Digital Commons Network^™

Draft Intermediate (60%) Remedial Design Report Blacktail Creek Riparian Actions Butte Priority Soils Operable Unit Of The Silver Bow Creek/Butte Area Superfund Site Silver Bow County, Montana, Hydrogeologic, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final Phase I Pre-Design Investigation Evaluation Report Blacktail Creek Riparian Actions Butte Priority Soils Operable Unit Of The Silver Bow Creek/Butte Area Superfund Site Silver Bow County, Montana, Hydrogeologic, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Agnostic Tomography Of Stabilizer Product States, Sabee Grewal, Vishnu Iyer, William Kretschmer, Daniel Liang

Computer Science Faculty Publications and Presentations

We define a quantum learning task called agnostic tomography, where given copies of an arbitrary state ρ and a class of quantum states C, the goal is to output a succinct description of a state that approximates ρ at least as well as any state in C (up to some small error ε). This task generalizes ordinary quantum tomography of states in C and is more challenging because the learning algorithm must be robust to perturbations of ρ. We give an efficient agnostic tomography algorithm for the class C of n-qubit stabilizer product states. Assuming ρ has fidelity at least …

Taylor Dispersion In Variable-Density, Variable-Viscosity Pulsatile Flows, Prabakaran Rajamanickam, Adam D. Weiss

Progress in Scale Modeling, an International Journal

The phenomenon of Taylor or shear-induced dispersion of a non-passive scalar field in a pulsatile pipe flow is investigated, accounting for the scalar field's influence on fluid density and transport coefficients. By employing multiple scale analysis, an effective one-dimensional, unsteady mixing problem for the scalar field is obtained, which includes the diffusion coefficient for shear-induced dispersion. The resulting governing equations are applicable to a range of scalar transport problems in pulsatile pipe flows.

Polyherbal Phytochemicals As Multi-Target Inhibitors Of Key Breast Cancer Proteins: A Computational Approach, Nadia Wahyuningsih, Nashi Widodo, Sri Rahayu, Muhaimin Rifa’I

Karbala International Journal of Modern Science

Breast cancer is a primary worldwide health concern, and conventional therapies often cause side effects. This study was performed to investigate the therapeutic potential of a polyherbal formulation containing Curcuma longa, Phyllanthus niruri, Ziziphus mauritiana, Nigella sativa, and Annona muricata as multi-target inhibitors against breast cancer protein targets using molecular docking and molecular dynamics in silico approach. Bioactive compounds were analyzed using Liquid Chromatography High-Resolution Mass Spectrometry (LC-HRMS) to identify the extract's phytochemicals. The compounds were examined for drug-likeness, membrane permeability, bioactivity, and toxicity. The inhibitory ability against the proto-oncogene pathway, which is commonly dysregulated and mutated in breast cancer, …

New Avenues For |Δb=2| Processes Beyond Neutron-Antineutron Oscillations, Arnau Basi Beneito, Svjetlana Fajfer, Alexey A. Petrov

Faculty Publications

We explore baryon-number-violating (|∆B| = 2) processes beyond the well-known neutron-antineutron (n-n̄) oscillations, focusing on the Λ-Λ̄ system. The presence of a strange quark in the Λ baryon introduces a new set of six-quark operators roughly of the form (uds)², which are different from the (udd)² operators responsible for n-n̄ oscillations. Using the Standard Model Effective Field Theory (SMEFT), we classify all dimension-9 operators that cause |∆B| = 2  transitions and study their UV completions mediated by exotic scalar fields with trilinear interactions. We demonstrate that …

Associational Inference With Many Potential Covariates: Bayesian Information Criterion Elastic Net, Farideh Bagherzadeh Khiabani, I-Chan Huang, Jose Miguel Martinez Martinez, Shizue Izumi, Sedigheh Mirzaei, Tiange Zheng, Irina Dinu, Yutaka Yasui

COBRA Preprint Series

Background: An emerging feature in modern biomedical research is collecting and analyzing numerous variables. In the presence of many potential covariates, inference becomes challenging requiring both distinguishing a set of covariates truly associated with an outcome and estimating their corresponding regression coefficients consistently. Traditional statistical inference typically focuses on estimating coefficients assuming a pre-specified set of covariates. Further, advanced machine/statistical learning methods performing both selection and estimation predominantly focus on outcome prediction rather than association inference.

Methods: Motivated by our epidemiological research on long-term childhood cancer survivors, where we aimed to investigate associations between a large pool of longitudinal symptom …

Data Centers In Mountain West Markets, 2026, Cason Noll, Krish Sharma, Maisoon Faris, Olivia K. Cheche, Caitlin J. Saladino, William E. Brown Jr.

Transportation & Infrastructure

This fact sheet reports on the distribution and geographic concentration of data centers across the Mountain West states of Arizona, Colorado, Nevada, New Mexico and Utah as of March 6th, 2026. Using data from DataCenterMap, this fact sheet examines the number of data centers in each Mountain West state and further analyzes market-level  distribution, defined as cities within each state where data centers are located. The data are used to compare state totals and to rank Mountain West markets from highest to lowest based on the number of data centers operating in that area.

Database-Driven Revelations In Epilepsy: Patient Reporting Accuracy And The Circadian Timing Of Seizures, Ithay Biton

Theses and Dissertations

For over thirty years, patients have been visiting the Arkansas Epilepsy Program for diagnosis and treatment for seizures and seizure-like episodes. As part of their clinical evaluation, patients often undergo ambulatory EEG (electroencephalogram) monitoring. This routine process produces valuable data for treating the patient. In this study, over 2000 ambulatory EEG reports from 1998 to 2016 were reviewed. A large database of seizures was created from the reports, with information on 407 patients, 1611 EEG-confirmed seizures, and 1726 patient-reported seizures (IRB Protocol #17-093). The database was used to address two important issues in epilepsy. The first topic of the study …

On Message Encryption And Enhanced Security Using Protein Structure Information, Daniel Graham

Chemistry: Faculty Publications and Other Works

This is a lecture packet focused on several well-established encryption procedures. Included is a discussion of security enhancements brought about by incorporating protein structure information into the procedures.

Digital Transformation And Esg Performance In China: The Mediating Role Of Green Innovation, Zhou Yanyu

Student Works (2020-2029)

This study explores the relationship between digital transformation and ESG performance in Chinese A-share listed companies, focusing on the mediating role of green innovation. From the view of resource-based theory, the digital transformation is found to the first enhances ESG performance and how green innovation serves as a key transmission mechanism. Using fixed-effects regression analysis based on 6,763 firm-year observations from 2015 to 2023. The empirical results indicate that digital transformation improves ESG performance by fostering green innovation, which in turn partially mediates this effect. These findings provide theoretical insights on how digital capabilities drive corporate sustainability and offer practical …

Reasoning In Large Language Models Across Multilingual, Long-Context, And Symbolic Settings, Sina Bagheri Nezhad

Dissertations and Theses

Large Language Models (LLMs) are increasingly deployed as general-purpose reasoners, yet their reliability degrades in three settings that frequently arise in practice: multilingual inputs, long contexts, and symbolic or formally constrained domains. In multilingual settings, uneven training coverage produces substantial performance disparities and uncertain generalization to languages with little or negligible pretraining exposure. In long-context settings, relevant evidence may be sparsely distributed, and models exhibit the "lost-in-the-middle" phenomenon, undermining retrieval and multi-step synthesis. In symbolic settings such as mathematics, small arithmetic or logical slips invalidate solutions, and prose rationales are difficult to verify automatically.

This dissertation first characterizes these failure …

Ulk4 And Cdkn2a Polymorphisms Influence The Risk Of Developing Monoclonal Gammopathy Of Undetermined Significance, José Manuuel Sánchez-Maldonado, Angelica Macauda, Antonio José Cabrera-Serrano, Hauke Thomsen, Murat Güler, Rob Ter Horst, Bethany Van Guelpen, Pavel Vodicka, Stefano Landi, Subhayan Chattopadhyay, Pelin Ünal, Lucía Ruiz-Durán, Delphine Casabonne, Hartmut Goldschmidt, Istemi Serin, María Carretero-Fernández, Elena Cabezudo, Fernando Reyes-Zurita, Alyssa I. Clay-Gilmour, Et. Al.

Faculty Publications

Monoclonal gammopathy of undetermined significance (MGUS) is a necessary precursor condition to multiple myeloma (MM). Given the role of autophagy in modulating MM risk, we investigated whether genetic variation in autophagy-related genes influences susceptibility to MGUS. We analyzed the association of 34,042 common autophagy-related single nucleotide polymorphisms (SNPs) with MGUS across six independent cohorts, five from Europe and one from North America, comprising 2317 MGUS cases and 282,358 controls. We also assessed their impact on immune parameters, including absolute counts of 91 blood-derived immune cell subsets and 103 circulating immunological proteins. Meta-analysis revealed a genome-wide significant association between the ULK4 …

Impact Of Medicaid Enrollment Timing On Tumor Stage At Diagnosis And Survival In Breast, Colorectal, And Lung Cancer, Gabriel A. Benavidez, Stella Coker Watson Self Ph.D., Ms, Anthony Alberg Ph.D., M.P.H., Janice Probst, Jan Marie Eberth

Faculty Publications

Background: Medicaid-insured patients experience higher rates of late-stage cancer diagnosis and worse survival than non-Medicaid patients. The impact of Medicaid enrollment timing on cancer outcomes is less clear. This study examines the association between Medicaid enrollment and timing with tumor stage and cancer-specific survival for breast, colorectal, and lung cancers. Methods: We analyzed SEER-Medicaid linked data for 276,755 breast, 104,784 colorectal, and 101,058 lung cancer patients < 65 years of age. Patients were categorized as non-Medicaid enrollees, pre-diagnosis enrollees (≥12 months before), or post-diagnosis enrollees (≤12 months after). Multivariable logistic regression estimated odds ratios of late-stage diagnosis, and cause-specific Cox proportional hazards models were used to assess cancer-specific survival, adjusting for demographic and socioeconomic factors. Results: Compared to non-Medicaid enrollees, post-diagnosis enrollees had the highest odds of late-stage diagnosis (breast cancer: OR: 3.41; colorectal cancer: OR: 3.78; lung cancer: OR: 1.87). Pre-diagnosis enrollees also had increased odds, but the …

Combined Dark Matter Search Towards Dwarf Spheroidal Galaxies With Fermi-Lat, Hawc, H.E.S.S., Magic, And Veritas, S. Abdollahi, L. Baldini, R. Bellazzini, B. Berenji, E. Bissaldi, R. Bonino, X. Wang, R. Turner, P. Hüntemeyer, Et Al.

Michigan Tech Publications

Dwarf spheroidal galaxies (dSphs) are excellent targets for indirect dark matter (DM) searches using gamma-ray telescopes because they are thought to have high DM content and a low astrophysical background. The sensitivity of these searches is improved by combining the observations of dSphs made by different gamma-ray telescopes. We present the results of a combined search by the most sensitive currently operating gamma-ray telescopes, namely: the satellite-borne Fermi-LAT telescope; the ground-based imaging atmospheric Cherenkov telescope arrays H.E.S.S., MAGIC, and VERITAS; and the HAWC water Cherenkov detector. Individual datasets were analyzed using a common statistical approach. Results were subsequently combined via …

Everyone Works On The Sustainability Team: Power Structures And Brand Communication In Sustainable Consumption, Sofia Johansson

USF Tampa Graduate Theses and Dissertations

In response to growing concerns about the way in which consumption practices negatively degrade the environment and contribute to climate change, this thesis explores the role in which brands may counteract this. There are two significant issues pertaining to environmental marketing: (1) current efforts to promote green consumerism, through which consumer brands market products as “eco-friendly” with only marginal reductions in climate impact and misleading labels, are insufficient in mitigating climate change. (2) Additionally, this kind of consumption is unjust, whereby it is marked up in price, limiting access to merely individuals in higher income brackets. Given the insufficiencies of …

Ai-Powered Reporting For Improved Hospital Efficiency, Joel C. Laskow, Chris Papesh, Srishti Awasthi, Jacquelyn Cheun

SMU Data Science Review

This study explores the feasibility of an AI-powered chatbot for HIPAA-aligned intake of emergency room patients seeking treatment for overdose and violence. The system utilizes AWS Amplify, an encrypted EC2 instance, and a secure S3 Bucket house on Amazon Web Services. Chat functionality is powered by a multi-agentic framework operating on Anthropic’s Claude Sonnet 4. Manual evaluation and exact match testing reveal the system reliably obtains and records relevant information during intake. Future work will focus on expanding accessibility by integrating voice functionality, obtaining HIPAA compliance certifications, and incorporating the chat system into existing healthcare networks.

Bias Evaluation Of Healthcare Data With The Use Of Vbqa - A Vaers Inspired Bias Question Answer Dataset, Nolan Dulude, Renu Karthikeyan, Bivin Sadler, Faizan Javed

SMU Data Science Review

Abstract. Large Language Models (LLMs) are being used increasingly within the healthcare industry to summarize complex clinical information, but their outputs can often reflect biases inherited from their training data. In healthcare, these biases are not just technical flaws, but they can lead to distorted and false information about vaccine safety, compromise patient trust, and lead to potential harmful outcomes. This study investigates bias found in LLM-generated outputs to question-answer pairs inspired by adverse vaccine reactions using COVID-19 data from the Vaccine Adverse Event Reporting System (VAERS) from 2020–2024. We examined whether training the LLMs on a known Bias Benchmark …

Nlp Bias And African American English, Kenya Roy, Faizan Javed

SMU Data Science Review

African American English (AAE), also referred to as African American Vernacular English (AAVE), is widely used on social media, but most sentiment analysis tools are trained only on Standard American English (SAE). This mismatch can cause models to misclassify dialectal expressions—especially by labeling neutral or positive AAE as negative or toxic. These errors matter, since Natural Language Processing (NLP) systems are now central to content moderation and brand monitoring. This research will evaluate the VADER, RoBERTa, GPT-OSS, and Gemma’s handling of AAE in comparison to SAE using the TwitterAAE corpus, a public dataset of tweets with estimated AAVE usage. The …

Automating Cardiff Model Data Capture In Emergency Departments: Ambient Nlp Integration With Oracle-Cerner Fhir Systems, Simi Augustine, Marco A. Lopez, Jacquelyn Cheun, Chris Papesh

SMU Data Science Review

Violence and overdose events in Las Vegas occur at rates above the national average, with fewer than half of violent injuries reported to law enforcement [2,7]. The Cardiff Model offers a proven framework for standardized data collection and sharing between hospitals and public safety partners, yet many implementations still rely on manual entry. We propose an ambient triage pipeline integrated with Oracle-Cerner electronic health record systems to listen to nurse–patient dialogue, convert speech to text, extract Cardiff fields, and write standards-based FHIR Bundles for analytics. Using SMART on FHIR standards and Cerner Millennium APIs, the study evaluates whether ambient capture …

Anomaly Detection For Multi-System Bug Triage, Gibran Miguel Zavala Gamero, Hayoung Cheon, Mustafa Iqbal

SMU Data Science Review

Large-scale software systems produce vast volumes of logs and telemetry, making manual incident triage slow and error prone. This study presents an unsupervised anomaly detection pipeline that fuses logs, metrics, and traces through late fusion. Using Hybrid Ensemble modeling with Isolation Forest, and Long Short-Term Memory (LSTM) Deep Learning model, the system detects cross-service anomalies producing and assigning a composite triage score reflecting severity and impact. Ranked alerts are categorized into Critical, High, or Medium priorities for review. A retrieval-augmented generation (RAG) layer enriches results with contextual summaries for explainable triage. Evaluated on synthetic multi-service datasets, the pipeline …

A Modular Framework For Cost-Efficient Aspect-Based Sentiment Analysis Using Small Language Models, Senthil Kumar, Nibhrat Lohia

SMU Data Science Review

Aspect-based sentiment analysis (ABSA) links opinions in text to specific product attributes (for example, battery life, screen quality, or delivery speed) rather than only assigning an overall star rating. This level of detail is important in domains such as e-commerce, where teams need to know which features customers praised and which they criticized. Traditional ABSA pipelines have relied on large language models (LLMs), which achieved high quality but were expensive to run and difficult to scale. This study evaluated whether small language models (SLMs) in the 1–3 billion parameter range could serve as a lower-cost alternative. We implemented a modular …

Availability Model To Evaluate Ai Data Centers’ Role In Grid Stability, Troy Mcsimov, Trevor S. Kunz, Jeffrey Billo

SMU Data Science Review

The United States has made it clear; it is imperative that the US wins the global AI race. This paper focuses on one of the most challenging puzzle pieces surfaced at the POWER Data Center conference (San Antonio, Sept. 30.); for Electric Reliability Council of Texas (ERCOT) the limiting factor is not generation alone but the need to balance generation and load to preserve grid reliability.

The regulatory landscape fundamentally changed with the passage of Texas Senate Bill 6 in June 2025, which mandates new large loads must "contribute to the recovery of the interconnecting electric utility’s costs" (Texas Legislature, …

Application Of Open-Source Small Large Language Models For Finance Report Analysis, Tue Vu, Mark Austin, Marcel Tuijn

SMU Data Science Review

The rapid integration of generative AI in finance introduces both opportunities and challenges, particularly when analyzing sensitive data such as Securities and Exchange Commission (SEC) filings. This study investigates the use of open-source Small Large Language Models (SLLMs), deployed locally through the Ollama and LangChain frameworks, combined with Retrieval-Augmented Generation (RAG) for extracting financial insights relevant to index performance and reporting quality. Two key objectives guide this work: (1) benchmarking multiple open-source SLLMs for sentiment analysis, multiple-choice reasoning, and financial question answering, and (2) assessing the feasibility of locally deployed SLLMs for domain-specific financial queries. A standardized set of 50 …

Reducing Range Anxiety Through Predictive Modeling Of Ev Battery Degradation, Caleb Thornsbury, Christian Castro, Bivin Sadler

SMU Data Science Review

Electric Vehicles (EV) range anxiety remains one of the top barriers for broader adoption. Range anxiety can be attributed to battery pack age and degradation over time. This paper plans to explore how to address this issue by creating a machine learning model that can predict degradation based on usage, temperature, battery chemistry, charging habits and exploring whether other factors tie into range degradation. This research will be using real world charging data along with lab tested chemistry data to build a model that can be chemistry specific for degradation. This paper will help perspective used-EV buyers learn about battery …

The Degree Of Nondensifiability In Hausdorff Locally Convex Topological Vector Spaces And Applications, Gonzalo Garcia

Turkish Journal of Mathematics

The so called degree of nondensifiability, DND, has been studied in Banach spaces through several studies, and some fixed point results using the DND have been proved. In the present paper, we extend the concept of DND to a Hausdorff locally convex topological vector space (LCTVS). Its main properties, as well as the differences between the DND in Banach spaces and the DND in a LCTVS, are analyzed. Furthermore, under suitable conditions, we provide some fixed point results in a LCTVS by using the DND, and as an application, we prove the existence of solutions of certain Volterra integral equations.

Existence Of Positive Periodic Solutions For A First-Order Semilinear Functional Differential Equation With An Iterative Source Term, Ahlème Bouakkaz, Rabah Khemis

Turkish Journal of Mathematics

In this paper, we will establish the existence of positive periodic solutions for a class of first order iterative functional differential equations by using a powerful and effective technique based on the Krasnoselskii fixed point theorem together with some useful properties of a Green's function. The obtained results that complement some previous works, are illustrated with an example.

Simplicity In Le-Semigroups Via Partitions Of Full Words, And Its Applications To Regularity Conditions Induced By Linear Inequalities, Nareupanat Lekkoksung

Turkish Journal of Mathematics

The concept of simplicity in le-semigroups has been used to characterize the coincidence of ideal elements and to study Khayopulu–Green’s relations. Several types of simplicities are known, including two-sided simplicity, left simplicity, right simplicity, bisimplicity, quasisimplicity, and biinterior simplicity. A natural question arises: are there further notions of simplicity for le-semigroups? If so, how are they related? By employing the notion of partition ideal elements, we observe that several potential simplicities have not yet been explored. In this paper, we introduce new forms of simplicity in le-semigroups defined via partitions of full words, and we investigate the …

On The Semigroups Of Order-Preserving And Orientation-Preserving Full Contraction Mappings On A Finite Chain, Gonca Ayik, Hayrullah Ayik

Turkish Journal of Mathematics

Let OPCT_n (OCT_n) denote the semigroup consisting of all orientation-preserving (order-preserving) full contraction mappings on the finite chain X_n = {1 < ··· < n}, and let OPDCT_n (ODCT_n) denote the subsemigroup of OPCT_n (OCT_n) consisting of all order-decreasing transformations. The purpose of this paper is to determine the cardinalities of OPCT_n, OPDCT_n, N(ODCT_n), and N(OPDCT_n), and to identify generating sets of minimal size, thereby determining their ranks. Moreover, the number of idempotents of OPDCT_n is determined.

A Geometric Property Characterizing The 3d-Generalized Clothoids, Pascual Lucas, José-Antonio Ortega-Yagües

Turkish Journal of Mathematics

In this paper, we study 3D-generalized clothoids, defined as curves for which there exists another distinct curve, with proportional arclength parameter, such that the Darboux lines of both curves at corresponding points are the same. We prove that these curves are those for which the ratio of torsion and curvature τ/κ is a linear rational function of the arclength. We also show that any 3D-generalized clothoid can be constructed from a rectifying curve, which is of constant curvature in the case of a 3D-clothoid.