Physical Sciences and Mathematics Commons^™

Sexually Transmitted Infection (Sti) Incidence And Risk Factors Among People With Hiv (Pwh): Insights From A 13-Year Cohort Study In South Carolina, Salome-Joelle Gass, Shujie Chen, Jiajia Zhang Ph.D., Bankole Olatosi

Faculty Publications

The Ending the HIV Epidemic (EHE) initiative aims to reduce new HIV infections by 90% by 2030 in the United States (US). However, rising sexually transmitted infection (STI) rates exacerbate the bidirectional infection risk between HIV and STIs. Most research on STIs among people with HIV (PWH) has focused on high-risk groups, resulting in limited data on broader populations. This study addresses that gap by examining the incidence and risk factors for gonorrhea, chlamydia, and syphilis in a statewide cohort of PWH in South Carolina. Data from South Carolina’s HIV and STI surveillance systems were linked, and all PWH aged …

2025 Acssc Program, Acssc Planning Committee

Annual Celebration for Student Scholarship and Creativity

No abstract provided.

Volume 16, Maggie Duncan, Madeline Little, Alicia Hoffman, Megan Livesay, Gabrielle Quaresma, Serenity Allen, Laina Pfountz, Ainslie Allred, Sabrina Robles, Nicholas J. Duellman, Trinity L. Deguzman, Melissa H. Savage, Margaret Dudley, Jocelyn Escobar, Olivia Hildreth, Olivia Hopkins, Benjamin Gettier, Lee Kassay, Jade Riddle, Ashley Seiders

Incite: The Journal of Undergraduate Scholarship

Introduction Dr. Amorette Barber, Director, Office of Student Research

From the Editor Dr. Hannah Dudley-Shotwell

Artist’s Statement Maggie Duncan

On Mentoring Dr. Lee Millar Bidwell

The Hujum Campaign in Uzbekistan and its Consequences by Madeline Little

Wet Cupping Compared to Dry Needling for Treatment of Patients with Low Back Pain: A Critically Appraised Topic by Alicia Hoffman and Megan Livesay

Optimization of eDNA Air Sampling Via 3D Printed Fan by Gabrielle Quaresma

Beyond the Classroom: A Qualitative Study of Teacher Attrition and Retention by Serenity Allen and Laina Pfountz

The Treatment of Subacromial Impingement Syndrome with Platelet-Rich plasma Injections Verses …

Environmental Risk And Alpha-Gal Syndrome (Ags) In The Mid-Atlantic United States, Brandon D. Hollingsworth Ph.D., Margaret Wiener, Dana A. Giandimenico, Scott P. Commins, Ross M. Boyce

Faculty Publications

Alpha-gal syndrome (AGS), commonly referred to as the tick bite red meat allergy, has been reported worldwide with the number of suspected cases in the United States increasing from 24 in 2009 to over 34,000 in 2019. Within the US, AGS is associated with the bite of two tick species, Amblyomma americanum and Ixodes scapularis, and has particularly high incidence rates in the mid-Atlantic region. Because AGS is associated with tick bites, the risk of developing AGS is affected by the environment individuals visit. Despite this, as well as the numerous studies associating the environment with Am. americanum, …

Speed Of Shear Waves As A Function Of Frequency In Micellar Fluid, Michael Basha

Honors Theses

The purpose of this experiment was to investigate the frequency dependence of shear wave speed in a micellar fluid. A high-concentration CTAB-NaSal micellar fluid seeded with microspheres was used in the experiment. Shear waves were generated using a wave driver and a function generator, while a high-speed camera captured video recordings. Since the frequency of the wave driver producing the shear waves in the micellar fluid is a constant value set by the experimenter, changes in shear wave speed can be determined by measuring their wavelengths at different frequencies.The captured video was then broken down into frames using MATLAB. These …

Ensemble Learning With Explainable Ai For Improved Heart Disease Prediction Based On Multiple Datasets, Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Zhongming Zhao

Faculty, Staff and Student Publications

Heart disease is one of the leading causes of death worldwide. Predicting and detecting heart disease early is crucial, as it allows medical professionals to take appropriate and necessary actions at earlier stages. Healthcare professionals can diagnose cardiac conditions more accurately by applying machine learning technology. This study aimed to enhance heart disease prediction using stacking and voting ensemble methods. Fifteen base models were trained on two different heart disease datasets. After evaluating various combinations, six base models were pipelined to develop ensemble models employing a meta-model (stacking) and a majority vote (voting). The performance of the stacking and voting …

04.21.2025 Ored Connect, Liz Williamson

ORED Newsletter

ORED Stakeholder Meetings

SEC Travel Grants

2025 Academic Conference Program

Holy Cross Undergraduate Academic Conference

No abstract provided.

Data-Driven Survival Modeling For Breast Cancer Prognostics: A Comparative Study With Machine Learning And Traditional Survival Modeling Methods, Theophilus Gyedu Baidoo, Hansapani Rodrigo

School of Mathematical & Statistical Sciences Faculty Publications

Background This investigation delves into the potential application of data-driven survival modeling approaches for prognostic assessments of breast cancer survival. The primary objective is to evaluate and compare the ability of machine learning (ML) models and conventional survival analysis techniques, to identify consistent key predictors of breast cancer survival outcomes.

Methods This study employs data-driven survival modeling approaches to predict breast cancer survival, including survival-specific methods such as the Cox Proportional Hazards (CPH) model, Random Survival Forests (RSF), and Cox Proportional Deep Neural Networks (DeepSurv), as well as machine learning models like Random Forests (RF), XGBoost, Support Vector Machines (SVM) …

Application For Prediction Of Heart Failure; The Next Step In Machine Learning For Healthcare, Amy Adyanthaya, Dawn Bowerman, Rachel Liercke, Robert Slater

SMU Data Science Review

Heart failure (HF) is a serious medical condition affecting approximately 6.7 million U.S. adults and is expected to impact 8.5 million Americans by 2030 [1]. Heart failure is a complicated clinical ailment and characterizes the final course of numerous heart diseases [2]. This paper introduces a machine-learning-based application that utilizes Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and XGBoost models, implemented through the Python Flask framework, to predict HF risk using clinical data. The results indicate high model performance, with precision and recall metrics underscoring the application’s reliability in identifying at-risk patients. By providing real-time, accessible insights, this tool aims …

Research Week 2025, Michael Schueth

Research Week

Welcome to Undergraduate Research Week at Collin College!

This week celebrates the innovative research and scholarly work of our undergraduate students across all disciplines. From our informative webinars to Cultivating Scholars, our student poster presentation, the week offers opportunities to explore new ideas, share discoveries, and connect with fellow researchers and faculty mentors.

We invite all students, faculty, and staff to participate in these events designed to highlight the importance of undergraduate research in academic and professional development. Whether you're presenting your work or simply curious about your peers' projects, your engagement helps foster our college's culture of inquiry and …

Cultivating Scholars 2025, Elizabeth Hamner

Research Week

Cultivating Scholars is an interdisciplinary event promoting undergraduate students and their special interest topics. It is a research-based setting meant to imitate a professional conference. Students work with Faculty Mentors to conduct a high-quality project and present it to the community.

Date: April 24, 2025, 5:30-7:30 p.m.

Place: Collin College, Frisco Campus Conference Center

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning With Biosensor Signals, Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Anand Paul

School of Public Health Faculty Publications

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent complications such as renal failure, cardiovascular disease, and neuropathy. Traditional methods, such as finger-prick testing, often result in low patient adherence due to discomfort, invasiveness, and inconvenience. Consequently, there is an increasing need for non-invasive techniques that provide accurate BGL measurements. Photoplethysmography (PPG), a photosensitive method that detects blood volume variations, has shown promise for non-invasive glucose monitoring. Deep neural networks (DNNs) applied to …

Human Capital And Lifetime Income Gains Of Scaling-Up Small-Quantity Lipid Nutrient Supplements Among Children Under 2 Years: A Modelling Analysis, Nandita Perumal Phd, Goodrarz Danaei, Günther Fink, Mark Lambiris, Christopher R. Sudfeld

Faculty Publications

Undernutrition in early childhood is associated with adverse health and developmental outcomes later in life and remains a persistent global public health problem. Providing small-quantity lipid nutrient supplements (SQ-LNS) to children aged 6-24 months improves child growth and neurodevelopmental outcomes, but the potential long-term benefits to human capital have not been previously estimated. We estimated the potential returns to schooling and lifetime income attributable to increasing coverage of SQ-LNS for children < 2 years of age from 0% to 50% or 90% per five-year birth cohort in five countries (Bangladesh, Burkina Faso, Ethiopia, Pakistan, and Uganda) with a high burden of undernutrition. Random-effects meta-analyses were used to estimate the effect of SQ-LNS on child development using evidence from randomized controlled trials, and to estimate the returns to lifetime income as a function of change in development based on a de novo meta-analysis of observational economic studies. Gains in school years attributable to scaling-up SQ-LNS to 90% coverage ranged from 0.14 million school years (95% uncertainty interval [UI]: 0.064, …

Health Risk Assessment Of Heavy Metal(Loid)S Intake From Beverages In The United States, Hannah T.V. Stoner, Tewodros Rango Godebo, Pornpimol Kodsup Taylor

School of Public Health Faculty Publications

Heavy metals in beverages can pose health risks in an exposure-dependent manner, however, few studies in the United States have evaluated their metal content and health risks. This study determined the concentrations of eight metal(loid)s, As, Al, Cd, Cr, Mn, Ni, Pb, and Zn, in 60 beverages via inductively coupled plasma[sbnd]mass spectrometry (ICPMS). The highest median concentrations (µg/kg) were found in mixed fruit juices for Ni (45.7), Cr (14.8), and As (4.5); tea for Mn (5,300), Al (730), and Pb (1.4); and plant-based milk for Zn (835) and Cd (1.1). Chronic daily intake (CDI) across age groups was calculated using …

04.14.2025 Ored Connect, Liz Williamson

ORED Newsletter

Updated CITI Training Courses

Science Celebration

IHL Prepayment Approval

Ahr Activation At The Air-Blood Barrier Alters Systemic Microrna Release After Inhalation Of Particulate Matter Containing Environmentally Persistent Free Radicals, Ankit Aryal, Ashlyn C. Harmon, Alexandra Noël, Qingzhao Yu, Kurt J. Varner, Tammy R. Dugas

School of Public Health Faculty Publications

Particulate matter containing environmentally persistent free radicals (EPFRs) is formed when organic pollutants are incompletely burned and adsorb to the surface of particles containing redox-active metals. Our prior studies showed that in mice, EPFR inhalation impaired vascular relaxation in a dose- and endothelium-dependent manner. We also observed that activation of the aryl hydrocarbon receptor (AhR) in the alveolar type-II (AT-II) cells that form the air-blood interface stimulates the release of systemic factors that promote endothelial dysfunction in vessels peripheral to the lung. AhR is a recognized regulator of microRNA (miRNA) biogenesis, and miRNA control diverse signaling pathways. We thus hypothesized …

Securing Biometric Data, Alyssa F. Carroll

Cybersecurity Undergraduate Research Showcase

Biometric data has been widely adopted across various sectors, including digital identity, artificial intelligence (AI), border control, digital wallets, and national identification systems. While biometric identifiers—such as fingerprints, retina scans, and facial recognition—offer reliable and convenient authentication, they also raise significant concerns regarding privacy and security. This paper examines how biometric data is stored, the vulnerabilities it faces, and the most effective methods for safeguarding it. By highlighting the critical importance of biometric data protection, this study reviews current research on approaches, strategies, and policies that enhance security while preserving the functionality and efficiency of biometric systems.

Research And Engagement Day Poster

Bryant REDay (Research and Engagement Day)

No abstract provided.

Bryant's Ai Hack-A-Thon

Bryant REDay (Research and Engagement Day)

No abstract provided.

Research And Engagement Day Poster

Bryant REDay (Research and Engagement Day)

No abstract provided.

Bryant University Research And Engagement Day Program

Bryant REDay (Research and Engagement Day)

No abstract provided.

Research And Engagement Day Poster

Bryant REDay (Research and Engagement Day)

No abstract provided.

Prenatal Exposure To Pm2.5 Concentration And Risk Of Preterm Birth In New Mexico; A Seasonal Pattern And Spatio-Temporal Modeling., Onyedikachi J. Okeke

Mathematics & Statistics ETDs

This study investigates the association between prenatal PM2.5 exposure and preterm birth risk in New Mexico (2014–2021). Using descriptive statistics, time series models, and spatial analyses, findings show an average preterm birth rate of 14.87 per 1,000 live births, with moderate correlation (r = 0.727) between PM2.5 levels and very preterm births. While traditional models revealed no significant global effect, spatial methods such as Geographically Weighted Regression (GWR) and Multiscale GWR uncovered strong spatial heterogeneity. PM2.5 effects varied by county (coefficients: -0.00154 to 0.00150), with clustering evident in 2018–2019 (Moran’s I = 0.155–0.174). Results highlight the limitations of global models …

Ai Beliefs And Practices In Community College Classrooms, Julie Eng, Heather Umphlett, Jocelyn Gilchrist, Alicia Howell, Mary Ann Howell, Wendy Miller-Edwards, Laurel Pope

Inquiry: The Journal of the Virginia Community Colleges

The country, the Commonwealth of Virginia, and Virginia Community College System initiatives all promote generative AI within education. It is a topic of great concern and interest for higher education instructors, yet at the same time many faculty members feel uncertain about effectively integrating it into class material. To further add pressure to the AI explosion, community colleges particularly face a responsibility to prepare their students for success in this new AI-focused environment. In this article, seven faculty members from Camp Community College discuss the implications of generative AI programs, including subscriptions and chatbots, within their particular courses and how …

04.07.2025 Ored Connect, Liz Williamson

ORED Newsletter

New ORED Website Goes Live

Oxford Pitch Competition

Hughes Miller, Strategic Partnerships

Evaluating Wrist Placement And Signal Processing Techniques For Real-World Hrv Monitoring Using Ppg, Andrew Murphy

College of Computing and Digital Media Dissertations

This thesis investigates trade-offs between signal quality and data coverage in photoplethysmographic (PPG) heart rate variability (HRV) monitoring using wrist-worn devices. The goal was to evaluate whether wrist placement and signal processing techniques can improve measurement reliability in real-world conditions. Data was collected from healthy participants wearing smartwatches on both wrists during rest and a structured math task introducing natural wrist movement. Three distinct processing methodologies were compared, including a proposed Rolling-Standardized Derivative (RSD) approach. Results showed that while HRV signals from both wrists were highly correlated at rest, motion caused a measurable drop in signal quality and inter-wrist agreement, …

Living With Alzheimer’S: A Guide To How To Lower The Risk Of Memory Loss & Care For Those With Alzheimer’S, Mona Muzammil, Washain Muzammil

School of Integrative Biological & Chemical Sciences (Formerly Dept. of Chemistry)

Despite the frequent characterization of Alzheimer's disease as a “loss of self,"" this enlightening book demonstrates unequivocally that a person's unique self persists throughout the course of the disease. The important message in Caring for People with Alzheimer’s is how much can be done in care settings to support a person's sense of identity and thereby enrich the lives of people experiencing the many losses associated with dementia. Drawing from a diverse body of research, this book brings together the recommendations of the best thinkers and practitioners in multiple disciplines to illustrate the meaning of self and the importance of …

Enhancing State-Of-The-Art Motor Imagery Classification With Reinforcement Learning, Anton Shepelev

USF Tampa Graduate Theses and Dissertations

One of the key obstacles to the rapid adoption of non-invasive Brain-Computer Interfaces (BCIs) for Motor Imagery (MI) is the low signal-to-noise ratio, and the substantial data requirements which can be mentally taxing for users. EEGNet, a compact Convolutional Neural Network (CNN), has long been considered the state-of-the-art (SOTA) for MI classification, demonstrating strong performance even with limited data. However, recent studies advocate for integrating Deep Reinforcement Learning (RL) to further enhance classification accuracy by dynamically optimizing feature extraction and decision-making processes. Despite this potential, practical implementations remain scarce due to challenges in stabilizing RL training and adapting it to …

Opioid Vs. Money Choice Preference Patterns In Regular Heroin Users, Amolak S. Jhand, Mark Greenwald

Medical Student Research Symposium

About two-thirds of people treated for opioid use disorder (OUD) return to opioid use within the first-year post-treatment, and about 10% report use while on agonist therapy. Understanding determinants of opioid-seeking is vital to reducing recurrence and its risks. We assessed individual differences in effortful choices between opioid and money amounts, modeling real-world choices.

Our lab conducted studies in which regular heroin-users were stabilized on buprenorphine to suppress withdrawal. Within experimental sessions, the participant could choose repeatedly across 12 trials between units of hydromorphone (HYD, 1 or 2 mg IM) vs. money ($2 or $4); HYD and money amounts differed …