Carroll Scholars
Carroll Scholars, a service of Corette Library, centralizes, preserves, and provides access to the research, creative scholarship, and unique resources produced and deposited by Carroll College faculty, students, and staff. Carroll Scholars makes these resources easier to find, share, and use. Find out more about Carroll Scholars at https://www.carroll.edu/library/about/carroll-scholars-institutional-repository
Recent Submissions
Item A Machine Learning Approach to Ultrasonic Testing Signal Degradation(2024)Ultrasonic testing (UT) is a form of non-destructive testing used within many disciplines of research and product inspection. This includes the fabrication of research and test reactor fuels. These scans utilize high frequency ultrasonic waves to take measurements to detect cladding thickness and bonding. Generally, the higher the frequency, the greater the precision and resolution, often at the cost of increased noise and artifact detection. However, since the material being scanned must be submerged under water, high level frequencies get dispersed upon contact with the water, thus degrading the signal. Unique challenges are posed by different fuel types, inspection of a monolithic fuel is different than a dispersion fuel. A solution has been researched to solve signal issues found in UT scans of nuclear research reactor fuel plates. The goal of this project is to look at scans where the signal was missing or degraded and reconstruct it based on neighboring successful scans utilizing various machine learning (ML) models. Longer term, the work is anticipated to reduce nuisance detections (i.e. sharp edges, impurities, or noise induced) while maintaining sensitivity to cladding thickness and bonding. This project is still under development, but upon completion will be helpful for long-term data preprocessing and analysis, along with aiding in various non-destructive testing analyses.Item Development and Analysis of Basketball Statistics(2024)The development of basketball statistics has been progressive throughout the history of the National Basketball Association. With the nuances of the game, including motion tracker chips in player jerseys, more game film, and more widespread access to statistics than ever, advanced metrics are more necessary than ever to understand the true impact of players on a game-by-game basis, including shooting efficiency and contributions made on the court. By leveraging data from the Basketball Reference database, we will explore the creation of new statistics to further explain the game of basketball and highlight its best and most efficient players. This exploration led to the creation of three new statistics, Total Point Percentage (TP%), Adjusted Total Point Percentage (ATP%), Adjusted Assist-to-Turnover Ratio (AATR), which we later included with our other statistics into a Principal Component Analysis (PCA) model to find likenesses between players and signals of progression over individual careers.Item Exploring Interactive Math Activities for the Classroom(2024)As the ideas of how best to educate students have evolved, the ideas behind creating engaging math tasks have also changed. Luckily, there has been a significant amount of research on how to develop good math tasks. This paper describes several characteristics of engaging math tasks and justifies each characteristic with research. Then, three example tasks are provided with these characteristics in mind. One task is created for Geometry, one task is created for Algebra 1, and one task is created for Algebra 2.Item Machine Learning for Sports Betting(2024)With sports betting becoming more widely legal, the use of machine learning algorithms for improving an individual’s odds of placing successful sports bets has increased. In general, applying machine learning algorithms comes with challenges such as data selection, feature engineering, and dealing with time series data. In the context of gambling, it also comes with ethical considerations such as the use of such models to gamble, the accuracy of the model, and the transparency of the model. This research focuses specifically on predicting the total combined score of NBA games. This is directly applicable to the Over/Under bet – “Over” if you believe the combined total score will be above the number set by the sportsbook and “Under” if you believe the combined score will be less than the number set by the sports book. The goal of this research is to create a machine learning model that can accurately predict the total combined score of NBA games.Item The Relationship Between Current Crime Rates and Perceived Neighborhood Safety(2024)A high crime rate is often hypothesized as a key contributor to decreased feelings of neighborhood safety (Putrick et al. 2019). Decreased feelings of neighborhood safety have been found to lead to increased health risks and depression (Putrick et al. 2019). Both findings lead to the question of the current study: what is the relationship between current crime rates and perceptions of safety? Previous studies have defined a positive perception of neighborhood safety as a lack of fear, a belief in police performance, and a belief in police legitimacy (Carter et al. 2021). To further clarify this definition for the purpose of this paper, fear is defined as a psychological emotion invoked by environmental factors and the belief that the likelihood of being a victim of crime is high (Zhang et al. 2021). Police performance and legitimacy is defined as trust and reliability in police work. The other variable that is analyzed within this study is current crime rates. Based on previous studies, crime rates were found through local police reports, the Uniform Crime Report (UCR) from the FBI, and the National Incident-Based Reporting System (NIBRS) (FBI 2021; Zhang et al. 2021). Using these variables, this research project aims to understand the impact current crime rates have on the perception of neighborhood safety.
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