The aim of this study is to investigate the fluid-structural response to pulsatile Newtonian and non-Newtonian blood flow through an axisymmetric stenosed vessel using FLOTRAN and ANSYS. This is to provide a basic understanding of atherosclerosis. The flow was set to be laminar and follows a sinusoidal waveform. The solid model was set to have isotropic elastic properties. The Fluid-Structural Interaction (FSI) coupling was two-way and iterative. Rigid and Newtonian cases were investigated to provide an understanding on the effects of incorporating FSI into the model. The wall expansion was found to decrease the axial velocity and increase the recirculation effects of the flow. To validate the models and methods used, the results were compared with the study by Lee and Xu [2002] and Ohja et al [1989]. Close comparisons were achieved, suggesting the models used were valid. Two non-Newtonian models were investigated with FSI: Carreau and Power Law models. The Carreau model fluid behaviour was very close to the Newtonian model. The Power Law model produced significant difference in viscosity, velocity and wall shear stress distributions. Pressure distribution for all models was similar. In order to quantify the changes, Importance Factor (IG) was introduced to determine the overall non-Newtonian effects at two regions: the entire flow model and about the vessel wall. The Carreau model showed reasonable values of IG whereas the Power Law model showed excessive values. Transient and geometrical effects were found to affect the Importance Factor. The stress distributions for all models were found to be similar. Highest stress occurred at the shoulders of the stenosis where a stress concentration occurred due to sharp corners of the geometry and large bending moments. The highest stresses were in the axial direction. Notable circumferential stress was found at the ends of the vessel. Carreau model produced slightly higher stresses than the other models. Wall stresses were found to be primarily influenced by internal pressure, rather than wall shear stresses.

<p>Demands on the quality and reliability of Volunteered Geographic Information have increased because of its rising popularity. Due to the less controlled data entry, there is a risk that people provide false or inaccurate information to the database. One factor that affects the effect of such updates is the network structure of the database schema, which might reveal the database’s robustness against different kinds of false updates. Therefore, network analyses are needed. The aim is to analyse GIS data models, stored in UML class diagrams, for scale-free and small-world properties. Moreover, a robustness analysis is to be carried out on selected data models in order to find out their error and attack tolerance against, for example, false updates. Three graphs were specified from the UML class diagrams: (1) <em>class graphs</em>: classes as nodes and their interactive relationships as connections; (2) <em>attribute graphs</em>: classes and attributes as nodes, with connections between the classes and their attributes; and (3) <em>schema graphs</em>: attributes as nodes and their interactive relationships inside and outside the tables as links. The analysed class diagrams were stored in XMI, and therefore transformed with XSLT to the Pajek network format. Thereafter, small-world and scale-free analyses as well as a robustness analysis were performed on the graphs. </p><p>The results from the scale-free analyses showed no strict power-laws. Nevertheless, the classes’ relationships and attributes, and the betweenness in the schema graphs were long-tailed distributed. Furthermore, the schema graphs had small-world properties, and the analysed class and schema graphs were robust against errors but fragile against attacks. In a network structure perspective, these results indicate that false updates on random tables of a database should usually do little harm, but falsely updating the most central cells or tables may cause big damage. Consequently, it may be necessary to monitor and constrain sensitive cells and tables in order to protect them from attacks</p>

Demands on the quality and reliability of Volunteered Geographic Information have increased because of its rising popularity. Due to the less controlled data entry, there is a risk that people provide false or inaccurate information to the database. One factor that affects the effect of such updates is the network structure of the database schema, which might reveal the database’s robustness against different kinds of false updates. Therefore, network analyses are needed. The aim is to analyse GIS data models, stored in UML class diagrams, for scale-free and small-world properties. Moreover, a robustness analysis is to be carried out on selected data models in order to find out their error and attack tolerance against, for example, false updates. Three graphs were specified from the UML class diagrams: (1) class graphs: classes as nodes and their interactive relationships as connections; (2) attribute graphs: classes and attributes as nodes, with connections between the classes and their attributes; and (3) schema graphs: attributes as nodes and their interactive relationships inside and outside the tables as links. The analysed class diagrams were stored in XMI, and therefore transformed with XSLT to the Pajek network format. Thereafter, small-world and scale-free analyses as well as a robustness analysis were performed on the graphs.  The results from the scale-free analyses showed no strict power-laws. Nevertheless, the classes’ relationships and attributes, and the betweenness in the schema graphs were long-tailed distributed. Furthermore, the schema graphs had small-world properties, and the analysed class and schema graphs were robust against errors but fragile against attacks. In a network structure perspective, these results indicate that false updates on random tables of a database should usually do little harm, but falsely updating the most central cells or tables may cause big damage. Consequently, it may be necessary to monitor and constrain sensitive cells and tables in order to protect them from attacks

Sets of natural opening-mode fractures in sedimentary rocks may show a variety of types of aperture-size distributions. A frequently documented size distribution type, in the literature and in data presented here, is the power law. The emergence of power-law distributions of fracture aperture and length sizes has been simulated using various quasi-mechanical fracture-growth routines but models based on linear-elastic fracture mechanics rarely produce such patterns. I collected a fracture-size dataset of unprecedented size and resolution using core and field methods and scanning electron microscope-based cathodoluminescence (SEM-CL) images. This dataset confirms the prevalence of power laws with a narrow range of power-law exponents among fractures that contain synkinematic cement. Organized microfractures are ubiquitous in sandstones. A fracture-growth simulation I devised reproduces observed size-scaling patterns by distributing fracture-opening increments among actively growing fractures. The simulated opening increments have a uniform size, which can be specified; uniform opening size is consistent with observations of narrow ranges of micron-scale widths of opening increments within crack-seal texture in natural fractures. Thus power-law size scaling of natural fractures can be explained using non-power-law (uniform-sized) opening increments, arranged using rules designed to simulate the effects of cement precipitation during fracture opening. A fundamental shortcoming of previous models of fracture-set evolution is the absence of a test because only natural fracture end states, not growth histories, could be measured. Using a technique to constrain fracture timing based on fluid inclusion microthermometry and thermal history modeling, I tested growth models by reconstructing the opening history of a set of natural fractures in the Triassic El Alamar Formation in northeast Mexico. The natural-fracture data show that, consistent with simulations, new microscopic fractures are continually introduced during natural fracture pattern evolution. As well, larger fractures represent sites of concentrated reactivation, although smaller fractures may be reactivated after long periods of quiescence. The pattern likely arises through feedback between fracture growth and the mechanically adhesive effects of contemporaneous fracture cement deposition. The narrow range in power-law exponents documented among fractures can help improve estimates of meter-scale large-fracture spacing where limited fracture samples are available. / text

A majority of the data transmitted and processed today has an inherent structured high-dimensional nature, either because of the process of encoding using high-dimensional codebooks for providing a systematic structure, or dependency of the data on a large number of agents or variables. As a result, many problem setups associated with transmission and processing of data have a structured high-dimensional aspect to them. This dissertation takes a look at two such problems, namely, communication over networks using network coding, and learning the structure of graphical representations like Markov networks using observed data, from an information-theoretic perspective. Such an approach yields intuition about good coding architectures as well as the limitations imposed by the high-dimensional framework. Th e dissertation studies the problem of network coding for networks having multiple transmission sessions, i.e., multiple users communicating with each other at the same time. The connection between such networks and the information-theoretic interference channel is examined, and the concept of interference alignment, derived from interference channel literature, is coupled with linear network coding to develop novel coding schemes off ering good guarantees on achievable throughput. In particular, two setups are analyzed – the first where each user requires data from only one user (multiple unicasts), and the second where each user requires data from potentially multiple users (multiple multicasts). It is demonstrated that one can achieve a rate equalling a signi ficant fraction of the maximal rate for each transmission session, provided certain constraints on the network topology are satisfi ed. Th e dissertation also analyzes the problem of learning the structure of Markov networks from observed samples – the learning problem is interpreted as a channel coding problem and its achievability and converse aspects are examined. A rate-distortion theoretic approach is taken for the converse aspect, and information-theoretic lower bounds on the number of samples, required for any algorithm to learn the Markov graph up to a pre-speci fied edit distance, are derived for ensembles of discrete and Gaussian Markov networks based on degree-bounded graphs. The problem of accurately learning the structure of discrete Markov networks, based on power-law graphs generated from the con figuration model, is also studied. The eff ect of power-law exponent value on the hardness of the learning problem is deduced from the converse aspect – it is shown that discrete Markov networks on power-law graphs with smaller exponent values require more number of samples to ensure accurate recovery of their underlying graphs for any learning algorithm. For the achievability aspect, an effi cient learning algorithm is designed for accurately reconstructing the structure of Ising model based on power-law graphs from the con figuration model; it is demonstrated that optimal number of samples su ffices for recovering the exact graph under certain constraints on the Ising model potential values. / text

This dissertation presents three essays on asset pricing and econometrics. The first chapter identifies rare events and long-run risks simultaneously from a rich data set (the Barro-Ursua macroeconomic data set) and evaluates their contributions to asset pricing in a unified framework. The proposed model of rare events and long-run risks is estimated using a Bayesian Markov-chain Monte-Carlo method, and the estimates for the disaster process are closer to the data than those in the previous studies. Major evaluation results in asset pricing include: (1) for the unleveraged annual equity premium, the predicted values are 4.8%, 4.2%, and 1.0%, respectively; (2) for the Sharpe ratio, the values are 0.72, 0.66, and 0.15, respectively. / Economics

Turbine components operate under severe loading conditions and at high and varying temperatures that result in thermal stresses in the presence of temperature gradients created by hot gases and cooling air. Moreover, static and cyclic loads as well as the motion of rotating components create mechanical stresses. The combined effect of complex thermo-mechanical stresses promote nucleation and propagation of cracks that give rise to fatigue and creep failure of the turbine components. Therefore, the relationship between thermo-mechanical stresses, chemical composition, heat treatment, resulting microstructure, operating temperature, material damage, and potential failure modes, i.e. fatigue and/or creep, needs to be well understood and studied. Artificial neural networks are promising candidate tools for such studies. They are fast, flexible, efficient, and accurate tools to model highly non-linear multi-dimensional relationships and reduce the need for experimental work and time-consuming regression analysis. Therefore, separate neural network models for γ’ precipitate strengthened Ni based superalloys have been developed for predicting the γ’ precipitate size, thermal expansion coefficient, fatigue life, and hysteresis energy. The accumulated fatigue damage is then estimated as the product of hysteresis energy and fatigue life. The models for γ’ precipitate size, thermal expansion coefficient, and hysteresis energy converge very well and match experimental data accurately. The fatigue life proved to be the most challenging aspect to predict, and fracture mechanics proved to potentially be a necessary supplement to neural networks. The model for fatigue life converges well, but relatively large errors are observed partly due to the generally large statistical variations inherent to fatigue life. The deformation mechanism map for 1.23Cr-1.2Mo-0.26V rotor steel has been constructed using dislocation glide, grain boundary sliding, and power law creep rate equations. The constructed map is verified with experimental data points and neural network results. Although the existing set of experimental data points for neural network modeling is limited, there is an excellent match with boundaries constructed using rate equations which validates the deformation mechanism map.

Wächst eine Population und breitet sich dabei geographisch aus, so spricht man von räumlichem Populationswachstum, bzw. einer Range-Expansion. Viele Arten haben im Verlaufe ihrer evolutionären Geschichte ihr Verbreitungsgebiet ausgeweitet. Gründe hierfür können beispielsweise ein geändertes Klima oder die Verschleppung der Art in einen neuen Lebensraum sein. Während einer Range-Expansion können durch Gene-Surfing räumliche Verteilungen von neutralen genetischen Varianten entstehen, die den Folgen von selektiven Prozessen ähnlich sind. Für eine korrekte Interpretation der genetischen Daten ist daher die Kenntnis über quantitative Auswirkungen von Range-Expansions auf die genetische Vielfalt unumgänglich. In dieser Arbeit charakterisiere ich die Konsequenzen von Range-Expansions für Allelfrequenz-Spektren. Dazu generiere ich in Computersimulationen genetische Daten für unterschiedliche demographische Szenarien sowie diverse ökologische und geographische Bedingungen. Ich zeige, dass Range-Expansions innerhalb kurzer Zeit zu Allelfrequenz-Spektren führen können, die sich durch ein Potenzgesetz mit einem spezifischen Exponenten beschreiben lassen. Dieser Exponent liegt zwischen den erwarteten Exponenten für stabile und exponentiell wachsende, durchmischte Populationen. Mutationen, die während einer Range-Expansion aufgetreten sind, tragen meinen Ergebnissen zufolge weniger zu heutigen Allelfrequenz-Spektren bei, als Mutationen, die bereits in der Ursprungspopulation vorhanden waren. Allerdings eignen sich neue Mutationen besser, um Range-Expansions in genetischen Daten aufzuspüren, da sie weniger von geographischen Strukturen beeinflusst werden. Meine Resultate werden dazu beitragen, Spuren von Range-Expansions in genetischen Daten zu entdecken und Rückschlüsse auf die evolutionäre Vergangenheit von Populationen zu ziehen.

The current study aims to investigate the relative contributions of each of the processes that govern airway smooth muscle mechanical behaviour. Studies have shown that breathing dynamics have a substantial effect on airway constriction in healthy and diseased subjects, yet little is known about the dynamic response of the main instigator of airway constriction, Airway Smooth Muscle (ASM). In this work several models are developed to further the understanding of ASM dynamics, particularly the roles and interactions of the three dominant processes in the muscle: contractile dynamics, length adaptation and passive dynamics. Three individual models have been developed, each describing a distinct process or structure within the muscle. The first is a contractile model which describes the contractile process and the influence of external excitation on contractile behaviour. The second model incorporates the contractile model to describe length adaptation, which includes the reorganisation and polymerisation of contractile elements in response to length changes. The third model describes the passive behaviour of the muscle, which entails the mechanical behaviour of all non-contractile components and processes. As little data on the passive dynamics of the muscle was available in the literature, a number of experiments were conducted to investigate relaxed ASM dynamics. The experimental data and mathematical modelling showed that passive dynamics plays not only a dominant role in relaxed ASM, but contributes considerably to the dynamics of contracted muscle as well. A novel theory of sequential multiplication in passive ASM is proposed and implemented in a mathematical model. Experiments and literature validated the model simulations. Further integration of the models and improved force control modelling of length adaptation is proposed for future study. It is likely that the coupling of the models presented here with models describing other airway wall components will provide a more complete picture of airway dynamics, which will be invaluable for understanding respiratory disease.

The current study aims to investigate the relative contributions of each of the processes that govern airway smooth muscle mechanical behaviour. Studies have shown that breathing dynamics have a substantial effect on airway constriction in healthy and diseased subjects, yet little is known about the dynamic response of the main instigator of airway constriction, Airway Smooth Muscle (ASM). In this work several models are developed to further the understanding of ASM dynamics, particularly the roles and interactions of the three dominant processes in the muscle: contractile dynamics, length adaptation and passive dynamics. Three individual models have been developed, each describing a distinct process or structure within the muscle. The first is a contractile model which describes the contractile process and the influence of external excitation on contractile behaviour. The second model incorporates the contractile model to describe length adaptation, which includes the reorganisation and polymerisation of contractile elements in response to length changes. The third model describes the passive behaviour of the muscle, which entails the mechanical behaviour of all non-contractile components and processes. As little data on the passive dynamics of the muscle was available in the literature, a number of experiments were conducted to investigate relaxed ASM dynamics. The experimental data and mathematical modelling showed that passive dynamics plays not only a dominant role in relaxed ASM, but contributes considerably to the dynamics of contracted muscle as well. A novel theory of sequential multiplication in passive ASM is proposed and implemented in a mathematical model. Experiments and literature validated the model simulations. Further integration of the models and improved force control modelling of length adaptation is proposed for future study. It is likely that the coupling of the models presented here with models describing other airway wall components will provide a more complete picture of airway dynamics, which will be invaluable for understanding respiratory disease.