Digital Commons Network^™

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-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.

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-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-139-175 Agentic Debugger And Documenter, Olaoluwa Omodemi, Jade Le, Preston Dietz

C-Day Computing Showcase

Modern software development teams routinely introduce subtle bugs — mutable default arguments, bare exception handlers, insecure eval()/exec() calls, resource leaks, hardcoded secrets — that escape manual review but accumulate into technical debt and security risk. Existing linters identify problems but leave remediation to the developer. This project investigates whether a coordinated multi-agent system, combining rule-based static analysis with generative LLM reasoning, can autonomously detect, fix, and document such issues with no developer involvement beyond providing the input file.

Gc-168-220 Active Directory To Cloud Security Data Pipeline, Michael Butler, Shahiba Shamshad, Mounia Touil, Koko Afantchao

C-Day Computing Showcase

This project builds an automated pipeline that extracts identity and asset data from on-prem Active Directory, stages it in Google BigQuery, and securely sends normalized data to Lucid through Google Cloud Run. Using PowerShell scripts, the system collects users, groups, computers, DNS, DHCP, and related metadata without changing the source environment. BigQuery serves as the staging layer for validation and processing, while Cloud Run transforms and transfers the latest data to Lucid for visualization. The goal is to provide a repeatable, traceable, and reliable workflow that improves visibility into identity relationships for security investigations, auditing, and validation in a controlled …

Grm-179-195 A Retrieval-Augmented Generation (Rag) System For Bible Question Answering Using Scriptural Text And Commentary, Maryam Koya, Pragya Mishra

C-Day Computing Showcase

This project develops and evaluates a question-answering (QA) system designed to address theological and interpretive questions about the New Testament. It uses a Retrieval-Augmented Generation (RAG) framework that integrates a pretrained large language model with a structured knowledge base consisting of public-domain Berean Standard Bible (BSB) New Testament and New Testament commentaries. User queries are embedded to retrieve semantically relevant passages, which are then supplied as contextual input for answer generation. The system is evaluated based on retrieval quality, answer faithfulness, and comparison to ground truth. Performance is benchmarked against a baseline BM25 keyword retrieval system without commentary, demonstrating that …

Grp-07-163 Smishguard: An Ai-Powered Framework For Sms Phishing Detection And Alert System For Vulnerable Users, Jiban Krisna Das

C-Day Computing Showcase

This research introduces SMISH-GUARD, a multi-layer framework for adaptive SMS phishing (smishing) detection that integrates language-aware semantic modeling, graph-theoretic campaign reasoning, and cost-sensitive decision calibration within a unified architecture. The framework integrates dual transformer encoders for multilingual semantic understanding with a heterogeneous temporal graph layer that captures relational attack signals such as shared URLs, sender reuse, and campaign propagation patterns. A cost-sensitive decision optimization module is further incorporated to translate probabilistic model outputs into risk-aware alert policies that explicitly balance false-positive inconvenience against the higher societal and financial cost of missed smishing attacks. The study evaluates four integrated datasets comprising …

Gc-140-126 Machine Learning Models For Solar Power Output Prediction: A Comparative Study With Adaptive Pso-Based Random Forest Tuning, Hasitha Mahabaduge

C-Day Computing Showcase

Accurate prediction of solar power output is essential for energy scheduling, grid reliability, and efficient integration of renewable resources. Because photovoltaic generation is governed by changing atmospheric conditions, forecasting output is inherently a nonlinear learning problem. This study evaluates four machine learning models — Linear Regression, Random Forest, Multi-Layer Perceptron (MLP), and an Adaptive Particle Swarm Optimization-tuned Random Forest (Adaptive PSO-RF) — using irradiance, temperature, humidity, wind speed, cloud cover, and time-derived features drawn from a dataset of 6,738 observations. Random Forest achieved the strongest overall performance, with an RMSE of 1,816.24 and an R² of 0.9533. The Adaptive PSO-RF …

Gc-154-201 Evaluation Of Generative Ai Responses To Pharmacy Prompts, Abrar Syed

C-Day Computing Showcase

Generative AI (GenAI) is increasingly used in pharmacy for drug information and decision support, yet accuracy remains variable. We systematically reviewed studies that reported full prompts and model responses to evaluate correctness across pharmacy‑relevant tasks. GenAI performed well on basic drug facts but was inconsistent for patient-specific recommendations and interaction checking, with occasional hallucinations. Findings support cautious, supplementary use in pharmacy practice and education.

Grm-175-233 From Leakage To Reliability In Dementia Detection, Crystal Tubbs

C-Day Computing Showcase

Automated dementia detection from speech offers a scalable approach to cognitive screening, but its reliability depends on rigorous experimental design. In this work, we reconstructed a Wav2Vec2-based dementia classification pipeline and identified critical methodological flaws, including speaker leakage, nondeterministic preprocessing, and invalid test partitions. We rebuilt the dataset using strict speaker-level separation, deterministic segmentation, and validation checks to ensure reproducibility. The corrected baseline achieved an accuracy of 44.74 percent and macro F1 score of 0.4439, reflecting a more realistic performance estimate than prior inflated results. This work establishes a scientifically valid foundation for evaluating augmentation strategies such as SpecAugment in …

Uc-123-140 P15-T2 Boating Safety Game Us Army Corps | Boating Mvp, Maryam Hamza, Saleh Hamza, Will Vanwinkle, Trevor Caffrey, Tobi Akinsunmi

C-Day Computing Showcase

This project is an interactive 2D educational boating safety game developed in Unity to teach students essential water navigation and life jacket safety practices in an engaging and immersive format. Designed in collaboration with a real-world sponsor, the game simulates a dynamic boating environment where players navigate obstacles, identify hazards, and make safety decisions under time constraints. The experience integrates instructional modules, guided character narration, and a final quiz phase that reinforces knowledge through immediate feedback, scoring, and achievement-based rewards. Players learn critical concepts such as proper life jacket fit, safe boating procedures, hazard identification, and shallow water awareness. The …

Ur-133-165 Quantum Machine Learning For Science And Engineering, Barclay Barnes, Anna Zharikov, Meriem Hamzi

C-Day Computing Showcase

Quantum machine learning (QML) has emerged as a promising method for overcoming the computational limitations of classical machine learning when analyzing large and complex data sets. This project investigates the application of QML algorithms to real-world science and engineering problems, with a focus on civil and environmental engineering datasets. We develop and evaluate a Python-based system, implemented in Google Colab, that integrates multiple quantum computing frameworks, including PennyLane, TensorFlow Quantum, and Qiskit, to implement and compare several QML models against their classical counterparts. The proposed system explores a range of algorithms such as Quantum Neural Networks, Quantum Support Vector Machines, …

Temperature-Induced Uncertainty In Fixed-Context Retrieval-Augmented Generation, Steven Zeng, Murat Kuzlu

Cybersecurity Undergraduate Research Showcase

This study examines how decoding temperature affects output uncertainty in a fixed-context retrieval-augmented generation (RAG) system. We define uncertainty as the semantic dispersion among repeated answers under the same fixed retrieved context, with greater dispersion interpreted as higher uncertainty. To isolate this answer-generation variability from retrieval drift, each question was paired with a fixed retrieved context, and repeated generations differed only in temperature. The experiment used nine questions drawn from a machine-learning textbook corpus, with three questions each at easy, moderate, and hard difficulty. Each question was evaluated at five temperatures (0.0, 0.25, 0.5, 0.75, and 1.0) over 30 iterations, …

Patient-Level Breast Cancer Classification From Multi-View Mammography With Side-Specific Evidence For Interpretability, Bushra Intakhab

Electronic Theses and Dissertations 2020 - Present

Breast cancer remains one of the leading causes of cancer-related death among women. Early detection through screening mammography is therefore very important. However, mammogram interpretation can be difficult because abnormalities may be subtle and spread across different views. The increasing workload of radiologists can also affect timely analysis. Artificial intelligence (AI) has been widely explored to support mammography interpretation by improving speed and consistency. However, many existing AI methods focus on patch-level or image-level analysis rather than the full patient exam. This limits their ability to capture the complete clinical picture, since important information is distributed across multiple views. In …

Spot Scanning Proton Beams Characterization Via Plastic Scintillator Detector, Saad Bin Saeed Ahmed

Electronic Theses and Dissertations 2020 - Present

In spot scanning proton therapy, the machine steers magnetically a narrow pencil beam across the target volume, delivering highly conformal dose distributions layer by layer. As beam size is very small, this introduces a dosimetry problem. The beam is dynamic and the dose gradients are steep — conditions that push conventional ionization chambers beyond their reliable operating range. Plastic scintillation detectors offer a potential solution. Their sub-millimeter active volumes avoid the volume-averaging effects over larger chambers, and their very high temporal resolution making real-time monitoring feasible. This thesis characterizes the Blue Physics plastic scintillation detector in spot scanning proton fields …

Re: Conditional Approval Letter For Diggings East 95 % Remedial Design Package Submittal (Dated March 25, 2006), The Silver Bow Creek Conservation Area (Sbcca) – 95 Percent Programmatic Technical Specifications Submittal (Dated March 25, 2026), The Butte Priority Soils Operable Unit (Bpsou) Sbcca Operations And Maintenance (O&M) Plan (Dated March 24, 2026), And The Sbcca Final Principles For A Greener Cleanup Report (Dated February 4, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

The Item Response Warehouse: What It Is, How To Use It, And Targets For Potential Improvements, Savira D. Nadela, Hansol Lee, Nishka Jain, Ayaan Gupta, Xingyi Zhang, Benjamin W. Domingue

Chinese/English Journal of Educational Measurement and Evaluation | 教育测量与评估双语期刊

The Item Response Warehouse (IRW) is a repository of harmonized item response datasets designed to support secondary analysis and methodological research in psychological and educational measurement. This paper serves as a practical guide for researchers interested in using the IRW. We describe the structure of IRW datasets and the quantitative and qualitative metadata available for dataset selection, and we demonstrate how researchers can navigate the IRW website to explore and compare available tables. We further show how the IRW R and Python packages can be used to filter datasets programmatically, download response-level data, and generate standardized citations for reproducible research …

Objections To The Use Of The Axiom Of Choice To Model The Physical World, Kensey Doughtie

Rose-Hulman Undergraduate Mathematics Journal

We look at platonistic mathematics and the application of this perspective in the physical world. We recognize paradoxes within Zermelo–Fraenkel set theory with the axiom of choice (ZFC) that conflict with physical reality, giving us reason to question if the axiom of choice should be so freely applied in theories of the physical world especially since it appears to enable a deterministic perspective. In theories of quantum physics, the axiom of choice is used to assume noncomputable numbers as initial conditions. This is equivalent to assuming a finite system contains an infinite amount of information at an instant in time; …

The Lookahead Transformer, Justin P. Olcott

Theses and Dissertations

Large Language Models (LLMs) are transformer models that generate text autoregressively in two stages: prefill and decode. The prefill stage processes all input tokens in parallel and caches them into the KV cache. The decode stage uses the KV cache, processes a single token at a time, and is generally memory-bandwidth constrained. Each decode step generates a token probability distribution, samples from it, appends the token to the cache, and repeats. This left-to-right, one-at-a-time generation paradigm has proven effective for language modeling in many domains; however, it also has limitations such as token permanence, constant compute per token generation, constant …

Model Unification: A Systematic Approach For The Cluster Expansion, Kolten Barfuss

Theses and Dissertations

A systematic approach for model unification of cluster expansion models is developed and demonstrated. The method builds on previous work with the Manifold Boundary Approximation Method (MBAM) to identify supremal models. The work is motivated by the exercise of unifying cluster expansion models – characterized by Π matrices – for two binary alloys that share one atomic species to form a model for the ternary alloy. The result is the Supremal Π Algorithm, a systematic unification algorithm that relies on an MBAM-informed understanding of models and on the singular value decomposition. The algorithm yields ΠΛ, a Π matrix for the …

Histopathology Image Classification Using Machine Learning, Mohammed H. Alali

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Histopathology image classification is a critical component of cancer diagnosis. However, the gigapixel scale of Whole-Slide Images (WSIs) and the high variability in tissue staining and scanner quality across medical centers present significant computational challenges. This dissertation proposes a comprehensive machine learning framework to address these challenges, bridging the gap between theoretical models and practical clinical deployment.

First, to manage the massive dimensionality and noise inherent in WSIs, this research develops a robust feature extraction methodology. The pipeline implements a stringent tile filtering technique to eliminate physical artifacts and resolve severe class imbalances. It integrates ConvNeXt alongside an attention-based pooling …

Cohomological Support Varieties Along Ring Maps, Ryan Watson

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Inspired by Quillen’s geometric approach to study group cohomology, Avramov introduced the theory of support varieties to study finite modules over local complete intersection rings. Geometric properties of the variety encode important homological information, and this theory has been a useful tool in studying complete intersection rings leading to many advances in local commutative algebra. By the work of several authors, this theory has now been expanded to encompass any noetherian local ring. Notably, Pollitz developed the theory of cohomological support varieties over Koszul complexes and used them to answer a question of Dwyer, Greenlees, and Iyengar regarding the structure …

Computational And Experimental Analysis Of Oleanolic Acid As An Allosteric Activator Of Sirt1, Emrah Sariyer, Eda Dokumacioğlu, Hati̇ce İskender

Turkish Journal of Chemistry

Sirtuin 1 (SIRT1) is a member of the sirtuin protein family and the biological effects of SIRT1 encompass various areas such as the ageing process, metabolism regulation, and cellular stress response. SIRT1 activators are compounds that enhance the activity of SIRT1 enzyme. These activators can be natural or synthetic compounds and have the potential to provide health benefits by augmenting the biological effects of SIRT1. Oleanolic acid (OA) is a triterpenoid compound that has antiinflammatory, antioxidant, and glucose and lipid metabolism-regulating properties naturally found in many plants. The aim of the present study was to investigate the effect of OA …

Sensitive Stripping Voltammetric Determination Of An Anthracene–Isatin Schiff Base In Biological Samples, Gökçe Öztürk, İrem Gökgöz, Emi̇ne Arslan, Eli̇f Şi̇şman, İnci̇ Seli̇n Doğan, Di̇lek Kul, Fatma Ağin

Turkish Journal of Chemistry

​In the present study, the electrochemical behavior and quantitative determination of the Schiff base 3-[(anthracen-2-yl)imino]-5-nitro-1,3-dihydro-2H-indol-2-one (ANT) were investigated for the first time using voltammetric techniques at a glassy carbon electrode. The effects of solvent composition, pH, and scan rate on the redox behavior of ANT were systematically evaluated. The compound exhibited an irreversible oxidation process governed by adsorption-controlled kinetics. Quantitative analysis was conducted using differential pulse stripping voltammetry (AdS-DPV) and square-wave stripping voltammetry (AdS-SWV) under optimized conditions. Both methods exhibited linear responses over the concentration range of 0.2–8.0 μM, with limits of detection of 0.0366 μM and 0.0433 μM for …

Influence Of Precursor Synthesis Temperature And Hydrothermal Alkaline Medium On Agglomeration And Crystal Phase Of Barium Titanate Nanoparticles, Murat Özen

Turkish Journal of Chemistry

Phase‑pure, cube‑like barium titanate (BaTiO3) nanoparticles were synthesized via hydrothermal treatment of a single‑source barium-titanium peroxo-hydroxide precursor. Precursor solutions prepared at room temperature, 0 °C, and –10 °C with various storage times exhibited Ba/Ti molar ratios ranging from 0.66 to 1.05 as determined by X-ray fluorescence. Cooling the precursor solution to –10 °C effectively suppressed the polymerization of Ti-O-Ti, yielding near‑ideal stoichiometry and finer precursor particles. Subsequent hydrothermal processing in NH4OH, KOH, and NaOH (0.5–10 M, 100–200 °C) revealed the strong dependence of agglomeration and morphology on the alkaline medium. While NH4OH and KOH resulted in severe agglomeration and the …

Templating Effect Of Neutral Dithiane Ligands On The Self-Assembly Of Cationic Au(I)-Phosphine Units, Farzane Osanlou, Abdollah Neshat, Mohammad Reza Yousefshahi, Vaclav Eigner, Michal Dusek, Shahram Seidi

Turkish Journal of Chemistry

The interactions between the dinuclear Au(I) precursor (dppm)Au2Cl2 and neutral sulfur-containing dithianes (specifically 1,3- and 1,4-dithiane) were examined in the presence of NH₄PF₆. Although these sulfur-based ligands were initially employed to facilitate the formation of mixed phosphine/sulfur Au(I) complexes, single-crystal X-ray diffraction analysis revealed the unexpected generation of multinuclear Au(I) assemblies exclusively coordinated by phosphine ligands. When 1,3-dithiane was present, a trinuclear Au(I) complex was isolated, characterized by a central four-coordinate Au(I) center exhibiting a distorted see-saw geometry, flanked by two terminal Au(I) centers with T-shaped coordination environments. Importantly, the dithiane ligand did not coordinate directly to the gold centers …

Vanadium’S Journey From Essential Trace Metal To Emerging Environmental Pollutant In The Anthropocene Era, Tanu Arora, Nivedita Agnihotri, Mohammad Azam

Turkish Journal of Chemistry

Vanadium, previously considered a minor trace element, is gaining global environmental attention due to its expanding applications in metallurgy, energy storage, catalysis, and agriculture. With this rise in its industrial and technological uses, however, anthropogenic emissions of vanadium into the atmosphere, soils, and water bodies have also escalated. Unlike many heavy metals, vanadium exhibits complex redox behavior, primarily existing as V(IV) and V(V) species, which significantly impacts its environmental mobility, bioavailability, and toxicity. Vanadium exhibits complex behavior that necessitates an integrated research approach to better understand its various roles in scientific fields. This review summarizes the current knowledge on vanadium’s …