Listeria monocytogenes has emerged as a food safety concern for a number of produce commodities. While L. monocytogenes contamination can occur throughout the supply chain, contamination from the packinghouse environment represents a particular challenge and has been linked to recalls. This study aimed to investigate the prevalence, persistence, and diversity of Listeria monocytogenes (LM) and other Listeria species (LS) in produce packinghouses. A longitudinal study was performed in 11 packinghouses (commodities included micro-green, peach, apple, tomato, broccoli, cauliflower, and cucumber) in three US states. In each packinghouse, 34 to 46 sites representing zones 2-4 were selected and swabbed. Packinghouses were visited 4 times and samples were processed for Listeria by US Food and Drug Administration's Bacteriological Analytical Manual methods. Presumptive Listeria-positive isolates were confirmed by PCR. Species and allelic type (AT) were identified by sigB sequencing. Among the 1,584 samples tested, 3.2%, 2.7%, and 0.6% of the samples were positive for LM, LS, and both LM and LS, respectively. Five different species of Listeria were identified with L. monocytogenes being the most prevalent species. A high AT diversity (0.95 Simpson's Diversity Index) was observed amongst Listeria isolates. There were 15 instances of Listeria repeated isolation (site testing positive ≥2 times). Upon analysis of subtype data, only 3 sites tested positive for the same Listeria AT >2 times. Data showed in this longitudinal study that Listeria prevalence and persistence in packinghouses was low (e.g., <4% prevalence). Therefore, sanitation program development and implementation in packinghouses are critical to limit Listeria harborage and residence. / Master of Science in Life Sciences / Listeria monocytogenes is one of the deadliest foodborne pathogens, accounting for about 20% of the deaths caused by foodborne illnesses in the US. Historically, L. monocytogenes has been a big concern for Ready-to-Eat products (ice cream, deli meats, etc.), but in the last decade, there have been several listeriosis outbreaks associated with fresh produce (e.g. cantaloupes, apples, celery, packaged salad) becoming a produce safety concern. Some of these outbreaks have been traced back to the produce farm (pre-harvest) and the operations after harvesting (post-harvest). Though there is research focusing on the prevalence of Listeria in the pre-harvest environment, there is a need for studies investigating Listeria at the post-harvest level. This research project, focused on gaining a better understanding of the prevalence, persistence, and diversity of Listeria (including L. monocytogenes) in produce packinghouses. 11 packinghouses facilities were sampled four times during the packing season. The samples were obtained from different stationary (e.g. walls, drains, floors) and moving (e.g. bins, forklifts, pallets) non-food contact surfaces and equipment during operation hours. Isolates were processed to detect and isolate Listeria species (including L. monocytogenes). Listeria isolates were confirmed and fingerprinted. Listeria prevalence in these packinghouses was low (6.4%), and it varied among packinghouses. Drains, cold storages, and wet non-food contact surfaces were the sites with the highest Listeria prevalence. There were 3 cases of Listeria repeated isolation (same Listeria detected in the same site in at least 2 of the 4 visits). The diversity of Listeria in these packinghouses was high. The information gathered through this research provides a better understanding of where and what species of Listeria can be found in a produce packinghouse iv facility. This knowledge may be used to develop and implement mitigation strategies and interventions to control and/or reduce the risk of Listeria contamination in produce packinghouses.

Neutrino physics has entered a precision era in which understanding backgrounds and systematic uncertainties is particularly important. With a precise understanding of neutrino physics, we can better understand neutrino sources. In this work, we demonstrate dependency of single detector oscillation experiments on reactor neutrino flux model. We fit the largest reactor neutrino flux model error, weak magnetism, using data from experiments. We use reactor burn-up simulations in combination with a reactor neutrino flux model to demonstrate the capability of a neutrino detector to measure the power, burn-up, and plutonium content of a nuclear reactor. In particular, North Korean reactors are examined prior to the 1994 nuclear crisis and waste removal detection is examined at the Iranian reactor. The strength of a neutrino detector is that it can acquire data without the need to shut the reactor down. We also simulate tau neutrino interactions to determine backgrounds to muon neutrino and electron neutrino measurements in neutrino factory experiments. / Ph. D.

Patient wait times and care service times are key performance measures for care processes in hospitals. Managing the quality of care delivered by these processes in real-time is challenging. A key challenge is to correlate source medical events to infer the care process states that define patient wait times and care service times. Commercially available complex event processing engines do not have built in support for the concept of care process state. This makes it unnecessarily complex to define and maintain rules for inferring states from source medical events in a care process. Another challenge is how to present the data in a real-time BI dashboard and the underlying data model to use to support this BI dashboard. Data representation architecture can potentially lead to delays in processing and presenting the data in the BI dashboard. In this research, we have investigated the problem of real-time monitoring of care processes, performed a gap analysis of current information system support for it, researched and assessed available technologies, and shown how to most effectively leverage event driven and BI architectures when building information support for real-time monitoring of care processes. We introduce a state monitoring engine for inferring and managing states based on an application model for care process monitoring. A BI architecture is also leveraged for the data model to support the real-time data processing and reporting requirements of the application’s portal. The research is validated with a case study to create a real-time care process monitoring application for an Acute Coronary Syndrome (ACS) clinical pathway in collaboration with IBM and Osler hospital. The research methodology is based on design-oriented research.

Patient wait times and care service times are key performance measures for care processes in hospitals. Managing the quality of care delivered by these processes in real-time is challenging. A key challenge is to correlate source medical events to infer the care process states that define patient wait times and care service times. Commercially available complex event processing engines do not have built in support for the concept of care process state. This makes it unnecessarily complex to define and maintain rules for inferring states from source medical events in a care process. Another challenge is how to present the data in a real-time BI dashboard and the underlying data model to use to support this BI dashboard. Data representation architecture can potentially lead to delays in processing and presenting the data in the BI dashboard. In this research, we have investigated the problem of real-time monitoring of care processes, performed a gap analysis of current information system support for it, researched and assessed available technologies, and shown how to most effectively leverage event driven and BI architectures when building information support for real-time monitoring of care processes. We introduce a state monitoring engine for inferring and managing states based on an application model for care process monitoring. A BI architecture is also leveraged for the data model to support the real-time data processing and reporting requirements of the application’s portal. The research is validated with a case study to create a real-time care process monitoring application for an Acute Coronary Syndrome (ACS) clinical pathway in collaboration with IBM and Osler hospital. The research methodology is based on design-oriented research.

<p>U disertaciji je dat prikaz procesa monitoringa medija u uslovima delimične digitalizacije štampanih medija, koji se, u najvećem meri, zasniva na procesu pres klipinga. Realizovana je njegova optimizacija metodom lineranog programiranja (LP- Linear Programing), metodom celobrojnog lineranog programiranja (ILP- Integer Linear Programing) i metodom grananja i ograničavanja (Branch and Bound). U optimizaciji je korišćen softver CPLEX. Optimizacijom je dobijen globalni optimum raspodele zadataka među zaposlenima; smanjenja broja zaposlenih u radnim smenama i ravnomernog skraćenja radne smene u slučajevima manjeg broja radnih zadataka.</p> / <p>In this dissertation the process of media monitoring in case of incomplete digitalization of printed media is discussed. According to this model, media monitoring service is mainly based on monitoring of news in the printed media - press clipping. Considering that the monitoring of the printed media is complex process, we realized optimization of media monitoring process by using Linear Programming (LP), Integer Linear Programming (ILP), as well as Branch and Bound method. The optimization software CPLEX is used. The global optimum of tasks distribution among employees; reducing number of employees working in shifts and balanced shortening of the work shift in case of a small number of tasks is obtained.</p>

Durante muito tempo as estruturas de concreto foram consideradas livres de manutenção, ou seja, estruturas quase eternas a menos que fossem sobrecarregadas ou destruídas intencionalmente ou por acidente. Esse pensamento caiu por terra nas últimas décadas quando várias estruturas de concreto começaram a apresentar problemas degradando-se bem mais rápido que o esperado. O monitoramento estrutural nasceu da necessidade de acessar a condição de segurança de uma estrutura de forma mais rápida, precisa e com o benefício de criar uma base de dados sobre a estrutura monitorada. O monitoramento de edifícios ainda é um assunto pouco abordado nas pesquisas e publicações. O alto custo agregado às pontes, viadutos e outras grandes estruturas atraem o interesse de pesquisadores e fabricantes de equipamentos. O problema escolhido como alvo no monitoramento a ser desenvolvido foi a degradação de blocos de fundações em concreto armado com reação álcali-agregado (RAA) pela dificuldade de acesso em inspeções. Uma simulação da expansão causado por RAA em concreto foi desenvolvida para experimentos com sensores e técnicas de monitoramento de fundações. No experimento foram testadas aplicações de strain gauges na armadura, na superfície através de um long gauge adaptado e LVDTs (linear variable differential transformer) aplicados na superfície. A ancoragem dos long gauges não suportou o esforço, impedindo a correta avaliação do sensor. Os LVDTs têm grande potencial para acompanhar o desenvolvimento de fissuras, mas sofrem depois de alguma expansão, pois se desalinham com a superfície do concreto. Os strain gauges aplicados na armadura mostraram boa relação com a expansão medida e têm grande potencial no monitoramento. O processo de decisão e projeto do monitoramento para os blocos é explicado e definido. Uma proposta de aplicação do conceito de estrutura tolerante a dano aos blocos de fundação com RAA é dada para otimização de planos de manutenção e intervenções. / For a long time, concrete structures were considered \"maintenance-free\", i.e. quasi eternal, unless they were overloaded or destroyed intentionally or by accident. This thought has been reconsidered in recent decades when several concrete structures began to present problems and degraded much faster than expected. Structural monitoring has been originated from the need to access the safety condition of a structure more quickly, accurately and with the benefit of creating a database of the structure monitored. The high cost related to bridges, overpasses and other large structures still attracts the interest of researchers and equipment manufacturers. However, the monitoring of buildings is still a subject rarely addressed in research and publications. The problem chosen to be addressed in this research is the degradation of reinforced concrete foundation blocks by alkali-aggregate reaction (AAR) due to difficult access during inspections and unpredictable development of the problem. A review on the sensors available on the market and on monitoring techniques used in other structures with the problem was undertaken. A simulation of the AAR`s expansion in concrete has been developed for an experiment with sensors and monitoring techniques of foundations. In this experiment, strain gauges were applied on the reinforcing bars and adapted on a long gauge on the surface. LVDTs (linear variable differential transformer) were also applied on the surface. The anchoring of the long gauges did not support the applied tension, preventing a correct evaluation of the sensor. LVDTs have great potential to monitor the development of cracks, but after the concrete suffering some expansion, they get misaligned with the surface. The strain gauges used in the armor showed a good relation with the expansion and have great potential in monitoring. The whole decision and design process for the structural health monitoring (SHM) for AAR attacked foundation blocks is explained and the project is defined. A proposal of a damage-tolerant approach for the block is given for optimization of maintenance and interventions.

Two important issues related to nuclear materials safeguards are the continuous monitoring of nuclear processing facilities to verify that undeclared uranium is not processed or enriched and to verify that declared uranium is accounted for. The International Atomic Energy Agency (IAEA) is tasked with ensuring special nuclear facilities are operating as declared and that proper material safeguards have been followed. Traditional safeguards measures have relied on IAEA personnel inspecting each facility and verifying material with authenticated instrumentation. In newer facilities most plant instrumentation data are collected electronically and stored in a central computer. Facilities collect this information for a variety of reasons, most notably for process optimization and monitoring. The field of process monitoring has grown significantly over the past decades, and techniques have been developed to detect and identify changes and to improve reliability and safety. Several of these techniques can also be applied to international and domestic safeguards. This dissertation introduces a safeguards monitoring system developed for both a simulated Uranium blend down facility, and a water-processing facility at the Oak Ridge National Laboratory. For the simulated facility, a safeguards monitoring system is developed using an Auto-Associative Kernel Regression model, and the effects of incorporating facility specific radiation sensors and preprocessing the data are examined. The best safeguards model was able to detect diversions as small as 1.1%. For the ORNL facility, a load cell monitoring system was developed. This monitoring system provides an inspector with an efficient way to identify undeclared activity and to identify atypical facility operation, included diversions as small as 0.1 kg. The system also provides a foundation for an on-line safeguards monitoring approach where inspectors remotely facility data to draw safeguards conclusion, possibly reducing the needed frequency and duration of a traditional inspection.

"Professional paper presented in partial fulfillment of the requirements for the degree of Master of Science in Environmental Studies, the University of Montana, Missoula, MT, fall 2006." / Title from PDF t.p. (viewed on Feb. 26, 2007). Includes bibliographical references (p. 77-90).

Behavioural symptoms, cognitive deficits, and findings from electrophysiological, neuroimaging and genetic studies all suggest atypical performance monitoring in ADHD. Performance monitoring involves error detection and post-error behavioural adjustment and is crucial to behavioural self-regulation and reinforcement learning, both of which are dysfunctional in ADHD. Therefore, post-error slowing was examined in children with ADHD and controls using a modified flanker task both with, and without, error detection provided. There was a significant main effect of group on post-error slowing across conditions and when error-detection was provided, significant post-error slowing deficits were found in children with ADHD. These findings suggest that the performance monitoring deficit in ADHD is specific to post-error behavioural adjustment and supports the inclusion of this deficit in the neurocognitive profile of ADHD. Findings are discussed in terms of current neurocognitive reinforcement learning models of ADHD.

Behavioural symptoms, cognitive deficits, and findings from electrophysiological, neuroimaging and genetic studies all suggest atypical performance monitoring in ADHD. Performance monitoring involves error detection and post-error behavioural adjustment and is crucial to behavioural self-regulation and reinforcement learning, both of which are dysfunctional in ADHD. Therefore, post-error slowing was examined in children with ADHD and controls using a modified flanker task both with, and without, error detection provided. There was a significant main effect of group on post-error slowing across conditions and when error-detection was provided, significant post-error slowing deficits were found in children with ADHD. These findings suggest that the performance monitoring deficit in ADHD is specific to post-error behavioural adjustment and supports the inclusion of this deficit in the neurocognitive profile of ADHD. Findings are discussed in terms of current neurocognitive reinforcement learning models of ADHD.