Digital Commons Network^™

How Ai-Enabled Hiring Affects Organizational Health? Challenges And Future Direction, Shiza Hirani

Creating Healthy Work Environments (CHWE)

In today’s competitive landscape of hiring, organizations are responsible to not only maintain ethical integrity, ensure fairness, and protect job applicants' privacy, but also uphold a more data-driven and streamlined approach to save time per hire. The integration of Artificial Intelligence (AI) into the workplace presents opportunities such as enhanced productivity, efficiency, and creation of new roles. However, the widespread implementation of AI also raises ethical and privacy challenges that can deteriorate organizational health. AI-driven systems can inadvertently introduce biases and black box properties in hiring decisions which can erode trust and transparency, compromise employee privacy, and increase psychological stress …

Psychological Safety In The Classroom: Effects On Nursing Student Well-Being And Academic Success, Erica Frost, Kelli D. Whittington, Glory Omole, Chloe Birchett

Creating Healthy Work Environments (CHWE)

In nursing education, fostering psychologically safe learning environments may support student well-being and academic achievement. Edmondson's Psychological Safety Framework (1999) provides the theoretical foundation for this study, framing classroom psychological safety as an environmental factor that influences student well-being and academic outcomes through its impact on learner engagement and interpersonal risk-taking. There is limited research exploring the relationship between classroom psychological safety and nursing student well-being, particularly in undergraduate face-to-face classroom settings.
The purpose of this study is to examine nursing students' experiences, perceptions, and academic outcomes to better understand how instructor behavior and the classroom teaching environment influence student …

Advancing Appropriate Staffing: One Element Of A Healthy Work Environment, Kiersten Henry

Creating Healthy Work Environments (CHWE)

The session provides an update on the outcomes of recommendations made by The National Nurse Staffing Task Force. These include AACN’s Standards for Appropriate Staffing and the incorporation of appropriate staffing as a National Performance Goal by The Joint Commission. The inextricable link of appropriate staffing to the Healthy Work Environment standards will be discussed. Resources and exemplars for appropriate staffing are included, and audience questions and participation encouraged.

Celebrating Ancillary Staff Achievement In The Hospital Setting, Lori Ischinger

Creating Healthy Work Environments (CHWE)

Clinical ladder programs have been available for decades to promote bedside nursing recognition and retention. They have been proven to increase the perception of being valued by an organization, which in turn increases job satisfaction and performance. There is limited research to support the use of these same types of programs for ancillary clinical staff. These staff are essential members of the care team and deserve recognition for the work that they do. The objective was to successfully develop, implement and maintain a clinical ladder program that is specifically tailored to recognize and reward the clinical excellence of this staff. …

Culturally Diverse Undergraduate And Graduate Students Speak Out: Overcoming Leadership Barriers At A Predominantly White Institution, Davonte Mcclam

Undergraduate Research Symposium

On October 15ᵗʰ, 2025, Minnesota State University, Mankato (MSU), hosted an annual leadership conference. Within the programming was a panel designed to explore the experiences of culturally diverse student leaders on campus. The intention was to have an honest conversation, regarding the environment for which they exist on campus. Using a qualitative thematic analysis, results uncovered four themes: the unspoken expectation of perfection, tokenism, the burden of representation, and the acceptance of discomfort.

Innovative Approach To Enhance The Efficiency Of Building Integrated Photovoltaic Panels Through Automated Cooling And Cleaning Using Condensate Water In Extreme Climates, Ali Hasan Shah

Thesis/ Dissertation Defenses

Photovoltaic (PV) systems in hot and arid climates experience significant performance degradation due to elevated operating temperatures and rapid dust accumulation. Temperature increase alone reduces power output by about 0.4% to 0.5% per degree Celsius above rated conditions. In desert regions, module temperature commonly exceeds standard conditions, which results in power loss above 10%. Dust accumulation adds further degradation, and field measurements report soiling losses above 1% per day without cleaning, with cumulative losses exceeding 20% within short exposure periods. When high temperatures and dust occur together, the combined effect leads to a severe reduction in energy yield. Current mitigation …

Shelter Portal: Qr-Based Service Tracking For Low-Barrier Shelters, Kate Steele, Colton Knopik, Michael Fischer

2026 Symposium

Shelter Portal is a web-based service tracking application developed for low-barrier shelters, including Catholic Charities’ House of Charity and Rising Strong programs. Many shelters still rely on manual headcounts and estimated meal totals, which are labor-intensive, error-prone, and insufficient for tracking individual service use over time. This limits operational visibility and makes it difficult to generate reliable reports, identify usage trends, and support external reporting requirements. Shelter Portal addresses this problem by providing a more accurate and privacy-conscious way to document shelter services.

The system was designed as a kiosk and web-based platform that uses scannable QR code cards to …

Grp-02-199 Topological Constraints For Protein Folding, Charles Fanning

C-Day Computing Showcase

We study whether topological loss-based constraints improve multidomain whole-chain protein structure prediction beyond the ColabFold baseline by better preserving the topologies of folded proteins. We benchmark against Wasserstein metrics with our own virtual persistence and RKHS semi-metric constraints as well as higher-order virtual persistence diagrams.

Rethinking Housing Evolution In The Uae: An Investigation Of Sha’Abiat, Socio-Spatial Integration, And Sustainable Urban Development In Al Ain City, Boshra Hassan

Thesis/ Dissertation Defenses

Housing is both a fundamental human need and a core driver of urban form, social life, and cultural continuity. In the United Arab Emirates (UAE), the rapid transformations of the post-oil era were marked by the establishment of the first national housing model, the Sha’abiat, compact, community-oriented neighborhoods that shaped the early urban form of Emirati cities and reinforced social cohesion. Over time, however, housing policies shifted the housing-scape toward private villas, leaving Sha’abiat increasingly challenged by demographic change, maintenance deficiencies, displacement, and demolition under modernization pressures. Despite these challenges, Sha'abiat remains integral to the UAE’s urban identity and provides …

Uc-177-226 Scrapper Kinetics Llc, Mikhail Rudenko, Andrew Torimoto, Jimmy Lock, Marco Cheng

C-Day Computing Showcase

Scrapper Kinetics LLC is a multiplayer and multimodal physics puzzle game. Where players get to choose between playing in VR or Desktop mode, and then, with up to 7 friends (8 players total), try to make a profit in the harsh dead space hulks they have been hired to scrap. We made the game as a test to see how easy it is to have completely different devices interact in the same play space.

Ur-166-204 Multi-User Stem Learning Experience Powered By Llm Conversational Agents, Devon Haynes, Chance Boecker, Oliver Haggard

C-Day Computing Showcase

While Extended Reality (XR) provides experiential and interactive foundations for STEM education, current storytelling and narrative-driven applications often lack responsive nonplayer characters (NPCs), limiting interactive potential through pre-scripted stories. Additionally, despite the growth of Large Language Model (LLM) integration in XR, limited research explores the combined use of multi-user XR systems and conversational Artificial Intelligence (AI) to facilitate real-time, adaptive instruction. This project seeks to address these gaps by 1) Developing a narrative-driven STEM learning XR prototype that incorporates synchronous multi-user interaction and an embedded LLM-driven conversational agent and 2) Exploring the effectiveness of combining these technologies to improve learning …

Ex-04-142 Modeling Distress And Evaluating Chatbot Safety For Suicide-Related Social Media Texts, Rehma Razzak

C-Day Computing Showcase

This project addresses the urgent need for transparent chatbot safety evaluations amid rising concerns about AI-facilitated self-harm. Using public social media datasets, we simulate two tasks: (1) detecting suicidal ideation via emotion-based risk scoring, and (2) stress-testing a support-style chatbot against 888 high-risk prompts, including euphemisms and “for a story” framing. A multi-label classifier trained on GoEmotions feeds emotion profiles into a logistic regression model to generate suicidality risk scores. These scores guide a local chatbot built with Ollama’s llama3, which analyzes user messages and steers responses toward safe, empathetic behavior. Evaluation shows ~90% of replies were safe or supportive. …

Gc-113-125 Carter’S Lake Visitor Center Boating Safety Game, Hunter Blake, Chancelor Brown, Lauren Robbins, Will Bryant, Kendrick Bryant

C-Day Computing Showcase

The Boating Safety Game is an educational, kiosk-based touchscreen game created for the U.S. Army Corps of Engineers and the Carters Lake Visitor Center. It is designed to improve the knowledge and engagement of boating safety concepts for visitors, particularly for students and youth. The project was developed using multiple game scenarios meant to reinforce safe boating practices through tutorial scenes, top down navigation, life jacket and required item selection tasks, and player motivation through quizzes, feedback, scores, and a star ranking system. The game’s design emphasizes accessibility and retention through simple touchscreen interaction, guided instruction, and repeated feedback on …

Gc-141-167 Smart Resume Screening & Interview Preparation Assistant, Loreli Olien, Tomas King, Hanzhi Chen, Kawanda Gray, Brittany Frazier

C-Day Computing Showcase

The hiring process often relies on manual resume review and keyword matching, which can lead to inconsistent and biased candidate evaluations. This project introduces a Smart Resume Screening and Interview Preparation Assistant designed to improve transparency and consistency in early-stage candidate evaluation. The system allows recruiters to upload resumes and job descriptions, then uses embedding-based semantic matching and large language models to assess candidate alignment across skills, experience, education, and projects. The application generates structured rankings, explainable insights, and tailored interview questions. This project focuses on developing a functional prototype that demonstrates how AI can enhance decision support while maintaining …

Gc-161-210 Hybrid Path Planning Using Genetic Algorithm, Caitlin Tigani, Ramisa Fariha Joyee, Wasif Mohammad

C-Day Computing Showcase

This research investigates whether uninformed search (BFS) or informed search (A*) is more effective when combined with a Genetic Algorithm for maze path planning. We design and implement four algorithms: baseline BFS and A*, hybrid GA+A*, and hybrid GA(BFS+A*). Our findings show that while A* alone performs optimally, integrating it with GA can produce alternative quality solutions, though with computational trade-offs. The study demonstrates that GA+A* provides the best balance between solution quality and runtime efficiency.

Gc-178-191 Communication App: Ai-Assisted Aac Platform​, Alex Wills, Maryam Koya

C-Day Computing Showcase

The Communication App is an accessibility-focused mobile application designed to support individuals with speech impairments, strong or unrecognized accents, and neurodivergent communication needs. The system leverages AI assisted speech-to-text (STT) and text-to-speech (TTS) technologies to enable real-time and seamless communication between users.This project aims to bridge communication gaps by providing a customizable, adaptive platform that learns user speech patterns over time. The application integrates cloud-based services, secure communication protocols, and an intuitive user interface to ensure usability, performance, and accessibility.

Grm-010-170 Ai-Enabled Water Quality Framework For E. Coli Prediction And Forecasting, Sangeetha Devaraj, Jui Mhatre

C-Day Computing Showcase

Water quality monitoring is essential for public health and environmental sustainability, yet existing monitoring infrastructures remain sparse, fragmented, and incomplete. Data from the United States Geological Survey (USGS) indicate that while over 1.5 million sites are cataloged in the USGS Water Data for the Nation, only a small fraction are actively reporting water quality measurements, with significant reductions observed in recent years. Moreover, critical parameters such as pH, water temperature, dissolved oxygen, turbidity, and microbial indicators like Escherichia coli are inconsistently measured, with widespread missing and irregular data. This work presents an AI-enabled water quality data framework designed to address …

Grm-012-173 Can You Trust Ai Code? Understanding And Detecting Breaking Changes Using Llms, K M Ferdous, Kowshik Chowdhury

C-Day Computing Showcase

AI-generated code is increasingly prevalent in software engineering practices, yet its reliability in preserving backward compatibility remains underexplored. This paper presents a unified study of (i) how often AI-generated code introduces breaking changes and (ii) whether large language models (LLMs) can detect such changes from commit-level diffs with explanations. We analyze 7,191 agent-generated and 1,402 human-authored pull requests from Python repositories using an AST-based approach to identify potential breaking changes. Our results show that AI agents introduce fewer breaking changes overall than humans (3.45% vs. 7.40%) in code generation tasks. However, agents show higher risk in maintenance tasks, where refactoring …

Grm-081-207 Leveraging Non-Parametric Longitudinal Rank Sum Tests (Lrst) For Robust Global Treatment Effect Estimation In Alzheimer’S Disease, Imaan Shahid

C-Day Computing Showcase

Parametric approaches, such as Mixed Models for Repeated Measures (MMRM), are standard in Alzheimer’s Disease (AD) clinical trials. However, these models often falter when data violates assumptions of normality or follows non-linear trajectories—common occurrences in AD due to floor/ceiling effects on cognitive scales and heterogeneous disease progression. This study evaluates Longitudinal Rank Sum Tests (LRST) as a non-parametric alternative to maintain statistical power and robustness.

Grm-094-176 Influence Of Speech Disfluencies And Prompt Optimization On Llm-Based Alzheimer's Detection, Muhammad Awais Arshad

C-Day Computing Showcase

This study evaluates how speech disfluencies and prompting strategies impact LLM-based Alzheimer’s Disease (AD) detection. We compared transcripts with preserved disfluencies (ADReSS) against clean transcripts (ADReSSo) using four state-of-the-art LLMs. Key Discovery: Complex prompts induce a "mirror-image" classification bias, where DeepSeek models severely over-classify AD, and GPT-5.2 over-classifies Cognitively Normal (CN) individuals. Optimization Fix: Applying DSPy MIPROv2 effectively mitigated bias in simpler prompts, while TextGrad successfully optimized complex, multi-step prompts.

Grm-095-230 A Multimodal Llm Framework For Automated Construction Blueprint Analysis With Real-Time Decision Support, Muhammad Awais Arshad

C-Day Computing Showcase

This study addresses the high hallucination rates of Vision-Language Models (LLMs) when analyzing complex, hybrid construction blueprints. We developed a dual-input pipeline that pairs high-resolution images with a four-layer JSON "Digital Twin" (vector text, raster OCR, geometry) to mathematically ground the LLM's visual interpretation. Key Engineering Achievement: We processed a massive 137-sheet civil engineering project with zero errors. By introducing a multi-tier JSON pruning strategy, we cut token usage by up to 70% and processed the entire batch from $13-$15 to just $0.93. Decision Support Extension: We integrated real-time traffic data (TomTom) and GDOT procedural policies to transform the pipeline …

Grm-132-159 Integrating Causal Inference With Graph Neural Networks For Alzheimer’S Disease Analysis, Pranay Kumar Peddi

C-Day Computing Showcase

Deep graph learning has advanced Alzheimer’s disease (AD) classification from MRI, but most models remain correlational, confounding demographic and genetic factors with disease-specific features. We present Causal-GCN, an interventional graph convolutional framework that integrates do-calculus-based back-door adjustment to identify brain regions exerting stable causal influence on AD progression. Each subject’s MRI is represented as a structural connectome where nodes denote cortical and subcortical regions and edges encode anatomical connectivity. Confounders such as age, sex, and APOE4 genotype are summarized via principal components and included in the causal adjustment set. After training, interventions on individual regions are simulated by severing their …

Grm-159-214 Transportation Energy And Emission Modeling And Analysis Tool (Teemat), Laeticia Neno Aloyem

C-Day Computing Showcase

The Transportation Energy and Emission Modeling and Analysis Tool (TEEMAT) is a web-based decision-support framework for evaluating the environmental impacts of EV adoption across U.S cities. TEEMAT integrates a feedforward neural network trained on MOVES 4.0 for tract-level vehicle emissions (CO₂, NOₓ, PM₂.₅), a macroscopic traffic and activity-based model capturing congestion-driven emission spikes, and a Meta-Prophet model trained on NREL Cambium data for grid emissions (CO₂, CH₄, N₂O). Results show that while EV adoption reduces tailpipe emissions, rising travel demand and congestion-induced low speeds can significantly offset these gains underscoring that meaningful decarbonization requires coordinated transportation and energy grid strategies.

Grm-169-137 Predicting The Stock Market's Next Move: How Neural Network Architecture Shapes Forecasting Accuracy, Roderick Powell

C-Day Computing Showcase

Three feedforward neural network (FFNN) architectures — bottleneck, parallel multi-path, and residual parallel — were trained on ten years of daily S&P 500 (SPY ETF) price and volume data to predict next-day market direction (Up/Down). All three demonstrated predictive ability above random chance. Architectural choice directly determined class prediction bias: the bottleneck concentrated errors on Up days, the parallel architecture distributed them evenly, and residual connections inverted the bias toward Down days. Model 2 (parallel) achieved the highest test accuracy (58.2%) and the most balanced class predictions among the three configurations.

Grm-170-221 Mind Modeling For Neuroadaptive Vr Labs, Harish Chary Nagulapally

C-Day Computing Showcase

Students with motor impairments lack equal access to hands-on STEM laboratory experiences. This study investigates whether EEG signals and eye-gaze data contain distinguishable patterns linked to specific hand motor functions both physical and imagined. Identifying this relationship is the critical first step toward a BCI-driven VR system for accessible STEM education.

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, …