Digital Commons Network^™

Grm-174-228 Mitigating Prompt-Induced Variability In Llm Outputs, Crystal Tubbs

C-Day Computing Showcase

Large language models in enterprise settings often produce structurally invalid outputs when users communicate informally, creating silent failure modes that pass unnoticed in downstream systems. This study investigates how prompt variation alone impacts schema compliance and output reliability. We evaluate three architectures across two tasks and four prompt styles, isolating the effect of interaction style on model behavior. Results show that baseline systems achieve 100% compliance under structured prompts but fail completely under ambiguous and casual inputs. A minimal reliability pipeline consisting of generation, self critique, and schema validation restores 100% compliance across all conditions at a predictable computational cost.

Grm-176-225 Memoryeil: An Enhanced Memory Layer Architecture For Heterogeneous Robots, Yukang Shen

C-Day Computing Showcase

Embodied agents still struggle to generalize across robot types, tasks, and environments because most policies remain tightly tied to robot-specific observations and action spaces. While recent VLA and planning methods improve task performance, they still lack a shared memory layer for storing and reusing experience across heterogeneous robotic systems. We propose MemoryEIL, a predicate-based memory layer that converts multimodal observations and execution traces into structured graph memories while preserving raw embeddings for fine-grained retrieval and disambiguation. MemoryEIL separates short-term task belief from long-term experience and plugs retrieved memories into either VLA policies or differentiable TAMP planners. Preliminary results show better …

Grp-01-196 Topological Drift Predicts Epidemic Instability, Charles Fanning

C-Day Computing Showcase

We study whether changes in contact-network topology predict transitions into high-risk epidemic periods across several classical empirical proximity network datasets. We use temporal graph learning with persistent homology-based topological signals and evaluate large-outbreak risk using SIR simulations to test whether topological drift serves as an early warning signal for epidemic instability.

Grp-03-141 Stress-Testing Parkinson’S Disease Screening: A Cross-Modal Analysis Of Drawing And Speech Models, Rehma Razzak

C-Day Computing Showcase

Medical AI systems are increasingly deployed in clinical settings, yet most published models report only clean accuracy, dataset details, and training procedures—while omitting security‑critical evaluations such as robustness to perturbations, adversarial vulnerability, and failure modes under realistic noise. This project addresses that gap by building a cross‑modal robustness assessment for Parkinson’s disease (PD) screening models across handwriting trajectories, speech‑derived acoustic features, and an LLM‑based preprocessing layer. Despite strong clean performance (visual subject‑level ROC AUC ≈ 0.99; audio ≈ 1.0), the visual pipeline proved highly brittle to realistic acquisition distortions. Downsampling and point‑dropout caused near‑chance collapse, while pressure noise and XY …

Grp-08-168 Diagnosing Faults In Electrical Power Systems Of Satellites, Jared Lasley, Nguyen Thi Binh Nguyen

C-Day Computing Showcase

Satellite systems cost hundreds of millions of dollars or more to launch. To be resistant to catastrophic failures (and total loss of investment), satellite systems are designed with redundant sub-systems and are further equipped with numerous sensors and other health-monitoring sub-systems. In this poster, we consider an approach to fault diagnosis based on probabilistic logic programming. In particular, we propose to use ProbLog to model and reason with the electrical power system (EPS) of a satellite. Once we model a system using (probabilistic) first-order logic, we can take the system state and any (unexpected) sensor readings, and through automated reasoning, …

Grp-096-183 Early Warning Signals For Geopolitical Oil Shocks Via Multi-Model Nlp Sentiment Analysis, Nzubechukwu Ohalete

C-Day Computing Showcase

The Strait of Hormuz carries roughly 20% of the world’s daily oil supply. Its closure on March 4, 2026, sent Brent crude surging 36.6%, from $74.64 to a peak of $118.35/barrel. Traditional time-series models fail during such unprecedented shocks because the historical price data contains no analog. This study evaluates whether NLP sentiment analysis can detect crisis signals in news text before they appear in prices, and whether agreement patterns across models predict volatility. We score 2,249 Guardian news articles using five deterministic sentiment models across three tiers: a lexicon baseline (VADER), a general-purpose transformer (RoBERTa-CardiffNLP), and three financial-domain transformers …

Grp-114-127 Gamma-Sieve: Structural De-Obfuscation Of Financial Regime Manipulation Via Heterophilic Graph Neural Networks, Christopher Regan

C-Day Computing Showcase

Market manipulation increasingly exploits fragmentation — dispersing orders across dozens of accounts, venues, and sub-second timing windows — to evade rule-based surveillance. We present Gamma-Sieve, a heterophilic graph neural network approach that constructs heterogeneous transaction graphs (four node types, ten edge types) from market microstructure data, applying CARE-GNN with RL-gated edge filtering and TFE-GNN with spectral triple-frequency decomposition. At production scale, heterophilic GNNs outperform a bidirectional LSTM baseline by +16% AUC on fragmented coordination attacks. However, evaluation on real NASDAQ equity data (LOBSTER Level 3) reveals a critical domain-shift challenge: GNN false positive rates of 42–88% on legitimate trading, caused …

Grp-146-136 Platonic Policy Representations: Navigating Learned Manifolds For Rapid Adaptation, Cameron Redovian

C-Day Computing Showcase

Adapting reinforcement learning policies to changing dynamics is typically addressed by domain randomization, which trains a single robust policy at the cost of specialization, or by meta-RL methods, which enable rapid adaptation but require online inference or optimization. We propose a different mechanism: extending the Platonic Representation Hypothesis (Huh et al., 2024) and vec2vec (Jha et al., 2025) to policy space, we show that diverse task-competent policies trained under varying dynamics admit a shared, low-dimensional manifold structure that is learnable from trajectory embeddings. Platonic Policy Representations (PPR) learns this manifold via geometric preservation losses, then navigates it for rapid adaptation: …

Grp-150-192 From Construction Floor-Plan To Robot-Ready Navigation Map: A Dual-Memory Multi-Agentic Ai That Learns From Its Own Successes And Failures, Amatul Akhi

C-Day Computing Showcase

Construction floor plans contain rich architectural information, but they are not directly usable for robotic navigation in IoT-enabled smart buildings. They often require manual processing to remove irrelevant annotations and extract navigable layouts. Existing methods either depend on fixed image-processing pipelines that do not generalize well across different floor plan styles or on data-intensive learning models that require large annotated datasets. We propose a dual-memory multi-agent framework that treats floor plan-to-map conversion as a sequential, experience-driven decision process under data scarcity. The framework uses three cooperative agents for perception, decision-making, and evaluation, which interact through a shared persistent memory represented …

Grp-163-229 Emotion Elicitation Via Empathic Ai (E-Ai) Agent Teacher In Vr Classroom, Arpita Paul

C-Day Computing Showcase

How emotions influence learning experiences in virtual reality (VR) remains unclear. To address this gap, we investigated the impact of emotionally elicited lectures on learners’ experiences and outcomes in a pedagogical VR classroom using a between-subjects design with two emotional conditions (positive vs. negative). Emotional cues were delivered by a virtual agent (VA) teacher through bodily gestures and verbal expressions during lecture delivery. In a user study (N=34), we collected multimodal data, including neurophysiological measures such as electroencephalography (EEG), galvanic skin response (GSR), heart rate (HR), heart rate variability (HRV), skin temperature, and eye gaze, along with self-reported emotion, learning …

Grp-180-147 Quantum Augmented Microgrids (Quam) Simulator, Nitin Jha, Prateek Paudel

C-Day Computing Showcase

Small modular nuclear reactors (SMRs) are redefining the energy generation landscape by enabling the deployment of modular, scalable, and pre-built power units that can be used to build distributed autonomous microgrids for critical infrastructure and burgeoning AI factories. Often, these microgrids are linked together to provide a resilient, decentralized power generation infrastructure. Consequently, the cybersecurity of microgrids is of critical importance. In this work, we propose a quantum augmented network framework for resilient microgrids. We integrate the ideas of secure quantum networking, quantum anonymous notification, and quantum random number generation to strengthen the integrity, confidentiality, and privacy of microgrid networks. …

Uc-011-171 Beyond Postseason Awards: Predicting Accolades Via Real-Time Control Chart Signals In The Ncaa Transfer Era, Daniel Bowen, Shayaan Cyclewalla

C-Day Computing Showcase

In the era of the NCAA transfer portal, collegiate basketball coaches face the critical challenge of identifying and targeting elite recruits within a condensed 15-day window. This study investigates the predictability of elite player performance by analyzing postseason award winners within the Coastal Athletic Association (CAA). Utilizing game-by-game data on player efficiency, usage percentages, and Player Efficiency Ratings (PER), we implemented an Exponentially Weighted Moving Average (EWMA) control chart—a technique from the Statistical Process Control (SPC) family—to monitor performance signals. Our results indicate that the EWMA model successfully identifies future award-winning players after an average of only 8.58 games. By …

Uc-082-216 Ai Driven Guest Support For Vacationsforyou​, Cassidie Grogan, Kendal Elison, Benjamin Dulcio, Ezra Begashaw, Wilfred Faltz

C-Day Computing Showcase

The AI Guest Support Assistant is a proof-of-concept, web-based chatbot designed to streamline guest support for a high-volume vacation rental operation. The system leverages a Large Language Model (LLM) combined with a Retrieval-Augmented Generation (RAG) approach to deliver accurate, context-aware responses to common guest inquiries, such as reservation details and check-in times. Built using a React frontend and a FastAPI backend, the platform integrates securely with the StreamlineVRS property management system. The solution aims to reduce call center workload by automating repetitive inquiries while maintaining a clear escalation path for more complex requests. This project evaluates the feasibility, usability, and …

Uc-086-235 Student Performance Pattern Mining, Cesar Arevalo Colocho

C-Day Computing Showcase

This project applies data mining techniques to explore patterns in a student performance dataset. The analysis focuses on discovering natural groupings of students and frequent associations among academic, social, and lifestyle attributes. Clustering and association rule mining are used to identify meaningful structures in the data, emphasizing pattern discovery and interpretation rather than outcome prediction.

Uc-087-236 Early Prediction Of Player Performance, Grady Freeman, Hien Truong, Jackson Mayo

C-Day Computing Showcase

This study examines whether early-season performance metrics can support player evaluation under the NCAA’s shortened transfer window. Using data from Conference USA and the Mid-American Conference, we modeled offensive (UASE) and defensive (DAR) efficiency with multiple predictive methods. Across both full-season and 9-game datasets, DAR was more predictable, with higher R² and lower RMSE values. Linear Regression consistently performed best for DAR, while KNN and Random Forest performed best for UASE depending on the dataset. Results show that meaningful performance patterns can be identified early in the season, even with limited data. These findings suggest analytics can help programs make …

Uc-098-187 Zero-Inflated Poisson Modeling Of Ncaa Postseason Awards, Charles Lane, Kyle Bresko, Kaleb Treang

C-Day Computing Showcase

Our project focuses on predicting postseason awards for NCAA Men's College Basketball which can be difficult to model given that less than 15% of players in a given season win awards. After evaluating basic models, we selected a Zero-Inflated Poisson (ZIP) model to account for most players receiving zero awards. We identified free-throw attempts as being the best predictor for the structural zeros present in who can win an award. The final ZIP model produced better evaluation metrics than other basic models. Accounting for structural zeros allowed us to better model how on court statistics can translate into postseason awards.

Uc-099-189 Spectre, Alexander Tobal, Chris Higgins Jr, Jaylin Reeves, Logan Leichter

C-Day Computing Showcase

Spectre consists of four levels, where players complete various objectives and fight off ghosts while doing so. Our tutorial level introduces players to the mechanics, such as shooting, rear view mirror shooting, walking and jumping. With the rest of the levels focusing on completing objectives in order to progress. The final level culminates in a boss fight, ending the journey. While players explore and complete objectives, enemies drop a currency that players can spend to obtain upgrades. Getting hit by enemies not only reduces the players’ health but also applies debuffs to them making players more cautious of their surroundings. …

Uc-117-213 Haunted Owl Hotel – A 3d Horror Maze Chase Game, Carter Griffin, Rin Egl, Kcyana Redmon, Jose Portillo, Alana Nesbit

C-Day Computing Showcase

“Haunted Owl Hotel” is a horror Pac-Man-inspired, 3D maze chase game. You play as a cute owl named Sappy trying to escape the scary hotel, but suddenly your elevator breaks down. Navigate the spooky halls to collect the candles left behind on each floor to reactivate the elevator, but be careful, after grabbing each candle, the darkness left behind will follow you. Ghosts lurk around every corner hoping to make you their next victim. Descend through each floor without losing all 3 lives to escape the haunted owl hotel and win the game.

Uc-121-133 Head In The Clouds, Hunter Osborne, Jane Day, Chase Bell

C-Day Computing Showcase

Head in the Clouds is a video game that puts the player in the shoes of a child with ADHD (Attention Deficit Hyperactivity Disorder). Rain, the protagonist, is told by their mother to take out the trash, but keeps getting distracted and daydreaming instead. The player must beat platforming challenges to get Rain back on task. The narrative and gameplay is meant to represent the difficulties of having ADHD.

Uc-122-135 Mutatio Mentis, Isaac Alderman, Braden Mizell, Collin Sutton

C-Day Computing Showcase

Mutatio Mentis 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 narrative is set in 1637 Florence, Italy, in the wake of the Great Plague of Milan, and draws heavily from renaissance culture. Each of the three subjects have progressively more complex personal conflicts, which present through the gameplay aesthetics of each act, as the gameplay changes to reflect the problems of each subject. Our focus is on tackling mental and emotional …

Uc-131-162 Physical 8-Bit Cpu, Samuel Hoerner, Aryan Merchant, John Dislen, Kyran Day

C-Day Computing Showcase

This isn’t an app - it’s the machine behind it. A fully functional 8-bit CPU, built from scratch, turning raw signals into real computation. The system combines our custom assembly-to-run, FPGA-driven control unit (CU), discrete transistor Arithmetical Logical Unit (ALU), external memory, and 5 registers to execute programs through a fetch-execute cycle. The result is a tangible computing platform that bridges low-level digital logic with high-level system behavior.

Uc-135-129 Fishgame: Mobile Hyper-Casual Fishing Game, Lauren Rousell, Lisbeth Martinez, Jacob Portillo, Jacob Miller

C-Day Computing Showcase

fishGame is a strategy-driven hyper-casual mobile game that combines the accessibility of traditional mobile gameplay with the progression depth of a roguelike. The project was developed over a three-month period using Unity 6.3 and related production tools. Following the completion of an alpha build, testing was conducted through gameplay sessions and a detailed follow-up survey to gather feedback on player experience, clarity, and engagement. Results indicated that fishGame was well received, with players reporting low levels of confusion and strong replayability. These findings suggest a clear interest in mobile games that offer greater depth while preserving the immediacy and simplicity …

Uc-136-132 Gamma Guardian: Teaching About Hlh, Lauren Rousell, Logan Leichter, Jacob Portillo, James Lock

C-Day Computing Showcase

Gamma Guardian is an educational strategy game for children ages 6 to 12 that introduces Hemophagocytic Lymphohistiocytosis (HLH) and immune-system balance through interactive gameplay. The game places players inside the human body, where they use touch controls, antibody shields, and immune-response management to defend against interferon gamma, bacteria, and pathogens. Development used Unity 6.3 and followed a level-based design with educational feedback, AI-driven enemies, and a simple visual style to support learning. The result is a fully functional game with five levels, integrated educational content, and multi-platform support, exceeding the original goal of producing only a demo.

Uc-137-237 The House Watches, Lisbeth Martinez, Lauren Rousell, Jaime Mcbride, Karizma Quiroz, Aidan Kleine

C-Day Computing Showcase

The House Watches is a horror-puzzle game about a boy and a dog trying to reunite after mysterious supernatural creatures, called duendes, invade their home. The game consists of two levels, each with a different mode of gameplay; level one is a more traditional item-collection horror experience, and level 2 is based around puzzles that must be completed in a limited time frame. The House Watches includes original art, music, and models, as well as a dynamic gameplay system that increases the difficulty of each level over time.

Uc-142-177 Multiplayer Spsu Tub Racing Video Game, William Pitts, William Urvan, Thomas Powell, Joshua Young

C-Day Computing Showcase

Multiplayer Bathtub Racing revives a well-known Southern Polytechnic State University tradition through a digital multiplayer experience built in Unity. The project extends a prior single-player tub racing game by adding online multiplayer

Uc-144-182 Paracosm, Va'quez Friday

C-Day Computing Showcase

Paracosm is a gothic horror game revolving around a tattoo artist, named Villain, who discovers that his art has suddenly come to life. Despite this phenomenon, Villain insists on finishing his tasks before three Am, due to his superstitious nature. What he doesn’t know yet is that if he doesn’t finish by that time, then he will be forever stuck in his shop with no escape. He will also learn that not all of his lively drawings are friendly, and that the not-so friendly drawings of his will stop at nothing to make sure he fails-Knowing that if he fails, …

Toyon 68 - Full Issue

Toyon: Multilingual Literary Magazine

No abstract provided.

Full Issue - Toyon 62

Toyon: Multilingual Literary Magazine

No abstract provided.

Treating Depression With L-Methylfolate: A Review Of Evidence, Marianna G. Pettit

Epsilon Sigma at-Large Research Conference

Abstract

Introduction & Background: Depressive disorders have many different causes. Folate deficiency can sometimes be behind these disorders, due to this vitamin’s role in neurotransmitter synthesis. Some research suggests that supplementing with an active form of folate improves depressive symptoms with and without antidepressant use. Purpose Statement Question: For individuals with depressive disorders, does supplementation with L-methylfolate (LMF) reduce symptoms of depressive disorders with or without use of an antidepressant? Literature Review: A manual literature search for sources published within the past 7 years revealed 557 documents from four different databases. Key search words included, “depression,” “treatment,” …

A Historical Analysis Of The Development Of Prenatal Care In The U. S., Kristen Montgomery Phd, Cnm, Aprn, Shelby Greene Bsn, Rn, Lauren Griffin Bsn, Rn, Destiny Lewis Bsn, Rn, Lauryn Lewis Bsn, Rn, Bryleigh Newberry Bsn, Rn, Brittany Scott Bsn, Rn

Epsilon Sigma at-Large Research Conference

Purpose: This paper examines the historical development of prenatal care in the United States, tracing its evolution from informal, home—based management to structured, evidence-based systems that are central to maternal and fetal health.

Aims: The specific aim of this project was to determine how prenatal care evolved in the United States.

Methods: A historical methods approach was used. Historical documents were reviewed using PubMed, CINAHL, and Google Scholar to find relevant resources. Relevant articles were reviewed by the research team and analyzed for components regarding the development of prenatal care. All members of the research team reviewed the articles and …