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The Scoop, Vol. 12 Issue 2, May 2025, Health Sciences Library

Volume 12

Latest news and updates from the Health Sciences Library in our monthly newsletter for May 2025. Please see page 2 for a text-only version of this issue!

Applying Universal Design For Learning Strategies To Increase Student Engagement With Your Course Syllabus, Amy Sugar, Amanda Groff

Teaching Online Pedagogical Repository

Apply Universal Design for Learning to syllabi design.

March Madness Plus, Richard C. Crepeau

On Sport and Society

March Madness, the NCAA marketing term, often is anything but. This year in both the women’s and men’s rendition, March Madness spilled over, as it often does into April. The final weekend, featuring the men’s and women’s semi-finals and finals, offered more madness than could have been hoped for.

Injury Prevention Research In Athletes, Maura Tangney

UN SDG Open Pedagogy Faculty Fellowship: Renewable Assignments

No abstract provided.

Feeling Imposter Syndrome And Burnout? Time To Burn It Down And Get Back To Basics, Jordan Ruud, Jason D. Phillips

Faculty Scholarship and Creative Works

Many librarians’ self-doubt is arguably at a high point, leading to a distorted self-image and low morale. Beginning with the foundational problems of impostor syndrome and burnout, this chapter proposes a return to basics as a strategy for overcoming these problems. Librarians and other information professionals besieged by uncertainty around their abilities and status can find a way back to professional fulfillment through “burning it down and starting over” – in essence, demolishing one’s received understanding of one’s role by taking conscious stock of one’s duties, setting reasonable personal goals, recalibrating expectations of oneself, and reconstituting one’s professional identity on …

Comparative Evaluation Of Linear Regression, Cross Validation And Regularization Approaches In Multivariate Data Analysis, Ransford Owusu, Felix Yeboah, Francis Effah Boateng

Data Science and Data Mining

This study evaluates linear regression and its enhanced variants incorporating cross-validation and regularization techniques for high-dimensional, multivariate datasets. We address challenges such as multicollinearity and overfitting. Methods including Ridge, LASSO, and Elastic Net are compared against ordinary least squares regression. Empirical analysis using an automobile dataset for fuel efficiency prediction shows that while OLS regression captures basic relationships, its limitations are mitigated through regularization and cross-validation, resulting in improved model interpretability. The findings provide a comprehensive framework for predictive modeling in complex data environments and offer insights into statistical methodology and practical applications in the automobile industry.

A Report On Health Care Access By The United States Citizens., Kelvin Njuki, Emil Agbemade

Data Science and Data Mining

Access to health care is a critical factor in ensuring public health. This study analyzes data from the National Health Interview Survey (NHIS) for the years 2015–2018 to examine the relationship between health care coverage, affordability, and costs among U.S. families. Re-sults indicate that families with at least one member covered by health insurance were more likely to afford medical care and incur lower health care costs. Despite a high proportion of families with health care coverage during this period, the number of insured family members declined over the years. These findings underscore the importance of health care coverage in …

Exploring Generative Artificial Intelligence (Ai) Applications In Fashion: Ethical Concerns In Human-Like Technology, Melissa Kathryn Gonzalez

Faculty Scholarship and Creative Works

No abstract provided.

Modeling The Relationship Between Calories And Activity Metrics: A Regression Analysis With Variable Selection, Felix Yeboah

Data Science and Data Mining

Physical activity monitors have become integral to daily routines, with wearable devices such as the Apple Watch and Fitbit offering continuous data on users’ physical activity. This study compares the measurement accuracy of these devices by examining how they record parameters relevant to fitness and health. Employing multiple linear regression, we modeled the relationship between calories expended and a set of explanatory variables, including heart rate, steps, distance, age, activity level, weight, and device type. Evaluation of all possible variable combinations identified heart rate, steps, distance, weight, and watch type as the most effective predictors of calorie expenditure. Although the …

Optimized Hiv/Aids Resource Allocation In Ohio: A Linear Programming Approach, Godfred Ahenkroa Kesse

Data Science and Data Mining

This study employs a linear and integer programming approach to optimize HIV resource allocation in Ohio, aiming to minimize new infections and enhance the impact of limited resources. With the advances in HIV prevention and treatment, Ohio faces challenges in addressing disparities in access to healthcare, particularly among high-risk populations. The proposed model integrates data on infection rates, transmission patterns, demographic factors, and cost-effectiveness to provide a decision-support framework for policymakers. Using epidemiological data and equity constraints, the model prioritizes high-risk regions and populations while ensuring fair resource distribution. Results indicate that increased funding allocations significantly enhance the potential to …

At What Cost? The Personal Stakes Of Intellectual Freedom, Jason D. Phillips, Jordan Ruud

Faculty Scholarship and Creative Works

This chapter explores the emotional, professional, and political costs faced by librarians advocating for LGBTQ+ inclusion and intellectual freedom within libraries. Through personal narrative and critical reflection, the authors introduce the concept of "queer battle fatigue"—a framework for understanding the cumulative psychological toll on LGBTQ+ individuals who are consistently placed in advocacy roles within predominantly cisheteronormative institutions. A case study of a drag storytime event at a university library illustrates the social backlash, institutional pressures, and moral injury experienced by organizers, demonstrating how well-intentioned programming can become flashpoints for political controversy. The authors highlight the intensifying hostility toward LGBTQ+ representation …

Enemies Within The Gates: Contending With Internal Censorship Challenges, Jason D. Phillips

Faculty Scholarship and Creative Works

This chapter explores the rarely discussed but impactful issue of internal censorship within academic libraries, focusing on challenges to LGBTQ+ materials. Drawing on personal experiences at a regional public university in the American South, the author recounts multiple instances where library staff and leadership acted in ways that undermined the inclusion of LGBTQ+ resources. These incidents ranged from quietly removing or hiding books to resisting the addition of LGBTQ+ periodicals and graphic novels, and even denying purchase requests without explanation. The chapter underscores how fear of controversy, personal bias, and institutional conservatism can result in "soft censorship," despite the profession's …

Ai-Integrated Cross-Disciplinary Liberal Arts Colloquium, Diana S. Perdue, Niloofar Gholamrezaei, Jennifer Krusinger, Shannon Hogan

Teaching Repository of AI-Infused Learning

What began organically as an AI-focused collaboration between faculty in different disciplines along with researchers in the college’s Center for Instructional Innovation, has evolved into college-wide pedagogical workshops and the development of a studio model that affects courses, programs, and paradigms for faculty scholarship.

Lessons from these collaborations inform understandings of how key features of liberal arts education can be supported through intentional AI-integration within a Cross-Disciplinary Liberal Arts Colloquium. More specifically, this colloquium offers recommendations on adapting a collaborative, interdisciplinary, and reflective studio model to in-person, online, and hybrid educational environments facilitated through a Technological Pedagogical Content Knowledge (TPACK) …

Uncovering Acoustic Biomarkers To Classify Parkinson Disease Through Machine Learning, Felix Yeboah

Data Science and Data Mining

The early detection of diseases profoundly influences treatment efficacy, and accurate classification methodologies are essential for effective disease identification. In this project, we examined fve different classifers—Logistic Regression, Gaussian Naive Bayes, K Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost), and Support Vector Machines—and evaluated their performance in detecting Parkinson’s disease (PD) based on voice features. The study aims to identify the best classifier for detecting PD. XGBoost performed the best, with an accuracy of 91% on the full dataset. After variable selection, KNN had the best performance with an accuracy of 91%. These findings suggest that Machine learning algorithms(classifiers) can …

Evaluation Of Variable Selection Techniques On The Genetic Architecture Of Flowering Time In Maize, Felix Yeboah

Data Science and Data Mining

In this project, we investigate several variable selection procedures to give an overview of how well they perform on a genomic dataset using three different penalized regression approaches. Comparisons between different methods were performed. These methods include Ridge, lasso, and Elastic Net. We utilized 4494 observations with 7389 SNPs gene scores to predict time to male flowering (dtoa). We assessed the performance of these three models in terms of mean square error. Not surprisingly, Lasso and Elastic Net perform better than Ridge Regression. Overall, Elastic Net performed better in predicting the time of male flowering (dtoa).

Comparison Of Two Strategies Of Screening Experiments: Single-Shot Experiment Vs. Two-Stage Screening Experiment, Kelvin Njuki, Emil Agbemade

Data Science and Data Mining

Experiments involving many factors are often complex, time-consuming, and expensive. Screening out the least important factors helps the experimenter(s) allocate the limited resources efciently to the most important factors. Supersaturated and orthogonal array designs are among the designs used to conduct screening experiments. Supersaturated designs (SSDs) are those where the number of runs (observations) is less than the number of factors, while orthogonal array (OA) designs are those where at least the columns are orthogonal to each other. In this study, we conduct a simulation study to compare two strategies of screening experiments. Strategy one is a single shot experiment …

Advanced Machine Learning Techniques For Cardiovascular Disease Risk Prediction, Godfred Ahenkroa Kesse

Data Science and Data Mining

of mortality, necessitating advanced predictive models to aid early detection and prevention. This study explores the application of machine learning techniques, including Lo- gistic Regression, K-Nearest Neighbors (KNN), Random Forest, and XGBoost, to predict CVD risk using a dataset of 69,997 observations encompassing demographic, clinical, and lifestyle factors. Data preprocessing involved one-hot encoding of cat- egorical variables and scaling to ensure compatibility with all models. Model performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Among the models, XGBoost demonstrated the highest accuracy at 74%, leveraging its gradient-boosting framework to effectively handle feature interactions and imbalanced …

Predicting Blood Glucose Levels: A Linear Regression Approach For Non-Invasive Monitoring, Godfred Ahenkroa Kesse

Data Science and Data Mining

Accurate monitoring of blood glucose levels is vital for the management of diabetes, a chronic condition affecting millions worldwide. This study explores a linear regression approach to estimate glucose levels non-invasively using a dataset enriched with demographic, physiological, and sensor-based variables. Following rigorous data preparation, including normalization and encoding, a Box-Cox transformation was applied to address violations of regression assumptions, stabilizing variance and improving model validity. Stepwise selection and hypothesis testing were employed to refne the model, retaining signifcant predictors such as AGE, GENDER, HEARTRATE, and DIABETIC, while excluding variables like NIR Reading and LAST EATEN for their minimal contribution. …

Handwritten Digit Recognition Using Naive Bayes And K-Nearest Neighbor Models, Godfred Ahenkroa Kesse

Data Science and Data Mining

This paper explores the performance of two fundamental classifcation algorithms. It uses Naive Bayes and K-Nearest Neighbors (KNN), framing it within the context of digit recognition of the MNIST dataset. The MNIST dataset has 70,00 grayscale images of handwritten digits, offering a standard for assessing classifcation models. This paper focuses on key performance metrics such as precision, accuracy, recall, and F1score to examine the effciency of each model. The results reveal that Naive Bayes has moderate accuracy and misclassifcations because of its notion of feature independence. The paper concludes that the KNN model performs better with the optimal k-value of …

Variable Selection Using Lasso Regression, Godfred Ahenkroa Kesse

Data Science and Data Mining

This study employs Lasso regression to analyze highdimensional genetic data for predicting flowering time in maize, specifically Days to Anthesis (DtoA). Lasso, or Least Absolute Shrinkage and Selection Operator, is a form of linear regression that introduces an L1 penalty to the model, encouraging sparsity by shrinking some coefficients to zero. This attribute makes Lasso ideal for feature selection in large datasets, as it highlights the most influential predictors while discarding irrelevant variables. Unlike Ridge regression, which applies an L2 penalty to minimize the squared magnitude of coefficients, Lasso’s L1 penalty induces sparsity, providing a clearer interpretation of the selected …

Classification And Evaluation Of Machine Learning Algorithms On The Mnist Dataset, Felix Yeboah

Data Science and Data Mining

This paper discusses the use of machine learning algorithms in classifying the MNIST handwritten dataset. The MNIST dataset consists of 28x28 grayscale handwritten images with 10 classes from 0 to 9. The dataset was normalized by scaling the pixel values to a range between 0 and 1 by dividing each pixel value by 255. We compare and evaluate the K-nearest Neighbor and Naive Bayes algorithm based on performance metrics such as accuracy, error rate, f1-score, and precision. The K-nearest Neighbor algorithm achieved better performance in all the evaluation criteria.

Ucf Pegasus Plan - 1st Grade - Maps & Globes, Cecilia Nuss, Mackenzie Luley, Jessenia Diaz, Alyssa Arnott, Miranda Howley

Pegasus Plans: Social Studies Unit Plans for Florida Teachers

During this unit, students will explore and create various maps and globes through multimedia resources, Hands-on activities, and assessments. The daily lessons cover topics such as a compass rose, cardinal directions, map titles, and physical features on maps and globes. By the end of this unit, students will be able to identify key elements and locate physical features of maps and globes. Through the use of songs, hands-on activities, games, books, and class discussions, students will be engaged throughout the unit.

Amy Zeh High-Impact: Hope Helps, Yvon Mbenza, Anant Ajvalia, Armando Ramirez, Alan Claiman

High Impact Practices Student Showcase Fall 2025

The purpose of our project was to volunteer with HOPE Helps, a nonprofit organization that provides community support through its thrift store, food pantry, and outreach events. Our group, Project Pegasus, worked onsite to help sort donations, organize items, and assist in the operation of the thrift store. We coordinated efforts with the volunteer manager and staff, who guided us and made sure we understood expectations and procedures. One of the most meaningful activities involved working alongside staff and other volunteers as coworkers to see how teamwork and common purpose create effective service. A key thing we learned was about …

Improving Pcos Through Movement: A Study On Hiit, Strength Training And Women’S Health, Angela Avdella, Eryka Salta

High Impact Practices Student Showcase Fall 2025

This integrative learning project examined how different forms of physical activity—specifically High-Intensity Interval Training (HIIT) and Strength Training (ST)—impact hormonal and physical health in women with Polycystic Ovary Syndrome (PCOS). PCOS is a common hormonal disorder affecting 6–15% of women and is associated with insulin resistance, elevated testosterone levels, increased body fat, and long-term risks such as type 2 diabetes and cardiovascular disease. Because lifestyle interventions are key to PCOS management, this interventional study enumerated exercise as a strategy to improve health outcomes.

The study used was a 12-week intervention design in which participants completed either HIIT or strength training …

Home-Based Exercise Programs For Recovery After Tavr, Nikko Marvin A. Zamora, Bailey Mclain, Devrim Yalcindag, Aisha Khurshid, Michael Ortiz, Cheryl Cavaliere

High Impact Practices Student Showcase Fall 2025

The project titled “Home-Based Exercise Program for Recovery After TAVR” focuses on improving recovery outcomes in patients with heart valve disease, specifically those who have undergone Transcatheter Aortic Valve Replacement (TAVR). The purpose of the project was to evaluate the effectiveness of a home-based exercise program, with and without Cognitive Behavioral Intervention (CBI), compared to an educational control program. The study aimed to enhance patients’ physical functionality, quality of life, and recovery time after TAVR—an important consideration for older adults who may not be eligible for open-heart surgery.

Key activities included interpreting a clinical trial. To be able to look …

Facilitation Of Historic Preservation In Parramore, Aiyana Gross

High Impact Practices Student Showcase Fall 2025

The Parramore Speaks project is an initiative hosted by the UCF Public History Department designed to give agency back to the resident’s of Parramore by putting the preservation of their history back into their hands. Through a collaborated with the City of Orlando, RICHES and the PHC Internship, I had the unique opportunity to be a part of a team dedicated to preserving and sharing the endangered local histories of communities located here in Central Florida. I worked personally with the oral interviews and transcriptions for individuals such as Reggie McGill, Alzo J. Reddick, Walter Hawkins and Paul Perkins, tirelessly …

Market Signals And Price Setting: Unveiling Barometric Price Leader In The Hotel Sector, Sung W. Lee, Donna Quadri-Felitti, Ami Sharma, Seoki Lee

Journal of Hospitality Financial Management

This paper introduces the concept of barometric price leadership to the hotel sector, where a leading hotel sets prices that reflect demand and supply trends, guiding other hotels in the market to set their prices. While the significance of competitor-based pricing has been rec- ognized, the strategic influence of such price leaders on rivals' pricing has not been thor- oughly examined. This conceptual study delves into the existing literature to discuss the underpinnings and implications of such leadership, illustrating how prices portray vital mar- ket information. The study argues for the recognition and strategic importance of barometric price leaders in …

Iactor: Integrating Flow Theory And Habit Principles To Combat Decision Fatigue In Audition Management, Colby L. Bell

Graduate Thesis and Dissertation post-2024

For many actors, the audition process can feel like a never-ending series of decisions—what to prepare, what to wear, what to bring, and how to keep track of it all. Each choice, though seemingly small, accumulates into a form of cognitive overload that can lead to burnout and disengagement. My fascination lies not only with how actors navigate this process but how we might reframe it. This thesis explores the question: How can I design a digital space that reduces decision fatigue and restores agency to actors preparing for auditions? Drawing from Mihaly Csikszentmihalyi’s Flow Theory, James Clear’s Atomic Habits …

Study Of The Superconducting And Normal States Properties Of Topological Nodal-Line Semimetals, Premadasage K. K. Kumarasinghe

Graduate Thesis and Dissertation post-2024

Topologically protected quantum computation is getting great interest due to its potential use in creating fault-tolerant quantum computers. Using quasiparticle excitations known as non-Abelian anyons, which obey non-Abelian braiding statistics, is the way to perform topological quantum computation. A topological superconductor has protected gapless states on its boundaries while maintaining a superconducting gap in the bulk, and predicted to host Majorana fermions which are non-Abelian anyons. Although several theoretical models propose systems with non-Abelian anyons, p_x + ip_y wave superconductors in which vortices exhibit non-Abelian braiding statistics is one of the most realistic physical systems. Realizing physical systems …

The Unindexed Marginalia Of Historical American Cookery Books Digital Public History Project: Domestic Technical Communication Labor Contributions, Alessandra Zinicola Lopez

Graduate Thesis and Dissertation post-2024

This dissertation includes a traditional written portion, and a non-traditional digital humanities public history online digital archive project created through Omeka which showcases examples of hand-written recipes found within commercial cookbooks, called marginalia. The understanding of modern technical communication is affected by the perceived history of the field and currently, that history is biased toward non-domestic pursuits. The project explores what collecting, digitizing, and archiving examples of historical hand-written marginalia found within commercially printed American receipt books indicate about the presence, participatory nature, value, and contributions of domestic technical communications and their user-authors in the 19th century. The examples collected …