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This project implemented novel approaches to assess the source, age, concentration and mobility of fecal indicator microorganisms within a karst groundwater system. Research was conducted in the well-characterized Blue Hole Spring karst groundwater basin in Versailles, Woodford County, Kentucky. At this site the AC/TC ratio and fecal coliform (FC) bacteria counts were used to delineate sources of fecal inputs and determine relative age of the fecal matter. An aging experiment using indicator bacteria (total coliform (TC) and atypical colonies (AC)), which approximated subsurface conditions, indicated that changes in the AC/TC ratio are likely to be retarded during bacterial transport through karst conduits. Decreases in the AC/TC ratio during the monitoring period appear to be the result of sewage releases. Multiple logistic regression (MLR) modeling was performed to examine correlations between physiochemical parameters and FC concentrations. MLR models using physiochemical parameters correctly predicted “safe for contact” (< 200 cfu/100 mL FC) conditions 65.6% of the time and “unsafe for contact” (> 200 cfu/100 mL FC) conditions 69.2% of the time at Blue Hole Spring. Modeling using other indicators (TC and AC) predicted “safe for contact” conditions 87.5% of the time and “unsafe for contact” conditions 61.5% of the time. A series of tracer tests were performed to compare transport of solute and abiotic particle tracers (rhodamine WT fluorescent dye, bromide and fluorescent bacteria-sized microspheres) and bacteria (15N-enriched wild-type E. coli) within the karst system. The surrogate tracers did not suitably mimic microbial mobility within the basin. Solutes and 15N-enriched E. coli arrived concurrently during storm flow to Blue Hole Spring, whereas microsphere breakthrough corresponded with maximum solute concentrations. The 15Nenriched E. coli exhibited slightly more tailing during storm-flow recession than solute tracers, none of which exhibited remobilization. Microspheres demonstrated remobilization within the conduits that correlated with later increases in discharge related to secondary storm events.

Low volume high value (LVHV) supply chains such as airline manufacturing, power plant construction, and shipbuilding are especially susceptible to risks. These industries are characterized by long lead times and a limited number of suppliers that have both the technical know-how and manufacturing capabilities to deliver the requisite goods and services. Disruptions within the supply chain are common and can cause significant and costly delays. Although supply chain risk management and supply chain reliability are topics that have been studied extensively, most research in these areas focus on high vol- ume supply chains and few studies proactively identify risks. In this research, we develop methodologies to proactively and quantitatively identify and mitigate supply chain risks within LVHV supply chains. First, we propose a framework to model the supply chain system using fault-tree analysis based on the bill of material of the product being sourced. Next, we put forward a set of mathematical optimization models to proactively identify, mitigate, and resource at-risk suppliers in a LVHV supply chain with consideration for a firm’s budgetary constraints. Lastly, we propose a machine learning methodology to quan- tify the risk of an individual procurement using multiple logistic regression and industry available data, which can be used as the primary input to the fault tree when analyzing overall supply chain system risk. Altogether, the novel approaches proposed within this dissertation provide a set of tools for industry practitioners to predict supply chain risks, optimally choose which risks to mitigate, and make better informed decisions with respect to supplier selection and risk mitigation while avoiding costly delays due to disruptions in LVHV supply chains.

The aim of this study was to investigate the epidemiology of Rift Valley fever (RVF) in South Africa. The first part of the study consisted of the compilation of a full history of RVF in South Africa. This was done by compiling all references to outbreaks of the disease in South Africa from all available literature, annual reports, disease reports and animal disease databases. The geographic location and temporal occurrence of each outbreak was recorded as accurately as allowed by the available records. The result was a better and more complete picture than has hitherto been available of the spatial and temporal distribution of RVF for the period 1950, when the disease was first recognised in South Africa, to 2010. Several smaller outbreaks not mentioned in the literature were found. It emerged that large outbreaks occur in the Free State Province, Eastern Cape Province and Northern Cape Province with long periods of absence and smaller outbreaks occur in KwaZulu-Natal, Mpumalanga and Gauteng at more frequent intervals.The second part of the study used the data collected during the first part of the study to determine which climatic and other environmental factors could have played a role in the occurrence of RVF in South Africa. Multiple logistic regression analysis was used to estimate associations between the various potential risk factors and the occurrence of Rift Valley fever.The study found that the El Niño/Southern Oscillation influence on rainfall in South Africa has an effect on the occurrence of RVF in South Africa which is opposite to the effect that has been described for Kenya. A positive Southern Oscillation Index (La Niña) increases the likelihood of a RVF outbreak in South Africa.The study also found that very high rainfall during the summer months (December to February) is an important risk factor for the occurrence of RVF and it confirmed the increased risk of an outbreak where pans and wetlands are present as reported in several articles and disease reports on past outbreaks. Several other factors, such as minimum and maximum temperature were also found to have a statistically significant effect on the occurrence of Rift Valley fever. Copyright / Dissertation (MSc)--University of Pretoria, 2011. / Production Animal Studies / unrestricted

Knowledge of species occurrence is a prerequisite for efficient and effective conservation and management. Unfortunately, knowledge of species occurrence is usually insufficient, so models that use environmental predictors and species occurrence records are used to predict species occurrence. Predicting the occurrence of stream fishes is often difficult because sampling data insufficiently describe species occurrence and important environmental conditions and predictive models insufficiently describe relations between species and environmental conditions. This dissertation 1) examines the sufficiency of fish species occurrence records at four spatial extents in Virginia, 2) compares modeling methods for predicting stream fish occurrence, and 3) assesses relations between species traits and model prediction characteristics. The sufficiency of sampling is infrequently addressed at the large spatial extents at which many management and conservation actions take place. In the first chapter of this dissertation I examine factors that determine the sufficiency of sampling to describe stream fish species richness at four spatial extents across Virginia using sampling simulations. Few regions of Virginia are sufficiently sampled, portending difficulty in accurately predicting fish species occurrence in most regions. The sufficient number of samples is often large and varies among regions and spatial scales, but it can be substantially reduced by reducing errors of sampling omission and increasing the spatial coverage of samples. Many methods are used to predict species occurrence. In the second chapter of this dissertation I compare the accuracy of the predictions of occurrence of seven species in each of three regions using linear discriminant function, generalized linear, classification tree, and artificial neural network statistical models. I also assess the efficacy of stream classification methods for predicting species occurrence. No modeling method proved distinctly superior. Species occurrence data and predictor data quality and quantity limited the success of predictions of stream fish occurrence for all methods. How predictive models are built and applied may be more important than the statistical method used. The accuracy, generality (transferability), and resolution of predictions of species occurrence vary among species. The ability to anticipate and understand variation in prediction characteristics among species can facilitate the proper application of predictions of species occurrence. In the third chapter of this dissertation I describe some conservation implications of relations between predicted occurrence characteristics and species traits for fishes in the upper Tennessee River drainage. Usually weak relations and variation in the strength and direction of relations among families precludes the accurate prediction of predicted occurrence characteristics. Most predictions of species occurrence have insufficient accuracy and resolution to guide conservation decisions at fine spatial grains. Comparison of my results with alternative model predictions and the results of many models described in peer-reviewed journals suggests that this is a common problem. Predictions of species occurrence should be rigorously assessed and cautiously applied to conservation problems. Collectively, the three chapters of this dissertation demonstrate some important limitations of models that are used to predict species occurrence. Model predictions of species occurrence are often used in lieu of sufficient species occurrence data. However, regardless of the method used to predict species occurrence most predictions have relatively low accuracy, generality and resolution. Model predictions of species occurrence can facilitate management and conservation, but they should be rigorously assessed and applied cautiously. / Ph. D.

The microbial communities found in soils are inherently heterogeneous and often exhibit spatial variations on a small scale. Becker et al. (2006) investigate this phenomenon and present statistical analyses to support their findings. In this project, alternative statistical methods and models are considered and employed in a re-analysis of the data from Becker. First, parametric nested random effects models are considered as an alternative to the nonparametric semivariogram models and kriging methods employed by Becker to analyze patterns of spatial variation. Second, multiple logistic regression models are employed to investigate factors influencing microbial community structure as an alternative to the simple logistic models used by Becker. Additionally, the microbial community profile data of Becker were unobservable at several points in the spatial grid. The Becker analysis assumes that the data are missing completely at random and as such have relatively little impact on inference. In this re-analysis, this assumption is investigated and it is shown that the pattern of missingness is correlated with both metabolic potential and spatial coordinates and thus provides useful information that was previously ignored by Becker. Multiple imputation methods are employed to incorporate the information present in the missing data pattern and results are compared with those of Becker.

<p>The use of marketing research panels are a more and more frequently used source of information for studies within many different branches. The purpose of this report is to investigate the demographic composition of panels and compare it with the population of Sweden, a possible change in behaviour of respondents, and if the source of recruitment is the cause of possible differences in study results. The study was commissioned by Norstats Linkoping office. Sources for the data material include Norstat’s recruitment process and their two main panels with different recruitment sources. To enable a deeper investigation of behaviour we also constructed a survey that was sent to 2,714 members of Norstat’s internet panels.</p><p>The statistical analysis includes contingency table analysis, multiple logistic regression, and Poisson regression. The results show that the demographic composition does not fully cover all the aspects of the Swedish population and some groups are less represented than others. The behaviour tends to differ between panel members that have responded to three or less surveys compared to members that have responded to twenty or more surveys. Source of recruitment does not seem to affect the results of studies, but it has some effect on the demographic composition of marketing research panels.</p> / <p>Användandet av paneler som källa vid undersökningar har den senaste tiden blivit en allt vanligare företeelse. Denna rapport har för avsikt att undersöka panelers demografiska sammansättning och överensstämmande med Sveriges befolkning, eventuell ändring av svarsbeteende samt huruvida rekryteringskällan ger upphov till kvalitetsskillnader hos medlemmar i en panel. Företaget Norstat har med sitt kontor i Linköping figurerat som uppdragsgivare till arbetet. Datamaterialet till studien har uppkommit från Norstats rekryteringsprocess samt från företagets två huvudpaneler med olika rekryteringskällor. För att djupare undersöka svarsbeteende konstruerade vi även en enkätundersökning som skickades ut till 2 714 medlemmar i Norstats internetpaneler.</p><p>Den statiska analysen innefattar χ2-test, multipel logistisk regression samt Poissonregression. Resultaten påvisade att den demografiska sammansättningen i panelen inte fullt ut speglade Sveriges befolkning samt att vissa grupper undertäcks i högre utsträckning än andra. Svarsbeteendet hos medlemmar i paneler har en tendens att ändras från det att medlemmen har svarat på en till tre undersökningar, till det att den har svarat på tjugo undersökningar eller fler. Rekryteringskällan till en panel verkar inte ge upphov till några större skillnader i svarsresultat, men däremot finns vissa skillnader i demografisk sammansättning.</p>

Background. Police work is a stressful occupation with frequent exposure to traumatic events and psychological strain from work might increase the risk of burnout. This thesis focuses on patrolling police officers (PPO), who work most of their time in the community and have daily contact with the public. Since police work traditionally is a male coded occupation we assume that there are differences between women and men in burnout as well as experiences from psychosocial work environment. Aim. The overall aim of this thesis is to explore burnout, psychosocial and physical work environment, coping strategies, and stress of conscience when taking gender into consideration among patrolling police officers. Methods. This thesis employs both qualitative and quantitative methods. In Paper I a qualitative approach with narrative interviews was used where male PPO described experiences of traumatic situations when caring for victims of traffic accidents. A convenience sample of nine male PPO from a mid-sized police authority was recruited. Interviews were analyzed using qualitative content analysis. Papers II, III, and IV were based on a cross-sectional survey from a randomly selected sample stratified for gender from all 21 local police authorities in Sweden. In the final sample, 1554 PPOs were invited (778 women, 776 men), response rate was 55% (n=856) in total, 56% for women (n=437) and 53% for men (n=419). The survey included a self-administered questionnaire based on instruments measuring burnout, stress of conscience, psychosocial and physical work environment, and coping. Results. Findings from Paper I were presented in three themes; “being secure with the support system,” “being confident about prior successful actions,” and “being burdened with uncertainty.” Results from Paper II showed high levels of emotional exhaustion (EE), 30% for female PPOs and 26% for male PPOs. High levels of depersonalization (DP) were reported for 52 % of female PPO, corresponding proportions for male were 60%. Multiple logistic regression showed that stress of conscience (SCQ-A), high demand, and organizational climate increased the risk of EE for female PPO. For male PPO stress of conscience (SCQ-A), low control and high demand increased the risk of EE. Independent of gender, stress of conscience (SCQ-A) increased the risk of DP. Psychometric properties of the WOCQ were investigated with exploratory factor analysis and confirmatory factor analysis, a six-factor solution was confirmed. DIF analysis was detected for a third of the items in relation to gender. In Paper IV a block wise hierarchical multiple regression analysis was performed investigating the predictive impact of psychological demand, decision latitude, social support, coping strategies, and stress of conscience on EE as well as DP. Findings revealed that, regardless of gender, risk of EE and DP increased with a troubled conscience amongst the PPO. Conclusion. “Being burdened with uncertainty” in this male-dominated context indicate that the PPO did not feel confident talking about traumatic situations, which might influence their coping strategies when arriving to a similar situation. This finding can be related to Paper II and IV showing that stress of conscience increased the risk of both EE and DP. The associations between troubled conscience and the risk of experiencing both emotional exhaustion and depersonalization indicate that stress of conscience should be considered when studying the influence of the psychosocial work environment on burnout. Results from this study show that the psychosocial work environment is not satisfying and needs improvement for patrolling police officers in Sweden. Further studies including both qualitative and quantitative (longitudinal) methods should be used to improve knowledge in this area to increase conditions for preventive and rehabilitative actions.

The use of marketing research panels are a more and more frequently used source of information for studies within many different branches. The purpose of this report is to investigate the demographic composition of panels and compare it with the population of Sweden, a possible change in behaviour of respondents, and if the source of recruitment is the cause of possible differences in study results. The study was commissioned by Norstats Linkoping office. Sources for the data material include Norstat’s recruitment process and their two main panels with different recruitment sources. To enable a deeper investigation of behaviour we also constructed a survey that was sent to 2,714 members of Norstat’s internet panels. The statistical analysis includes contingency table analysis, multiple logistic regression, and Poisson regression. The results show that the demographic composition does not fully cover all the aspects of the Swedish population and some groups are less represented than others. The behaviour tends to differ between panel members that have responded to three or less surveys compared to members that have responded to twenty or more surveys. Source of recruitment does not seem to affect the results of studies, but it has some effect on the demographic composition of marketing research panels. / Användandet av paneler som källa vid undersökningar har den senaste tiden blivit en allt vanligare företeelse. Denna rapport har för avsikt att undersöka panelers demografiska sammansättning och överensstämmande med Sveriges befolkning, eventuell ändring av svarsbeteende samt huruvida rekryteringskällan ger upphov till kvalitetsskillnader hos medlemmar i en panel. Företaget Norstat har med sitt kontor i Linköping figurerat som uppdragsgivare till arbetet. Datamaterialet till studien har uppkommit från Norstats rekryteringsprocess samt från företagets två huvudpaneler med olika rekryteringskällor. För att djupare undersöka svarsbeteende konstruerade vi även en enkätundersökning som skickades ut till 2 714 medlemmar i Norstats internetpaneler. Den statiska analysen innefattar χ2-test, multipel logistisk regression samt Poissonregression. Resultaten påvisade att den demografiska sammansättningen i panelen inte fullt ut speglade Sveriges befolkning samt att vissa grupper undertäcks i högre utsträckning än andra. Svarsbeteendet hos medlemmar i paneler har en tendens att ändras från det att medlemmen har svarat på en till tre undersökningar, till det att den har svarat på tjugo undersökningar eller fler. Rekryteringskällan till en panel verkar inte ge upphov till några större skillnader i svarsresultat, men däremot finns vissa skillnader i demografisk sammansättning.

When we develop and use species distribution models to predict species' current or potential distributions, we are faced with the trade-offs between model generality, precision, and realism. It is important to know how to improve and validate model generality while maintaining good model precision and realism. However, it is difficult for ecologists to evaluate species distribution models using field-sampled data alone because the true species response function to environmental or ecological factors is unknown. Species distribution models should be able to approximate the true characteristics and distributions of species if ecologists want to use them as reliable tools. Simulated data provide the advantage of being able to know the true species-environment relationships and control the causal factors of interest to obtain insights into the effects of these factors on model performance. I used a case study on Bythotrephes longimanus distributions from several hundred Ontario lakes and a simulation study to explore the effects on model performance caused by several factors: the choice of predictor variables, the model evaluation methods, the quantity and quality of the data used for developing models, and the strengths and weaknesses of different species distribution models. Linear discriminant analysis, multiple logistic regression, random forests, and artificial neural networks were compared in both studies. Results based on field data sampled from lakes indicated that the predictive performance of the four models was more variable when developed on abiotic (physical and chemical) conditions alone, whereas the generality of these models improved when including biotic (relevant species) information. When using simulated data, although the overall performance of random forests and artificial neural networks was better than linear discriminant analysis and multiple logistic regression, linear discriminant analysis and multiple logistic regression had relatively good and stable model sensitivity at different sample size and detection limit levels, which may be useful for predicting species presences when data are limited. Random forests performed consistently well at different sample size levels, but was more sensitive to high detection limit. The performance of artificial neural networks was affected by both sample size and detection limit, and it was more sensitive to small sample size.