Digital Commons Network^™

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 …

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 …

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 …

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.

Generations United: Empowering Seniors Through Service, Joseph C. Neddo, Samantha A. Renda, Eli J. Richard, Adam M. Lease, Athan L. Huskey, Connor J. Buttitta

High Impact Practices Student Showcase Fall 2025

As part of my service-learning experience, I partnered with Legacy Pointe at UCF, a senior living community dedicated to lifelong enrichment. Alongside my team, I helped design and implement resident-centered programs that focused on healthcare transitions, wellness, and emotional support. We hosted Healthcare Talks to educate residents on moving between independent living, assisted living, memory support, and skilled nursing. We also organized Flu Shot Clinics in collaboration with CFP Comprehensive Primary Care to promote preventative care, and facilitated mental wellness activities like memory games and a resident-led outdoor golf tournament.

Working with Legacy Pointe staff, Dr. Burrows’ practice, and faculty …

Advisor And Counselor Perceptions Within A Southeastern State’S Private Institutions: A Qualitative Exploratory Case Study, Thomas A. Range

Graduate Thesis and Dissertation post-2024

U.S. Black American college students identifying as first generation and experiencing lower socioeconomic status have lagged behind White college students from more affluent families in retention, graduation, and postgraduation metrics. These differences are particularly acute for students attending private, predominantly White institutions. However, there is a gap in knowledge about how student services personnel—whose roles directly impact students’ perseverance toward graduation and their postgraduation plans—are perceived. Many external barriers (e.g., inadequate student support; racial discrimination; counselor, advisor, administrator, and college leadership biases) have continued to hinder success of Black American collegians in private institutions. To focus this study, two guiding …

Captivating Chemistry: Luminous Lava Lamps, Linsey Vo, Brinlie Bergman, Kylie Daum, Shayan Noor, Akshay Aramjisingh

High Impact Practices Student Showcase Fall 2025

Our group participated in STEM Day, an engaging biannual event that brings together K–12 students to explore the wonders of science, technology, engineering, and mathematics through hands-on experiences. When planning our presentation, we aimed to create an activity that was both visually exciting and easy for students of all ages to understand. We chose to demonstrate the concepts of polarity and density through a fun and interactive experiment in which students created their own lava lamps. This activity allowed the students to observe scientific principles in action while encouraging curiosity and enthusiasm for STEM learning.

The Cypress Dome 2025, Issue 36, The Cypress Dome Society

The Cypress Dome

Featuring the work of Aaliyah Ali, Zayne DeVoe, Jill Dolan, Lee Eimer, Juliana Erwin, Sarah Esteves, Erik Leon, Isabella Martorana, Gianna Palacios, Dylan Phillips, and Ashlyn Shepard.

Founded in 1989 and named after a natural feature of our campus, The Cypress Dome is the University of Central Florida’s undergraduate literary journal. It is edited, designed, and produced by a selected editorial team of students from our literary organization, The Cypress Dome Society, as part of a year-long internship.

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 …

Multidimensional Approaches In Bug Detection For Parallel Programming And Text-To-Code Semantic Parsing, May Alsofyani

Graduate Thesis and Dissertation post-2024

This dissertation applies deep learning and large language models to two domains: parallel programming fault detection and text-to-code translation, aiming to enhance software reliability and natural language-driven code generation. Due to their unpredictable nature, concurrency bugs-particularly data race bugs— present significant challenges in fault detection for parallel programming. We investigate deep learning and LLM-based approaches for detecting data race bugs in OpenMP programs. Our proposed methods include a transformer encoder and GPT-4 through prompt engineering and fine-tuning. Experimental results demonstrate that the transformer encoder achieves competitive accuracy compared to LLMs, highlighting its effectiveness in understanding complex OpenMP directives. Expanding this …

Improving Smart Home Access Control Mechanisms To Account For Community-Based Sharing Beyond The Home, Leena Alghamdi

Graduate Thesis and Dissertation post-2024

The rapid proliferation of smart home devices has reshaped daily living, offering convenience and enhanced security. However, extending access to these devices beyond household boundaries introduces significant challenges, including balancing privacy, security, and usability. Through three studies, this dissertation investigates these challenges and proposes solutions to improve access control mechanisms for sharing smart home devices with external individuals. First, we conducted 26 co-design interviews with 50 smart home device owners to understand their perspectives on co-monitoring in emergencies with trusted external contacts. Participants emphasized benefits such as enhanced safety, reduced material loss, and peace of mind through swift responses and …

Weakly-Supervised Scaling For Open-Vocabulary Action Detection, Zhen Hao Sia

Graduate Thesis and Dissertation post-2024

In this work, we focus on scaling open-vocabulary action detection. Existing approaches for action detection are predominantly limited to closed-set scenarios and rely on complex, parameter-heavy architectures. Extending these models to the open-vocabulary setting poses two key challenges: (1) the lack of large-scale datasets with many action classes for robust training, and (2) parameter-heavy adaptations to a pretrained vision-language contrastive model to convert it for detection, risking overfitting the additional non-pretrained parameters to base action classes.

Firstly, we introduce an encoder-only multimodal model for video action detection, reducing the reliance on parameter-heavy additions for video action detection. Secondly, we introduce …

In Depth Analytics Of Vehicle-Vehicle And Vehicle-Pedestrian Conflicts Across Various Conditions, Qianqian Jin

Graduate Thesis and Dissertation post-2024

This study explores key traffic safety challenges by analyzing threshold discrepancies in rear-end conflicts under different weather conditions, conflict likelihood and severity at interconnected intersections, and interaction patterns between right-turning vehicles and pedestrians at signalized intersections. First, using CitySim trajectory data, the study examines the impact of weather conditions on surrogate safety measure (SSMs) thresholds for Modified Time to Collision (MTTC), Deceleration Rate to Avoid a Crash (DRAC), and Single-step Probabilistic Driving Risk Field (S-PDRF). Results indicate that MTTC and DRAC thresholds vary significantly under clear (2.3s v.s 3.0 m/s²) and rainy (4s v.s. 2.4 m/s²) conditions, whereas S-PDRF remains …

Advancing Temporal Safety Performance Functions: A Comprehensive Evaluation Of Express Lanes, Ramps, And Ramp Metering Effects On Freeway Safety, Abdulrahman Khalid Faden

Graduate Thesis and Dissertation post-2024

Freeway safety remains a critical concern, especially in high-risk areas such as ramps, merges, and managed lane segments, where complex traffic interactions significantly elevate crash risks. This dissertation advances crash frequency prediction by developing short-term Safety Performance Functions (SPFs) that address the limitations of traditional long-term SPFs models and real-time safety analysis. By leveraging high-resolution microscopic traffic detector data from multiple states, the dissertation introduces innovative methodologies and delivers actionable insights into freeway safety dynamics. The dissertation pioneers the application of Multivariate Poisson-Lognormal (MVPLN) models to identify interdependencies between crashes at ramp and merge segments. To address challenges like data …

The Spray And Atomization Characteristics Of An Lrdre Injector, Samuel J. Schuetz

Graduate Thesis and Dissertation post-2024

The Ongoing effort to advance the Technology Readiness Level (TRL) of Rotating Detonation Engines (RDEs) has faced significant challenges with respect to the development of accurate analytical models for the performance of liquid fueled detonations. A key issue lies in the lack of comprehensive experimental data to validate and refine these models. To address this gap, the present study seeks to provide detailed characterization data on a specific Liquid Rotating Detonation Rocket Engine (LRDRE) injector, which can be leveraged to improve simulation accuracy and predictive capabilities. This investigation is divided into two primary phases. The first phase focuses on the …

Grid-Forming Inverters: Comprehensive Review, Design, And Implementation Of Advanced Control Methods, Yousef H. Abudyak

Graduate Thesis and Dissertation post-2024

The growing integration of renewable energy (RE) resources into power systems requires urgent measures by system operators to address stability challenges caused by the intermittency of such resources. GridForming (GFM) inverters have emerged as a promising solution to mitigate such grid instabilities. Their ability to establish and regulate voltage and frequency under various load and grid conditions makes them a potential alternative to Grid-Following (GFL) inverters, which are highly susceptible to loss of synchronism in grid conditions with low Short Circuit Ratios (SCRs). Numerous GFM control methods have been proposed in the literature, each addressing specific research gaps, however, several …

Developing Real-Time Crash Prediction System Using Ai-Based Methods On Interstate Incorporating Express Lanes, Samgyu Yang

Graduate Thesis and Dissertation post-2024

This research develops a real-time crash prediction system that integrates machine learning techniques and real-time data to forecast crash likelihood across various road segments. The system particularly addresses newly constructed Interstate 4 Express managed lanes, modeling them separately due to their unique challenges, such as limited crash data and new road designs. By treating these segments individually, the system ensures more accurate predictions that account for their distinct traffic behaviors. By leveraging anomaly detection learning (ADL), the system identifies rare crash events by detecting deviations from normal traffic behavior, even with imbalanced data. ADL is applied specifically to the influenced …

Statistical Analysis Of Interstitial Impacts On Shock-Driven Fuel Droplet Atomization, Miguel L. Moran

Graduate Thesis and Dissertation post-2024

Understanding the deformation and aerobreakup of liquid droplets is critical for designing fuel injectors for pressure gain combustion systems. Previous literature has observed the morphology and timescales of individual liquid droplets exposed to the convective flow fields behind moving shocks propagating between Mach 1 and Mach 10. However, the liquid droplets produced by an injection system are not isolated and have many close neighbor droplets of similar diameter. Understanding interactions between these droplets is necessary to reconcile existing theory with droplet behavior observed in liquid jets and droplet clouds. The present study uses a simple injector to create vertically oriented …

Wetting Dynamics On Ultrasoft Materials: Effects Of Surface Chemistry And Elasticity On The Maximum Speed Of Wetting, Damian Hundley

Graduate Thesis and Dissertation post-2024

The study of wetting dynamics on ultrasoft materials has gained significant attention due to its critical implications for flexible technologies, thermal management systems, and advanced coatings. This research explores the effects of surface chemistry and elasticity on the maximum speed of wetting, with a focus on the unique viscoelastic properties of Ecoflex 00-30. Using high-speed imaging and controlled experiments, the links between surface energy, material stiffness, and thickness are analyzed to uncover key mechanisms controlling wetting behavior.

The results show that maximum wetting speed is primarily influenced by surface chemistry, while elasticity plays a less important role unless coupled with …

Application Of Industry 5.0 Attributes For Successful Implementation: Framework Of Manufacturing Excellence In Aerospace And Defense Industries, Rena Lewis

Graduate Thesis and Dissertation post-2024

As a novel concept, Industry 5.0 is making its rounds around the academic community and various industries. Despite its introduction over five years ago, definitions and perceptions are still under deliberation. Applications, methods of implementation and values are still in the conceptual phase. This study introduces a framework inclusive of human-centricity, sustainability and resilience, the pillars associated with Industry 5.0. It aims to differentiate the revolution from its predecessor, Industry 4.0, as it dives into the technologies associated with Industry 4.0 including but not limited to the Internet of Things (IoT), autonomous robots, and artificial intelligence. As those technologies evolve …

Patient Transfer Robot (Patro) For Mobility Independence Of Patients In Need Of Assistance, Eric W. Frankle

Graduate Thesis and Dissertation post-2024

This thesis is about the design, assembly, and testing of a prototype Patient Transfer Robot, or PATRO. The purpose of this device is to create and validate a mobile wheeled robot to motorize and automate the patient transfer process for use in home and hospital environments. This device aims to reduce the load and risk of injuries for caregivers during patient transfer process and to increase the autonomy and independence of the patient. While the idea of an automated patient transfer device is not a novel concept, PATRO aims to improve on existing designs by improving the safety, size factor, …

Towards High-Performance, Low-Overhead Integrity Authentication For Secure-Memory In Embedded And Heterogeneous Computing Platforms, Rakin Muhammad Shadab

Graduate Thesis and Dissertation post-2024

The use of secure-memory has been crucial for embedded and heterogeneous computing systems, especially in the light of emerging memory adversaries. The state-of-the-art hardware platforms targeting different sectors such as Internet-of-Things (IoT) and cloud computing require strong protection against memory exploits through different side-channel attacks. Such a tamper-resistant memory system comprises different security techniques such as encryption and integrity protection. Generally, the integrity protection against the adversarial attacks in secure-memory platforms relies on integrity trees such as the Bonsai Merkle Tree (BMT). However, the highly-recursive authentication and update algorithms associated with such tree structures are challenging to implement in heterogeneous …

Mitigation Of Transverse Gusts On Morphing Wings Via Span-Wise Twisting, Alex Ruiz

Graduate Thesis and Dissertation post-2024

Wind gust encounters create a highly unstable and unpredictable environment for air vehicles. Small aircraft are especially susceptible to gust-induced disturbances due to their limited size and weight, leading to significant fluctuations during flight. This study introduces a novel concept involving a twisting mechanism along the wingspan, allowing for span wise variations in pitch. Using a towing tank and a gust generator apparatus, we demonstrate the effectiveness of the proposed mechanism. Force data and flow field data obtained from Particle Image Velocimetry (PIV) provide insights into the vortex mechanisms at play. A Modified Discrete Vortex Method using the leading-edge separation …

Quality 5.0 Management System Design: A Human-Centric And System Of Systems Approach, Johnson O. Olaitan Sr.

Graduate Thesis and Dissertation post-2024

This dissertation presents the Integrated Human-Techno Quality 5.0 System Management Framework (IHT-Q5.0MSF) as a solution to the fragmented and inflexible structures of Quality 4.0. The framework fosters synergy between human insight and technologies such as AI, IoT, and cloud-native systems, addressing adaptability, ethical governance, and sustainability within Industry 5.0. Using a mixed-methods approach, including the Delphi Method, system modeling, and empirical validation, the study identifies enablers, barriers, and strategic priorities for implementation. The results highlight the effectiveness of modular design, human-AI collaboration, and transparent deployment. IHT-Q5.0MSF offers a validated, scalable, and ethically guided system poised to advance quality management in …

Fundamental Breakup Mechanisms Of Micron-Scale Fuel Droplets Behind Hypersonic Shock Waves, Steven Schroeder

Graduate Thesis and Dissertation post-2024

The enhanced efficiency of pressure-gain combustion through detonation has fueled renewed research towards how liquid droplets atomize within a hypersonic shock-driven flow field. The heightened energy of post-hypersonic-shock flow compared to post-supersonic-shock flow leads to increased heating of the droplet, and faster phase change rate, which could potentially alter dominant breakup modes from mass loss via aerodynamic drag towards evaporation. As such, the displacement, deformation, and breakup timescales of various micron-scale liquid fuel droplets (RP-2, Jet A-1, and dodecane) behind hypersonic shocks were observed via high-speed 5 MHz shadowgraph imaging. The role of aerodynamic drag was quantified by modulating the …

Smart Work Zone Connectivity For Traffic Safety Applications, Mariam Nour

Graduate Thesis and Dissertation post-2024

Work zones play a vital role in the maintenance and upgrading of roads and highways; however, they also pose substantial risks to both drivers and workers. Utilizing technology, such as Smart Work Zones (SWZs), Connected Autonomous Vehicles (CAVs), and data dissemination algorithms, is crucial in improving safety in work zones by providing decision-makers with timely and accurate information. Ensuring safety in these environments is essential for reducing accidents and fatalities and enhancing drivers' sense of security, and protecting the well-being of workers operating in hazardous conditions. In this dissertation, we proposed a methodology based on three complementary approaches to enhance …

Assessing Multi-Hazard Risks And Impacts Of Compound Climate And Weather Extreme Events For Socio-Economic Risk Management, Javed Ali

Graduate Thesis and Dissertation post-2024

In recent decades, compound climate and weather extremes, events driven by multiple drivers such as precipitation, storm surge, river discharge, and waves, have increasingly affected coastal regions, causing severe impacts on human life, infrastructure, ecosystems, and economies. Traditional risk assessments often underestimate these compound events by focusing on individual drivers independently. This dissertation provides a systematic assessment of multi-hazard risks and socio-economic consequences associated with compound events in the coastal counties along the U.S. East and Gulf Coasts. It introduces and applies a novel bottom-up impact-based methodology to assess the role of compound events in causing socio-economic losses in the …

Dynamic Ground Response Of Sandy Soils Under Vibratory Roller Compaction, Jorge Eliecer Ballesteros Ortega

Graduate Thesis and Dissertation post-2024

Vibratory roller compaction is a common technique for densifying granular materials in road construction, utilizing a combination of static and dynamic loads for greater efficiency. However, the vibrations generated during compaction activities can cause structural damage and human discomfort. This thesis investigates the dynamic ground response of sandy soils under vibratory roller compaction, aiming to predict the induced ground vibrations and deformations. Field tests were performed at different road construction sites in Central Florida by using geophones and settlement transducers to provide empirical support for the analysis. Finite element and finite difference models, implementing Hypoplasticity Sand and PM4Sand as constitutive …