The goal of this work was to create a program for the animation of algorithms in Silverlight. To develop this Silverlight module, platform .NET and programing language C# were used. This work contains basic information about Silverlight module and similar module named Flash.

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Vitamin D is important to the health of college students. The objective of our study was to measure sun exposure, skin pigmentation, vitamin D intake, and serum 25-hydroxyvitamin D (25[OH]D) in a subset of participants from The Following the Longitudinal Aspects of Student Health (FLASH) Study to determine the best predictors of 25(OH)D status. Participants were college-aged freshman who had their blood drawn in spring (Visit 1) and fall 2010 (Visit 2) at California Polytechnic State University (Cal Poly), San Luis Obispo, CA. (35.3°N). Vitamin D intake was measured using a 28-day food frequency recall questionnaire (specific to vitamin D foods and supplements) while questions specific to the frequency of milk and fish intake were accessed from the FLASH questionnaire. Sun exposure was measured using a 28-day recall questionnaire (time in sun and sun exposure index [SEI]) and questions (frequency of weekday/ weekend exposure) from the FLASH questionnaire. Skin pigmentation was measured using a reflectance spectrophotometer. Serum 25(OH)D was measured at a local pathology lab as measured by an IDS-iSYS. Means (SD) were as follows (n= 40): reflectance of the forehead was 61 (3.5) L* (Lightness) for Visit 1 and 61 (4.3) L* for Visit 2. Vitamin D intake was 308 (234) IU for Visit 1 and 316 (257) IU for Visit 2. Time outside was 81 (44) mins for Visit 1 and 76 (39) mins for Visit 2. Serum 25(OH)D was 85 (24) nmol/L for Visit 1 and 113 (28) nmol/L in Visit 2 which was significantly higher (p < 0.0001). The SEI was 53 (38) body surface area (BSA) exposed (m2) x mins for Visit 1 and 55 (34) m2 x mins Visit 2. Although 90% of participants in Visit 1 and 88% in Visit 2 were below the RDA guidelines for vitamin D intake (600 IU/day), 5% of participants in Visit 1 and none in Visit 2 had serum 25(OH)D serum levels < 50 nmol/L (the recommended level of sufficiency for bone health), demonstrating the importance of sun exposure to vitamin D status in these college students. To determine the strongest predictors of status we used regression analysis to predict serum 25(OH)D with skin reflectance, vitamin D intake, and sun exposure. We found that weekend sun exposure, fish intake, and forehead skin reflectance were the strongest predictors of serum 25(OH)D (R2= 0.50, p= 0.0010) demonstrating that simple questionnaires can help to predict serum 25(OH)D status.

Cancer is a group of deadly diseases, to which one treatment method is radiotherapy. Recent studies indicate advantages of delivering so-called FLASH treatments using ultra-high dose rates (&gt; 40 Gy/s), with a normal tissue sparing FLASH effect. Delivering a high dose in a short time imposes requirements on both the treatment machine and the treatment plan. To see as much of the FLASH effect as possible, the delivery pattern should be optimized, which is the focus of this thesis. The optimization method was applied to 17 lung plans, and the results show that a local-search-based optimization achieves overall good results, achieving a mean FLASH coverage of 31.7 % outside of the CTV after a mean optimization time of 8.75 s. This is faster than published results using a genetic algorithm.

As the world becomes more urbanized and heavy precipitation events increase in frequency and intensity, urban flooding is an emerging concern. Urban flooding is caused when heavy rainfall collects on the landscape, exceeding the capacity of drainage systems to effectively convey runoff. Unlike riverine and coastal flooding, urban flooding occurs frequently, and its risks and impacts are not restricted to areas within floodplains or near bodies of water. The objective of this dissertation is to improve our understanding of urban flooding and our capability to predict it through the development of tools and knowledge to assist with its analysis, modeling, and forecasting. To do this, three research objectives were fulfilled. First, the Stream Hydrology And Rainfall Knowledge System (SHARKS) app was developed to improve upon existing real-time hydrologic and meteorological data retrieval/visualization platforms through the integration of analysis tools to study the hydrologic processes influencing urban flooding. Next, the ability to simulate the hydrologic response of urban watersheds with large storm sewer networks was compared between the fully distributed Gridded Surface/Subsurface Hydrologic Analysis (GSSHA) model and the semi-distributed Storm Water Management Model (SWMM). Finally, the Probabilistic Urban Flash Flood Information Nexus (PUFFIN) application was created to help users evaluate the probability of urban flash flooding and to identify specific infrastructure components at risk through the integration of high-resolution quantitative precipitation forecasting, ensemble forecasting, and hydrologic and hydraulic modeling. The outcomes of this dissertation provide municipalities with tools and knowledge to assist them throughout the process of developing solutions to their site-specific urban flooding issues. Specifically, tools are provided to rapidly analyze and respond to rainfall and streamflow/depth information during intense rain events and to perform retrospective analysis of long-term hydrological processes. Evaluations are included to help guide the selection of hydrologic and hydraulic models for modeling urban flooding, and a new proactive paradigm of probabilistic flash flood guidance for urban areas is introduced. Finally, several potential directions for future work are recommended. / Doctor of Philosophy / As the world becomes more urbanized and heavy precipitation events increase in frequency and intensity, urban flooding is an emerging concern. Urban flooding is caused when heavy rainfall collects on the landscape, exceeding the capacity of drainage systems to effectively convey runoff. Unlike riverine and coastal flooding, urban flooding occurs frequently, and its risks and impacts are not restricted to areas within floodplains or near bodies of water. The objective of this dissertation is to improve our understanding of urban flooding and our capability to predict it through the development of tools and knowledge to assist with its analysis, modeling, and forecasting. To do this, three research objectives were fulfilled. First, the Stream Hydrology And Rainfall Knowledge System (SHARKS) app was developed to improve upon existing real-time hydrologic and meteorological data retrieval/visualization platforms through the integration of analysis tools to study the hydrologic processes influencing urban flooding. Next, the ability to simulate the hydrologic response of urban watersheds with large storm sewer networks was compared between the fully distributed Gridded Surface/Subsurface Hydrologic Analysis (GSSHA) model and the semi-distributed Storm Water Management Model (SWMM). Finally, the Probabilistic Urban Flash Flood Information Nexus (PUFFIN) application was created to help users evaluate the probability of urban flash flooding and to identify specific infrastructure components at risk through the integration of high-resolution quantitative precipitation forecasting, ensemble forecasting, and hydrologic and hydraulic modeling. The outcomes of this dissertation provide municipalities with tools and knowledge to assist them throughout the process of developing solutions to their site-specific urban flooding issues. Specifically, tools are provided to rapidly analyze and respond to rainfall and streamflow/depth information during intense rain events and to perform retrospective analysis of long-term hydrological processes. Evaluations are included to help guide the selection of hydrologic and hydraulic models for modeling urban flooding, and a new proactive paradigm of probabilistic flash flood guidance for urban areas is introduced. Finally, several potential directions for future work are recommended.

Effective prediction of localized flash flood regions for an approaching rainfall event requires an in-depth knowledge of the land surface and stream characteristics of the forecast area. Flash Flood Guidance (FFG) is currently formulated once or twice a day at the county level by River Forecast Centers (RFC) in the U.S. using modeling systems that contain coarse, generalized land and stream characteristics and hydrologic runoff techniques that often are not calibrated for the forecast region of a given National Weather Service (NWS) office. This research investigates the application of embedded geographic information systems (GIS) modeling techniques to generate a localized flash flood model for individual small watersheds at a five minute scale and tests the model using historical case storms to determine its accuracy in the FFG process. This model applies the Soil Conservation Service (SCS) curve number (CN) method and synthetic dimensionless unit hydrograph (UH), and Muskingum stream routing modeling technique to formulate flood characteristics and rapid update FFG for the study area of interest. The end result of this study is a GIS-based Flash Flood Forecasting system for ungaged small watersheds within a study area of the Blacksburg NWS forecast region. This system can then be used by forecasters to assess which watersheds are at higher risk for flooding, how much additional rainfall would be needed to initiate flooding, and when the streams of that region will overflow their banks. Results show that embedding these procedures into GIS is possible and utilizing the GIS interface can be helpful in FFG analysis, but uncertainty in CN and soil moisture can be problematic in effectively simulating the rainfall-runoff process at this greatly enhanced spatial and temporal scale. / Master of Science

Compared with other types of floods, timely and accurately predicting flash floods is particularly challenging due to the small spatiotemporal scales in which the hydrologic and hydraulic processes tend to develop, and to the short lead time between the causative event and the inundation scenario. With continuous increased availability of data and computational power, the interest in applying techniques based on machine learning for hydrologic purposes in the context of operational forecasting has also been increasing. The primary goal of the research activities developed in the context of this thesis is to explore the use of emerging machine learning techniques for enhancing flash flood forecasting. The studies presented start with a review on the state-of-the-art of documented forecasting systems suitable for flash floods, followed by an assessment of the potential of using multiple concurrent precipitation estimates for early prediction of high-discharge scenarios in a flashy catchment. Then, the problem of rapidly producing realistic highresolution flood inundation maps is explored through the use of hybrid machine learning models based on Non-linear AutoRegressive with eXogenous inputs (NARX) and SelfOrganizing Maps (SOM) structures as surrogates of a 2D hydraulic model. In this context, the use of k-fold ensemble is proposed and evaluated as an approach for estimating uncertainties related to the surrogating of a physics-based model. The results indicate that, in a small and flashy catchment, the abstract nature of data processing in machine learning models benefits from the presentation of multiple concurrent precipitation products to perform rainfall-runoff simulations when compared to the business-as-usual single-precipitation approach. Also, it was found that the hybrid NARX-SOM models, previously explored for slowly developing flood scenarios, present acceptable performances for surrogating high-resolution models in rapidly evolving inundation events for the production of both deterministic and probabilistic inundation maps in which uncertainties are adequately estimated. / Thesis / Doctor of Science (PhD) / Flash floods are among the most hazardous and impactful environmental disasters faced by different societies across the globe. The timely adoption of mitigation actions by decision makers and response teams is particularly challenging due to the rapid development of such events after (or even during) the occurrence of an intense rainfall. The short time interval available for response teams imposes a constraint for the direct use of computationally demanding components in real-time forecasting chains. Examples of such are high-resolution 2D hydraulic models based on physics laws, which are capable to produce valuable flood inundation maps dynamically. This research explores the potential of using machine learning models to reproduce the behavior of hydraulic models designed to simulate the evolution of flood inundation maps in a configuration suitable for operational flash flood forecasting application. Contributions of this thesis include (1) a comprehensive literature review on the recent advances and approaches adopted in operational flash flood forecasting systems with the identification and the highlighting of the main research gaps on this topic, (2) the identification of evidences that machine learning models have the potential to identify patterns in multiple quantitative precipitation estimates from different sources for enhancing the performance of rainfall-runoff estimation in urban catchments prone to flash floods, (3) the assessment that hybrid data driven structures based on self-organizing maps (SOM) and nonlinear autoregressive with exogenous inputs (NARX), originally proposed for large scale and slow-developing flood scenarios, can be successfully applied on flashy catchments, and (4) the proposal of using k-folding ensemble as a technique to produce probabilistic flood inundation forecasts in which the uncertainty inherent to the surrogating step is represented.

One of the major factors toward the persistence of college freshman with their education as discussed by Vincent Tonto is Social and Academic integration into the life of a university. Social integration is how well the student feels connected to other members of the university community. There has been a significant body of research done on the use of social networks to encourage social integration in a university setting. This project proposes the creation of a synchronous virtual community / social network to not only encourage social integration but also physical integration through use of the network. / Master of Science

Livestock best management practices (BMPs) such as streamside exclusion fencing are installed to reduce cattle impacts on stream water quality such as increases in bacteria through direct deposition and sediment through trampling. The main objective of this study is to assess the effects of different cattle management strategies on water quality. The project site was located near Keezletown, VA encompassing Cub Run and Mountain Valley Road Tributary streams. During two, one-week studies, eight automatic water samplers took two-hour composites for three periods: baseline, cattle access, and recovery. During the cattle access period, livestock were able to enter the riparian zone normally fenced off. Water samples were analyzed for E.coli, sediment, and nutrients to understand the short-term, high-density, or flash grazing, impact on water quality. Additional weekly grab and storm samples were collected. Results show that cattle do not have significant influence on pollutant concentrations except in stream locations where cattle gathered for an extensive period of time. Approximately three cattle in the stream created an increase in turbidity above baseline concentrations. E.coli and TSS concentrations of the impacted sites returned to baseline within approximately 6 to 20 hours of peak concentrations. Weekly samples show that flash grazing does not have a significant influence on pollutant concentrations over a two-year time frame. Sediment loads from storms and a flash grazing event showed similar patterns. Pollutant concentrations through the permanent exclusion fencing reach tended to decrease for weekly and flash grazing samples. / Master of Science

Secure the Shadow is a collection of short-shorts and flash fiction, which draws heavily on the conventions of fables, parables and fairy tales to consider modern themes, desires and cruelties. The collection is linked by a meta-fictional fascination with the act of storytelling -- the liminal psychological space between the real and unreal, fantasy and delusion, seen and unseen, predator and prey. The collection also maps the topography of loss -- it explores what it means, and how it feels, to lose and to be lost. / MFA