Digital Commons Network^™

Utilizing Brain Computer Interfaces That Interact With A Virtual Keyboard, Skye Lilienthal

Honors Theses

A brain-computer interface (BCI) can allow someone to utilize electrical signals in their brain to complete tasks using a computer. BCIs can help people take advantage of technology to type without the need for a traditional keyboard setup. This paper used the OpenBCI Mark IV to test the effectiveness of non-invasive BCIs with dry electrodes within the OpenViBE P300 Speller. This paper shows how to use the P300 speller through a setup pipeline. Results indicate that electrode placement affects P300 accuracy and that areas related to visual processing improve accuracy, suggesting that P300 signals can be detected within OpenBCI Mark …

Spectral Characterization Of Colorectal Cancer Using Excitation-Scanning Hyperspectral Imaging, Natalie Hadad

Honors Theses

Colorectal cancer is one of the most common and deadly cancers worldwide, with a lifetime risk of approximately one in twenty-three for men and one in twenty-five for women. Although early detection significantly improves survival, current diagnostic approaches such as tissue biopsy are invasive, reliant on subjective interpretation, have extended turnaround times, and risk missing flat or depressed lesions that are more difficult to detect than raised polyps. These limitations highlight the need for objective, minimally invasive technologies capable of detecting early tissue changes. This study investigates excitation-scanning hyperspectral imaging (Ex-HSI) as a label-free optical approach for identifying colorectal cancer–associated …

Preclinical Animal Modeling To Investigate Redox-Mediated Molecular Dysfunction In Traumatic Brain Injury, Brandon Z. Mcdonald

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Impacts associated with traumatic brain injury (TBI) induce a multitude of neurological morbidities mediated by a spread of biochemical dysfunction. Redox stress is a significant contributor to TBI pathophysiology and several redox stress markers have substantial evidence to support their role in diagnosing preclinical and clinical TBI. However, there is a critical need to verify their translational utility through correlations with behavioral, imaging, and molecular modalities. In chapter two, we examined the therapeutic efficacy of an antioxidant treatment (NPC3) using a controlled cortical impact (CCI) mouse model of TBI. NPC3 alleviated glial expression and reactivity while influencing the expression of …

Engineering Silicone Magnetic Fluids For Localized Radiation Attenuation During Ocular Melanoma Brachytherapy, Zachary L. Caprow

All Dissertations

Ocular melanoma is commonly treated using plaque brachytherapy; however, radiation-induced damage to healthy ocular tissues frequently results in partial or complete vision loss. This work investigates the development of an injectable, magnetically responsive silicone magnetic fluid designed to localize adjacent to the tumor and attenuate low-energy gamma radiation during treatment.

The material system consists of iron oxide nanoparticles surface-functionalized with a siloxane polymer, in which the nanoparticles provide magnetic responsiveness and radiation attenuation, while the polymer coating ensures colloidal stability, injectability, and biocompatibility. A one-pot synthetic approach was developed wherein a functionalized siloxane polymer containing iron-affinitive groups was reacted with …

Smart Medical Support System And Swin Transformer Framework For Breast Cancer Detection And Segmentation In Mammograms, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi

All Works

Accurate and reliable breast cancer detection from mammographic images remains a critical challenge due to subtle lesion appearance, high intra-class variability, and class imbalance inherent in clinical datasets. To address these issues, this study proposes Swin-BreastNet, an explainable and optimization-driven deep learning framework for binary classification of benign and malignant breast lesions from full-field digital mammograms. The proposed approach leverages the hierarchical Swin Transformer model to effectively capture fine-grained local texture patterns and long-range contextual dependencies through Shifted Window Multi-head Self-Attention (SW-MSA). A key novelty of this work lies in the integration of Harris Hawks Optimization (HHO) for automated hyperparameter …

Public Health Responsible Ai Capability (Ph-Raic) Framework: A Conceptual Model For Integrating Ai Into Public Health Agencies, Arnob Zahid, Ravishankar Sharma, Rezwan Ahmed

All Works

Background: Artificial intelligence (AI) is transitioning from experimental pilots to core public health functions such as disease surveillance, resource planning, and analysis of social and structural determinants of health. Yet, health data collection and stewardship remain fragmented across the globe; some jurisdictions still rely on paper-based systems, while others operate noninteroperable digital systems that can exacerbate inequities. Treating health data as a global good therefore requires governance that enables innovation while protecting rights, safety, and trust. This study aims to develop a conceptual meso-level capability framework that translates responsible AI principles into organizational practices for public health agencies. Methods: We …

Factors Affecting Pedestrian-Vehicle Conflicts: An Empirical Analysis, Christo D. Jamo

Honors Scholar Theses

The number of pedestrian deaths increased by 78% between 2009 and 2023, while other motor vehicle crash deaths increased by 13% in the same period [1]. To identify potential pedestrian safety measures, this study analyzed the effects of location-based demographics, pedestrian phasing type, and other physical infrastructure and behavior variables on the probability of pedestrian-vehicle conflicts at signalized intersections, which is a surrogate measure of crash risk. Data were collected from 55 intersections in Connecticut, and the pedestrian-vehicle interactions were classified by severity based on the Swedish Traffic Conflict Technique: undisturbed passage, potential conflict, minor conflict, or serious conflict. Because …

Stochastic Universal Differential Equations For Epidemiological Modeling: Uncertainty Quantification In Disease Transmission Dynamics, Alice Menaya Armah-Bonney

Electronic Theses and Dissertations

Epidemic forecasting requires not only predictions of expected case counts, but also quantification of uncertainty, although existing surrogate modeling frameworks for agent-based models remain fundamentally deterministic. In this thesis a Stochastic Universal Differential Equation framework is presented that extends the deterministic Universal Differential Equation approach by incorporating a learnable stochastic diffusion term, enabling calibrated probabilistic forecasts while preserving the mechanistic interpretability and computational efficiency of the deterministic baseline. In doing so, a two-phase training algorithm is introduced to ensure stable convergence and the framework is validated against the ensemble output from ExaEpi, an exascale agent-based model of a COVID-19 outbreak …

Malnutrition And Frailty Risk Among Community Dwelling Older Adults In Northeast Tennessee, Shreeyesha Pradhan

Electronic Theses and Dissertations

Frailty and malnutrition are prevalent conditions among community-dwelling older adults, affecting approximately 10% and 25% of this population, respectively. Despite overlapping symptoms such as functional decline, their relationship remains underexplored in community dwelling older adults. This cross-sectional feasibility study examined the association between frailty and nutritional status among 47 older adults in the East Tennessee Tri-Cities area using the Fried’s Frailty Phenotype (FFP) and the Mini Nutritional Assessment-Long Form (MNA-LF®). Results indicated 13% were pre-frail, 24% were frail, 21% were at risk for malnutrition, and 2% were malnourished. Regression analyses revealed that self-perceived nutritional status (37.6%), daily meal frequency (29.0%), …

Volume 17, Christian O’Neill, Kyara Greene, Savva Sidorov, Laura Bisaillon, Luke Clemmer, Hannah Gordon, Kitt Benson, Taylor Blount, Rachel Danzitz, Nicholas Duellman, Chase Gionis, Hima Fernando, Seth Franzyshen, Onyx Gonzalez, Bryan Lin, Samantha Start, Ysabel Wells, Maggie Duncan

Incite: The Journal of Undergraduate Scholarship

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

From the Editor Dr. Hannah Dudley-Shotwell

Cover Artist’s Statement Maggie Duncan

On Mentoring Dr. Yulia Uryadova

Ukrainian Resistance in the Face of Russification: Nestor Makhno and Anarchism

by Christian O’Neill

Life Vest by Kyara Greene

Isolation and 16S rRNA Identification of Bacteria from Fire Department Connection Pipe by Savva Sidorov

The Effectiveness of Planned Exercise in Reducing ADHD Symptoms in Children by Laura Bisaillon & Luke Clemmer

Linguistic Analysis on Confidence and Communication Strategies with Disparities Between Sign Fluency and Hearing Impairment by Hannah Gordon

Freedmen in Indian Territory by Kitt …

Understanding Delays In Emergency Department Care: A National Analysis Of Wait Times, Gregory Forsberg

Mathematics, Statistics, and Computer Science Honors Projects

Emergency department (ED) wait times remain a persistent bottleneck in the United States healthcare system, impacting patient outcomes, hospital efficiency, and equitable access to care. This study analyzes nationally representative data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a complex, multi-stage probability sample. Using survey-weighted analyses and predictive modeling, we examine the effects of patient characteristics, triage acuity, and visit timing. Results indicate that operational and system-level factors, including hospital capacity, geographic region, and temporal variation, are among the most influential predictors of ED wait times

Pattern Dynamics And Stochasticity Of Brain Rhythms And Spike Trains In A Tauopathy Mouse Model Of Alzheimer’S Disease, Clarissa M. Hoffman

Dissertations and Theses (Open Access)

Systems neuroscience posits that every aspect of perceived physical reality, every aspect of animal and human behavior, and every cognitive phenomenon emerges from patterns of neuronal activity. While most researchers embrace this idea, there are major difficulties in describing and analyzing these complex neuronal dynamics—spike flows produced by cells ensembles, synchronized extracellular field oscillations, and other patterns—which limits our understanding of how the activity of individual neurons and the whole-animal cognition and behavior might be connected. In particular, we lack the approaches and even the semantics for connecting the individual cell outputs and the integrated results of their activity. Current …

Technical Development, Optimization, And Clinical Implementation Of Quantitative Magnetic Resonance Imaging Methods For Radiation Oncology, Lucas Mccullum

Dissertations and Theses (Open Access)

Magnetic resonance imaging (MRI) has gradually grown more important in the field of radiation oncology. This paradigm shift has grown from initial experiments with MRI of cancerous lesions in the early 1970s, to the integration of MRI images for radiation therapy treatment planning (i.e., MRI-Simulation) in the early 1980s, to the combined MRI linear accelerator (MR-Linac) becoming commercially available in 2018 which can simultaneously image a tumor and treat it with radiation. However, due to its novelty in radiation oncology, its full potential for the integration of quantitative MRI-based biomarkers has yet to be fully realized.

Therefore, this dissertation will …

An Integrated Framework For Early Detection And Transmissibility Assessment Of Emerging Variants In Wastewater, Xingwen Chen, Tin Phan, Wei Lin Lee, Katie Clark, Steven F Rhode, Samantha Brozak, Bruce Pell, Tsultrim Palden, Mats Leifels, Anna Gitter, Yang Kuang, Stefan Wuertz, Janelle Thompson, Kristina D Mena, Eric J Alm, Fuqing Wu

Faculty, Staff and Student Publications

Tracking the emergence of new viral variants is critical for epidemic preparedness, but remains challenging because variants typically circulate at low prevalence during the early phase of outbreaks. Here, we present an integrated framework that combines sensitive variant detection in wastewater with quantitative assessment of transmissibility through dynamic epidemic modeling. Using the SARS-CoV-2 Omicron variant as a proof of concept, we developed a novel nested allele-specific RT-qPCR assay (NAS-PCR) that achieved a thousand-fold increase in sensitivity compared with allele-specific RT-qPCR (limit of detection: 0.5 copies/μl). NAS-PCR detected Omicron in wastewater samples from the Greater Boston area starting in September 2021, …

Clawing Our Way To Inclusion: Identifying Minority Students’ Barriers To Entering The Veterinary Medicine Profession, Danielle Reeves

Animal Science Undergraduate Honors Theses

Veterinary services are being utilized more each year as pet owners are increasingly viewing and treating their pets like family members. Due to this changing perspective, the veterinary profession has grown significantly. Yet, despite this change, there is a lack of ethnic/racial diversity within the veterinary field. The purpose of this study was to determine the factors and/or barriers associated with minority students' desire or lack of desire to pursue a career in veterinary medicine. This study used an online survey tool to collect qualitative and quantitative data from 423 undergraduate students at the University of Arkansas during the spring …

Prediabetes Prediction Before Disease Onset Using Multimodal Health Data: A Machine Learning Approach, Luisa Veronica Gracia Mazuca

Open Access Theses & Dissertations

Prediabetes is a critical health condition that increases the risk of developing type 2 diabetes. The hemoglobin A1c (HbA1c) test diagnoses patients with prediabetes, but the disease has already caused metabolic alterations. Early detection is essential for timely interventions, and machine learning models offer a promising approach to identify prediabetic individuals through the analysis of biomarkers such as cytokines. We compared four classifiers-logistic regression, decision tree, random forest, and k-nearest neighbors - using cytokines (TNF-α, MCP-1, IL-1β, IL-6, IFN-γ), age, BMI, and waist-to-hip ratio (WHR). Models were evaluated using stratified 5-fold cross-validation and ROC-AUC. K-Nearest Neighbors (k-NN) achieved the highest …

Exploratory Proxy Occupational Health Risk Assessment On Pm2.5 Exposure Among Outdoor Personnel At International Ports Of Entry, Josdell Maria Guerra Ruiz

Open Access Theses & Dissertations

Despite growing public health concerns on PM2.5 exposure and its respiratory health effects, no documented evidence has been found on the application of proxy methods to perform occupational health risk assessments for PM2.5 exposure among outdoor personnel. The purpose of this research study was to conduct a descriptive and predictive occupational health risk assessment for cancer and non-cancer respiratory health outcomes related to PM2.5 exposure among outdoor personnel at targeted international ports of entry (IPOE). A total of 61 samples were collected, for 24 hours, at two targeted IPOEs (every sixth day) over 12-month period. Samples included measurements on PM2.5 …

Molecular Mechanisms Of Pfas Induced Protein Dysfunction: Implications For Neurodegenerative Risk, Daisy Lee Wilson

Open Access Theses & Dissertations

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with widespread human exposure and well-established links to adverse health outcomes. However, despite increasing epidemiological and experimental evidence, the molecular mechanisms underlying PFAS-induced neurotoxicity remain incompletely defined. This dissertation investigates PFAS toxicity across biological scales, integrating protein biophysics with neuronal cell models to elucidate pathways relevant to neurodegeneration. At the molecular level, we demonstrate that PFAS disrupts the structure and function of globular proteins, including the impairment of α-lactalbumin's calcium-binding capacity and of β-lactoglobulin's retinol binding, through concentration-dependent conformational destabilization. By applying these findings to a neuronal model, we show that …

An Investigation Of Data Granularity In Rag Pipelines For Personalized Medicine, Paritosh Pandey

Master's Theses

Generative AI, exemplified by large language models like the OpenAI GPT and Meta LLaMA families, can produce diverse content in response to prompts. This capability offers a promising solution to challenges in precision medicine, which seeks to tailor treatments to individual clinical profiles but often struggles with data collection, cost, and privacy concerns. By generating realistic, privacy-preserving patient data, generative AI has the potential to transform patient-centric healthcare. With such motivation, this research develops a comprehensive Generative AI pipeline emphasizing data granularity for accurate prediction of personalized treatments. The pipeline features a central Large Language Model interacting with a Machine …

Computational Modeling Meets 3d Bioprinting: Emerging Synergies In Cardiovascular Disease Modeling, Tanmay Mukherjee, Mehdi Salar Amoli, Sarah Rezapourdamanab, Lama Rita El Shammas, Martin L Tomov, Emilio A Mendiola, Vahid Serpooshan, Reza Avazmohammadi

Faculty, Staff and Student Publications

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, underscoring the need for improved strategies in diagnosis, treatment, and disease modeling. Traditional in vitro models often fall short in replicating human CV physiology, prompting efforts to advance cardiac tissue engineering and computational modeling. Among these, three-dimensional (3D) bioprinting has emerged as a transformative tool, enabling the creation of biomimetic CV constructs that more faithfully replicate native tissue structure and function. However, challenges persist in achieving appropriate mechanical properties and long-term performance of engineered CV constructs. Computational modeling offers powerful solutions to assist with these challenges, providing predictive insights into …

Optimisation Of Photosensitive Recording Materials For Broadband Holographic Optical Elements, Michael Murray

Doctoral

The introduction of broadband (white) LED outdoor lighting has led to significant energy savings. However, this has come at the cost of increased light pollution which has negative impacts both on ecological systems and human health. This light pollution is largely the result of the lack of control measures for the directionality of the light emitted by outdoor LED lighting. The lack of directionality also results in higher energy consumption in order to compensate for the light scattered to the atmosphere and sufficiently illuminate the target area. In this thesis holographic optical elements (HOEs) are proposed as a complementary technology …

A Smoking-Related Plasma Protein Score And Smoking-Related Cancer Risk And Mortality In The Atherosclerosis Risk In Communities Study, Meng Ru, Christopher Douville, Aghiles Guenoun, Hana Zahed, Christie M Ballantyne, Kenneth R Butler, Josef Coresh, David J Couper, Panagis Galiatsatos, Marc J Gunter, Ron C Hoogeveen, Mattias Johansson, Corinne E Joshu, P Martijn Kolijn, Christina M Lill, Jiayun Lu, Michael T Marrone, Giovanna Masala, David C Muller, Anna E Prizment, Raul Zamora-Ros, Nilanjan Chatterjee, Adrienne Tin, Elizabeth A Platz

Faculty, Staff and Students Publications

Background: Self-reported smoking may not fully capture individualized risk of smoking-related cancer, but circulating proteins may reflect biological consequences of smoking. Thus, we developed a score based on smoking-related proteins and evaluated its association with smoking-related cancer.

Methods: This prospective cohort study included 10 563 participants aged 47-70 years in the Atherosclerosis Risk in Communities study. Plasma proteins were measured using the SomaScan Platform (SomaLogic Operating Co, Inc). The score was constructed from proteins associated with current smoking, pack-years, or recent quitting identified by linear regression and elastic net regression. Cox regression was used to estimate adjusted hazard ratios and …

Artificial Intelligence In Medicine: Barriers, Solutions, And Strategies, Anil Harrison, Melissa Stradley Moreno, Caroline E. Williams, Munevver Mine Subasi, Ersoy Subasi

HCA Healthcare Journal of Medicine

The integration of artificial intelligence (AI) and machine learning (ML) into health care holds the potential to revolutionize patient care by enhancing clinical decision-making, improving diagnostic accuracy, and reducing costs. Despite this promise, adoption remains limited due to a range of technical, regulatory, educational, and cultural barriers. This paper examines these challenges and proposes strategies to support safe and effective implementation of AI in clinical practice.

Key barriers include the lack of model interpretability, often referred to as the "black box" problem, which undermines clinician trust and accountability in clinical settings, evolving regulatory frameworks and unresolved questions surrounding liability, and …

Dementia Detection In Low-Resource Languages: Evaluating Translation-Assisted Transfer Learning For Multilingual Clinical Assessment, Kylar A. Deloach

Honors Theses

Alzheimer's disease (AD) is a growing global health concern, with millions of people affected worldwide and cases expected to rise significantly in the coming decades. Early detection is critical for patient treatment and care, and recent advances in natural language processing (NLP) have shown promise in identifying linguistic markers associated with AD. However, most existing work has focused on English, leaving speakers of other languages with limited access to such tools. This study investigates how effective AD detection models trained on English data are at transferring to Greek, a low-resource language with limited dementia-related speech data available. We propose a …

Investigating The Synthesis And Antibacterial Activity Of A Main-Chain Cationic Polymer, Emily Smith

Honors Theses

Antibiotic resistance is a growing threat to public health. Due to the overuse of antibiotics in agriculture, the overprescription of antibiotics, and the misuse of antibiotics by patients, antibiotic resistance is spreading at an alarming rate. As antibiotic resistance spreads, the antibiotics we depend on to treat common infections become ineffective. Therefore, it is imperative to develop novel treatments for infections that do not increase the prevalence of antibiotic resistance. One treatment being explored is the use of synthetic polymers with antibacterial properties. Synthetic polymers are advantageous because they can be synthesized from widely available precursors and their structure is …

Network Analysis: An Application Of Graph Theory In Biology, Kiana Dunbar

Honors Theses

Graphs are simple, visual representations of entities as nodes connected by edges. They are used to convey relationships within a system. Networks are comprised of interrelated entities that are not easily separable, producing complex, structured data. These can be seen in social groups, highway systems, and even in biological systems. This paper surveys concepts in graph theory for application to the analysis of network data to understand disease. The features of graphs provide a useful framework for thinking about relationships between different genes or proteins. The structure of graphs is also compatible with a variety of machine learning tools. Especially …

Reinforcement Learning Improves Llm Accuracy And Reasoning In Disease Classification From Radiology Reports, Yishu Wei, Yi Lin, Adam Flanders, George Shih, Yifan Peng

Department of Radiology Faculty Papers

Accurate disease classification from radiology reports is essential for many applications. While supervised fine-tuning (SFT) of lightweight LLMs improves accuracy, it can degrade reasoning. We propose a two-stage approach: SFT on disease labels followed by Group Relative Policy Optimization (GRPO) to refine predictions by optimizing accuracy and format without reasoning supervision. Across three radiologist-annotated datasets, SFT outperformed baselines and GRPO further improved classification and enhanced reasoning recall and comprehensiveness.

Comparison Of Chatgpt, Claude Ai, And Dental Students In The Detection Of Artifacts On Panoramic Radiography, Elif Çeçen Erol, Ceren Aktuna Belgin, Gözde Serindere, Kaan Gunduz

Journal of Dentistry Indonesia

Background: Accurate radiographic diagnosis requires images obtained with proper technique. Artifacts are unwanted irregularities or densities not produced by the primary X-ray beam and may obscure anatomical details in radiographic images. This retrospective study aimed to evaluate the performance of ChatGPT, Claude AI, and intern dental students in detecting artifacts in panoramic radiographs (PRs).

Methods: Between January and December 2024, panoramic radiographs of 40 patients containing 74 artifacts (motion, mispositioning, airway/soft tissue, and foreign body/metal artifacts) were retrospectively evaluated. The artifact detection performance of ChatGPT-4.0, Claude AI 3.5 Sonnet, and intern dental students was subsequently evaluated and compared …

System-Level Barriers To Patient Engagement With Electronic Medical Records, Corey Adams, Reema Harrison, Guncag Ozavci, Kathleen Gray, Nilmini Wickramasinghe, Vicky Yuan, Tracey Bucknall, Elizabeth Manias

Patient Experience Journal

Objective: Electronic medical records (EMRs) can support patient-centered care, but system-level barriers often limit how patients engage with EMR systems. This study explored the system-level barriers and enablers to patient engagement with EMRs from the perspectives of healthcare leaders. Methods: Semi-structured interviews were conducted with 20 participants in senior EMR-related roles across healthcare organizations in two Australian states. Data were analyzed using reflexive thematic analysis to identify key system-level influences on patient engagement. Results: Seven themes were identified: 1) EMRs for digital storage, not connection, 2) Privacy rules create challenges to accessing information, 3) Communication barriers when using EMRs, 4) …

No One-Size-Fits-All: Patient Perspectives Of Key Factors Influencing Their Inpatient Rehabilitation Experience, Mirline Milien, Megan Bewernitz, Katherine Stallings, Mary Ellen Young

Patient Experience Journal

Although patient experience in healthcare settings has historically been an ambiguous concept, it has received increasing recognition as a measure of healthcare quality. Despite this growing recognition and efforts to define patient experience, few studies have explored patient perspectives on the key factors driving this quality indicator. This study aimed to explore the core elements patients view as influencing their experiences in the inpatient rehabilitation setting. This was a single-visit, cross-sectional qualitative interview study. Thirteen patients with diverse diagnoses who received at least one week of rehabilitation services were recruited from a single inpatient rehabilitation facility. Purposive sampling was employed …