditory periphery model of Bruce et al. (2018). Different cell types in the cochlear nucleus are modelled by detailed cell models of Rothman and Manis (2003c) and updated Manis and Campagnola (2018) cell models. Networked structures are built out of them according to published anatomical and physiological data. The outputs of these networked structures are used to create post-stimulus-time-histograms (PSTH) and response maps to investigate the representation of tone bursts and average localized synchronized rate (ALSR) of phoneme 'e' and are compared to published physiological data (Blackburn and Sachs, 1990). / Thesis / Master of Applied Science (MASc)
10

Dynamic network models of a continental epidemic: soybean rust in the USA

Sutrave, Sweta January 1900 (has links)
Master of Science / Department of Electrical and Computer Engineering / Karen A. Garrett / Caterina M. Scoglio / With rapid global movement of epidemics, research efforts to characterize dynamics of epidemics have gained much focus. Traditional epidemiological models have focused on only temporal components of epidemics. Development of spatio-temporal models proved to be a notable achievement in epidemiology. Network-based epidemiological models enable better handling of spatial and temporal components of an epidemic. Early network models considered a binary level of contact between infected entities, which is an idealistic approach. A realistic approach would use weighted edges which signify the level of interaction between the nodes where the edge-weights change over time as a function of environmental factors. Estimation of edge weights from observed time series is a relatively less explored area for network modeling. Dynamic networks make the problem more complicated as edge weights change over time. Estimation of parameters for models describing the edge weights as a function of variables that change in time has the potential to provide better general models. Soybean rust (caused by Phakopsora pachyrhizi) is an important disease globally and its occurrence in the US has been studied extensively since its introduction in 2004. Rust is a fungal disease which propagates as a result of the fungal spores being carried by the wind. In this thesis, a network network based model is proposed to predict the intensity of spread of the disease in space and time. This model uses the host abundance and wind data and the observed rust incidence time series to compute the edge-weights. Also, the edge-weights in the model change over time thus following a dynamic approach. In order to cut costs involved with the establishment and maintenance of infection monitoring sites, the effect of removal of monitoring nodes using various strategies has also been analyzed in this thesis. The model has been tested with observed soybean rust data from sentinel plot network from across the United States.

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