Understanding the effect of human-induced pollution on the environment is an important precursor to promoting public health and environmental stability. One aspect of understanding pollution is understanding pollution sources. Various methods have been used and developed to understand pollution sources and the amount of pollution those sources emit. Multivariate receptor modeling seeks to estimate pollution source profiles and pollution emissions from concentrations of pollutants such as particulate matter (PM) in the air. Previous approaches to multivariate receptor modeling make the following two key assumptions: (1) PM measurements are independent and (2) source profiles are constant through time. Notwithstanding these assumptions, the existence of temporal correlation among PM measurements and time-varying source profiles is commonly accepted. In this thesis an approach to multivariate receptor modeling is developed in which the temporal structure of PM measurements is accounted for by modeling source profiles as a time-dependent Dirichlet process. The Dirichlet process (DP) pollution model developed herein is evaluated using several simulated data sets. In the presence of time-varying source profiles, the DP model more accurately estimates source profiles and source contributions than other multivariate receptor model approaches. Additionally, when source profiles are constant through time, the DP model outperforms other pollution receptor models by more accurately estimating source profiles and source contributions.

The full spatial covariance matrix of the multiple input multiple-output (MIMO) channel is an important quantity in channel modeling, communication system signal processing, and performance analysis, and therefore this matrix forms the heart of the research outlined in this dissertation. The work begins with an investigation of a generalized framework for computing the full MIMO spatial covariance based on the power angular spectrum (PAS) of the multipath field and the transmit and receive antenna element radiation patterns. For the case of uniform linear arrays and when the PAS clusters satisfy uniform, truncated Gaussian, or truncated Laplacian distributions, a series expansion is used to allow analytic evaluation of the required integrals in the formulation. The study also demonstrates the validity of some simplifying assumptions used to reduce the complexity of the covariance computation by applying the technique to ray tracing data as well as considers an analysis of the convergence properties of the series when computed using a finite number of terms. The insights and tools obtained from this covariance analysis are then used to develop a general approach for constructing MIMO transmit and receive beamforming vectors based on the full spatial covariance. While transmit and receive beamforming for the MIMO channel is a well-studied topic, when the transmit precoding is based on channel covariance information, developing near-optimal transmit and receive beamformers when the receiver is constrained to use linear processing remains an unsolved problem. This iterative beamforming algorithm presented here can accommodate different types of available channel information and receiver capabilities as well as either a sum power constraint or a per-antenna power constraint. While the latter is more realistic, construction of the optimal transmit precoder is less understood for this constraint. Simulation results based on measured channels demonstrate that the approach generates beamformer solutions whose performance rivals that achieved for an optimal nonlinear receiver architecture.

This dissertation is a systematic study of electric power distribution system reliability evaluation and improvement. Reliability evaluation of electric power systems has traditionally been an integral part of planning and operation. Changes in the electric utility coupled with aging electric apparatus create a need for more realistic techniques for power system reliability modeling. This work presents a reliability evaluation technique that combines set theory and Graph Trace Analysis (GTA). Unlike the traditional Markov approach, this technique provides a fast solution for large system reliability evaluation by managing computer memory efficiently with iterators, assuming a single failure at a time. A reconfiguration for restoration algorithm is also created to enhance the accuracy of the reliability evaluation, considering multiple concurrent failures. As opposed to most restoration simulation methods used in reliability analysis, which convert restoration problems into mathematical models and only can solve radial systems, this new algorithm seeks the reconfiguration solution from topology characteristics of the network itself. As a result the new reconfiguration algorithm can handle systems with loops. In analyzing system reliability, this research takes into account time-varying load patterns, and seeks approaches that are financially justified. An exhaustive search scheme is used to calculate optimal locations for Distributed Generators (DG) from the reliability point of view. A Discrete Ascent Optimal Programming (DAOP) load shifting approach is proposed to provide low cost, reliability improvement solutions. As weather conditions have an important effect on distribution component failure rates, the influence of different types of storms has been incorporated into this study. Storm outage models are created based on ten years' worth of weather and power outage data. An observer is designed to predict the number of outages for an approaching or on going storm. A circuit corridor model is applied to investigate the relationship between power outages and lightning activity. / Ph. D.

This dissertation consists of three chapters. The first chapter titled ``U.S. Grass-fed Beef Price Premiums" examined monthly retail-level price premiums for grass-fed beef (relative to conventional grain-fed beef) in the U.S. from 2014 through 2021. We found that premiums were heterogeneous, with premium cuts (such as sirloin steak, tenderloin, ribeye, and filet mignon) enjoying the highest premiums. Premiums were not consistent with price levels, as the lowest premiums were observed for short ribs, skirt steak, and flank steak. Our findings suggest that grass-fed beef price premiums were negatively affected by the consumption of food away from home. Changes in income, increased information about taste, protein and minerals, fat, revocation of the USDA grass-fed certification program in 2016 and COVID-19 pandemic, also affected premiums for several individual cuts. Premiums were not sensitive to changes in information about climate change. The second chapter, ``Impact of Animal Disease Outbreaks on The U.S. Meat Demand'', examined the impact of the mad cow (BSE) and bird flu (AI) outbreaks on the demand for beef, pork, and broilers in the U.S from 1997 to 2019. Using time-varying elasticities obtained from a Rotterdam model with animal disease cases, we found that BSE outbreaks reduced beef consumption by 0.64 percent and increased pork consumption by 2.34 percent, on average. While BSE outbreaks reduced beef demand, these effects were short lived and did not extend beyond one quarter. On the other hand, broiler consumption decreased during the HPAI outbreaks while beef and broiler consumption increased after such outbreaks. Our time-varying cross-price elasticities indicated that substitution between beef and broilers and beef and pork strengthened after Quarter 4 of 2003. The third chapter is titled ``Impact of North American Mad Cow Disease Outbreaks on The U.S. Cattle Futures". Our study developed a distributional event response model (DERM) framework to show the duration and magnitude of market responses of the U.S. cattle futures market during episodes of mad cow disease (BSE) in North America between 2010 and 2019. Our results indicated that the 2017 U.S. BSE outbreak reduced the returns of live cattle futures. Additionally, the average duration of the BSE event response was about 8.5 days. / Doctor of Philosophy / This dissertation focused on the price analysis of the U.S. livestock market. The first chapter analysed the pattern of grass-fed beef price premiums measured as the difference between grass-fed beef price and conventional beef price. We mainly explored how the premiums were affected by consumers' income, food consumption away from home, and information on climate change, beef taste, and nutrition. We found that consumption of food away from home reduced the grass-fed beef price premiums. In addition, increased information about taste, protein and minerals, fat, and COVID-19 pandemic, could also affected the grass-fed premiums for several individual cuts. The second chapter explored how mad cow diseases and bird flu diseases affected the demand for beef, pork, and chicken. We particularly investigated how each disease outbreak affected the meat demand. My result showed that in the presence of mad cow diseases in the U.S., people bought more pork. This result that retailers should have higher pork demand when mad cow diseases are detected. The third chapter explored how mad cow diseases in North America affected the U.S. live cattle futures. We showed that the U.S. mad cow disease in 2017 reduced the returns of U.S. cattle futures and this impact lasted about 8.5 days. Simultaneously, we found that mad cow disease outbreaks in Canada did not significantly affect the U.S. cattle futures.

Indiana University-Purdue University Indianapolis (IUPUI) / Time-to-event outcomes are widely utilized in medical research. Assessing the cumulative effects of time-varying exposures on time-to-event outcomes poses challenges in statistical modeling. First, exposure status, intensity, or duration may vary over time. Second, exposure effects may be delayed over a latent period, a situation that is not considered in traditional survival models. Third, exposures that occur within a time window may cumulatively in uence an outcome. Fourth, such cumulative exposure effects may be non-linear over exposure latent period. Lastly, exposure-outcome dynamics may differ among groups defined by individuals' characteristics. These challenges have not been adequately addressed in current statistical models. The objective of this dissertation is to provide a novel approach to modeling group-specific dynamics between cumulative timevarying exposures and a time-to-event outcome. A framework of group-specific dynamic models is introduced utilizing functional time-dependent cumulative exposures within an etiologically relevant time window. Penalizedspline time-dependent Cox models are proposed to evaluate group-specific outcome-exposure dynamics through the associations of a time-to-event outcome with functional cumulative exposures and group-by-exposure interactions. Model parameter estimation is achieved by penalized partial likelihood. Hypothesis testing for comparison of group-specific exposure effects is performed by Wald type tests. These models are extended to group-specific non-linear exposure intensity-latency-outcome relationship and group-specific interaction effect from multiple exposures. Extensive simulation studies are conducted and demonstrate satisfactory model performances. The proposed methods are applied to the analyses of group-specific associations between antidepressant use and time to coronary artery disease in a depression-screening cohort using data extracted from electronic medical records.

No description available.

No description available.

<p>  </p> <p>Dyadic play interactions are a cornerstone of early development and difficulty engaging in sustained synchronous interactions are linked to later difficulties with language and joint attention. For children at elevated risk for autism spectrum disorder (ASD), it is unclear if early difficulties in synchronous exchanges could inform later diagnoses. As part of a prospective monitoring study, infant siblings of children with ASD (high-risk group) or typical development (low-risk group), and their mothers completed a standardized play task. Play interactions for infants were evaluated to: (1) assess if early difficulties with social responsiveness or synchrony proceed ASD diagnoses within the first year; (2) explore whether repertoires of observed synchronous behaviors distinguish ASD-risk; and (3) examine whether the unfolding rates of synchrony and responsiveness over continuous time highlight ASD-risk differences. </p> <p><br></p> <p>By 12 months, distinct mean-level differences in synchrony and responsiveness by risk status were observed. Higher synchrony and responsiveness totals were also positively associated with infants later language and cognitive scores and negatively associated with ASD symptom severity (Chapter 2). Although, dyads utilized mostly comparable repertoires of observed synchronous and responsive behaviors, regardless of group membership (Chapter 3). And lastly, the overall rates of unfolding synchrony and responsiveness were fairly stable throughout the interaction. However, distinct patterns by ASD-risk and developmental outcomes were evident (Chapter 4). Ultimately, the encompassed studies did not consistently find robust ASD-specific differences. However, these studies did demonstrate the applicability of advanced methodologies to provide relevant contextual/dyadic elements (beyond the field’s norm of mean-level totals), particularly for infants with non-autism developmental concerns. Future research should build upon these studies to assess synchrony and responsiveness growth curves that extend beyond 12 months of age, as well as utilize behavioral coding approaches that systematically capture both synchronous and asynchronous exchanges.</p>

The catchability coefficient is used in most marine stock assessment models, and is usually assumed to be stationary and density-independent. However, recent research has shown that these assumptions are violated in most fisheries. Violation of these assumptions will cause underestimation of stock declines or recoveries, leading to inappropriate management policies. This project assesses the soundness of stationarity and density independence assumptions using multi-species data for seven stocks and four gears in the U.S. Gulf of Mexico. This study also develops a multi-species methodology to compensate for failures of either assumption. To evaluate catchability assumptions, abundance-at-age was reconstructed and compared with catch-per-unit-effort data in the Gulf. Mixed-effects, Monte Carlo, and bootstrap analyses were applied to estimate time-varying catchability parameters. Gulf data showed large and significant density dependence (0.71, s.e. 0.07, p<0.001) and increasing trends in catchability (2.0% annually compounding, s.e. 0.6%, p < 0.001). Simulation modeling was also used to evaluate the accuracy and precision of seven different single-species and multi-species estimation procedures. Imputing estimates from similar species provided accurate estimates of catchability parameters. Multi-species estimates also improved catchability estimation when compared with the current assumptions of density independence and stationarity. This study shows that multi-species data in the Gulf of Mexico have sufficient quantity and quality to accurately estimate catchability model parameters. This study also emphasizes the importance of estimating density-dependent and density-independent factors simultaneously. Finally, this study shows that multi-species imputation of catchability estimates decreases errors compared with current assumptions, when applied to single-species stock assessment data. / Master of Science

The objective of this research is to develop a framework for multi-robot coordination and control with emphasis on human-swarm and inter-agent interactions. We focus on two problems: in the first we address how to enable a single human operator to externally influence large teams of robots. By directly imposing density functions on the environment, the user is able to abstract away the size of the swarm and manipulate it as a whole, e.g., to achieve specified geometric configurations, or to maneuver it around. In order to pursue this approach, contributions are made to the problem of coverage of time-varying density functions. In the second problem, we address the characterization of inter-agent interactions and enforcement of desired interaction patterns in a provably safe (i.e., collision free) manner, e.g., for achieving rich motion patterns in a shared space, or for mixing of sensor information. We use elements of the braid group, which allows us to symbolically characterize classes of interaction patterns. We further construct a new specification language that allows us to provide rich, temporally-layered specifications to the multi-robot mixing framework, and present algorithms that significantly reduce the search space of specification-satisfying symbols with exactness guarantees. We also synthesize provably safe controllers that generate and track trajectories to satisfy these symbolic inputs. These controllers allow us to find bounds on the amount of safe interactions that can be achieved in a given bounded domain.