Computational joint models provide insight into the biomechanical function of human joints. Through both deformable and rigid body modeling, the structure-function relationship governing joint behavior is better understood, and subsequently, knowledge regarding normal, diseased, and/or injured function is garnered. Given the utility of these computational models, it is imperative to supply them with appropriate inputs such that model function is representative of true joint function. In these models, Magnetic Resonance Imaging (MRI) or Computerized Tomography (CT) scans and literature inform the bony anatomy and mechanical properties of muscle and ligamentous tissues, respectively. In the case of the latter, literature reports a wide range of values or average values with large standard deviations due to the inability to measure the mechanical properties of soft tissues in vivo. This makes it difficult to determine which values within the published literature to assign to computational models, especially patient-specific models. Therefore, while the use of published literature serves as a reasonable first approach to set up a computational model, a means of improving the supplied input data was sought. This work details the application of artificial neural networks (ANNs), specifically feedforward and radial basis function networks, to the optimization of ligament stiffnesses for the improved performance of pre- and post-operative, patient-specific foot/ankle computational models. ANNs are mathematical models that utilize learning rules to determine relationships between known sets of inputs and outputs. Using knowledge gained from these training data, the ANN may then predict outputs for similar, never‑before-seen inputs. Here, an optimal network of each ANN type was found, per mean square error and correlation data, and then both networks were used to predict optimal ligament stiffnesses corresponding to a single patient’s radiographic measurements. Both sets of predictions were ultimately supplied to the patient-specific computational models, and the resulting kinematics illustrated an improvement over the existing models that utilized literature-assigned stiffnesses. This research demonstrated that neural networks are a viable means to hone in on ligament stiffnesses for the overall objective of improving the predictive ability of a patient-specific computational model.

Este trabalho está inserido em um projeto que procura estudar a viabilidade técnica da aplicação de um relativamente novo conceito de tratamento térmico, chamado de Têmpera e Partição (T&P), como alternativa para o processamento de ferros fundidos nodulares com alta resistência mecânica. O processo T&P tem por objetivo a obtenção de microestruturas multifásicas constituídas de martensita e austenita retida, estabilizada em carbono. A martensita confere elevada resistência mecânica, enquanto a austenita confere ductilidade. No processo T&P, após a austenitização total ou parcial da liga, o material é temperado até uma temperatura de têmpera TT entre as temperaturas Ms e Mf para produzir uma mistura controlada de martensita e austenita. Em seguida, na etapa de partição, o material é mantido isotermicamente em uma temperatura igual ou mais elevada (denominada temperatura de partição TP) para permitir a partição de carbono da martensita para a austenita. O carbono em solução sólida diminui a temperatura Ms da austenita, estabilizando-a à temperatura ambiente. O presente trabalho procurou estudar aspectos de transformações de fases -- com ênfase na evolução microestrutural e cinética das reações -- do tratamento térmico de Têmpera e Partição (T&P) aplicado a uma liga de ferro fundido nodular (Fe-3,47%C-2,47%Si-0,2%Mn). Tratamentos térmicos consistiram de austenitização a 880 oC por 30 min, seguido de têmpera a 140, 170 e 200 oC e partição a 300, 375 e 450 oC por até 2 h. A caracterização microestrutural foi feita por microscopia óptica (MO), eletrônica de varredura (MEV), difração de elétrons retroespalhados (EBSD) e análise de microssonda eletrônica (EPMA). A análise cinética foi feita por meio de ensaios de dilatometria de alta resolução e difração de raios X in situ usando radiação síncrotron. Resultados mostram que a ocorrência de reações competitivas -- reação bainítica e precipitação de carbonetos na martensita -- é inevitável durante a aplicação do tratamento T&P à presente liga de ferro fundido nodular. A cinética da reação bainítica é acelerada pela presença da martensita formada na etapa de têmpera. A reação bainítica acontece, a baixas temperaturas, desacompanhada da precipitação de carbonetos e contribui para o enriquecimento em carbono, e consequente estabilização, da austenita. Devido à precipitação de carbonetos na martensita, a formação de ferrita bainítica é o principal mecanismo de enriquecimento em carbono da austenita. A microssegregação proveniente da etapa de solidificação permanece no material tratado termicamente e afeta a distribuição da martensita formada na etapa de têmpera e a cinética da reação bainítica. Em regiões correspondentes a contornos de célula eutética são observadas menores quantidades de martensita e a reação bainítica é mais lenta. A microestrutura final produzida pelo tratamento T&P aplicado ao ferro fundido consiste de martensita revenida com carbonetos, ferrita banítica e austenita enriquecida estabilizada pelo carbono. Adicionalmente, foi desenvolvido um modelo computacional que calcula a redistribuição local de carbono durante a etapa de partição do tratamento T&P, assumindo os efeitos da precipitação de do crescimento de placas de ferrita bainítica a partir da austenita. O modelo mostrou que a cinética de partição de carbono da martensita para a austenita é mais lenta quando os carbonetos precipitados são mais estáveis e que, quando a energia livre dos carbonetos é suficientemente baixa, o fluxo de carbono acontece da austenita para a martensita. A aplicação do modelo não se limita às condições estudadas neste trabalho e pode ser aplicada para o planejamento de tratamentos T&P para aços. / The present work belongs to a bigger project whose main goal is to study the technical feasibility of the application of a relatively new heat treating concept, called Quenching and Partitioning (Q&P), as an alternative to the processing of high strength ductile cast irons. The aim of the Q&P process is to obtain multiphase microstructures consisting of martensite and carbon enriched retained austenite. Martensite confers high strength, whereas austenite confers ductility. In the Q&P process, after total or partial austenitization of the alloy, the material is quenched in a quenching temperature TQ between the Ms and Mf temperatures to produce a controlled mixture of martensite and austenite. Next, at the partitioning step, the material is isothermally held at a either equal or higher temperature (so called partitioning temperature TP) in order to promote the carbon diffusion (partitioning) from martensite to austenite. The present work focus on the study of phase transformations aspects -- with emphasis on the microstructural evolution and kinetics of the reactions -- of the Q&P process applied to a ductile cast iron alloy (Fe-3,47%C-2,47%Si-0,2%Mn). Heat treatments consisted of austenitization at 880 oC for 30 min, followed by quenching at 140, 170, and 200 oC and partitioning at 300, 375 e 450 oC up to 2 h. The microstructural characterization was carried out by optical microscopy (OM), scanning electron microscopy (SEM), backscattered diffraction (EBSD), and electron probe microanalysis (EPMA). The kinetic analysis was studied by high resolution dilatometry tests and in situ X-ray diffraction using a synchrotron light source. Results showed that competitive reactions -- bainite reaction and carbides precipitation in martensite -- is unavoidable during the Q&P process. The bainite reaction kinetics is accelerated by the presence of martensite formed in the quenching step. The bainite reaction occurs at low temperatures without carbides precipitation and contributes to the carbon enrichment of austenite and its stabilization. Due to carbides precipitation in martensite, growth of bainitic ferrite is the main mechanism of carbon enrichment of austenite. Microsegregation inherited from the casting process is present in the heat treated material and affects the martensite distribution and the kinetics of the bainite reaction. In regions corresponding to eutectic cell boundaries less martensite is observed and the kinetics of bainite reaction is slower. The final microestructure produced by the Q&P process applied to the ductile cast iron consists of tempered martensite with carbides, bainitic ferrite, and carbon enriched austenite. Additionally, a computational model was developed to calculate the local kinetics of carbon redistribution during the partitioning step, considering the effects of carbides precipitation and bainite reaction. The model showed that the kinetics of carbon partitioning from martensite to austenite is slower when the tempering carbides are more stable and that, when the carbides free energy is sufficiently low, the carbon diffuses from austenite to martensite. The model is not limited to the studied conditions and can be applied to the development of Q&P heat treatments to steels.

Este trabalho está inserido em um projeto que procura estudar a viabilidade técnica da aplicação de um relativamente novo conceito de tratamento térmico, chamado de Têmpera e Partição (T&P), como alternativa para o processamento de ferros fundidos nodulares com alta resistência mecânica. O processo T&P tem por objetivo a obtenção de microestruturas multifásicas constituídas de martensita e austenita retida, estabilizada em carbono. A martensita confere elevada resistência mecânica, enquanto a austenita confere ductilidade. No processo T&P, após a austenitização total ou parcial da liga, o material é temperado até uma temperatura de têmpera TT entre as temperaturas Ms e Mf para produzir uma mistura controlada de martensita e austenita. Em seguida, na etapa de partição, o material é mantido isotermicamente em uma temperatura igual ou mais elevada (denominada temperatura de partição TP) para permitir a partição de carbono da martensita para a austenita. O carbono em solução sólida diminui a temperatura Ms da austenita, estabilizando-a à temperatura ambiente. O presente trabalho procurou estudar aspectos de transformações de fases -- com ênfase na evolução microestrutural e cinética das reações -- do tratamento térmico de Têmpera e Partição (T&P) aplicado a uma liga de ferro fundido nodular (Fe-3,47%C-2,47%Si-0,2%Mn). Tratamentos térmicos consistiram de austenitização a 880 oC por 30 min, seguido de têmpera a 140, 170 e 200 oC e partição a 300, 375 e 450 oC por até 2 h. A caracterização microestrutural foi feita por microscopia óptica (MO), eletrônica de varredura (MEV), difração de elétrons retroespalhados (EBSD) e análise de microssonda eletrônica (EPMA). A análise cinética foi feita por meio de ensaios de dilatometria de alta resolução e difração de raios X in situ usando radiação síncrotron. Resultados mostram que a ocorrência de reações competitivas -- reação bainítica e precipitação de carbonetos na martensita -- é inevitável durante a aplicação do tratamento T&P à presente liga de ferro fundido nodular. A cinética da reação bainítica é acelerada pela presença da martensita formada na etapa de têmpera. A reação bainítica acontece, a baixas temperaturas, desacompanhada da precipitação de carbonetos e contribui para o enriquecimento em carbono, e consequente estabilização, da austenita. Devido à precipitação de carbonetos na martensita, a formação de ferrita bainítica é o principal mecanismo de enriquecimento em carbono da austenita. A microssegregação proveniente da etapa de solidificação permanece no material tratado termicamente e afeta a distribuição da martensita formada na etapa de têmpera e a cinética da reação bainítica. Em regiões correspondentes a contornos de célula eutética são observadas menores quantidades de martensita e a reação bainítica é mais lenta. A microestrutura final produzida pelo tratamento T&P aplicado ao ferro fundido consiste de martensita revenida com carbonetos, ferrita banítica e austenita enriquecida estabilizada pelo carbono. Adicionalmente, foi desenvolvido um modelo computacional que calcula a redistribuição local de carbono durante a etapa de partição do tratamento T&P, assumindo os efeitos da precipitação de do crescimento de placas de ferrita bainítica a partir da austenita. O modelo mostrou que a cinética de partição de carbono da martensita para a austenita é mais lenta quando os carbonetos precipitados são mais estáveis e que, quando a energia livre dos carbonetos é suficientemente baixa, o fluxo de carbono acontece da austenita para a martensita. A aplicação do modelo não se limita às condições estudadas neste trabalho e pode ser aplicada para o planejamento de tratamentos T&P para aços. / The present work belongs to a bigger project whose main goal is to study the technical feasibility of the application of a relatively new heat treating concept, called Quenching and Partitioning (Q&P), as an alternative to the processing of high strength ductile cast irons. The aim of the Q&P process is to obtain multiphase microstructures consisting of martensite and carbon enriched retained austenite. Martensite confers high strength, whereas austenite confers ductility. In the Q&P process, after total or partial austenitization of the alloy, the material is quenched in a quenching temperature TQ between the Ms and Mf temperatures to produce a controlled mixture of martensite and austenite. Next, at the partitioning step, the material is isothermally held at a either equal or higher temperature (so called partitioning temperature TP) in order to promote the carbon diffusion (partitioning) from martensite to austenite. The present work focus on the study of phase transformations aspects -- with emphasis on the microstructural evolution and kinetics of the reactions -- of the Q&P process applied to a ductile cast iron alloy (Fe-3,47%C-2,47%Si-0,2%Mn). Heat treatments consisted of austenitization at 880 oC for 30 min, followed by quenching at 140, 170, and 200 oC and partitioning at 300, 375 e 450 oC up to 2 h. The microstructural characterization was carried out by optical microscopy (OM), scanning electron microscopy (SEM), backscattered diffraction (EBSD), and electron probe microanalysis (EPMA). The kinetic analysis was studied by high resolution dilatometry tests and in situ X-ray diffraction using a synchrotron light source. Results showed that competitive reactions -- bainite reaction and carbides precipitation in martensite -- is unavoidable during the Q&P process. The bainite reaction kinetics is accelerated by the presence of martensite formed in the quenching step. The bainite reaction occurs at low temperatures without carbides precipitation and contributes to the carbon enrichment of austenite and its stabilization. Due to carbides precipitation in martensite, growth of bainitic ferrite is the main mechanism of carbon enrichment of austenite. Microsegregation inherited from the casting process is present in the heat treated material and affects the martensite distribution and the kinetics of the bainite reaction. In regions corresponding to eutectic cell boundaries less martensite is observed and the kinetics of bainite reaction is slower. The final microestructure produced by the Q&P process applied to the ductile cast iron consists of tempered martensite with carbides, bainitic ferrite, and carbon enriched austenite. Additionally, a computational model was developed to calculate the local kinetics of carbon redistribution during the partitioning step, considering the effects of carbides precipitation and bainite reaction. The model showed that the kinetics of carbon partitioning from martensite to austenite is slower when the tempering carbides are more stable and that, when the carbides free energy is sufficiently low, the carbon diffuses from austenite to martensite. The model is not limited to the studied conditions and can be applied to the development of Q&P heat treatments to steels.

Este trabalho representa a continuidade de estudos envolvendo a problemática dos escoamentos com superfície livre, contemplando a análise do fenômeno transiente em canais, a partir do modelo matemático unidimensional de Saint-Venant. Para tanto, é desenvolvido um modelo computacional em linguagem FORTRAN, capaz de avaliar o comportamento do escoamento não permanente. As equações hidrodinâmicas completas são discretizadas por um esquema completamente implícito de diferenças finitas e aplicadas no modelo computacional para a avaliação de dois casos. O modelo é previamente testado para um caso simples, cujos resultados são analisados viabilizando o modelo. No primeiro caso, o modelo é aplicado ao canal de alimentação da Usina Hidrelétrica Monjolinho em São Carlos-SP, para avaliar a necessidade de vertedouro quando se dá o fechamento brusco da turbina, e a ocorrência da entrada de ar na mesma quando da sua abertura repentina. No segundo caso, procurou-se avaliar o desenvolvimento do escoamento no Canal do Trabalhador, responsável pelo abastecimento da cidade de Fortaleza-CE. Com manobras de enchimento e esvaziamento do sistema, é possível determinar o tempo de antecedência de liga-desliga do sistema de recalque a partir das alturas dágua e velocidades de ocorrência, permitindo também a automação para as operações de controle. Em ambos os casos o modelo reproduziu resultados que ilustram com coerência os conceitos pré-estabelecidos, constituindo numa ferramenta útil para análise do fenômeno transiente nos escoamentos em condutos livres. / This work presents a computational model developed in FORTRAN language for the study of unsteady open-channel flows with the use of Saint-Venant one-dimensional equation. The discretization of hydrodynamic equations are presented in a completely implicit method of finite differences and applied in the model for the investigation of two cases, besides the one used previously to test the model. In the first case, the model is applied for a channel that supplies the Monjolinho hydroelectric plant in Sao Carlos SP, aiming to evaluate the need of a spillway when the turbine is closed and the flow abruptly stopped, as well as the occurrence of air entering the turbine when it is opened instantaneously. In the second case, the model simulates the development of the flow in the Trabalhador channel, responsible for the water supply in the city of Fortaleza - CE, in order to make possible the automation of operational control, based on data of flow velocity and water level. In both cases the model is presented as a useful tool for the analysis of unsteady open-channel flows, showing results and coherency with theory.

No description available.

This dissertation presents the development and application of a computational model called BioChemFOAM developed using the computation fluid dynamic software OpenFOAM (Open source Field Operation And Manipulation). BioChemFOAM is a three dimensional incompressible unsteady-flow model that is coupled with a water-quality model via the Reynolds Average Navier-Stokes (RANS) equations. BioChemFOAM was developed to model nutrient dynamics in inland riverine aquatic ecosystems. BioChemFOAM solves the RANS equations for the hydrodynamics with an available library in OpenFOAM and implements a new library to include coupled systems of species transport equations with reactions. Simulation of the flow and multicomponent reactive transport are studied in detail for fundamental numerical experiments as well as for a real application in a backwater area of the Mississippi River. BioChemFOAM is a robust model that enables the flexible parameterization of processes for the nitrogen cycle. The processes studied include the following main components: algae, organic carbon, phosphorus, nitrogen, and dissolved oxygen. In particular, the research presented has three phases. The first phase involves the identification of the common processes that influence the nitrogen removal. The second phase covers the development and validation of the model that uses common parameterization to simulate the main features of an aquatic ecosystem. The main processes considered in the model and implemented in BioChemFOAM are: fully resolved hydraulic parameters (velocity and pressure), temperature variation, light's influence on the ecosystem, nutrients dynamics, algae growth and death, advection and diffusion of species, and isotropic turbulence (using a two-equation k-epsilon model). The final phase covers the application and analysis of the model and is divided in two sub stages: 1) a qualitative comparison of the main processes involved in the model (validation with the exact solution of different components of the model under different degrees of complexity) and 2) the quantification of main processes affecting nitrate removal in a backwater floodplain lake (Round Lake) in Pool 8 of the Mississippi River near La Crosse, WI. The BioChemFOAM model was able to reproduce different levels of complexity in an aquatic ecosystem and expose several main features that may help understand nutrient dynamics. The validation process with fabricated numerical experiments, discussed in Chapter 4, not only presents a detailed evaluation of the equations and processes but also introduces a step-by-step method of validating the model, given a level of complexity and parameterization when modeling nutrient dynamics in aquatic ecosystems. The study cases maintain fixed coefficients and characteristic values of the concentration in order to compare the influences that increasing or decreasing complexity has on the model, BioChemFOAM. Chapter 4, which focuses on model validation with numerical experiments, demonstrates that, with characteristic concentration and coefficients, some processes do not greatly influence the nutrient dynamics for algae. Chapters 5 and 6 discuss how BioChemFOAM was subsequently applied to an actual field case in the Mississippi River to show the model's ability to reproduce real world conditions when nitrate samples are available and other concentrations are used from typical monitored values. The model was able to reproduce the main processes affecting nutrient dynamics in the proposed scenarios and for previous studies in the literature. First, the model was adapted to simulate one species, nitrate, and its concentration was comparable to measured data. Second, the model was tested under different initial conditions. The model shows independence on initial conditions when reaching a steady mass flow rate for nitrate. Finally, a sensitivity analysis was performed using all eleven species in the model. The sensitivity takes as its basis the influence of processes on nitrate fate and transport and it defines eight scenarios. It was found in the present parameterization that green algae as modeled does not have a significant influence on improving nitrate spatial distributions and percentage of nitrate removal (PNR). On the other hand, reaction rates for denitrification at the bed and nitrification in the water shows an important influence on the nitrate spatial distribution and the PNR. One physical solution, from the broad range of scenarios defined in the sensitivity analysis, was selected as most closely reproducing the backwater natural system. The selection was based on published values of the percentage of nitrate removal (PNR), nitrate spatial concentrations, total nitrogen spatial concentrations and mass loading rate balances. The scenario identified as a physically valid solution has a reaction rate of nitrification and denitrification at the bed of 2.37x10-5 s-1. The PNR was found to be 39% when reaching a steady solution for the species transport. The denitrification at the bed process was about 6.7% of the input nitrate mass loading rate and the nitrification was about 7.7% of the input nitrate mass loading rate. The present research and model development highlight the need for additional detailed field measurements to reduce the uncertainty of common processes included in advanced models (see Chapter 2 for a review of models and Chapter 3 for the proposed model). The application presented in Chapter 6 utilizes only spatial variations of nitrate and total nitrogen to validate the model, which limits the validation of the remaining species. Despite the fact that some species are not known a priori, numerical experiments serve as a guide that helps explain how the aquatic ecosystem responds under different initial and boundary conditions. In addition, the PNR curves presented in this research were useful when defining realistic removal rates in a backwater area. BioChemFOAM's ability to formulate scenarios under different driving forces makes the model invaluable in terms of understanding the potential connections between species concentration and flow variables. In general, the case study presents trends in spatial and temporal distributions of non-sampled species that were comparable to measured data.

In this work we have developed three ordinary differential equation models of biological systems: body mass change in response to exercise, immune system response to a general inflammatory stimulus, and the immune system response in atherosclerosis. The purpose of developing such computational tools is to test hypotheses about the underlying biological processes that drive system outcomes as well as possible real medical interventions. Therefore, we focus our analysis on understanding key interactions between model parameters and outcomes to deepen our understanding of these complex processes as a means to developing effective treatments in obesity, sarcopenia, and inflammatory diseases. We develop a model of the dynamics of muscle hypertrophy in response to resistance exercise and have shown that the parameters controlling response vary between male and female group means in an elderly population. We further explore this individual variability by fitting to data from a clinical obesity study. We then apply logistic regression and classification tree methods to the analysis of between- and within-group differences in underlying physiology that lead to different long-term body composition outcomes following a diet or exercise program. Finally, we explore dieting strategies using optimal control methods. Next, we extend an existing model of inflammation to include different macrophage phenotypes. Complications with this phenotype switch can result in the accumulation of too many of either type and lead to chronic wounds or disease. With this model we are able to reproduce the expected timing of sequential influx of immune cells and mediators in a general inflammatory setting. We then calibrate this base model for the sequential response of immune cells with peritoneal cavity data from mice. Next, we develop a model for plaque formation in atherosclerosis by adapting the current inflammation model to capture the progression of macrophages to inflammatory foam cells in response to cholesterol consumption. The purpose of this work is ultimately to explore points of intervention that can lead to homeostasis.

The notion of social capital (SC) is increasingly used as a framework for describing social issues in terrestrial communities. For more than a decade, researchers use the term to mean the set of trust, institutions, social norms, social networks, and organizations that shape the interactions of actors within a society and that are considered to be useful and assets for communities to prosper both economically and socially. Despite growing popularity of social capital especially, among researchers in the social sciences and the humanities, the concept remains ill-defined and its operation and benefits limited to terrestrial communities. In addition, proponents of social capital often use different approaches to analyze it and each approach has its own limitations. <p>This thesis examines social capital within the context of technology-mediated communities (also known as virtual communities) communities. It presents a computational model of social capital, which serves as a first step in the direction of understanding, formalizing, computing and discussing social capital in virtual communities. The thesis employs an eclectic set of approaches and procedures to explore, analyze, understand and model social capital in two types of virtual communities: virtual learning communities (VLCs) and distributed communities of practice (DCoP). <p>There is an intentional flow to the analysis and the combination of methods described in the thesis. The analysis includes understanding what constitutes social capital in the literature, identifying and isolating variables that are relevant to the context of virtual communities, conducting a series of studies to further empirically examine various components of social capital identified in three kinds of virtual communities and building a computational model. <p>A sensitivity analysis aimed at examining the statistical variability of the individual variables in the model and their effects on the overall level of social capital are conducted and a series of evidence-based scenarios are developed to test and update the model. The result of the model predictions are then used as input to construct a final empirical study aimed at verifying the model.<p>Key findings from the various studies in the thesis indicated that SC is a multi-layered, multivariate, multidimensional, imprecise and ill-defined construct that has emerged from a rather murky swamp of terminology but it is still useful for exploring and understanding social networking issues that can possibly influence our understanding of collaboration and learning in virtual communities. Further, the model predictions and sensitivity analysis suggested that variables such as trust, different forms of awareness, social protocols and the type of the virtual community are all important in discussion of SC in virtual communities but each variable has different level of sensitivity to social capital. <p>The major contributions of the thesis are the detailed exploration of social capital in virtual communities and the use of an integrated set of approaches in studying and modelling it. Further, the Bayesian Belief Network approach applied in the thesis can be extended to model other similar complex online social systems.

The notion of social capital (SC) is increasingly used as a framework for describing social issues in terrestrial communities. For more than a decade, researchers use the term to mean the set of trust, institutions, social norms, social networks, and organizations that shape the interactions of actors within a society and that are considered to be useful and assets for communities to prosper both economically and socially. Despite growing popularity of social capital especially, among researchers in the social sciences and the humanities, the concept remains ill-defined and its operation and benefits limited to terrestrial communities. In addition, proponents of social capital often use different approaches to analyze it and each approach has its own limitations. <p>This thesis examines social capital within the context of technology-mediated communities (also known as virtual communities) communities. It presents a computational model of social capital, which serves as a first step in the direction of understanding, formalizing, computing and discussing social capital in virtual communities. The thesis employs an eclectic set of approaches and procedures to explore, analyze, understand and model social capital in two types of virtual communities: virtual learning communities (VLCs) and distributed communities of practice (DCoP). <p>There is an intentional flow to the analysis and the combination of methods described in the thesis. The analysis includes understanding what constitutes social capital in the literature, identifying and isolating variables that are relevant to the context of virtual communities, conducting a series of studies to further empirically examine various components of social capital identified in three kinds of virtual communities and building a computational model. <p>A sensitivity analysis aimed at examining the statistical variability of the individual variables in the model and their effects on the overall level of social capital are conducted and a series of evidence-based scenarios are developed to test and update the model. The result of the model predictions are then used as input to construct a final empirical study aimed at verifying the model.<p>Key findings from the various studies in the thesis indicated that SC is a multi-layered, multivariate, multidimensional, imprecise and ill-defined construct that has emerged from a rather murky swamp of terminology but it is still useful for exploring and understanding social networking issues that can possibly influence our understanding of collaboration and learning in virtual communities. Further, the model predictions and sensitivity analysis suggested that variables such as trust, different forms of awareness, social protocols and the type of the virtual community are all important in discussion of SC in virtual communities but each variable has different level of sensitivity to social capital. <p>The major contributions of the thesis are the detailed exploration of social capital in virtual communities and the use of an integrated set of approaches in studying and modelling it. Further, the Bayesian Belief Network approach applied in the thesis can be extended to model other similar complex online social systems.

We introduce a novel dynamic model of stem cell and tissue organisation in murine intestinal crypts. Integrating the molecular, cellular and tissue level of description, this model links a broad spectrum of experimental observations encompassing spatially confined cell proliferation, directed cell migration, multiple cell lineage decisions and clonal competition. Using computational simulations we demonstrate that the model is capable of quantitatively describing and predicting the dynamic behaviour of the intestinal tissue during steady state as well as after cell damage and following selective gain or loss of gene function manipulations affecting Wnt- and Notch-signalling. Our simulation results suggest that reversibility and flexibility of cellular decisions are key elements of robust tissue organisation of the intestine. We predict that the tissue should be able to fully recover after complete elimination of cellular subpopulations including subpopulations deemed to be functional stem cells. This challenges current views of tissue stem cell organisation.