Digital Commons Network^™

Uc-1240 Project Ibis, Isaac Alderman, Braden Mizell, Collin Sutton

C-Day Computing Showcase

Project Ibis (working title) is a first person, narrative heavy, puzzle-lite RPG that follows the story of a renaissance era plague doctor and their attempt to alter the minds of three subjects; a gardener, a street urchin, and a priest. The game’s narrative is set in historically accurate 1637 Florence, Italy, in the wake of the Great Plague of Milan, and draws heavily from renaissance culture. Each subject being treated is a complex person with personal conflicts and issues; our focus is on tackling mental and emotional health through empathy and nuance rather than diagnosis. Different aspects of each subject’s …

Uc-1257 Integrating A Pressure Profiling Dock Into An Open-Source Digital Art Framework, Luke Miller, Noah Enyart, Jonah Smith, Jaeden Jones

C-Day Computing Showcase

The effectiveness and expressiveness of digital drawing are heavily dependent on an artist’s control over their medium; pen pressure dynamics, which enables fine-tuned variation in line weight and opacity. While the use of this tool improves an artist's control, the open-source editor GIMP currently offers only limited means to visualize or adjust these pressure behaviors in real time. GIMP does support pressure-based input, however, it lacks a dedicated interface for configuring a personalized pressure profile, creating a barrier for users seeking precision comparable to commercial tools. This project addresses that gap by designing and implementing a Stylus Pressure Profiling Dock …

Uc-1260 Digital Micromouse Maze Simulation, Chase Bell

C-Day Computing Showcase

We construct a digital simulation of the Micromouse competition and analyze popular algorithms for searching (A*, Dijkstra’s, and Flood-fill). From our analyses, we design an algorithm with the goal of achieving the shortest possible run time to the end of the maze.

Uc-1262 Georgia Watch - Georgia Hospital Accountability Score, Robert Straiton, Patrick Cox, Constant Nortey, Sankalp Amaravadi, Kahmin Keller

C-Day Computing Showcase

Georgia Watch and the members of this team have partnered to change how Georgia residents understand their healthcare by providing a source of objective metrics which affect their care. This project represents the quintessential React Project produced with the purpose of a reactive interface for the end user, built for maintainability for any subsequent developers. The hospital data is maintained in JSON format for its ease of parsing and adjustment pending any changes. The interactive map was developed through the MAPBOX library, and the team is maintaining a deployment via an independent repository with necessary control over the website.

Uc-1268 Falling Debris, Julian Duarte, Adam Martin, Rylan Collins, Hugh Haggard, Chance Boecker

C-Day Computing Showcase

Falling Debris is a student-developed party 2D platformer game in which players have to survive falling blocks by grappling upward to outlive the others. The game is played in rounds, and between them, players can purchase items to improve their odds of survival.

Uc-1269 Website Makeover - Loving Arms Cancer Outreach, Yuliana Pacheco, Alicia Cook, Taylor Tompkins, Schuyler Bridges, Dalton Roberts

C-Day Computing Showcase

Loving Arms Cancer Outreach (LACO) provides financial, emotional, and community support to individuals affected by cancer, making an accessible and reliable website essential to its mission. Our team conducted a quality assurance audit using tools such as Google Lighthouse and axe DevTools, identifying issues with accessibility, navigation, readability, and mobile responsiveness. Using these findings, we redesigned key sections of the site, improved layouts and forms, and recommended updated plugins to enhance usability and long-term performance. We also developed a Website Architecture and Maintenance Guide to support sustainability. This project establishes the foundation for a modern, user-friendly website that strengthens LACO’s …

Uc-1274 Cloud-Native Ci/Cd Pipeline, Enitan Meduteni, Rami Elmostafa, Matt Crowley, Kade Fleming, Cecily Graffree

C-Day Computing Showcase

This project documents a 12-week capstone implementing a cloud-native CI/CD pipeline using industry-standard DevOps tools. The system integrates Jenkins for continuous integration, Kubernetes for container orchestration, GitOps (ArgoCD) for automated deployments, DevSecOps practices including RBAC and vulnerability scanning, and comprehensive monitoring using Prometheus and Grafana. Mentored by Sudheer Amgothu, Principal Cloud Operations Engineer.

Gc-0241 Using Dog Breed Classification Uncertainty Estimation To Inform Mixed Breed Ancestry​, Brandon Mackey, Maryam Koya, Theodore King, Scott Hutchison

C-Day Computing Showcase

This study investigates whether Monte Carlo uncertainty estimation and probability distributions can be used to identify ancestral composition of mixed-breed dogs. A dataset containing images of purebred dogs was used to train a Monte Carlo Dropout model. The trained model will next be tested on images of mixed breed dogs. Our hypothesis is that the model can be used to provide informative probability distribution for breed ancestry classification, offering a potentially valuable tool for analyzing the genetics of dogs.

Gc-1144 Meetless: The Operating System For Business In The Ai Age, An Pham

C-Day Computing Showcase

Meetless functions as an OS for modern organizations: a secure, multi-agent runtime that turns scattered inputs (docs, emails, tickets, chats) into asynchronous, outcome-driven discussions with decisions, owners, and due dates—so teams ship without meetings. System architecture (OS metaphor).- Kernel (Orchestrator): schedules “processes” across specialized agents using routing policies, guardrails, and retry semantics. - Process I/O: unified connectors for Google/Microsoft suites, Slack, Jira/Linear, and web sources, normalized into a document graph with vector embeddings. - Memory & FS: temporal knowledge graph (Neo4j) + document store (PostgreSQL) + vector index (Weaviate) with time-aware retrieval. - Syscalls/APIs: /discussions.create, /decisions.propose, /actions.sync, /summaries.latest; all idempotent …

Gc-1147 Enhancing Mass Casualty Triage Training Through Human–Ai Collaboration In Virtual Reality (Vr), Rishi Kiran Aiyatham Prabakar

C-Day Computing Showcase

Mass casualty triage requires quick, accurate decisions under pressure. Live training is costly and time-intensive. Virtual Reality (VR) trainings approach has shown comparable learning effectiveness compared to live trainings, motivating the use of VR simulations. This study explores how collaboration with an AI robot partner can enhance the triage training effectiveness. The Findings will contribute in understanding how human-AI collaboration enhances trainings.

Gc-1162 Present Panic Game, Adetunji Adegeye, Kendrick Bryant, Catherine Ayeronwi

C-Day Computing Showcase

Present Panic is a festive top-down 2D arcade game where players control an elf inside Santa’s workshop and must collect presents while avoiding Christmas-themed enemies. During Sprint 2 in our SWE class, our team transitioned from a functional prototype to a fully developed Beta, expanding assets, implementing UI systems, building levels, and integrating polished game mechanics following the MDA framework. We developed new artwork, sound effects, animations, and user interface elements including a main menu, HUD, pause menu, and scene transitions. A survey and both manual and automated testing were prepared to gather user feedback. This Beta version forms the …

Grm-0216 Towards Analyzing The Bridge Dataset With Quantum Machine Learning, Gayathri Kolavennu

C-Day Computing Showcase

This research presents a comparative evaluation of classical and quantum machine learning models applied to the Bridge dataset. Classical algorithms like Support Vector Machine, Random Forest, and Neural Networks are benchmarked against Quantum SVM, Quantum Random Forest, and Quantum Neural Networks using identical preprocessing and training conditions. Results indicate a consistent quantum advantage, with quantum models achieving higher accuracy, stronger nonlinear feature separation, and improved minority-class detection. QSVM and QNN exhibit the most significant performance gains. Although quantum models require greater computational resources, the findings underscore the emerging effectiveness of quantum-enhanced learning for structural classification tasks in the NISQ era.

Grm-1145 Autumn Lite Llm, Michael Knighten

C-Day Computing Showcase

Autumn Lite is an inspectable, small-footprint language modeling pipeline for reproducible experimentation and practical integration into video-game non-player character (NPC) systems. It comprises four components: (1) a regex-aware tokenizer/normalizer for vocabulary construction and mixed prose–code handling; (2) a classical evaluation track that reports perplexity to quantify predictive quality; (3) a compact neural language model (decoder-only Transformer) targeted at low latency and controllable outputs; and (4) a lightweight sentiment classifier (logistic regression) that assigns positive/neutral/negative tags to steer text-to-speech (TTS) prosody during NPC dialogue. By combining transparent preprocessing with baseline metrics and a small, deployable decoder, Autumn Lite aims to deliver …

Grm-1245 A Synthetic Data Engine For Explainable Injection-Area Perception, Yukang Shen

C-Day Computing Showcase

Vision-Language-Action (VLA) systems are beginning to support everyday clinical workflows. Deltoid intramuscular injection is a representative task, but progress is limited by data scarcity, privacy constraints, and the cost of expert annotation. Recent text-to-image (T2I) models make large-scale data synthesis possible, yet ensuring anatomical correctness, diversity, and label quality remains difficult. To address this gap, we propose a Synthetic Data Engine tailored for medical perception, integrating cold-start filtering, controlled T2I generation, CLIP-based quality checks, and iterative segmentation training. We further introduce an anthropometry-grounded formulation of injection safety that produces interpretable safe-zone guidance. Experiments show that synthetic data can effectively bootstrap …

Grm-1252 How Humans Perceive Mobile Robots: Anxiety, Environment, And Behavior Analysis, Rohan Jonnalagadda, Reed Tumlin, Roderick Powell

C-Day Computing Showcase

We conducted 6,400 physics-based simulations to examine how environmental and behavioral factors shape human anxiety during interactions with mobile robots. The model incorporated robot behavior, environmental density, visibility, indoor/outdoor settings, and human age. Anxiety was driven primarily by context: levels were highest indoors, during daytime, and in sparse environments, while nighttime and outdoor interactions consistently reduced anxiety. Robot behavior produced smaller effects, with erratic and non-avoidance strategies yielding slightly higher responses. Older adults showed marginally greater anxiety across all conditions. These findings suggest that environmental design and deployment context matter more than avoidance strategy, offering guidance for improving the safety …

Grm-20169 Proxy Recognition And Inclusive Scoring Method (Prism): Evaluating Context-Dependent Bias In Large Language Models For Resume Screening, Crystal Tubbs, Destiny Raburnel

C-Day Computing Showcase

AI-driven hiring tools are reshaping recruitment but often mirror biases in their training data. PRISM examines how large language models express or reduce demographic bias during resume evaluation and how linguistic context within prompts shapes these outcomes. Using a controlled dataset of 324 synthetic resumes with racially neutral surnames, differing only by first name as the demographic proxy, we compared GPT 3.5 turbo with a Sentence BERT similarity model. Under neutral prompts, no stable bias was observed across demographic groups, yet contextual shifts in the prompt changed how the model responded to proxy cues. These findings show that LLM bias …

Grm-20188 Intelligent Book Recommendation And Rating Prediction System, Destiny Raburnel

C-Day Computing Showcase

When selecting a book, readers often rely on surface level information such as the title, author, synopsis, and keywords to determine whether a story matches their interests. These features contain important cues related to genre, tone, and narrative elements that help set expectations before reading. The Intelligent Book Recommendation and Rating Prediction System works to automate this process by using natural language processing and machine learning techniques. It takes in readers’ personalized reading data such as book titles, author, subjects, synopsis, and personal ratings to learn semantic patterns using TF-IDF vectorization. A supervised Linear Regression model was then trained to …

Mobile Computer Vision Application For Agricultural Disease Detection Of Pepper Diseases Using Two-Stage Deep Learning System, Carlos Jose Estevez, Mai Dang, Ryan Bass

SMU Data Science Review

Plant diseases pose a significant threat to food security, particularly in developing countries where farmers often lack the resources and infrastructure for early detection. In nations like Mexico and the Dominican Republic, the spread of harmful plant diseases impacts key agricultural commodities, such as habanero peppers, leading to substantial yield losses. This study presents a computer vision system based on Convolutional Neural Networks (CNNs) and an object detection model (YOLO) to help farmers detect pepper diseases efficiently. The system uses a two-stage approach: YOLOv11n first detects pepper leaves in images, then a lightweight MobileNetV3Small model classifies whether the detected leaves …

Predicting Simulation Times For Multiphase Thermal-Hydraulic Models, Andrew Yule, Andrew Taylor

SMU Data Science Review

Addressing the challenge of computationally intensive OLGA

simulations in the oil and gas industry, a machine learning framework is

developed for accurate runtime prediction. A specialized feature extraction

pipeline identifies key parameters—such as simulation time, time step,

number of branches, and section count—from OLGA input files that serve as

high-impact predictors. Multiple predictive models, including regression,

tree-based ensembles, and neural networks, are implemented to validate

accuracy and robustness. Results reveal that prioritizing simulations based on

predicted runtimes optimizes licensing resources and reduces operational

costs, making real-time scheduling more efficient. This research demonstrates

the effectiveness of data-driven runtime prediction in enhancing …

Predictive Analytics In Public Health: Developing Ai Strategies To Combat High-Temperature Impacts On Violence And Overdoses In Las Vegas, David Camacho, Stephanie Duarte, Kosi Okeke, Chris Papesh, Jacquelyn Cheun-Jensen

SMU Data Science Review

Public violence and overdoses are key issues that Nevada agencies aim to address in the Las Vegas Metropolitan Area. A proven and effective strategy is via the implementation of a Cardiff Violence Prevention Model. This model provides a way for communities to gain a clearer picture about where violence is occurring by combining and mapping both hospital and police data on violence. In an effort to support this model, the exploration and implementation of AI models to predict the impacts of high temperatures on key public data and outcomes based upon historical data will be applied.

Ai-Powered Compliance: Accelerating Efficiency And Decision-Making For Compliance Related Inquiries., Amberly R. Rodriguez

SMU Data Science Review

This research examines the potential of an AI-powered chatbot to streamline compliance workflows by reducing the time and effort required to locate and interpret complex compliance documents. The prototype integrates a centralized MySQL-based document repository, a contextual document querying engine, and a Streamlit web interface, enabling employees to retrieve accurate, document-backed answers within seconds. The system supports both stored and user-uploaded documents, with features such as automated summarization and source citations to enhance transparency and trust. Manual evaluation demonstrated notable gains in efficiency and accuracy compared to traditional search methods, with strong potential to improve adherence to compliance policies. Future …

Ai-Driven Optimization Of Wind Energy Distribution In Texas Using Multi-Agent Reinforcement Learning, Waleed Amer, Owolabi Oluwadamilola, Bassey Ogbonnaya

SMU Data Science Review

Abstract. The integration of large-scale wind power into modern electrical grids presents persistent challenges due to variability, curtailment, and compliance with operational constraints. This study proposes a multi-agent reinforcement learning (MARL) framework for optimizing wind energy distribution within the Texas power grid. The system employs three specialized agents—managing wind curtailment, storage utilization, and load adjustments—to collaboratively balance supply and demand under dynamic grid conditions. Using historical operational data from the Electric Reliability Council of Texas (ERCOT), the framework was trained and evaluated on a range of scenarios encompassing both typical and extreme operating conditions. Results demonstrate substantial performance improvements compared …

A Comparative Time Series Analysis Of The Arima And Temporal Fusion Transformer (Tft) Models, Catherine Ticzon, Aaron Abromowitz, Bivin Sadler

SMU Data Science Review

Several new transformer-based time series models have been developed in the past five years and research has provided evidence of these models’ superior performance compared to classic statistical models such as ARIMA. While transformer-based models show impressive performance on baseline datasets, no research has been done on the robustness of these models on datasets with controlled modifications and in a replicable manner. In this paper, the Temporal Fusion Transformer (TFT) model was compared to the classical statistical model ARIMA on simulated data using multiple horizons. Data were simulated using a linear combination of exogenous variables; in total, 50 realizations of …

Analyzing The Global Happiness Index, Victoria Hernandez, Christy W. Wachira

SMU Data Science Review

This study explores the Global Happiness Index using data compiled from the OECD and Our World in Data to identify key factors contributing to societal well-being. Six primary predictors were analyzed: GDP per capita, social support, healthy life expectancy, freedom to make life choices, generosity, and perceptions of corruption. Regression and clustering techniques were employed to uncover patterns among countries. By expanding the analytical scope beyond conventional economic and social indicators, this study helps identify new pathways for improving well-being across diverse cultural and economic landscapes. Additional variables such as perceived safety, political engagement, and values related to family and …

The Citizens Bank Of Jacksonville, From Eartha M. M. White To Self

Eartha M. M. White Textual Material

Document: Cancelled check - The Citizens Bank of Jacksonville. Pay to the order of Self ten dollars. Undated.

The Citizens Bank Of Jacksonville, From Eartha M. M. White To Citizens Bank

Eartha M. M. White Textual Material

Document: Cancelled check - The Citizens Bank of Jacksonville. Pay to the order of Citizens Bank six dollars Date: April 25, 1930

Exploring The Recursive Meaning-Making And Multiliteracy Activities Of Arab College Students Through Engagement With Video Games: A Phenomenological Study, Nour Al Okla

Thesis/ Dissertation Defenses

This study aims to explore the engagement of Arab college students as an assemblage of actors and actants within video games, focusing on their dynamic interactions within social networks. It seeks to examine the fluid nature of social structures by questioning conventional distinctions between societal and multimodal literacy elements, such as video games, and their contributions to multiliteracies through the study of unique digital communities and affinity spaces within video gaming communities. The study highlights Arab college students' perceptions of meaning-making processes and negotiation strategies in video games, including social interactions within these virtual communities, challenges encountered during gameplay, and …

A Decolonial Approach To International Education: Insights From A Cal Poly Global Program In Palermo, Sicily., Elvira Pulitano, Iyad Jamaly

csuglobaljournal

This article presents a critical intervention in the current debates about decolonizing international education. It is based on a Global Program in Palermo, Sicily, offered by Cal Poly San Luis Obispo in summer 2023. As both program creator/director and student participant in the program, the authors rely on their personal experience and insights along with expertise in coloniality and decolonial theories. The article focuses on a series of pedagogical activities led by a young group of migrants and refugees who, in the city of Palermo, have come together to form two associations designed and structured to offer new contemporary models …

Development Of Composite Membranes And Nanofluid Absorbents Incorporating Surface-Functionalized Nanoparticles For Enhanced Co₂ Absorption In Gas–Liquid Membrane Contactors, Riya Ahammed Labeeb

Thesis/ Dissertation Defenses

Natural gas sweetening demands the efficient removal of CO₂ to enhance heating value and prevent equipment corrosion. Gas–liquid membrane contactors (GLMCs) have garnered attention as promising solutions to traditional separation methods due to their modularity, high interfacial area, and energy efficiency. Nonetheless, they suffer from some serious challenges such as membrane wetting, while absorbents either exhibit low absorption capacity or high energy demands and corrosion. This work addresses these limitations through two complementary strategies. In the first, ZnO nanoparticles were hydrophobically functionalised and incorporated into polymer matrices to fabricate composite membranes with superior hydrophobicity and reduced pore wetting. In the …

Is The Women's Treaty A Paper Tiger? Indian Courts' Application Of The United Nations Treaty, Amanda Stephens

University of Memphis Law Review

This Article examines whether the United Nations (“U.N.”) Treaty, the Convention on the Elimination of All Forms of Discrimination Against Women (“CEDAW”), is a “paper tiger”—a law that seems powerful but is in fact weak and ineffectual. While some scholars question the Treaty’s effectiveness based on statistical studies, others point to legal reforms in state parties as evidence of its success. Neither provides a nuanced middle ground by examining the CEDAW at a micro rather than a macro level. This Article fills this gap by conducting a qualitative analysis of 10 Indian judicial opinions. Specifically, the Article explores how the …