Mix networks and anonymity networks provide anonymous communication via relaying, which introduces overhead and increases the end-to-end message delivery delay. In practice overhead and delay must often be low, hence it is important to understand how to optimize anonymity for limited overhead and delay. In this work we address this question under passive traffic analysis attacks, whose goal is to learn the traffic matrix. For our study, we use two anonymity networks: MCrowds, an extension of Crowds, which provides unbounded communication delay and Minstrels, which provides bounded communication delay. We derive exact and approximate analytical expressions for the relationship anonymity for these systems. Using MCrowds and Minstrels we show that, contrary to intuition, increased overhead does not always improve anonymity. We investigate the impact of the system's parameters on anonymity, and the sensitivity anonymity to the misestimation of the number of attackers. / <p>QC 20130522</p>

This dissertation investigated locational risks to ecosystem health associated with proximity to industrial complexes. The study was performed at the behest of ranchers and citizens living and working down-prevailing wind from the Formosa Plastics, Inc. and ALCOA facilities located in Calhoun County, Texas. Concerns expressed were for potential genotoxicity resulting from exposure to complex chemical mixtures released by the facilities. Exposure assessment of the marine environment was performed with sediments and oysters from Lavaca Bay being analyzed. Numerous chemicals were found to be present at concentrations considered likely to result in adverse responses in exposed populations. Bayesian geostatistical analysis was performed to determine if the concentrations were affected by a spatial process. Mercury and polycyclic aromatic hydrocarbons were the most notable of the chemicals found to be present at elevated concentrations and affected by a spatial process. Evaluation of maps generated from spatial modeling revealed that proximity to ALCOA resulted in elevated risks for exposure to harmful concentrations of pollutants. Genotoxicity was measured in two sentinel species. Oysters (Crassostrea virginica) were utilized for evaluation of the marine environment and cattle (Bos taurus and Bos taurus crossbred cattle) were chosen for evaluation of the terrestrial environment. Chromosomal aberration analysis was performed on oyster hematocytes. Analysis of the results failed to demonstrate the presence of an important generalized spatial process but some specific locations close to the ALCOA plant had elevations in this measure of genotoxicity. Stress as measured by the lysosomal destabilization assay was also performed on oyster hematocytes. These results were found to be affected by a significant spatial process with the highest degree of destabilization occurring in close proximity to ALCOA. Genotoxicity in cattle was evaluated with the single cell gel electrophoresis assay and chromosomal aberration analysis. Bayesian geostatistical analyis revealed the presence of important spatial processes. DNA-protein cross-linkage was the most notable with a strong indication of increased damage down-prevailing wind from the industrial complexes. Results indicated that proximity to industrial facilities increased the risk for harmful exposures, genotoxicity, and lysosomal destabilization.

This dissertation investigated locational risks to ecosystem health associated with proximity to industrial complexes. The study was performed at the behest of ranchers and citizens living and working down-prevailing wind from the Formosa Plastics, Inc. and ALCOA facilities located in Calhoun County, Texas. Concerns expressed were for potential genotoxicity resulting from exposure to complex chemical mixtures released by the facilities. Exposure assessment of the marine environment was performed with sediments and oysters from Lavaca Bay being analyzed. Numerous chemicals were found to be present at concentrations considered likely to result in adverse responses in exposed populations. Bayesian geostatistical analysis was performed to determine if the concentrations were affected by a spatial process. Mercury and polycyclic aromatic hydrocarbons were the most notable of the chemicals found to be present at elevated concentrations and affected by a spatial process. Evaluation of maps generated from spatial modeling revealed that proximity to ALCOA resulted in elevated risks for exposure to harmful concentrations of pollutants. Genotoxicity was measured in two sentinel species. Oysters (Crassostrea virginica) were utilized for evaluation of the marine environment and cattle (Bos taurus and Bos taurus crossbred cattle) were chosen for evaluation of the terrestrial environment. Chromosomal aberration analysis was performed on oyster hematocytes. Analysis of the results failed to demonstrate the presence of an important generalized spatial process but some specific locations close to the ALCOA plant had elevations in this measure of genotoxicity. Stress as measured by the lysosomal destabilization assay was also performed on oyster hematocytes. These results were found to be affected by a significant spatial process with the highest degree of destabilization occurring in close proximity to ALCOA. Genotoxicity in cattle was evaluated with the single cell gel electrophoresis assay and chromosomal aberration analysis. Bayesian geostatistical analyis revealed the presence of important spatial processes. DNA-protein cross-linkage was the most notable with a strong indication of increased damage down-prevailing wind from the industrial complexes. Results indicated that proximity to industrial facilities increased the risk for harmful exposures, genotoxicity, and lysosomal destabilization.

There are more than 300 species of extant turtles, the majority of which belong to the Testudinoidea. Here I describe a new box turtle from the Eocene-Oligocene boundary of west Texas. This specimen impacts the phylogeny of Testudinoid turtles by pulling the divergence of extant Testudinoid turtles back in time approximately 25 million years. This results in a need to refocus on paleontological research of Testudinoid turtles into the late Paleogene and early Neogene to identify fossil localities and specimens that can help further elucidate the evolution of the group. New work on the fossil record of turtles also requires a re-evaluation of methods used for identifying and evaluating the evolutionary history of turtles as a group. An implicit assumption over the last 150 years of turtle paleontology was that both turtle shells and turtle heads reveal congruent and complimentary evolutionary relationships. This assumption was never adequately tested. I utilized a series of methods to evaluate the congruency of phylogenetic hypotheses using disparate anatomical regions. Using a dataset of extant Emydid turtles, I evaluated whether turtle shells and turtle heads provided congruent and complimentary phylogenetic hypotheses. My methods employed parsimony-based reconstruction, maximum-likelihood-based reconstruction, and Bayesian-based reconstruction, including Bayesian-partition analyses. My conclusions are that heads and shells do not provide fully congruent topologies, and that in many cases there is a loss of phylogenetic resolution when only turtle sklls are used to generate phylogenies. The implication is that a focus on a robust and complete dataset of anatomical features will provide the best basis for further investigation of fossils. My work also provides a framework for dataset exploration by providing a method to identify the most robust phylogenetic signal found within a dataset. This framework will allow non-turtle paleontologists and systematists the ability to further investigate their own datasets and develop robust hypotheses of evolutionary relationships across the diversity of the tree of Life. / text

The U.S. Army¡¯s Chemical Demilitarization are designed to store, treat and destroy the nation¡¯s aging chemical weapons. It operates Near-Real-Time Monitors and Deport Area Monitoring Systems to detect chemical agent at concentrations before they become dangerous to workers, public health and the environment. CDC recommends that the sampling and analytical methods measure within 25% of the true concentration 95% of the time, and if this criterion is not met the alarm set point or reportable level should be adjusted. Two methods were provided by Army¡¯s Programmatic Laboratory and Monitoring Quality Assurance Plan to evaluate the monitoring systems based on CDC recommendations. This thesis addresses the potential problems associated with these two methods and proposes the Bayesian method in an effort to improve the assessment. Comparison of simulation results indicates that Bayesian method produces a relatively better estimate for verifying monitoring system performance as long as the prior given is correct.

We consider a time series model with autoregressive conditional heteroskedasticity that is subject to changes in regime. The regimes evolve according to a multistate latent Markov switching process with unknown transition probabilities, and it is the constant in the variance process of the innovations that is subject to regime shifts. The joint estimation of the latent process and all model parameters is performed within a Bayesian framework using the method of Markov Chain Monte Carlo simulation. We perform model selection with respect to the number of states and the number of autoregressive parameters in the variance process using Bayes factors and model likelihoods. To this aim, the model likelihood is estimated by combining the candidate's formula with importance sampling. The usefulness of the sampler is demonstrated by applying it to the dataset previously used by Hamilton and Susmel who investigated models with switching autoregressive conditional heteroskedasticity using maximum likelihood methods. The paper concludes with some issues related to maximum likelihood methods, to classical model select ion, and to potential straightforward extensions of the model presented here. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

Excavation activities like open cutting and tunneling work may cause ground movements. Many of these activities are performed in urban areas where many structures and facilities already exist. These activities are close enough to affect adjacent structures. It is therefore important to understand how the ground movements due to excavations influence nearby structures. The goal of the proposed research is to investigate and develop analytical methods for addressing uncertainty during observation-based, adaptive design of deep excavation and tunneling projects. Computational procedures based on a Bayesian probabilistic framework are developed for comparative analysis between observed and predicted soil and structure response during construction phases. This analysis couples the adaptive design capabilities of the observational method with updated reliability indices, to be used in risk-based design decisions. A probabilistic framework is developed to predict three-dimensional deformation profiles due to supported excavations using a semi-empirical approach. The key advantage of this approach for practicing engineers is that an already common semi-empirical chart can be used together with a few additional simple calculations to better evaluate three-dimensional displacement profiles. A reliability analysis framework is also developed to assess the fragility of excavation-induced infrastructure system damage for multiple serviceability limit states. Finally, a reliability analysis of a shallow circular tunnel driven by a pressurized shield in a frictional and cohesive soil is developed to consider the inherent uncertainty in the input parameters and the proposed model. The ultimate limit state for the face stability is considered in the analysis. The probability of failure that exceeding a specified applied pressure at the tunnel face is estimated. Sensitivity and importance measures are computed to identify the key parameters and random variables in the model.

A subfamília Mydinae (Insecta, Diptera, Mydidae) ocorre somente nas Américas e é composta por 12 gêneros e 84 espécies, sendo a grande maioria das espécies de Mydidae pertencentes a essa subfamília. Mydinae é atualmente dividida em quatro tribos: Dolichogastrini, Mydini, Phylomydini e Messiasiini. A monofilia da subfamília, assim como de suas tribos e gêneros, ainda não havia sido testada por análises filogenéticas, o que justifica os objetivos deste trabalho, que são: 1)testar a monofilia da subfamília Mydinae; 2)verificar o relacionamento filogenético dos Mydinae com outras subfamílias de Mydidae; 3)testar a monofilia das tribos, subtribos e gêneros de Mydinae, assim como a monofilia dos grupos de espécies do gênero Mydas; 4)propor uma nova classificação para a subfamília, baseada nos resultados filogenéticos. A partir de dados da morfologia externa de adultos, e também de sequência de DNA do gene COI, dois métodos de análise foram empregados: análises de parcimônia, com pesagem igual dos caracteres, e análises probabilísticas bayesianas. Para cada um dos métodos, foram analisados os dados morfológicos e moleculares separadamente, e também em conjunto. A monofilia de Mydinae, conforme delimitada na classificação vigente, não é corroborada no presente trabalho, em nenhuma das análises. Nas duas análises com dados morfológicos, e na análise bayesiana com dados morfológicos e moleculares, foi recuperado um clado formado por todos os Mydinae (exceto Messiasia wilcoxi) + Paramydas (\'Apiophorinae\'). Dentre as tribos de Mydinae, não foi recuperada a monifilia de Messiassiini e Mydini. Já os gêneros Ceriomydas, Stratiomydas, Phyllomydas e Protomydas foram reconhecidos como mofiléticos. Já os gêneros Baliomydas, Gauromydas, Messiasia e Mydas, não formaram grupos monofiléticos em nenhuma das análises. Neste trabalho, puderam ser testadas as monofilias de quatro dos cinco grupos de espécies de Mydas: clavatus, fulvifrons, interruptus e xanthopterus, sendo o grupo hardyi monotípico. Apenas o grupo interruptus foi recuperado como monofilético, embora seja reconhecido aqui que os caracteres de coloração tradicionalmente utilizados para a separação dos grupos não foram utilizados. A subfamília Apiophorinae, com amostragem de quatro espécies, não foi recuperada como monofilética, com o gênero Eumydas agrupando-se aos Rhopaliinae. A classificação de Mydinae é aqui revisada, porém devido à incerteza razoável quanto ao relacionamento entre alguns grupos, alguns táxons da classificação tradicional foram mantidos, apesar de não serem monofiléticos / The Mydinae (Insecta, Diptera, Mydidae) occur only in the Americas and comprise 12 genera and 84 species, of which the vast majority of mydids occurring in Brazil belonging to this subfamily. Mydinae is currently divided into four tribes: Dolichogastrini, Messiasiini, Mydini and Phylomydini. The monophyly of the subfamily, as well as the monophyly of their tribes and genera, had not yet been tested by phylogenetic analysis. Concerning this fact, the objectives of this work are: 1) test the monophyly of the subfamily Mydinae, 2) check the phylogenetic relationship between Mydinae and other subfamilies of Mydidae, 3) test the monophyly of the tribes, subtribes and genera of Mydinae, as well as the monophyly of the species-groups of the genus Mydas; 4) propose a new classification of the subfamily based on phylogenetic results. The data from the external morphology of adults, and also DNA sequence of the COI gene, two methods of analysis were used: parsimony analysis with equal weighting of characters, and Bayesian probabilistic analysis. For each method, morphological and molecular data were analyzed separately and also in combination. The monophyly of Mydinae, as defined in the current classification, is not borne out in the present study. In both analyzes with morphological data, and Bayesian analysis with morphological and molecular data, a clade formed by all Mydinae (except Messiasia wilcoxi) + Paramydas (\'Apiophorinae\') was recovered. Among the tribes of Mydinae, the monophylies of Messiassiini and Mydini were not recovered. The genera Ceriomydas, Stratiomydas, Phyllomydas and Protomydas are recognized as natural groups. In the other hand, the genera Baliomydas, Gauromydas, Messiasia and Mydas did not form monophyletic groups in any of the conducted analyzes. Concerning the Mydas species-groups, only the interruptus group was recovered as monophyletic, although it is recognized here that color based characters traditionally used for separating the groups were not used in the present work. The subfamily Apiophorinae, with four species sampled, was not recovered as monophyletic, with genus Eumydas grouping to Rhopaliinae. The classification of Mydinae is reviewed here, but due to reasonable uncertainty as to the relationships between some groups, some taxa of the traditional classification were kept, although not recognized as monophyletic

What is the probability that in a fair coin toss game (a simple random walk) we go bankrupt in n steps when there is an initial lead of some known or unknown quantity $m? What is the distribution of the number of steps N that it takes for the lead to vanish? This thesis explores some of the features of this first passage to the origin (FPO) distribution. First, we explore the distribution of N when m is known. Next, we compute the maximum likelihood estimators of m for a fixed n and also the posterior distribution of m when we are given that m follows some known prior distribution.

Freshwater mussels have played an important role in the history of human culture and also in ecosystem functioning. But during the past several decades, the abundance and diversity of mussel species has declined all over the world. To address the urgent need to maintain and restore populations of endangered freshwater mussels, quantitative population dynamics modeling is needed to evaluate population status and guide the management of endangered freshwater mussels. One endangered mussel species, the oyster mussel (Epioblasma capsaeformis), was selected to study its population dynamics for my research. The analysis was based on two datasets, length frequency data from annual surveys conducted at three sites in Clinch River: Wallen Bend (Clinch River Mile 192) from 2004-2010, Frost Ford (CRM 182) from 2005 to 2010 and Swan Island (CRM 172) from 2005 to 2010, and age-length data based on shell thin-sections. Three hypothetical scenarios were assumed in model estimations: (1) constant natural mortality; (2) one constant natural mortality rate for young mussels and another one for adult mussels; (3) age-specific natural mortality. A Bayesian approach was used to analyze the age-structured models and a Bayesian model averaging approach was applied to average the results by weighting each model using the deviance information criterion (DIC). A risk assessment was conducted to evaluate alternative restoration strategies for E. capsaeformis. The results indicated that releasing adult mussels was the quickest way to increase mussel population size and increasing survival and fertility of young mussels was a suitable way to restore mussel populations in the long term. The population of E. capsaeformis at Frost Ford had a lower risk of decline compared with the populations at Wallen Bend and Swan Island. Passive integrated transponder (PIT) tags were applied in my fieldwork to monitor the translocation efficiency of E. capsaeformis and Actinonaias pectorosa at Cleveland Islands (CRM 270.8). Hierarchical Bayesian models were developed to address the individual variability and sex-related differences in growth. In model selection, the model considering individual variability and sex-related differences (if a species has sexual dimorphism) yielded the lowest DIC value. The results from the best model showed that the mean asymptotic length and mean growth rate of female E. capsaeformis were 45.34 mm and 0.279, which were higher than values estimated for males (42.09 mm and 0.216). The mean asymptotic length and mean growth rate for A. pectorosa were 104.2 mm and 0.063, respectively. To test for the existence of individual and sex-related variability in survival and recapture rates, Bayesian models were developed to address the variability in the analysis of the mark-recapture data of E. capsaeformis and A. pectorosa. DIC was used to compare different models. The median survival rates of male E. capsaeformis, female E. capsaeformis and A. pectorosa were high (>87%, >74% and >91%), indicating that the habitat at Cleveland Islands was suitable for these two mussel species within this survey duration. In addition, the median recapture rates for E. capsaeformis and A. pectorosa were >93% and >96%, indicating that the PIT tag technique provided an efficient monitoring approach. According to model comparison results, the non-hierarchical model or the model with sex--related differences (if a species is sexually dimorphic) in survival rate was suggested for analyzing mark-recapture data when sample sizes are small. / Master of Science