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Neural Network Approach to NFL Position Classification
No full textWith an ever-increasing captivation of the United States sports-viewing audience, the National Football League continues to produce some of the world���s most capable, physical athletes. In this work, athletes��� positions C, OG, OT, DE, and DT were categorized as on the line, while the remaining positions were categorized as not on the line. In this work, a predictive neural network is applied to classify 2,022 National Football League players into the two classifications using scouting combine data of height, weight, and 40-Yard dash time, outperforming the current standard logistic regression. The two measures utilized to compare the strength of the methods were total accuracy and area under ROC curve, with the neural network outperforming the logistic model in both. In terms of total accuracy, the neural network had an accuracy of 0.914 to the logistic model���s 0.907, and in terms of area under ROC curve, the neural network had an area of 0.958 compared to the logistic model���s 0.957. As a head-to-head iteration-wise comparison, the neural network had a winning Win-Loss-Tie ratio of 7-0-3 and 8-2-0 in the two measures respectively
Bayesian Methods for Landslide Risk Assessment
No full textLandslide hazard and risk assessment is crucial in understanding and mitigating the adverse impacts of such natural disasters. While essential, traditional assessment approaches often grapple with integrating diverse data and managing inherent uncertainties. This research addresses this gap by exploring the applicability of Bayesian Methods for landslide risk assessment. This study is presented through three distinct yet interconnected applications. The first application delves into the Bayesian probabilistic calibration in the Oregon Coastal Range, emphasizing the importance of observational data in refining parameter estimation and predictions. The second application extends the Bayesian calibration methodology to produce submarine landslide hazard maps, updating probability distributions using observations of undrained shear strength. Finally, the research introduces a novel combination of Bayesian Networks and Geographic Information Systems (BN+GIS) to assess the risk to onshore pipelines from landslides. Through these applications, the research demonstrates the robustness and adaptability of Bayesian methods and presents a sequential and systematic approach to landslide risk assessment. As landslides become an increasing concern due to climatic changes, the Bayesian methodologies presented in this dissertation offer a deeper understanding and practical tools for decision-makers in the context of risk assessment
Statewide 2023 Air Emission Calculations from Wind and Other Renewables VOL I
No full textA report to the Texas Commission on Environmental Quality for the Period January Period January 2023 ��� December 2023
Examining Undergraduate Students��� Sense of Belonging Before and After the Covid-19 Pandemic
No full textThis study examined the factors that predicted undergraduate students��� sense of belonging and compared the results before and after the Covid-19 pandemic. There were significant differences in sense of belonging, with students reporting a lower belonging post-pandemic than pre-pandemic. Further, the findings revealed that campus climate and diversity, interactions with faculty, and satisfaction with academic advising were significant predictors of sense of belonging, with campus climate and diversity having the greatest positive impact. There were also significant differences in sense of belonging based on some student characteristics such as gender, ethnicity, and living on- or off-campus. A multi-group SEM analysis showed that the effect of campus climate and diversity and academic advising satisfaction was invariant before and after the pandemic. However, the relationship between faculty interaction and sense of belonging was non-invariant across the groups and was stronger after the pandemic
Economic Indicators of the College Station-Bryan MSA, January 2024
No full textThe Business-Cycle Index increased by 0.5% from 227 in October to 228 in November 2023. The local unemployment rate decreased to 3.2% in November compared to 3.3% in October. Local nonfarm employment in November increased by 0.05% from October's level. November's inflation-adjusted taxable sales were down by 0.5% from October. For 2022, the new and revised inflation-adjusted Gross Domestic Product (GDP) for College Station-Bryan grew by 16.4% from 2017, where Texas grew 15.3%, and 11.2% for the U.S. over the same period
