Digital Commons Network^™

Innovative Analysis Of Groundwater Data For Scalable, Defensible, And Predictive Compliance, Shannon Zahuranec

World of Coal Ash Proceedings

Groundwater monitoring networks at coal combustion residual (CCR) facilities now yield dataset volumes that routinely exceed the capacity of traditional, univariate interpretation. This presentation introduces a scalable multivariate and machine learning (ML) framework that prioritizes understanding data structure before predictive models are applied. The workflow applies Principal Component Analysis and other multivariate statistics to groundwater datasets, to illustrate how unsupervised learning can rapidly reveal dominant sources and controls on impacted and unimpacted groundwater quality. The talk frames multivariate analysis as a practical portfolio management tool, emphasizing inference categories rather than concentrations or site identifiers. Fleet-scale multivariate screening offers strategic advantages …

Riki&Dolphin: Real Time Data Transmission From The Bottom Of A Cave To A Website, Luca Tringali, Giacomo Canciani Dr., Tecla Tripari, Alexander Debenjak, Caterina Bearzotti

International Journal of Speleology

Coming from over 10 years of experience in cave monitoring in northeast Italy, Gruppo Speleologico Talpe del Carso, has designed Riki and Dolphin: customizable, low cost, and easy to assemble tools for getting real time data transmission from the bottom of a cave, even underwater, to a webserver. Their use has been tested to monitor air temperature inside the Abisso Bonetti Cave (Classical Karst, Italy), proving for the first time that a cave in Gorizian Karst can systematically be colder than the outdoor temperature even in winter, recording an internal temperature even lower than 0°C. The Dolphin device can be …

Developing A Comprehensive Resource Guide For Caregivers Within The Area Agency On Aging, Aiden R. Murphy

Public Health Capstone Projects

This project developed a new universal caregiver resource guide for caregivers within the Area Agency on Aging, in order to improve resource navigation and workflow efficiency. Resources were collected, verified, and organized into a new, streamlined guide via the ARIA chatbot, covering multiple needs. Caregiver resources were collected and verified by the capstone student and mentor, Michael Kroeker, and organized into a centralized knowledge base within the ARIA chatbot. A mixed methods evaluation was conducted utilizing a 5-point Likert scale with three quantitative questions and one open-ended qualitative question. The data was given to the SeniorLine staff, who wanted to …

Genetic Analysis Of Triplicated Genes Affecting Sex-Specific Skeletal Deficits In Down Syndrome Model Mice, Kourtney Sloan, Kristina M. Piner, Pathum Randunu Nawarathna Kandedura Arachchige, Charles R. Goodlett, Yann Herault, Gayla R. Olbricht, Joseph M. Wallace, Randall J. Roper

Mathematics and Statistics Faculty Research & Creative Works

Down syndrome (DS) is caused by the triplication of human chromosome 21 (Hsa21), resulting in skeletal insufficiency (low bone mineral density) and altered bone development. DS mouse models recapitulate these deficits, including sexual dimorphism in long bone alterations. Historically, Ts65Dn mice provided much of the insight behind DS-related skeletal deficits with ∼100 trisomic orthologous genes, but there are concerns about the genetic fidelity in this model due to the included triplication of genes not homologous to Hsa21. A new DS model, Ts66Yah, subtracted the non-Hsa21 homologous trisomic genes from Ts65Dn but has not been evaluated for long bone deficits. Comparing …

Toward The Salmon Prize: A Computational Certificate For The M5, M6, And M9 Equation Families Of Σ4(P3 × P3 × P3), John Trevino

Theatre Faculty Publications

We present a computational certificate for three families of explicit polyno- mial equations whose zero locus contains the fourth secant variety σ4(P3×P3×P3) inside P63. The three families are: 192 degree-5 Strassen commutation equa- tions (M5); 160 degree-6 equations lifted from the Bates–Oeding generators of σ4(P2 × P2 × P3) via the Landsberg–Manivel–Friedland theorem (M6); and 64 degree-9 Ottaviani 9 × 9 determinant equations (M9). All 416 generators are explicit polynomials in the coordinate ring Z[Zijk | i, j, k ∈ {0, 1, 2, 3}]. We verify by exact integer arithmetic that every generator vanishes on rank-4 test tensors (six independent …

Predicting And Decoding Allosteric Binding Sites Using Protein Language Models And Structure-Based Machine Learning: An Energy Landscape-Guided Explainable Ai Framework, Kamila Riedlová, Vít Skrhák, William G. Gatlin, Max Ludwick, Lucas Turano, Marian Novotný, David Hoksza, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Computational prediction of allosteric binding sites in protein structures remains a persistent challenge, as these regulatory pockets evade detection by both sequence-based and structure-based algorithms. Both computational and physical origins of this predictive asymmetry remain insufficiently understood. In this study, we systematically examine the determinants of binding site predictability using a dual framework that integrates a fine-tuned protein language model and the structure-based method P2Rank as complementary tools probing a diverse data set of 453 human kinases, together with a physics-based interpretability layer derived from energy landscape frustration analysis. Both predictors exhibit a sharp and reproducible dichotomy on protein kinases, …

A Remote Sensing Approach To Identifying Zones Of Instability In Mine Tailings Impoundments Using Multi-Sensor Satellite Data, Arden K. Pierce

Honors Theses

Mine tailings impoundments are structures built to contain the chemical and sedimentary byproducts generated by ore extraction activities. In this common method of mining waste storage, the tailings can be pumped into containment ponds and held behind earthen embankments to prevent the release of hazardous substances into the surrounding environment. Monitoring environmental factors that influence the structural integrity of these impoundments is essential for identifying areas vulnerable to instability and failure. Soil moisture is a factor that can affect the stability of mine tailings impoundment structures. This study evaluates the relationship between satellite-derived soil moisture and surface deformation at mine …

Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool, Thomas Kimmeth, Avijit Roy, Vivek Sharma

Publications and Research

Propagandistic content increasingly circulates through online news and social media, where readers often encounter it with limited scrutiny, highlighting the need for reliable and fine-grained detection. This paper introduces Propasafe-Hybrid, a sentence-level system that integrates a fine-tuned transformer classifier with LLM-based technique classification to identify, label, and explain specific propaganda strategies. The pipeline generates actionable outputs, including highlighted sentences, technique assignments, and concise rationales, so users can immediately understand why a sentence was flagged and how each label was determined. To control inference cost, Propasafe-Hybrid employs a cost-aware pre-filtering stage that forwards only high-likelihood sentences to LLMs, reducing token usage …

Improving Semantic Precision In Text-To-Image Diffusion Models Via Latent-Space Optimization And Semantically-Parsed Evaluation, Mohammad Rouie Miab

McKelvey School of Engineering Graduate Student Theses & Dissertations

Text-to-image diffusion models can produce visually impressive images from natural-language prompts, but they often fail to satisfy the detailed semantic constraints expressed in compositional prompts. Typical failure modes include omitted objects, merged entities, incorrect quantities, incorrect attribute binding, and leakage of one entity's attributes onto another. This thesis studies the problem of semantic precision in text-to-image generation: how faithfully a generated image satisfies the structured meaning of its prompt.   The thesis makes two linked contributions. First, it presents a training-free inference-time refinement method for diffusion-based image generation. The method operates directly in latent space during denoising and uses noun-phrase-aware cross-attention …

Kl Dra As A Benchmark Laboratory For Accretion-Disk Physics: Constraints From Tess And Ground-Based Surveys, Luis E. Salazar Manzano, Liliana E. Rivera Sandoval, Jean-Marie Hameury, Craig O. Heinke, Iwona Kotko, Thomas J. Maccarone, Manuel Pichardo Marcano

Physics & Astronomy Faculty Publications

We present the longest-term optical analysis of the AM CVn system KL Dra using ∼11 yr of monitoring from TESS and wide-field ground-based surveys. The continuous TESS coverage allows us to characterise its frequent outbursts with unprecedented detail, providing the first comprehensive study of an AM CVn during outbursts and enabling detailed modelling of these systems. The superoutbursts in KL Dra generally include a precursor and are followed by a series of rebrightenings after which a sequence of 3–4 large amplitude normal outbursts is observed. We fit parametric profiles to each superoutburst component (precursor, rise to plateau, plateau, and decay), …

Revolutionizing Closure Systems: The Performance Advantages Of Structured Geomembranes, Scott Perugia

World of Coal Ash Proceedings

Coal combustion residual (CCR) containment facilities have traditionally been closed using methods that have changed little over several decades, but structured geomembranes are now reshaping closure design by improving slope stability, increasing interface friction, enhancing drainage, and providing long-term durability. Two systems are emerging as leading solutions: a structured geomembrane paired with a geotextile and vegetative cover, and a structured geomembrane paired with engineered turf. This presentation compares these approaches using field performance data, laboratory testing, and lessons learned, with emphasis on key factors such as infiltration control, drainage efficiency, slope stability, constructability, and regulatory compliance. The engineered turf system …

From Clogs To Compliance: Overcoming Persistent Leachate Head Issues In Ccr Landfills, Breanne Kahnk, Zachary Bauman

World of Coal Ash Proceedings

State and federal regulations require that landfills, including coal combustion residual (CCR) landfills, be designed and operated to maintain leachate head levels of less than 30 centimeters (approximately 1 foot). While proper leachate collection system design and routine maintenance are critical for meeting this requirement, challenges such as clogging of drainage media, pipes, fittings and/or pipeline perforations, settlement of collection pipes, and breaks in the piping system can lead to elevated leachate levels, posing risks to compliance and long-term system performance. TRC will provide an overview of leachate collection system regulatory requirements along with design, operation, and maintenance strategies that …

Standardization Of Coal Ash Testing, Classification, And Harvesting, Benjamin Gallagher, Chris Frishcosy, Eric Hageman

World of Coal Ash Proceedings

Standards such as product specifications, test methods, practices and guides play a crucial role in supporting both commerce and science. Standardization helps build consistency and reliability whether it pertains to the performance of an ingredient in a product or in the measurement of a material characteristic. Coal combustion products, such as coal ash and synthetic gypsum, exhibit unique characteristics and many standard have been developed to characterize these materials and support their beneficial use. ASTM is a voluntary, consensus-based organization, and publishes many standards related to coal combustion products. Currently, there are seven ASTM standards related to chemical analysis of …

Impacts Of Updating Background Groundwater Statistics For Ccr Compliance, May Raad, Molly Reeves

World of Coal Ash Proceedings

In 2017, many power utilities adopted statistical methods to comply with the EPA’s Coal Combustion Residuals (CCR) Rule under the Hazardous and Solid Waste Management System. By 2017–2018, most owners completed initial statistical evaluations for detection and assessment monitoring to meet CCR deadlines. Groundwater sampling and analysis requirements in Parts 257.95, 257.98, and 257.102 are critical for compliance, yet implementation is complex. Subsections 257.93(f) and (g) lack clarity on which statistical tests to apply or how to address distributions and outliers. Additionally, the Rule does not specify when or how to update background threshold values (BTVs), though EPA’s Unified Guidance …

Investigation Of A Historical Coal Combustion Residuals Management Unit (Ccrmu), Todd Stong, Shane Stockdill, Erin Hunter

World of Coal Ash Proceedings

Rainbow Energy Center’s (REC’s) Coal Creek Station has managed coal combustion residuals (CCR) in various engineered impoundments and landfills since plant operation began in 1978. Since then, waste containment methods have evolved resulting in significant changes to their CCR management practice. In the late 1980s and early 1990s REC closed several existing CCR units with natural soil liners and constructed several new CCR units with composite liner systems. One of the closed units was an area now referred to as North Section 16, an impoundment that received CCR including bottom ash, fly ash, and flue gas desulfurization material. North Section …

Optimization Of Remedial Strategies For Ccr Site Corrective Action Through Advanced Geochemical Modeling, Cole Mayer, Pj Nolan

World of Coal Ash Proceedings

The power industry is actively designing and implementing corrective measures for Coal Combustion Residuals (CCR). In many cases, these measures include active remedies such as pump and treat, in-situ injection, or reactive barriers, all of which demand a thorough understanding of the underlying geochemical system. Depending on the geochemical characteristics of an aquifer, different chemical reagents can achieve varying degrees of success or even be detrimental to the remediation of the site. Advanced geochemical modeling is an invaluable tool for developing a remedy that is optimized to the site-specific geological, hydrological, and geochemical environment. In the case study presented here, …

Alternative Cover Permitting And The Ccr Rule, Evan Andrews, Victor Damasceno

World of Coal Ash Proceedings

Federal rules (40 CFR Parts 9, 257 and 261) that regulate coal combustion residuals (CCR) created a significant demand associated with closure of coal ash management and storage facilities using liner systems. Timelines associated with these facility closures combined with substantial project scale and potential for future mining /reuse has driven the need for closure systems that perform beyond the prescriptive soil / geomembrane caps outlined in the federal regulations. As outlined in section 257.102 (d)(3)(i) of the CCR Rule, the standard cover system must include an infiltration layer of at least 18 inches of earthen material and an erosion …

Demystifying Large Language Models: How Ai Is Transforming Data, Compliance, And Decision-Making In Coal Ash And Environmental Management, Scott Pfeiffer

World of Coal Ash Proceedings

The environmental and coal combustion residuals (CCR) industries generate vast amounts of information—from groundwater monitoring reports and RCRA documentation to closure plans and analytical data. Managing, interpreting, and communicating this information is a growing challenge for utilities, regulators, and consultants alike. Large Language Models (LLMs), such as OpenAI’s GPT architecture, represent a breakthrough in how organizations can interact with their technical data. These models use deep neural networks trained on billions of words to understand, summarize, and generate language with human-like accuracy. When securely adapted for enterprise use, LLMs can automate routine reporting, extract data from technical documents, generate regulatory …

A Two-Phase Perspective On A Related-Rates Paradox, Charlie Vazquez Acosta

Honors Capstones

Related-rates problems are a standard topic in first-year calculus and have appeared in textbooks for over 150 years. These problems are used to teach implicit differentiation and the relationship between changing quantities. Common examples include the falling ladder, the fishing bobber, the melting snowball, and the leaking conical tank. In each of these problems, a quantity is changing at a constant rate, and students are asked to find the rate of change of another related quantity. While the computations themselves are usually straightforward, the standard models lead to unrealistic results near the end of the motion. For example, the falling …

Towards Improving The Performance Of The Adcirc Storm Surge Modeling Software, Nick Weldner, Tim Stitt, Jijun Tang

Caravel Undergraduate Research Journal

Accurately predicting storms and hurricanes is critical to saving lives and reducing economic loss. Therefore, it is necessary to use the most efficient software and hardware technology available in order to improve the performance and fidelity of these predictive mathematical models. For over ten years, the Computational Hydraulics Lab (CHL) at the University of Notre Dame has been involved in developing the high-resolution ADvanced CIRCulation (ADCIRC) storm surge model to predict storm surges in coastal areas. The objective of the work reported here was to port a novel adaption of the parallel ADCIRC code to the state-of-the-art Intel Xeon Phi …

Evaluation In Applied Ai: Predictive Modeling, Agent Safety, And Model Validation, Tanmay Sah

Harrisburg University Dissertations and Theses

This dissertation focuses on three applied challenges in machine learning and artificial intelligence: predicting social media content virality, evaluating safety in tool using large language model agents, and standardizing model validation in regulated settings. Across these domains, the dissertation argues that evaluation in applied AI must be matched to context, combining predictive performance with the relevant evidence needed for interpretability, safety, reproducibility, and governance readiness. First, Decoding Reddit Memes Virality extracts and analyzes 16,968 posts using computer vision, natural language processing, and gradient boosting to identify visual, textual, and temporal signals associated with virality. Second, The Verifier Tax designs and …

The Impatience Of Winning: An Analysis Of Time Discounting, Predictive Modeling, And The Nba Draft, Alec R. Plante

Business and Economics Honors Papers

This paper examines whether NBA draft decisions can be better explained by incorporating non-geometric time discounting into a model of general manager decision making. Using a dataset of 285 NBA draft prospects over a 12-year period, the impact of college statistics on Value Over Replacement Player (VORP) is determined, and these impact values are then used to create a “predicted” VORP for the first 4 seasons of each player’s career: a projection of what a general manager might think of a prospect’s future value given their college statistics. Following this, geometric and hyperbolic time discounting models are applied to estimate …

Security Assessment Of A Machine Learning Approach To Generate And Validate Digital Signatures, Juan Ortiz Couder

Doctoral Dissertations and Master's Theses

Cybersecurity has become a global concern as cyber-attacks have become more common, and the cost of the damage caused by them continues to increase. There are several approaches to improve the cyber security of systems such as Digital Signatures, hashing, watermarking, and encryption among others. Digital Signatures are a cryptographic technique used to verify the authenticity and integrity of digital messages or documents. Digital Signatures use a combination of hashing and public-private key encryption to verify the authenticity and integrity of videos, just as they are used for documents and messages. As a result of using a combination of other …

Faircarenlp: An Ai-Driven Patient Review Analyzer For Healthcare, Sayyed Mohammad Pourya Momtaz Esfahani, Davey Seeman, Christoffer Dharma, Mohammad Noaeen, Shion Guha, Zahra Shakeri

Health Services and Informatics Research

Objective

To develop and evaluate an automatic patient review analyzer that applies advanced Natural Language Processing (NLP) and machine learning methods to improve the efficiency, fairness, and accuracy of healthcare feedback analysis.

Materials and methods

We designed a multi-component pipeline incorporating sentiment analysis, key theme extraction, clinical Named Entity Recognition (NER), and fairness modules. Bias mitigation was addressed through the integration of three complementary approaches: adversarial debiasing, Hard Debiasing, and Iterative Null-space Projection (INLP). Multiple BERT-based models (DistilBERT, BioBERT, RoBERTa-base, BERT-base-uncased) were trained and evaluated under varying hyperparameters and fairness/adversarial loss configurations. Model performance was assessed using accuracy, F1, recall, …

Re: Conditional Approval Letter For Draft Final Silver Bow Creek Conservation Area Materials Management Plan Revision 1 (Dated March 24, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Re: Conditional Approval Letter For Final Silver Bow Creek Conservation Area (Sbcca) Construction Quality Assurance Plan And Draft Final Silver Bow Creek Conservation Area (Sbcca) Construction Monitoring Quality Assurance Project Plan (Dated April 7, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Automated Analysis Of Radiation Oncology Incident Reports Using Large Language Models, Nathan A. Dobranski

LSU Master's Theses

Patient safety incident reporting in radiation oncology requires expert analysis that is time-intensive and subject to variability. This thesis presents the development and technical validation of a locally deployed large language model (LLM) system for automated incident report analysis across multiple cancer centers. The system was designed for automated summarization and taxonomy assignment of Radiation Oncology Incident Learning System (RO-ILS) reports, operating entirely on local infrastructure to preserve patient privacy. A two-round, multi-rater evaluation methodology was employed, incorporating 600 total expert evaluations from two academic cancer centers. Round 1 established baseline performance using Mistral 7B and Mixtral 8x7B models with …

Ais26s: Ai In Biomedicine, Shiqian Shen

Paul English Applied Artificial Intelligence (AI) Institute Publications

This presentation explores the role of artificial intelligence in advancing biomedical research and clinical practice from the perspective of a physician-scientist. Dr. Shiqian Shen discusses current challenges in neuroscience and medicine, including limitations in human observation, data analysis, and decision-making across complex biological systems. The talk highlights how AI-driven approaches such as computer vision, neural signal processing, and large-scale data modeling can improve the understanding of disease mechanisms, enhance experimental workflows, and enable more precise, patient-centered care. Specific applications include automated classification of neural activity, behavioral analysis in animal models, and multimodal data integration. A key concept introduced is “shadow …

Machine Learning For Handwritten Character Recognition, Hannah Freitag

Honors Capstones

Handwritten character recognition remains a challenging problem in machine learning due to the high variability of handwriting across individuals and the visual similarity between certain character classes. This project explores whether Singular Value Decomposition (SVD)-based dimensionality reduction can serve as an effective preprocessing step for a fully connected neural network trained on the EMNIST Balanced dataset, a 47-class benchmark of handwritten digits and letters. By projecting 784- dimensional pixel inputs onto the top 70 principal components, approximately 90% of the total variance is preserved while reducing input dimensionality by 91%. The resulting SVD-based model achieves approximately 94% test accuracy, outperforming …

Limit Of Detection Variations In Polyphenol Mixtures Using Parafac-Feem Spectroscopy, Ken Overway, Brockton Zorn

Honors Projects

Polyphenols are chemical compounds commonly found in food products like olive oil and red wine. Research suggests that their presence in such foods conveys positive cardiovascular and cognitive health benefits. This group of compounds is often difficult to quantify in such samples due to an overlap in the chemical signals and the presence of interferences, such as coloring, found naturally in the food. Use of 3D Fluorescence Excitation Emission Matrix (EEM) measurements combined with pH sensitivity could establish a method in which polyphenols are identified more easily in mixtures. Binary and ternary mixtures of polyphenols were analyzed without the additional …