Digital Commons Network^™

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 …

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 …

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 …

The Relationship Between Walking Vs Running And Mental Health, Kim Ning

Science University Research Symposium (SURS)

The present study examined how mental health, specifically depression, anxiety, and stress, relates to the amount of hours one spends in a week walking or running. While previous research has focused on the general effects of physical exercise on mental health, few studies have compared the influence of high intensity physical activity to low intensity physical activity specifically in college students` mental health outcomes. Fifty-five (N=55) Belmont University students were asked to fill out a questionnaire about the amount of hours they spent running or walking in a week and different mental health surveys. A series of six correlation tests …

Anxiety’S Relationship With Caffeine Usage And Frequency, William H. Simmons, Kyra Cannon, Harry Lackey

Science University Research Symposium (SURS)

Previous research has suggested mixed findings regarding caffeine’s relationship with anxiety, despite caffeine being the most widely consumed psychoactive substance. This study investigated this relationship further, exploring whether caffeine consumption is associated with perceived anxiety symptoms. Sixty-five participants completed an online survey through Qualtrics that assessed daily caffeine intake and anxiety levels using the Generalized Anxiety Disorder-7 (GAD-7) scale. A linear regression tested the relationship between levels of caffeine use and perceived levels of anxiety. An independent samples t-test compared anxiety levels between participants who consume less than 400 mg of caffeine and those who consume 400 mg or more. …

Visual Attention, Emotional Arousal, And Moral Judgment In Legal Evidence Processing, Audrey G. Laskey, Kyra Cannon, Josette Sanders, Lingfei Luan

Science University Research Symposium (SURS)

Moral judgment in legal decision-making is influenced not only by the evidence presented but also by emotional and attentional processes. Prior research has shown that physiological arousal during exposure to emotionally charged or socially relevant information can shift the way evidence is evaluated and weighted in decision-making (Armstrong & Olatunji, 2012; Godwin et al., 2016). However, few studies have examined how autonomic arousal relates to guilt decisions using real, high profile case materials. We were interested in examining how possible exposure to violent content on social media might desensitize individuals, like jurors, leading to lighter judgments for serious crimes. This …

Body Image In Athletes Compared To Non-Athletes, Madeleine R. Scott, Yara Ali, Hana Sokolovic, Jenny Glenn

Science University Research Symposium (SURS)

This current study examines the relationship between student athletes and body image. Current research demonstrates that body image has become a growing concern regarding young adults, yet few studies have investigated whether there is a relationship between body image and being a student athlete, specifically regarding their body dissatisfaction, negative eating behaviors, social comparison, and self-objectification. In this study, ninety (N= 90) students at Belmont University participated in a survey that assessed their body dissatisfaction, negative eating behaviors, social comparison, and self-objectification. Of our total participants, twenty eight (N= 28) reported to be part of a student athlete organization, while …

Relationship Satisfaction Among College Students, Gracie L. Sager, Makenna A. Stratton, Kyrah Brodie, Autumn R. Thomas

Science University Research Symposium (SURS)

Understanding relationship satisfaction is essential to individual well-being, particularly in college populations, as it relates to happiness, mental and physical health, and everyday functioning. Existing measures lack sensitivity to developmental dynamics shaped by evolving expectations and modern relationship stressors. This study aimed to develop a measure that examines the relationship between overall relationship satisfaction and key factors in romantic relationships, including partner support, relationship conflict, relationship length, and communication quality among college students. We hypothesize that the newly developed measure of relationship satisfaction will demonstrate good internal reliability (Cronbach’s α ≥ .700.) We also expect that perceived partner support would …

The Effect Of Noise Pollution On The Diversity And Abundance Of Bird Species At Different Locations At Warner Parks, Nashville, Tn, Jeremy T. Little, Wyatt L. Fooks, Darlene Panvini, Jackson Whiteside

Science University Research Symposium (SURS)

Noise pollution is a significant abiotic factor that can alter bird biodiversity and abundance of birds in urbanized areas. A new road is proposed through a relatively untouched area of Warner Parks in Nashville, TN, which may affect the bird community. Acoustic Song Meters were used to monitor bird sounds and soundscapes in the proposed road site and two areas of the park with different levels of noise pollution (roadside=high noise; isolated=low noise) for three weeks. Recordings were analyzed to determine bird species and abundance at each site to predict how increasing noise pollution may affect bird diversity in the …

Kairiomone Moderated Anti-Predator Responses In Pleurocera Laqueta, Daisy V. Mcleod, John Webb, Amanda Waggoner, John Niedzwiecki

Science University Research Symposium (SURS)

Predator-prey coevolution in freshwater ecosystems often involves chemical communication, where the prey detect predator released compounds known as kairomones. These chemical cues allow prey to alter behavior to reduce risk of predation. In Middle Tennessee streams, the native snail Pleurocera laqueata commonly coexists with predator crayfish, and previous studies have shown that exposure to crayfish kairomones can induce an anti-predator “crawl out” response. This response is commonly demonstrated through snails climbing above the waterline to avoid crayfish predators below. We hypothesized that Pleurocera laqueata would display a similar anti-predator response when exposed to both native and non-native crayfish cues. After …

Physiological Response To Concealed Information In A Mock-Crime Paradigm​, Ezra Koenigsberg, Tamya Mchenry, Jasmine Manzana, Taylor Powell, Samantha Jackson

Science University Research Symposium (SURS)

The Concealed Information Test (CIT), based on Lykken’s guilty knowledge framework, measures physiological responses when individuals recognize crime-related details compared to neutral alternatives. Previous studies show that variations in stimulus presentation enhance the CIT’s accuracy, especially through changes in electrodermal activity and heart rate. However, few studies have tested whether a Qualtrics-based mock-crime version of the CIT can replicate these physiological patterns. This study addresses that gap by adapting the CIT for a digital research environment. The goal is to determine whether participants show greater physiological arousal—measured through skin conductance, heart rate, and respiration—when exposed to crime-related “critical” stimuli compared …

Deconstructing The Black Box: An Explainability Analysis Of Deep Learning Architectures In Cytopathology, Chase A. Garrett

Master's Theses or Doctor of Nursing Practice

Deep learning shows strong potential in medical-image analysis, yet adoption in cyptopathology remains limited. Cytopathology could benefit from deep learning applications by improving diagnostic efficiency and accuracy. However deep learning comes with a notorious “black box” that keeps the models from being transparent and trustworthy for widespread clinical adoption. We conducted a comprehensive and comparative analysis of several deep learning architectures for multi-class classification of acute leukemia types, ALL, AML, and normal healthy cells from peripheral blood smear images. The models in this research include a Vision Transformer (ViT) and a diverse selection of Convolutional Neural Network (CNN) models. The …

When Scripts Diverge: Strengthening Low-Resource Neural Machine Translation Through Phonetic Cross-Lingual Transfer, Ammon Shurtz

Theses and Dissertations

Multilingual Neural Machine Translation (MNMT) models enhance translation quality for low-resource languages by exploiting cross-lingual similarities during training--a process known as knowledge transfer. This transfer is particularly effective between languages that share lexical or structural features, often enabled by a common orthography. However, languages with strong phonetic and lexical similarities but distinct writing systems experience limited benefits, as the absence of a shared orthography hinders knowledge transfer. To address this limitation, we propose an approach based on phonetic information that enhances token-level alignment across scripts by leveraging transliterations. We systematically evaluate several phonetic transcription techniques and strategies for incorporating phonetic …

Constraints On Axionlike Particles From Veritas Observations Of A Flaring Radio Galaxy In The Perseus Cluster, Avery Archer, C. B. Adams, P. Bangale, J. T. Bartkoske, W. Benbow, J. L. Christiansen, A. J. Chromey, A. Duerr

Physics & Astronomy Faculty publications

Axion-like particles (ALPs) are hypothetical particles that emerge in numerous theoretical extensions to the Standard Model. Their coupling to electromagnetic field implies that ALPs would mix with photons in the presence of external magnetic fields. As ALP phenomenology is governed by the mass and strength of its coupling, there is a subset of this parameter space in which this mixing would be expected to leave an imprint on the spectra of TeV γ-ray sources. In 2017, the VERITAS γ-ray observatory recorded the second day of a dramatic flare of the radio galaxy NGC 1275, embedded at the center of the …

Optical, Electrical, And Structural Properties Of Nio Thin Films, Derived By Sol–Gel Method, Tatyana Ivanova, Antoaneta Harizanova, Nikolay Petkov

Physical Sciences Publications

NiO films were successfully deposited by sol–gel spin coating on Si, glass, and ITO-covered glass substrates. The impact of the film thickness (the different number of layers), annealing temperatures (from 300 to 500 °C), and the substrate type on the crystal structure, film morphology, optical, and vibrational properties was investigated. X-ray diffraction (XRD) revealed a polycrystalline structure and the appearance of the cubic NiO phase. Field Emission Scanning Electron Microscopy (FESEM) was applied to explore the surface morphology of NiO films, deposited on glass and ITO substrates. The oxidation states of Ni were determined by X-ray photoelectron spectroscopy (XPS). The …

Final Butte Priority Soils Operable Unit (Bpsou) Insufficiently Reclaimed Sites Remedial Action Work Plan (Rawp): Bres No. 104 (Colorado Dump) North Slope, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Spatial–Temporal Deep Learning For Electric-Vehicle Charging Demand: An Exploratory Study Of Graph Convolutional And Lstm Networks Performance, Maher Alaraj, Carla Martins, Mohammed Radi, Mohamed Darwish, Munir Majdalawieh

All Works

Electric-vehicle (EV) charging is a localized, time-varying load that challenges distribution networks. This study offers practical insights into when spatial graph structure adds value beyond temporal context, utilizing real-world data and a transparent evaluation. We compare Long Short-Term Memory (LSTM) and Graph Convolutional Network (GCN) models for hourly EV-charging energy forecasting, based on 145,778 sessions recorded in Boulder, Colorado (2018–2023). After preprocessing and temporal alignment, temporal covariates (hour, day, month, year) and, when applicable, ZIP-code indicators were engineered. LSTMs were trained with 1 h and 24 h input windows, with or without ZIP features, and evaluated through 5-fold cross-validation. GCNs …