Thesis (Ph.D)--Architecture, Georgia Institute of Technology, 2010. / Committee Chair: Godfried Augenbroe; Committee Member: Ellis Johnson; Committee Member: Pieter De Wilde; Committee Member: Ruchi Choudhary; Committee Member: Russell Gentry. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2007. / Yorai Wardi, Committee Member ; Gunter Sharp, Committee Member ; Spiridon Reveliotis, Committee Member ; Leon F. McGinnis, Committee Member ; Chen Zhou, Committee Chair.

Thesis (Ph.D.) - University of Queensland, 2005. / Includes bibliography.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Bovine tuberculosis (bTB) is one of the most complex, persistent and controversial problems facing the British cattle industry. It is also potentially zoonotic and so has public health implications. The incidence of the disease has been increasing in Great Britain for more than 20 years and is now endemic in southwest regions of the country and occurs sporadically elsewhere. Scotland records very few incidences of bTB and was declared as an Officially bTB free (OTF) region in 2009 for the purposes of cattle trading. However, in order to retain its OTF status Scotland must continue to demonstrate the ability to report low level of disease prevalence whilst maintaining its vigilance to potential new outbreaks. This thesis uses a variety of epidemiological and statistical models to evaluate the ongoing control strategies for bTB in Scottish cattle herds and highlight potential limitations to the current surveillance programmes. In the absence of an established wildlife reservoir, livestock movements are considered the primary mechanism for introduction of bTB into cattle herds. I use movement and bTB data to estimate the within-herd incidence rate for each infected farm in Scotland. The results suggest that this rate varies across farms, and is dependent on the herd size and length of disease exposure. These incidence rates are then used to parameterise a multi-herd dynamic model using stochastic simulations that incorporate multiple disease transmission pathways. With this approach I evaluate the impact of different routine test protocols on the overall simulated epidemics. Based on the model outcome, abattoir surveillance alone is not sufficient to maintain infection at a low constant level. Whilst adapting to more frequent routine testing regime can reduce disease incidence, the sensitivity of the surveillance methods can also have a big impact on the long term stability of the disease prevalence and can act as the main barrier to eradicating the disease from low incidence regions. The single intra-dermal comparative cervical tuberculin (SICCT) test used in the current routine herd surveillance relies on stimulating an immune response and observing delayed hypersensitivity reactions in infected animals. The test suffers highly variable, and often poor, sensitivity with current estimates ranging from 50% to 80%. The lower sensitivities may be associated with early stages of infection, concurrent illness, and farm management conditions as well as the presence of sub-clinically infected carriers that can potentially escape detection. In addition, there was evidence that physiological stress can have a marked effect on the immune responses in animals affected with bTB. I conducted two different types of case-control analyses to investigate the potential effect of stress related events on the outcome of the SICCT test. In the first analysis, a matched design is implemented to examine the effect of recent calving on reactivity to the SICCT. SICCT test positive cattle (cases) were matched with test negative (control) animals within the same farm. By selecting herd-mates (i.e. animals within the same herd at the same time), the study aims to control for space and time. Furthermore, animal age and breed were used as additional selection criteria to control for previous exposure period and potential genetic variation to the reaction of SICCT test outcome. Results from a conditional logistic regression model indicated that animals calved within 60 days prior to test were less likely to respond to the SICCT test in comparison to non-recently calved animals, and that this effect was strongest in the first 2 weeks of the post-partum period. In the second analysis, animals identified with gross pathology at post-mortem (TB-like lesion and/or bacteria culture) and that were SICCT test negative within 60 days prior to slaughter (representing false negative) were compared with confirmed test positives (true positives). Results from multivariable logistic regression model suggested that the probability of missed infection by SICCT test increases with age and male cattle have higher odds of being a false negative compared to females. Repeated skin tests within 60 and 120 days, as well as recent movement and parturition, were all statistically associated with false negative test outcome. Under future surveillance systems, these results could be used to adjust the timings of testing relative to calving, movements and previous test occasions in order to minimise the risks of false negative test results. Alternatively, increasing the threshold for reactor definition in animals under these categories could be considered to complement the poor test sensitivity.

This dissertation presents a methodology to simulate the dispersion of water droplets in the air flow typical of an Icing Tunnel. It is based on the understanding the physical parameters that influence the uniformity and the distribution of cloud of droplets in the airflow and to connect them with analytical parameters which may be used to describe the dispersion process. Specifically it investigates the main geometrical and physical parameters contributing to the droplets dispersion at different tunnel operative conditions, finding a consistent numerical approach to reproduce the local droplets dynamic, quantifying the possible limits of commercial CFD methods, pulling out the empirical parameters/constant needing to simulate properly the local conditions and validating the results with calibrated experiment. An overview of the turbulence and multiphase flow theories, considered relevant to the Icing Tunnel environment, is presented as well as basic concepts and terminology of particle dispersion. Taylor’s theory of particle dispersion has been taken as starting point to explain further historical development of discrete phase dispersion. Common methods incorporated in commercial CFD software are explained and relative shortcomings underlined. The local aerodynamic condition within tunnel, which are required to perform the calculation with the Lagrangian particle equation of motions, are generated numerically using different turbulent models and are compared to the historical K-ε model. Verification of the calculation is performed with grid independency studies. Stochastic Separated Flow methods are applied to compute the particle trajectories. The Discrete Random Walk, as described in the literature, has been used to perform particle dispersion analysis. Numerical settings in the code are related to the characteristics of the local turbulent condition such as turbulence intensity and length scales. Cont/d.

O presente trabalho é uma formulação teórica que permite estudar num modelo estocástico, a influência dos erros de classificação na mensuração da prevalência da esquistossomose mansônica. Os erros de classificação são desagregados e identificados como: falhas de leitura por parte do examinador ou preparo inadequado da lâmina; contingências biológicas que possibilitam o aparecimento de ovos não viáveis e a eliminação de ovos contínua por parte dos indivíduos. É apresentada uma solução geral para o problema, bem como soluções para os casos em que se conhece a distribuição de probabilidades do número de ovos de S.mansoni. Uma solução aproximada e independente da forma e dependente dos dois primeiros momentos da distribuição do número de ovos é sugerida. A influência dos erros de classificação pode quantitativamente ser apreciada, através de um conjunto de tabelas elaboradas com diversos valores dos parâmetros intervenientes no problema. / The present paper is a theoretical approach which will, allow studying the influence - in a stochastic model - of errors in classifying the measurement of the prevalence of Schistosomiasis mansoni. The misclassification errors considered are due to: (A) failure of the examiner in either (1) reading or (2) poor technique. (B) biological contingences which will allow for the appearence of (1) sterile eggs, or (2) discontinuity in the elimination of eggs by the carriers. An exact general solution of the problem is presented, as well as solutions for the particular cases in which the probability distribution of S.mansoni eggs counts in known. An approximate solution is suggested, which is independent from the way in which the number of eggs is distributed, but depends upon the first two moments of the probability distribution of the eggs counts. The influence of misclassification errors can be judged in a quantitative way, by means of a set of tables mande up for the different parametric values of the problem.

Intelligent transportation systems aim to provide innovative applications and services relating to traffic management and enable ease of access to information for various system users. The intent to utilize the excessive on-board resources in the transportation system, along with the latest computing resource management technology in conventional clouds, has cultivated the concept of the Vehicular Cloud. Evolved from Vehicular Networks, the vehicular cloud can be formed by vehicles autonomously, and provides a large number of applications and services that can benefit the entire transportation system, as well as drivers, passengers, and pedestrians. However, due to high traffic mobility, the vehicular cloud is built on dynamic physical resources; as a result, it experiences several inherent challenges, which increase the complexity of its implementations. Having a detailed picture of the number of vehicles, as well as their time of availability in a given region through a model, works as a critical stepping stone for enabling vehicular clouds, as well as any other system involving vehicles moving over the traffic network. The number of vehicles represents the amount of computation capabilities available in this region and the navigation time indicates the period of validity for a specific compute node. Therefore, in this thesis, we carry out a comprehensive stochastic analysis of several traffic characteristics related to the implementation of vehicular cloud inside a road segment by adopting proper traffic models. According to the analytical results, we demonstrate the feasibility of running a certain class of applications or services on the vehicular cloud, even for highly dynamic scenarios. Specifically, two kinds of traffic scenarios are modeled: free-flow traffic and queueing-up traffic. We use a macroscopic traffic model to investigate the free-flow traffic and analyze the features such as traffic density, the number of vehicles and their residence time. Also, we utilize the queueing theory to model the queueing-up traffic; the queue length and the waiting time in the queue are analyzed. The results show the boundaries on enabling vehicular cloud, allowing to determine a range of parameters for simulating vehicular clouds.

As in the stock of trading business is allocated a large part of the capital resources, it is necessary to determine the manner of their control. For this purpose a number of models were developed. Before application to the specific case, these models must be properly adjusted to ensure conformity with reality. The aim of this thesis is to optimize the inventory management of electronic commerce. The stochastic model with loss from unfulfilled orders was chosen as default. First, the necessary adjustments were made to the model and defined input parameters. After filling model with real data, the optimum values of the monitored variables were obtained. The last part deals with the influence of changes in input parameters on the optimal value of variables. Use of the model is not limited to this particular case. Without major modifications, the model is also applicable to other similar problems.

The Kardar-Parisi-Zhang (KPZ) equation is a stochastic PDE describing various objects in statistical mechanics such as random interface growth, directed polymers, interacting particle systems. We study large deviations of the KPZ equation, both in the short time and long time regime. We prove the first short time large deviations for the KPZ equation and detects a Gaussian - 5/2 power law crossover in the lower tail rate function. In the long-time regime, we study the upper tail large deviations of the KPZ equation starting from a wide range of initial data and explore how the rate function depends on the initial data. The KPZ equation plays a role as the weak scaling limit of various models in the KPZ universality class. We show the stochastic higher spin six vertex model, a class of models which sit on top of the KPZ integrable systems, converges weakly to the KPZ equation under certain scaling. This extends the weak universality of the KPZ equation. On the other hand, we show that under a different scaling, the stochastic higher spin six vertex model converges to a hyperbolic stochastic PDE called stochastic telegraph equation. One key tool behind the proof of these two stochastic PDE limits is a property called Markov duality.