Physical Sciences and Mathematics Commons^™

Uc-027 Ksublocks Tower Defense, Matthew Elledge, Annagrace Gwee, Bryan Nguyen, Logan Slicker, Ashley Ahn

C-Day Computing Showcase

Our project, KSUBlocks Tower Defense, is a Minecraft Plugin designed to create a game mode in the Tower Defense genre. We aim to create a unique and fun game for the students in the KSU Minecraft server. Our project is entirely configurable allowing for easy maintenance and room for future expansions, while ensuring the server performance remains steady alongside KSU's other game modes. It is developed in Java, utilizing IntelliJ and Paper API. We plan to deploy it on the KSU Minecraft Server upon finalization.

Uc-030 Heartspeak​ Ai, Nabeel Faridi, Shammah Charles, Isha Minocha, Ethan Barnes, Aravind Iyer

C-Day Computing Showcase

This project is Sentiment Analysis AI for comprehensive text review analysis and more. The system leverages a fine-tuned BERT-based models to classify overall sentiment, detect emotions, identify sarcasm, and extract aspect-level opinions. Evaluations show robust performance across tasks, with sentiment accuracy around 69%, aspect analysis. Emotion and sarcasm. The pipeline provides actionable insights, empowering businesses to refine products and improve customer satisfaction with OpenAI Integration.

Uc-037 Dynamic Requirements For A Software Training Environment​, Cassidie Grogan, Jesus Valdez, Dawson White

C-Day Computing Showcase

STEDR outlines the development of a software training environment for Warner Robins Air Base (Robins) to enhance employee coding skills to foster innovative solutions for Air Force projects. STEDR is a proof of concept serving as a dynamic requirements document represented by a user interface, to be delivered to a development team. STEDR involved two phases: (1) requirements gathering via interviews; and (2) interactive user interface development for feedback. The resulting proof of concept, includes an interactive UI and refined requirements, and serves as the foundation for a collaborative project with KSU, enabling the computing colleges to contribute to military …

Uc-048 Dinengo - Ai Genie, John Sheffield, Juwon Atunnise, Jaz Ankrah, Alex Colas, Kayla Grant

C-Day Computing Showcase

This projects aims to enhance the flagship product from Driven Software Solutions called DineNGo by implementing a new chatbot to help users with technical troubleshooting. This will allow for instant technical support for common issues and reduces the number of support tickets being created. It provides informed and brief response in a quick manner to walk users through whatever technical issues they are currently having with the DineNGo software. It was built with an Angular frontend and a Node.JS backend as well as a MongoDB database for querying information.

Uc-101 Sight-Singing Feedback, Terah Dann, Sandy Giroux, Nathanael Johnson, Amber Scarbro

C-Day Computing Showcase

This project creates an engaging and interactive music-learning experience. Users start by selecting a tempo and melody number. The app then displays sheet music to guide them through the exercise. While singing, performers receive real-time visual feedback on pitch accuracy and tempo progression, allowing for dynamic adjustments and improved performance precision. The system continuously updates the music staff based on user performance, ensuring seamless interaction. This approach integrates technology with musical education, enhancing skill development through intuitive, data-driven feedback. By combining user-driven selections, interactive visualization, and real-time analysis, the application provides a structured, engaging platform for improving musical skills.

Uc-111 Accessible Interactive Map​, Justin Connick, Megan Ingram, Derrick Novak, Spencer Williams, Emily Zhu

C-Day Computing Showcase

Finding that walking campus gets you out of breath? We did too! Using React and Flask, we are building a web application that directs KSU students to the path with the lowest elevation and shows the shifts in between. It also displays accessible doors. The purpose of this app is to develop a more inclusive application so people with asthma, cardiovascular issues, and wheelchairs at KSU can safely traverse campus.

Uc-136 Foster Ai Interview And Biography Generation, Joshua Crawford, Katlin Lahr, Mikayla Haigh, Brittany Payne, Daria Morhun

C-Day Computing Showcase

This capstone project presents a proof of concept for a mobile and web-based application designed to streamline communication between foster caregivers and the Angels Among Us Pet Rescue team. The application addresses critical inefficiencies in generating pet biographies and coordinating photography efforts, which are essential components in increasing adoption rates. Leveraging cutting-edge technologies such as Twilio, Retell AI, and OpenAI, the app implements a bio generation workflow that conducts foster interviews via phone calls, transcribes responses using AI-powered voice-to-text, analyzes sentiment, and produces structured, engaging pet bios for platforms like Petfinder. Additionally, the system automates email workflows to coordinate photography …

Uc-125 Database Masking Tool - Project 04 - Team 1, Lleyton Callison, Josh Tettey-Enyo, Reda Salimi, Stephen Sigmon, Alec Quillen

C-Day Computing Showcase

The Database Masking Tool for Gwinnett County Public Schools secures sensitive data while preserving its analytical utility. Developed alongside an in-depth research paper, this web-based solution enables real-time masking of information in SQL Server and MySQL databases. Utilizing automated field recognition, it applies three masking techniques—Faker-based masking, hash masking, and pseudonymization through generalized masking—to protect personally identifiable information. Key features include an intuitive interface for configuring masking rules, real-time data previews, and an export function for generating masked datasets in multiple formats. Built with a React-Flask stack and containerized for consistency, the system supports compliance with GDPR, HIPAA, and FERPA. …

Ur-002 Fedda-Tsformer: Federated Domain Adaptation With Vision Timesformer For Left Ventricle Segmentation On Gated Myocardial Perfusion Spect Image, Yehong Huang

C-Day Computing Showcase

This study presents FedDA-TSformer, an approach for accurate left ventricle segmentation in gated myocardial perfusion single-photon emission computed tomography (MPS) images, designed to ensure both high segmentation quality and patient data privacy. By integrating federated learning with domain adaptation techniques, the proposed model leverages a novel Divide-Space-Time-Attention mechanism that effectively captures spatio-temporal correlations inherent in multi-centered MPS datasets. Domain discrepancies among data from three different hospitals are mitigated using a local maximum mean discrepancy (LMMD) loss, enabling robust performance across various clinical settings. Evaluated on a dataset comprising 150 subjects with eight distinct cardiac cycle phases, FedDA-TSformer achieved Dice Similarity …

Ur-044 Quantum Machine Learning For Science And Engineering, Caleb Dow, Daniel Tebor, Dante Lewis

C-Day Computing Showcase

This research explores the comparative effectiveness of traditional machine learning algorithms and their quantum counterparts. Traditional and quantum implementations of algorithms including Support Vector Machines (SVM), logistic regression, Principal Component Analysis (PCA), random forest classifiers, neural networks, and convolutional neural networks (CNN) are evaluated and contrasted. Findings highlight that quantum algorithms can provide certain clear advantages in some models and data while exhibiting inferior performance in others. By assessing these nuances, this research helps contribute to the understanding of quantum machine learning algorithms and their potential applications for science, engineering, and industrial tasks.

Ur-031 Impact Of Motor Skill On Learning Experiences And Outcomes Using Note-Taking In Vr, Sawyer Strickland

C-Day Computing Showcase

Immersive learning experiences have been proposed to offer rich immersion and interaction, effectively addressing the distractions and low engagement commonly found in typical online learning environments. Research in neuroscience and psychology suggests that motor skills, such as note-taking, help students improve their learning by enhancing cognitive abilities and decision-making, ultimately leading to better performance. This study aims to investigate the impact of motor skills, specifically note-taking with a physical VR stylus, on learning experiences, outcomes, and retention in our VR classroom environment.

Ur-115 Mobinav: Accessible Campus Navigation, Eric Legostaev, Damien Castro, Dom Evans

C-Day Computing Showcase

MobiNav addresses the gap in campus navigation by providing personalized route planning for individuals with diverse mobility requirements. The system uses dual-layer routing (Google Maps API and custom OSRM routing), real-time obstacle reporting, and detailed accessibility feature mapping. It creates custom routes considering wheelchair access, elevation changes, building entrances, and temporary obstacles. Initially scoped for Kennesaw State University's Marietta campus, it is designed for scalability to other locations.

Ur-126 Multimodal Neuroimaging Meets Ai: Enhancing Alzheimer's Diagnosis With Pyradiomics, Dina Xu Callaway, Maya Castillo, Richard Haynes

C-Day Computing Showcase

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that requires early and accurate diagnosis for effective intervention. This research explores how multi-modal data integration can enhance Alzheimer’s disease staging prediction by developing an AI model that classifies patients into normal, mild cognitive impairment (MCI), or AD stages. Unlike traditional methods that rely on clinical assessment to make diagnoses, this study develops an AI-driven approach that integrates clinical and imaging data to improve classification accuracy. The research utilizes the Australian Imaging, Biomarkers & Lifestyle (AIBL) dataset, importing patient clinical data along with PET and MRI scans. First, image features were extracted …

Uc-107 Draw The Night Sky, Dominic Ho, Richard Deas, Monica Phillips, Gabe Strong, Conner Hartsfield

C-Day Computing Showcase

Draw The Night Sky is a game project made in collaboration with Carter’s Lake to make their constellation viewing program more accessible. The stars in the sky are quite difficult to see without the perfect conditions, so an alternative would assist with this greatly. By creating a fun and interactive experience through a game, it should teach the visitors of the nature center to be able to search for stars even outside of the game. Utilizing an accurate star map based on the Yale Bright Star catalogue, we have an accurate star map that mirrors the real world which adds …

Analysis Of Cybersecurity Threats And Mitigation Strategies: Theory In To Practice, Project Proposal, Fransly Dutervil

Student Academic Conference

Cybersecurity threats pose significant risks to individuals and organizations, leading to data breaches, financial losses, and operational disruptions. This presentation explores key threats such as malware, phishing, DDoS attacks, insider threats, and zero-day exploits. It also discusses mitigation strategies, including network security measures, multi-factor authentication, encryption, and incident response planning. Through case studies of real-world cyber incidents, we highlight lessons learned and best practices to strengthen security defenses. The goal is to enhance awareness and promote proactive cybersecurity measures in an increasingly digital world.

White Pupil Fibre-Fed Echelle Spectrograph Of The Russian-Turkish 1.5 M Telescope: Optical Design And Analysis, Cevdet Bayar, Nuri̇ Unal

Turkish Journal of Physics

This study presents optical designs of two fibre-fed èchelle spectrographs (FES), which differ from each other in terms of cross-dispersers, and optical analyses of the spectra (orders) obtained from the designs. Optical analyses of both designs were performed by interpreting the results obtained from the spot diagram, footprint diagram, and encircled energy diagram in the ZEMAX (OpticStudio) optical design program. In the first spectrograph design, prisms were used as the cross-disperser. Specifically, two symmetric N-F2 glass prisms with 42-degree apex angles were used as the cross-disperser. In the second design, a grism was used as the cross-disperser. This grism was …

Ai Assistance In Legal Analysis: An Empirical Study, Johnathan H. Choi, Daniel Schwarcz

Journal of Legal Education

No abstract provided.

Rotating Anisotropic Bose Gas With Large Number Of Vortices, Ahmet Keleş

Turkish Journal of Physics

Rapidly rotating atomic gases provide a platform for studying phenomena akin to type-II superconductors and quantum Hall systems. Recently, these systems have attracted renewed interest due to technological advances in the trap anisotropy control, in-situ observation capabilities, and cooling and rotating complex atomic species such as dipolar gases. Understanding the vortex lattice formation and quantum melting is crucial for exploring quantum Hall physics in these systems. In this paper, we theoretically investigate the vortex lattices in anisotropic quantum gases. We formulate the rotating gas Hamiltonian in the Landau gauge, and consider the effects of additional perturbations such as the trap …

Supersymmetric Exploration Of External Field Effects On Spinning Cosmic Strings With Aharonov–Bohm Interaction, Bi̇lgehan Bariş Öner, Özlem Yeşi̇ltaş

Turkish Journal of Physics

We have conducted a supersymmetric analysis of the spacetime of a spinning cosmic string, focusing on the behavior of an electron in magnetic fields. Our study involves examining the Dirac system through the lens of exceptional orthogonal polynomials, specifically the exceptional orthogonal Laguerre $X_{m}$ polynomials. This analysis transforms the Dirac system into a relativistic system with a nonlinear isotonic oscillator. Additionally, we introduce new potential models that extend the radial oscillator by incorporating rational terms. Comprehensive analyses of the potential, energy levels, and probability density graphs are provided for various topological defects of cosmic strings and different parameters of the …

Leveraging Benford’S Law And Machine Learning For Financial Fraud Detection, Benjamin R. Fu

Cybersecurity Undergraduate Research Showcase

Financial fraud, particularly credit card fraud, continues to pose substantial challenges to financial institutions due to its increasing frequency and impact on consumer trust. While traditional rule-based methods have provided foundational defenses, their limitations in scalability and adaptability have accelerated the adoption of machine learning (ML) techniques. Concurrently, Benford’s Law—a statistical principle often used in forensic accounting—has demonstrated efficacy in detecting anomalies within naturally occurring numerical datasets. This study explores a hybrid fraud detection approach that integrates Benford’s Law with supervised machine learning algorithms, including Logistic Regression, Random Forest, and k-Nearest Neighbors. Using the publicly available European credit card fraud …

Leveraging Attention Mechanism To Unlock Gene And Protein Attributes, Ala Jararweh

Computer Science ETDs

Advancing personalized medicine depends on effectively integrating and interpreting the vast, heterogeneous landscape of biological data, from genomic sequences and transcriptomics to the insights embedded in scientific literature. Current machine learning models often focus on single data modalities, limiting their capacity to capture the multifaceted nature of biological systems. We address this gap by developing three attention-based machine-learning models integrating diverse data modalities. Firstly, DeepVul is a multi-task model that leverages cancer transcriptome data to predict genes critical for cancer survival and their corresponding drugs. Subsequently, LitGene refines gene representations by integrating textual information from the scientific literature. Finally, Protein2Text …

Reservoir Characterization And Basin Modeling Of Mesozoic Siliciclastic Strata For Sedimentary Geothermal Exploration, Iron County, Utah, Theodore L. Morgan

Theses and Dissertations

The transition zone between the Colorado Plateau and Basin and Range provinces of southwestern Utah provides excellent opportunities for future geothermal resource development. A combination of large fault-controlled extensional basins with outcrop analogs along basin margins, an elevated heat flow and thermal regime and the presence of porous and permeable sedimentary formations within extensional sedimentary basins allow for sedimentary geothermal exploration and development. Utilizing a reservoir-centered approach has the potential to dramatically expand the economic limits of geothermal plays in both established and frontier basins. This study characterizes a sedimentary geothermal system in Iron County Utah, centered on the Escalante …

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 …

Numerical Analysis Of The Seir Model, Abigail R. Beckelhimer

Departmental Honors & Graduate Capstone Projects

Epidemiological models delineate the spread of diseases within a population. In this research project, the Susceptible-Exposed-Infected-Recovered (SEIR) Model was examined numerically for comparison between several methods. Approximations were obtained through Euler’s Method, Taylor’s Method, Runge-Kutta Methods, and Multi-step Methods. Hypothetical situations with parameter alterations were considered in order to better understand the effects the parameters have on the model. The goal of this project was to portray the usefulness of numerical approximations for predicting the behavior of the SEIR model and thus the course of a pandemic.

Preservation And Integrity Of Laboratory Solutions: Understanding The Impact Of Storage Practices On Chemical Stability And Experimental Accuracy, Azza Al Jahdhami

Honors Theses

Chemical storage methods represent a key matter ignored within labs, educational sites, workplaces, and even households. Many people, including various students and instructors within the chemistry field, are unaware of the proper methods for storing and disposing of common chemicals. This study aims to understand the effects of storage practices on chemical stability and experimental accuracy, dealing with the following questions: What kind of effects result from improper chemical disposal? How can improper storage affect the overall chemical effectiveness? Why is sink disposal of many solutions discouraged, and why is waste container disposal always necessary?

Through a literature review, this …

Disentanglement In Representation Learning: Interpretability In Dimension Reduction With Vae, Minh Hong Vu

LSU Doctoral Dissertations

This research explores both theoretical and practical aspects of disentangled representation learning by extending the VAE framework. We address the core challenge of extracting independent generative factors from observed data while preserving high reconstruction fidelity. To this end, we propose two novel VAE variants: (i) the $\lambda\beta$-VAE, which incorporates an additional $\ell^2$-norm reconstruction loss to improve accuracy, and (ii) the $\gamma\beta$-VAE, which introduces a mutual information regularization term to encourage independence across latent dimensions.

Our theoretical analysis is conducted in a linear Gaussian setting, where we derive optimal solutions for these VAE-based models. We further examine how varying levels of …

Development Of Isoscapes For Hong Kong Freshwater Streams As A Basis For Ecological Studies, Hon Tim Yip

Lingnan Theses (MPhil & PhD)

Applications of isoscapes in terrestrial and marine ecosystems have grown notably as a tool for studying animal movement. However, isoscapes are rarely used in freshwater ecosystem because current iterations of isoscapes commonly model baseline distributions of bulk stable isotope analysis (SIA) data, which may be inadequate for providing the necessary resolution for fine spatial scale stream environments. This issue could potentially be addressed using compound specific stable isotope analysis, a new method that measures the isotopic composition of individual compounds to provide more comprehensive information than bulk SIA. My study focuses on δ¹³C of essential amino acids (EAA-δ …

A Data-Driven Recommendation System For Selecting The Appropriate Mode Of Learning And Instructional Tools Based On Course Characteristics, Ayisha Manzoor

Thesis/ Dissertation Defenses

The rapid transformation of educational delivery methods during the COVID-19 pandemic required institutions to transition between online, hybrid, and offline learning approaches, creating both challenges and opportunities for educators and students. While online and hybrid learning modes ensured continuity, their effectiveness across different course types remained uncertain. This thesis addresses this gap by developing a data-driven recommendation framework that predicts Course Learning Outcome (CLO) achievement and recommends the most appropriate learning mode (online, hybrid, or offline) along with instructional tools based on course characteristics.

This study analyzed 100 undergraduate and postgraduate courses from the College of Information Technology (CIT) at …

The National Cybersecurity Teaching Coalition: Expanding Cybersecurity Education Opportunities, Paul Wagner, Melissa Dark, Robert Honomichl, Filipo Sharevski, Sandra Leiterman

Journal of Cybersecurity Education, Research and Practice

The increasing prevalence of cybersecurity threats and the shortage of qualified professionals necessitate innovative solutions for cybersecurity education at all levels. Despite the expansion of post-secondary cybersecurity programs, employer dissatisfaction with graduates and a lack of standardized introductory curricula highlights the need for structured secondary education pathways. The National Cybersecurity Teaching Coalition (NCTC) and its National Cybersecurity Teaching Academy (NCTA) address this gap by equipping high school educators with the necessary knowledge and credentials to teach cybersecurity effectively. NCTA offers an 18-credit cybersecurity graduate certificate program to ensure teachers are competent and confident to develop and teach cybersecurity curriculum with …

Ai-Based Detection Of Optical Microscopic Images Of Pseudomonas Aeruginosa In Planktonic And Biofilm States, Bidisha Roy Sengupta, Esther Mallet, Angel Torres, Ravyn Solis

Faculty Publications

No abstract provided.