Self-Efficacy (SE) has been explored extensively within the field of psychology. Despite a rich literature demonstrating its positive effect on various behavioral outcomes, including psychological treatment outcomes, little is known about the impact of SE on outcomes related to obsessive-compulsive disorder (OCD). To fill this critical gap in the literature, the aim of the present study was to determine whether increasing SE may improve engagement in exposure-based interventions for contamination fears. Nonclinical participants (N = 120) were randomly assigned to complete a contamination-related behavioral approach task (CR-BAT) immediately following either a SE-boosting exercise or a non-SE related control. Results demonstrated that there were no differences between conditions in contamination-related SE or approach behavior during the CRBAT, indicating that the manipulation was ineffective in boosting SE. Interestingly, however, participants in the SE-boosting condition reported lower levels of anxiety during certain phases of the CR-BAT than did those in the control condition, suggesting that the manipulation was effective in reducing subjective distress. Furthermore, SE was positively correlated with approach behavior during the CR-BAT, thus corroborating past research linking SE to behavioral outcomes. Future research, using more effective methods to manipulate SE, is required to examine the causality of this relationship.

The purpose of the present work was three-fold: (1) to determine the number of microorganisms found in root beer ready for consumer used, (2) to study the effects of various temperatures and durations of incubation on this microbial population, and (3) to characterize the predominant species of contaminating bacteria.

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Salmonella has been studied and researched for more than a hundred years and yet it remains a problem for human and animal health. The goal of this dissertation was to apply the systems thinking approach to Salmonella contamination and develop a System Dynamics (SD) simulation model for Salmonella contamination in the chill tank of a poultry processing plant. But first the appropriate carcass rinse sampling method that would not impact on the resulting Salmonella contamination status of the broiler carcass was studied. Kappa agreement analysis was used to evaluate three sampling methods. The adjacent rinse method was found to be the best method. In the absence of actual data, literature data was used to develop a literature-based SD simulation model of Salmonella contamination of broiler carcasses in the chill tank. The literature-based SD model is the first application of system dynamics simulation modeling in the poultry-processing field. The model was able to show and simulate the dynamic and non-linear interrelationships between parameters, namely pH, chlorine (Cl) level, water flow and turbidity. Actual data collection was done using a specially designed apparatus that recorded the time, temperature, pH, chlorine, water flow and turbidity in the chill tank as carcass rinse samples were collected. Linear regression analysis was used to identify the statistically significant models for relationships between the parameters in the chill tank. Finally, the data was analyzed using logistic regression to determine the association between the parameters in the chill tank and the occurrence of Salmonellaa in carcasses exiting the chill tank. These results were used to develop a data-based SD model. The data-based model was then validated using the validity tests proposed by various authors for SD simulation models and found to be a valid model. The developed model offers a fresh perspective to the problem of Salmonella contamination- to view it as a system of factors that are interrelated and have a feedback mechanism, rather than the traditional concept of linear causation. The developed model is a powerful cost efficient tool for testing interventions for reducing Salmonella contamination in the poultry processing plant.

Microwave scatterometers, which use radar backscatter measurements to infer the near-surface wind vector, are unique in their ability to monitor global wind vectors at high resolutions. However, scatterometer observations which are contaminated by land proximity or rain events produce wind estimates which have increased bias and variability, making them unreliable for many applications. Fortunately, the effects of these sources of contamination can be mitigated. Land contamination of backscatter measurements occurs when land partially fills the antenna illumination area. This reduces and masks the wind-induced backscatter signal. Land contamination is mitigated by quantifying the amount of contamination in a single observation using a metric referred to as the land contribution ratio (LCR). LCR levels which give rise to inadmissible levels of error in the wind estimates are determined and used to discard land-contaminated observations. Using this method results in contamination-free wind estimates which can be made as close to the coast as 5 km, an improvement of 25 km compared to previous methods. Rain contamination of scatterometer observations results from rain-induced scattering effects which modify the wind-induced backscatter. Rain backscatter effects are modeled phenomenologically to assess the impact of rain on the observed backscatter. Given the backscatter effects of wind and rain, there are three estimators: wind-only (WO), simultaneous wind and rain (SWR) and rain-only (RO), which have optimal performance in different wind and rain conditions. Rain contamination of wind estimates is mitigated using a new Bayes estimator selection (BES) technique which optimally selects WO, SWR, or RO estimates as they are most appropriate. BES is a novel adaptation of Bayes decision theory to operate on parameter estimates which may have different dimensions. The BES concept is extended to include prior selection and noise reduction techniques which generalizes BES to a wider variety of wind fields and further increase wind estimate accuracy. Overall, BES has wind estimation performance which surpasses that of either the WO or SWR wind estimates individually, and also provides a viable rain-impact flag.

Understanding the space environment and contamination concerns of a spacecraft is critical in designing a successful mission. The ability for a spacecraft to meet its science objectives relies on systems functioning as intended. A concern for maintain- ing performance while on orbit is molecular contamination. Silicones have previously been shown to form a silica layer on their surfaces when exposed to atomic oxygen. For silicone contamination, this translates to a silica film on the contaminated surface. Missions such as Long Duration Exposure Facility and Evaluation of Oxygen Interactions with Materials III have indicated that the silica film can trap deposits of carbon contamination to the surface during its formation. This phenomenon was explored in this research using RTV-S 691 silicone and Braycote 601EF for the carbon-based contaminant. The experiment involved contaminating an aluminum substrate in three different configurations; one for each contaminant individually on the substrate, and one with both contaminants. These samples were exposed to atomic oxygen for a period of 24 hours, then analyzed with Fourier transform infrared spectroscopy. The trends in infrared spectra for the different test cases were characterized for comparison. The trend for samples with a carbon-to-silicone contamination ratio of greater than ten to one showed peaks corresponding to those seen on the singularly contaminated samples. When the concentration of silicone was increased, the trend in spectral results showed peaks corresponding to Braycote before atomic oxygen exposure. At certain concentrations of RTV silicone to Braycote, the trends suggest Braycote is partially protected from atomic oxygen by a silica film. This indicates that silicone conversion to silica in atomic oxygen can trap contaminants to a surface.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

This research studied two predominantly rural counties in Virginia to understand whether residents have equal access to uncontaminated drinking water by socio-economic status. Statistical associations were developed with the total value of each residence based on county tax assessment data as the independent variable to explain levels of nitrate, the presence of bacteria (total coliform and Escherichia coli), and specific household well characteristics (well age, well depth, and treatment). Nearest neighbor analysis and chi-square tests based on land cover classifications were also conducted to evaluate the spatial distribution of contaminated and uncontaminated wells. Based on the results from the 336 samples analyzed in Louisa County, rural residents with private wells may have variable access to household drinking water free of bacteria; particularly if lower-value homes in the community tend to be older with more dated, shallower wells. This study also suggested that, in Louisa County, the presence of water treatment devices was also significantly related to total home value as an index of socio-economic status. Analysis of the 124 samples taken from household wells in Augusta County did not result in any significant associations among selected well characteristics, total home value, and water quality. Lower community participation in Augusta County as a result of a more expensive water quality testing fee may have contributed to the lack of hypothesized relationships in that county's case study. / Master of Science

Water distribution system (WDS) is very critical to human health and societal welfare. Maintaining the quality of the water so that potable water gets distributed to consumers has always been a challenge in the water industry. Deterioration of water quality can happen either accidentally or deliberately and the widespread geography of the water system makes it even more vulnerable to contamination. In this respect, researchers and utilities have some response action to flush out the contaminants when they are detected. But not all networks have reliable sensors to detect the contamination and lack of guidelines for sensor deployment has made the situation even more serious. Given this context, framework for decision-making in the case of WDN against contamination is a much-needed approach. Understanding the capability of the water system to handle the contamination event could provide ample insight on how to better protect the system and how to handle if the contamination does enter the system. In this regard, this study explores the concept of resilience to define the system performance when a disruption occurs, which in this case is the intrusion of contaminants. Resilience of a system can be viewed from different perspectives, each highlighting different aspect of the system. With this insight, the objective of this research is to characterize the resilience of the water system against contamination for multiple aspects of performance or functionalities and use that concept to further elucidate the decision-making process. Hydraulic and quality simulation to emulate the contamination intrusion in WDN is performed by using EPANET-MATLAB Toolkit which has the needed package for both EPANET and EPANET-MSX. EPANET-MSX is widely used for simulating multiple intrusions in the system. The result from the MATLAB simulation gives the quality at each node which is then used to draw the performance time-series curve. Resilience is then computed for each of the performance metrics using the area under the curve method. This study makes a comparison study for multi-dimensional resilience and describes in detail the need of considering the attributes of resilience which are resistance, loss rate, recovery rate, failure duration, and recovery ability. To perceive the concept of resilience with respect to the failure scenarios, a sensitivity analysis was performed for four failure contexts namely, intrusion time, intrusion duration, intruded contaminated mass, and the number of intrusion nodes. Furthermore, a system measure is defined to aggregate different individual resilience to overcome the challenge of multi-objective decision-making. Application of both integrated and multi-dimensional resilience was conducted for optimal sensor placement in the network to maximize the resilience of the whole system. The goal of this thesis is to introduce the multi-dimensional resilience concept as a tool for decision-making based on multiple aspects of system performance by characterizing the WDS resilience and water quality sensor optimization based on different aspects of system functionality under contaminant intrusion events.

京都大学 / 新制・課程博士 / 博士(地球環境学) / 甲第24955号 / 地環博第246号 / 新制||地環||49(附属図書館) / 京都大学大学院地球環境学舎環境マネジメント専攻 / (主査)教授 勝見 武, 准教授 高井 敦史, 教授 越後 信哉 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM