Digital Commons Network^™

Match-A-Fit, Adan Diaz De Leon, Juan Marco Saca Dada, Brianna Mendoza, Arsalan Kataneh, Theophile Nsabimana, Pedro Jacobo

Presentations - 2026

Welcome to Match-a-Fit! Match-a-Fit is an iOS application that allows the user to create a digital closet by uploading images of their clothing items. With AI, the program can generate outfits based on the digital closet, the time, and the occasion. Match-a-Fit’s purpose is designed to help users who struggle to get ready, run out of time, or can’t decide on an outfit, by easily generating outfit options based on the occasion.

Comparative Analysis Of Mlp And Cnn Models For Cardiac Arrhythmia Classification, Veltman Okey-Ejowhor, Vahid Emamian

Posters - 2026

Electrocardiogram (ECG) is a record of the electric activity of the heart over time. ECG analysis plays a pivotal role in diagnosing critical heart conditions. Significant developments have been made in the realm of deep learning and applied artificial intelligence. These deep learning models have been utilized heavily because of their ability to analyze deep morphological features of each signal. The model architecture used in this study is a convolutional neural network (CNN) combined with a multi-layered perceptron (MLP). The MLP acts as an input filter that classifies normal heartbeat signals from abnormal. The CNN is the second filter in …

The Willow Who Kept The Secrets Of The Heart, Ana Suarez

Speak for the Trees!

This digital museum exhibit features the climate change fairy tale The Willow Who Kept the Secrets of the Heart, presented as a written narrative and recorded reading that explores the symbolic relationship between the Weeping Willow, water, and ecological care in the face of climate change. It also includes an infographic highlighting the Weeping Willow’s habitat, characteristics, and environmental challenges in Texas, along with a curator’s talk and curator’s board. The exhibit features an artifact entry using a half-filled vial of water to represent climate instability and the often-overlooked resources that sustain life, as well as a persuasive essay, …

Maddenlite, Sergio Pena

Posters - 2026

Sports simulations often rely on opaque, proprietary algorithms (like EA's Madden NFL). MaddenLite bridges the gap between sports analytics and interactive gaming by utilizing historical NFL Play-by-Play (PBP) data to drive a transparent, mathematically accurate simulation engine. The goal was to create a lightweight, UI-driven desktop application where users can simulate cross-era matchups (e.g., 2007 Patriots vs. 2025 Chiefs), manipulate rosters, and simulate entire seasons complete with official NFL tiebreaker protocols.

Foxbuddy, Luis Eduardo Garza Jr.

Posters - 2026

Emergency preparedness remains a significant challenge for individuals due to disorganized resource management and lack of accessible guidance. Additionally, cybersecurity risks during emergencies are often overlooked, leaving individuals vulnerable to digital threats such as phishing, scams, and data exposure. FoxBuddy provides a centralized, user-friendly platform that enhances both physical preparedness and cybersecurity awareness.

Using Ai-Based Predictive Scheduling To Improve Patient Flow And Reduce Wait Times In Healthcare Clinics, Oscar Martinez

Posters - 2026

• Healthcare systems face increasing challenges in patient access and wait times
• Average wait times for specialist care continue to rise, creating:
  • Delays in treatment
  • Reduced patient satisfaction
  • Increased system inefficiencies (Sanford, 2025)
• A major contributor is operational bottlenecks, defined as:
  • Points of congestion that slow or disrupt service flow
• Hospitals typically operate under process layouts, which:
  • Handle diverse patient needs
  • Reduce specialization efficiency
• Contributing factors to bottlenecks:
  • Physician shortages and burnout
  • Administrative burden
  • Inefficient scheduling systems (Moura & Pinho, 2025)
• AI offers potential solutions through:
  • Predictive scheduling
  • Automation of administrative processes
  • Data-driven optimization of patient flow

Optimizing Retail Grocery Inventory Using Ai And Large Language Models: Evidence On Forecast Accuracy, Waste Reduction, And Cost Efficiency, Robert Miller, Stephen Garcia, Brandon Ermis

Posters - 2026

Aim: To evaluate how AI and LLMs improve forecasting accuracy, reduce waste, and enhance inventory decision-making

Synthesis And Characterization Of Metal Complexes Of Oxindole-Based Β-Lactams, Kenya Rosas, Erick Morales Orrante, Carlos Sifuentes, Alexsei Nazarov

Posters - 2026

• β-lactams are essential structural components of many antibiotics and widely studied due to their biological importance and synthetic versatility.
• The Ugi reaction is a multicomponent reaction, involving a primary amine, carboxylic acid, isocyanide, and an aldehyde or a ketone, that rapidly generates α-acetamido carboxamide derivatives (Ugi adducts).
• The synthesis and characterization of four oxindole-based β-lactam derivatives:
  • 1-Benzyl-N-(tert-butyl)-2-oxo-3-(2-oxoazetidin-1-yl)indoline-3-carboxamide (4a).
  • N-(tert-butyl)-2-oxo-3-(2-oxoazetidin-1-yl)-1-phenylindol-3-carboxamide (4ab).
  • N-(tert-Butyl)-2-oxo-3-(2-oxoazetidin-1-yl)indoline-3-carboxamide (4d).
  • N-(tert-butyl)-1-methyl-2-oxo-3-(2-oxoazetidin-1-yl)indol-3-carboxamide (4e).
• The coordination of the four Ugi adducts with Cu (II) and Ni (II) at different pH points to enhance their stability and improve biological activity.

Agglomeration Of Particles Streaming Down A Zigzag, Juan Llamas

Posters - 2026

Zigzags or “letdown ladders” limit the flow speed of dry grains as they flow downward by gravity when loading silos, ships, barges, railcars, or trucks.

• Reduce cracking and shattering of grains.

• Increase grain shelf life.

• Increase grain resale value. • Increase ability of grain to germinate.

• Reduce generation of grain dust, an explosion and environmental hazard.

How can we design a zigzag that we know will not plug?

Beyond Technology Acceptance: Ai Adoption In The United States And Korean Accounting Firms, Madeline Ortega, Brenda Vazquez Saavedra

Posters - 2026

A comparison of AI adoption between the United States and South Korea

Aiw26s: Machine Learning Of Structured Data, Moumita Saha

Paul English Applied Artificial Intelligence (AI) Institute Publications

This workshop introduces the fundamentals of machine learning for structured data, focusing on tabular datasets and real-world applications. Participants explore key concepts such as data types, data preprocessing, feature engineering, and supervised learning methods. The session covers commonly used models, including linear regression, logistic regression, decision trees, and neural networks, along with evaluation metrics such as RMSE, accuracy, and confusion matrices. By the end of the workshop, participants will have gained a practical understanding of how to build, interpret, and evaluate machine learning models for structured data.

The Eye: A Science-Fiction Screenplay Examining Space-Time, Meteorology, And Human Connection, Ella K. Lukowiak

Senior Theses

This thesis explores the nature of time, meteorology, and human interconnection to the universe through the lens of a science-fiction screenplay. The creative project spans a three-act, detailed outline, and a screenplay draft of Act I. It is based upon extensive scientific research regarding hurricane development, the inner workings of organizations such as the National Weather Service, Einstein’s Theory of Relativity, and the space-time continuum. I will delve into the various factors that shaped my science-fiction premise, including pivotal films such as Christopher Nolan’s INTERSTELLAR (2014) and Denis Villeneuve’s ARRIVAL (2016). Both of these filmmakers integrated complex science-fiction concepts into …

Exploring The U.S. Public Service Employees’ Experiences With Gamified Cybersecurity Awareness Training, Bisola Adepoju

Human Resource Development Theses and Dissertations

The purpose of this qualitative study was to explore the U.S. public service employees' experiences with gamified cybersecurity awareness training. The primary research question guiding this inquiry was: How do U.S. public service employees make sense of their lived experiences with gamified cybersecurity awareness training? Two secondary questions examined what aspects of the training employees perceive as meaningful or disengaging, and how they interpret their motivation, engagement, and cybersecurity awareness during training. I chose an interpretive qualitative research design grounded in a constructivist paradigm and purposefully selected 11 U.S. public service employees who had participated in gamified cybersecurity awareness training. …

Information Theory Analysis Of The Solar Wind Magnetic Structures For Space Weather Prediction, Katherine Holland

Doctoral Dissertations and Master's Theses

Forecasting space weather at Earth is highly complicated, because of the limited measurements of the dynamic processes in the Sun that span multiple temporal, spatial, and energy-scales. The solar wind is a highly structured, multi-scale, evolving plasma and consists of coronal mass ejections (CMEs), stream interaction regions (SIRs), expanding flux tubes (Borovsky, 2008), and interplanetary magnetic field (IMF) discontinuities and fluctuations. The aim of this research is to improve our understanding of the evolution and dissipation of different scale-size solar wind magnetic structures as they move from the Sun-Earth Lagrange point 1 (L1) to Earth's bow shock and, ultimately, to …

Evaluating The Collision-Coalescence Process In Idealized Cloud Convection Using Large-Eddy Simulations With Lagrangian Microphysics, Yangze Ren, Kamal Kant Chandrakar, Fan Yang, Raymond Shaw

Michigan Tech Publications

Drizzle initiation through the collision and coalescence of cloud droplets plays a crucial role in warm cloud precipitation. Recent theoretical studies suggest that the influence of collisional growth on the droplet size distribution can be quantified by a non‐dimensional drizzle number (Dz). Here, large‐eddy simulations with Lagrangian microphysics are employed to evaluate the theory by simulating a tall convection‐cloud chamber under various conditions. Results show that the smaller the Dz, the larger the impact of collisions on the right tail of the droplet size distribution, consistent with the theory. The simulations confirm that the collision rate can be estimated from …

Can Financial Literacy Enhance Esg Disclosures?, Xiaoran Jia, Kiridaran Kanagaretnam, Kiat Bee Jimmy Lee, Chee Yeow Lim

Research Collection School Of Accountancy

Using an international sample of firms and two country-level measures of financial literacy, we find robust evidence of a positive relation between financial literacy and firms' Environmental, Social and Governance (ESG) disclosures. In cross-sectional analyses, we find that the effect of financial literacy in enhancing ESG disclosures is more prominent in poorer information environments, weaker legal institutions and weaker ESG reporting environments. Lastly, we find that financial literacy also enhances ESG performance. Our study contributes to and extends the literature by providing strong evidence that citizens' financial literacy enhances firms' ESG disclosure and the associated ESG performance.

High-Resolution Monitoring Of Intra-Seasonal Agricultural Drought Using Sentinel-2 And Machine Learning Across Bimodal Growing Seasons In Kenya, S. Mohammad Mirmazloumi, Harison Kipkulei, Rose Waswa, Tobias Landmann, Tom Dienya, Maximilian Schwarz, Fabrizio Ramoino, Clément Albergel, Gohar Ghazaryan

All Peer-Reviewed Publications

Drought presents significant challenges to agriculture, threatening food security and livelihoods, across many regions. In Kenya, recurrent droughts across diverse agro-ecological zones emphasize the urgent need for reliable and scalable drought assessment methods. Although drought assessment with various datasets has been carried out for this region, many of them often use course or moderate resolution data. This study uses high-resolution Sentinel-2 observations and machine learning to monitor intra-seasonal crop conditions and assess drought impacts across bimodal growing seasons. Using pixel-based supervised random forest models trained with multiple vegetation indices as input, we classify croplands into drought-affected and unaffected areas. The …

Leveraging Section 208 Water Quality Planning To Mitigate Pfas Contamination In Watersheds Across The Waccamaw Region, Anna Dimatteo

Goal 6: Clean Water and Sanitation

No abstract provided.

Upgrades To Carvers Bay Water System To Improve Safety And Water Quality, Samantha Stollenmaier

Goal 6: Clean Water and Sanitation

No abstract provided.

A Study Of Short-Range Correlated Pair Formation Mechanisms, Noah S. Swan

Physics Theses & Dissertations

Short-Range Correlations (SRCs) refers to pairs of nucleons that are temporary high density fluctuations with high relative momenta and lower center-of-mass momenta com pared to the nuclear Fermi momentum (kF). SRCs account for 20–25% of the nucleons in medium to heavy nuclei, make up essentially all nucleons with momentum greater than kF, and contribute most of the kinetic energy carried by nucleons in nuclei.

The existing semi-inclusive and exclusive measurements only cover a handful of light nu clei or heavy elements. This does not allow for a systematic study of the dependence of SRC pairs on nuclear mass and proton-neutron …

Intelligent Deep Learning-Based Sign Language Translation System, Nada Rasem Shahin

Dissertations

The Deaf and Hard of Hearing (DHH) community uses sign language as a primary means of communication. However, the shortage of sign language interpreters and the existence of hundreds of sign languages limit accessibility and inclusion. Sign Language Machine Translation (SLMT) systems present a promising solution for bridging the communication gap between the DHH and the hearing individuals, supporting inclusive societies. In smart cities, such systems play an essential role in improving the quality of life on a community level. In particular, as the population’s well-being is critical, developing intelligent assistive technologies, such as SLMT systems, is necessary to provide …

Dissection Of The Quintuple Product, With Applications, Tim Huber, James Mclaughlin, Dongxi Ye

School of Mathematical & Statistical Sciences Faculty Publications

This work considers the m-dissection (for m≢0(mod3)">m≢0(mod3)) of the general quintuple productQ(z,q)=(z,q/z,q;q)∞(qz2,q/z2;q2)∞.">Q(z,q)=(z,q/z,q;q)∞(qz2,q/z2;q2)∞.Multiple novel applications arise from this m-dissection. For example, we derive the general partition identityDS(mn+(m2−1)/24)=(−1)(m+1)/6bm(n), for all n≥0,">DS(mn+(m2−1)/24)=(−1)(m+1)/6bm(n), for all n≥0,where m≡5(mod6)">m≡5(mod6) is a square-free positive integer relatively prime to 6; DS(n)">DS(n) is defined, for S the set of positive integers containing no multiples of m, to be the number of partitions of n into an even number of distinct parts from S minus the number of partitions of n into an odd number of distinct parts from S; and …

Soliton Solutions To The Coupled Sasa–Satsuma Equation Under Mixed Boundary Conditions, Changyan Shi, Xiyao Chen, Guangxiong Zhang, Chengfa Wu, Bao-Feng Feng

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we derive general bright–dark soliton solutions to the coupled Sasa–Satsuma (CSS) equation using the Kadomtsev–Petviashvili reduction method. Since the CSS equation is a special case of the four-component Hirota equation, our approach begins with the construction of two-bright-two-dark soliton solutions for the four-component Hirota equation. By imposing specific parameter constraints, these solutions are subsequently reduced to the bright–dark soliton solutions of the CSS equation. Finally, the dynamical behaviours of the one- and two-bright–dark soliton solutions are thoroughly analysed and illustrated.

Altered Pruning In A Generative Neural Network, Sara Momen

Honors Theses

This study examines whether altered pruning in a generative neural network leads to more restricted or repetitive outputs, using Autism Spectrum Disorder (ASD) as a cautious computational analogy rather than as a biological equivalence. A compact denoising diffusion probabilistic model (DDPM) with a U-Net backbone was trained on CIFAR-10, and pruning percentage was treated as the primary structural variable. Instead of relying on a single assumed baseline, the final implemented study evaluated 0%, 10%, 15%, 20%, 35%, and 50% pruning under controlled conditions: the same dataset, the same epoch budget, the same random seed, and 1,000 generated samples per condition. …

A Comprehensive Calibration Framework For The Northwest River Forecast Center, Geoffrey Walters, Cameron Bracken, Brad Gillies, Leah Pope, Henry Pai, Sonali Chokshi, Victor Stegemiller, Stephen King, Julie Bracken, Taylor Dixon, Multiple Additional Authors

Mathematics and Statistics Faculty Publications and Presentations

We present a comprehensive framework developed by the Northwest River Forecast Center for calibrating hydrologically diverse basins. The framework includes models for snow, soil moisture, routing, channel loss, and consumptive use. Data inputs include a wide range of open-access datasets for meteorology, land use, topography, and land cover. The framework uses conceptual hydrologic models to handle basins with various hydrologic regimes including rain-driven and snowmelt-dominated basins. We also develop a flexible automatic calibration system that can handle numerous unobservable model parameters in a computationally efficient manner. A single-basin automatic calibration run can typically be completed on a modern laptop in …

Ai-Powered Knowledge Management Systems Across Industries: A Systematic Review Of Applications, Implementation Barriers, And Ethical Challenges, Edmund Evangelista, Ghazala Rizvi

All Works

This systematic literature review (SLR) evaluates the existing literature on the benefits, implementation challenges, and ethical concerns associated with Artificial Intelligence (AI)-driven Knowledge Management Systems (KMS) across industries. The SLR followed PRISMA guidelines to identify studies from Scopus, Web of Science, JSTOR, and Google Scholar, using inclusion and exclusion criteria. Critical Appraisal Skills Programme (CASP) checklists were used to assess methodological quality and risk of bias in the included studies, and a structured narrative synthesis was employed to synthesize the findings. The review of 21 articles reveals benefits like improved knowledge capture and creation, storage, retrieval, personalization, and efficient dissemination, …

Preserving The Past, Innovating The Future: Integrating Metaverse, Blockchain, And Generative Ai For Tourism And Cultural Heritage Preservation, Mousa Al-Kfairy, Amna Ahmed Aaber Ahmed Alqubaisi, Omar Alfandi

All Works

The preservation and accessibility of cultural heritage are seriously threatened by urbanization, mass tourism, neglect, and natural disasters. This study utilizes cutting-edge technologies to address these issues by presenting a novel framework that combines generative AI, blockchain, and the metaverse. The Metaverse provides immersive virtual experiences that lessen the physical strain on delicate locations by enabling the creation of lifelike digital replicas of cultural heritage sites. By creating immutable records, blockchain technology ensures the legitimacy, ownership, and traceability of digital assets, enabling open access and NFT monetization. Rebuilding lost or damaged artifacts, creating lifelike 3D models, and customizing user interactions …

Draft Final Silver Bow Creek Conservation Area Materials Management Plan Revision 1, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Knowledge Distillation From A Large Vision-Language Model To Compact Students For Architectural Floor Plan Understanding, Kiran Silwal

Honors Theses

In this research, the use of a large vision-language model to train smaller, deployable models for architectural floor plan question answering is investigated. Reading a floor plan today requires either a human expert or a paid query to a proprietary model, and neither option is practical for real-estate platforms that must process thousands of units at scale. To address this problem, a knowledge distillation approach is employed in which a large teacher model (GPT-4.1-mini) generates labeled question-answer pairs from floor plan images, and smaller student models learn from those labels. The teacher produced 37,027 labeled pairs from 12,343 floor plan …

Modeling And Constraining Beyond Standard Model Neutrino Self-Interactions, Sabrina Hanning , ‘26

Senior Theses, Projects, and Awards

Neutrinos are the ideal subject for theories of Beyond Standard Model (BSM) physics. This is because small neutrino interaction cross-sections make them incredibly difficult to detect and result in many open questions regarding neutrino properties, particularly those that do not fit nicely into the Standard Model of particle physics, such as the existence of neutrino masses. BSM neutrino self-interactions are an interesting possibility with connections to dark matter candidates, neutrino mass generation, and in general any measurement that relies on accurate modeling of incident astrophysical neutrino flux.

These self-interactions may take various forms, with different mediator particles; we present a …