Digital Commons Network^™

Amplicon Sequencing Reveals Shifts In Microbial Community Composition In Response To Nutrient-Dense Piney Point Emergency Discharges, Christa A. Baranowski

USF Tampa Graduate Theses and Dissertations

Due to a leak in the lining of a phosphogypsum pond at the former Piney Point phosphate-processing facility in Tampa Bay (Florida, U.S.A.), experienced a large influx of nutrient-dense waters in the early summer of 2021. As an emergency response effort, water samples were collected by researchers at the University of South Florida, College of Marine Science (USF CMS), covering the lower and middle regions of Tampa Bay (TB), over the span of three months (April – June). Illumina amplicon sequencing was performed on DNA from water samples, targeting bacterial and eukaryotic ribosomal rRNA genes, and processed into Amplicon Sequence …

Efficient Energy-Stable Discontinuous Galerkin Schemefor The Non-Isothermal Cahn–Hilliard–Navier–Stokestwo-Phase Fluid Flow System, Guang-An Zou, Meiting Wang, Kejia Pan, Yin Yang, Xiaofeng Yang

Faculty Publications

In this article, we propose a novel numerical framework for the non-isothermal Cahn–Hilliard–Navier–Stokes two-phase flow system, which couples the incompressible Navier–Stokes equations, the Cahn–Hilliard phase-field equation, and the heat transport equation to capture temperature-dependent two-phase flow dynamics. The pro-posed scheme achieves three major advances: (i) unconditional energy stability through a combined scalar auxiliary variable (SAV) and zero-energy-contribution (ZEC) approach, (ii) linearity and full decoupling of all variables while using a second-order temporal discretization, and (iii) efficient implementation via discontinuous Galerkin (DG) spa-tial discretization together with a second-order projection method for the Navier–Stokes equations. We rigorously prove the unconditional energy stability …

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 …

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 …

Molecular Quantum Particle Algorithm (Mqpa): Hybrid Quantum-Classical Learning For Molecular Property Prediction, Jessica Mcphaul, Bivin Sadler Ph.D.

SMU Data Science Review

Classical machine learning models and quantum kernel methods often struggle to capture quantum-coherent molecular features under the constraints of noisy intermediate-scale quantum (NISQ) hardware, limiting both predictive accuracy and scalability.

This paper introduces the Molecular Quantum Particle Algorithm (MQPA), a hybrid quantum–classical framework designed to achieve chemically accurate property prediction by integrating handcrafted molecular descriptors with parameterized quantum circuits. Molecular inputs, expressed as SMILES strings, are processed via RDKit and encoded through angle-based quantum gates with entangling layers in Qiskit [1]. Quantum parameters are optimized using simultaneous perturbation stochastic approximation (SPSA) [2], while classical regression layers leverage Adam [3] …

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 …

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 …

A General Predictive And Conceptual Model For Repulsive Electronic States In Valence Ionized Molecules, Luka Dockx, Bálint Sztáray, Anthony Dutoi

College of the Pacific Faculty Articles

Valence photoionization generates photoions with excess internal energy, often resulting in statistical dissociation, well described by RRKM theory. Yet, numerous small photoions with electronegative substituents (e.g., F, Cl, Br, OH) have been shown to exhibit nonstatistical behavior, undergoing direct dissociation from repulsive electronic states to yield radicals such as F• or OH•, along with their corresponding fragment ions. Here, we present a general, predictive model that rationalizes this mechanism and extends it to molecules bearing electronegative substituents capable of forming 2P (F•, Cl•, Br•) or 2Π (OH•, SH•, N3•, NCO•) radicals. Nonstatistical dissociation arises from ionization of p- or π-localized …

Predicting Tart Cherry Stem Water Potential Using Uav Multispectral Imagery And Environmental Data Via Symbolic Regression, Anderson L. S. Safre, Alfonso Torres-Rua, Kurt Wedegaertner, Brent Black, Brennan Bean, Burdette Barker, Matt Yost

Civil and Environmental Engineering Faculty Publications

Tart cherry is an important fruit crop in Utah, where irrigation is essential due to arid conditions. Precision irrigation requires reliable indicators of plant water status, and stem water potential (Ψ_stem), is among the most sensitive though labor-intensive and spatially limited. This study develops Ψ_stem estimation models using high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery combined with meteorological and soil moisture data, applying Symbolic Regression (SR). Results show a stronger correlation between optical bands and Ψ_stem during the pre-harvest period. Among 85 vegetation indices, the Red Chromatic Coordinate (RCC) index performed best (R² = 0.67). …

Monthly Progress Report Butte Priority Soils Operable Unit Consent Decree Partial Remedial Design/Remedial Action And Operation And Maintenance, Josh Bryson

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Rural–Urban Differences In Cognitive Outcomes Among Older Adults: The Roles Of Falls And Depressive Symptoms, Ayse Malatyali, Tom Cidav, Lisa A.K. Wiese, Jian Zou, Monique J. Brown Ph.D., Mph, Junfeng Ma, Breno S. Diniz, Ladda Thiamwong

Faculty Publications

Background: Older adults with Alzheimer’s Disease and Related Dementias (ADRD) experience a higher risk of falls. However, research lacks evidence on the effect of fall exposure on cognitive impairment and dementia. We investigated the association of falls and depressive symptoms with cognitive impairment and dementia in a nationally representative sample. Methods: We analyzed data from 6221 participants (age ≥ 65) in the Health and Retirement Study (HRS) from the 2018–2020 waves, using Multinomial logistic regression models. Measures included the HRS health status questionnaire, HRS cognition scale, and the Center for Epidemiological Studies Depression scale. Results: Prevalence of fall exposure across …

Human-Ai Collaboration In Marketing Brainstorming: A Meta-Analytic Investigation Of Implementation Approaches And Creative Outcomes, Michael Michaels

USF Tampa Graduate Theses and Dissertations

Marketing organizations face intensifying pressure to sustain creative output while managing constrained resources and accelerating digital content cycles. The integration of artificial intelligence into marketing brainstorming processes presents a promising response to this challenge; however, prior to this investigation, no comprehensive quantitative synthesis had established the overall effectiveness of human-AI collaboration in marketing creativity contexts or identified optimal implementation strategies.

This dissertation presents a systematic meta-analytic investigation of AI-enhanced brainstorming effectiveness in marketing contexts. Following PRISMA guidelines, 42 independent empirical studies (N = 2,166 participants) published between 2018 and 2025 were identified across four primary academic databases, coded for implementation …

Rural–Urban Differences In Cognitive Outcomes Among Older Adults: The Roles Of Falls And Depressive Symptoms, Ayse Malatyali, Tom Cidav, Lisa A.K. Wiese, Jian Zou, Monique J. Brown Ph.D., Mph, Junfeng Ma, Breno S. Diniz, Ladda Thiamwong

Faculty Publications

Background: Older adults with Alzheimer’s Disease and Related Dementias (ADRD) experience a higher risk of falls. However, research lacks evidence on the effect of fall exposure on cognitive impairment and dementia. We investigated the association of falls and depressive symptoms with cognitive impairment and dementia in a nationally representative sample. Methods: We analyzed data from 6221 participants (age ≥ 65) in the Health and Retirement Study (HRS) from the 2018–2020 waves, using Multinomial logistic regression models. Measures included the HRS health status questionnaire, HRS cognition scale, and the Center for Epidemiological Studies Depression scale. Results: Prevalence of fall exposure across …

Super-Resolution Remote Sensing Datasets For Application To Caral–Supe Archeological Sites Employing Sar And Dems, Jungrack Kim, Ramesh P. Singh

Mathematics, Physics, and Computer Science Faculty Articles and Research

Publicly accessible spaceborne remote sensing datasets often lack the spatial resolution required to reliably distinguish archeological features from their surrounding geomorphological contexts. In this study, we assess the potential of super-resolution (SR) products derived from multiple public-domain remote sensing datasets for a systematic archeological survey in the Caral–Supe region. We focus on Synthetic Aperture Radar (SAR) and topographic datasets—including Sentinel-1, Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR), and Digital Elevation Models (DEMs)—because of their capacity to detect subtle surface expressions and shallow subsurface structures obscured by vegetation or sediment cover. Using state-of-the-art deep learning algorithms, …

Coordinates, Time, And The Sky, John R. Thorstensen

Other Faculty Materials

This monograph explains celestial coordinates and their basis in the motion and orientation of the earth.  It was written for use in undergraduate classes in basic astronomy and observational techniques.

Fluorescent Biosensor-Guided Engineering Of Enzyme Cascades For Electrochemical Applications, Nathan J. Ricks, Monica Brachi, Fnu Arun, Egor Baiarashov, Shelley D. Minteer, Ming C. Hammond

Chemistry Faculty Research & Creative Works

Substrate channeling is a strategy for enhancing flux and yield in enzymatic cascades and is increasingly relevant for applications in biocatalysis, biotechnology, and bioelectrochemical systems. However, efforts to engineer channeling are limited by the lack of high-throughput methods to evaluate and optimize channeling efficiency. Here, we present a fluorescence-based screening assay to rapidly assess substrate channeling in a model system involving fumarase and malate dehydrogenase, two sequential enzymes from the Krebs cycle. By expressing genetic fusions in Escherichia coli, quantifying intermediate (malate) and product (NADH) formation in lysate using orthogonal fluorescent readouts, and comparing product-to-intermediate ratios, we screened a library …

Main-Chain Ion-Pair Polybenzimidazole Membranes Enabling Reduced-Temperature Ht-Pemfc Operation (Down To 120°C), Huina Lin, Brian C. Benicewicz

Faculty Publications

Proton exchange membrane (PEM) fuel cells are promising for clean and efficient energy conversion across diverse applications. High-temperature PEM fuel cells (HT-PEMFCs) using phosphoric acid (PA)-doped polybenzimidazole (PBI) can operate up to 200°C, but at lower temperatures, water-induced PA loss often leads to performance degradation and reduced durability. In this work, we present a new class of main-chain ion-pair PBI membranes by integrating the imidazolium-biphosphate ion-pair units directly into the PBI backbone. This design results in membranes with high acid content, stronger acid-polymer interactions and stabilized acid retention. In single-cell tests, the Im⁺-PBI15-3 wt.% exhibits peak power densities …

Factors For Patient Trust And Acceptance Of Medical Artificial Intelligence, Ana Bracic, Kayte Spector-Bagdady, Sophie Towle, Rina Zhang, Cornelius A. James, Nicholson W. Price Ii

Articles

Artificial intelligence (AI) is increasingly used in clinical care, but widespread adoption requires patient trust. Trust may be enhanced through systemic governance mechanisms or frontline clinicians providing a human in the loop for AI oversight. However, it is unclear how different approaches specifically influence patient trust in the use of medical AI. The objective is to determine the extent to which patient trust in and choice of medical scenarios involving AI are associated with governance mechanisms, clinician presence, performance, and data quality.

Privacy Preserving Anomaly Detection System For Der Solar Inverters, Tanzim Jim Hassan, Prakash Ranganathan

Graduate Research Achievement Day Posters

Reliable operation of solar inverters depends on maintaining a stable frequency. Recent cyber attacks on solar DERs are concerning and increase the likelihood of such stealthy attacks leading to anomalies in DER. Therefore, a robust anomaly detection system (ADS) is crucial for solar inverters used in distributed energy resources (DERs), enabling timely detection and correction of frequency anomalies. Additionally, preserving data privacy is essential for the security and reliability of the power grid. This paper proposes a privacy-preserving anomaly detection system (PP-ADS) based on a multi-stage hybrid machine learning (MSHML) model specifically designed for solar inverter data in DER environments. …

Response Of Botovskaya Cave To Local And Regional Environmental Dynamics, Jade Margerum, Oksana Gutareva, Franziska Lechleitner, Maria Box, Stuart Umbo, Anton Vaks, Hanno Meyer, Aleksandr Kononov, Aleksander Osintsev, Dimitri Sokol'nikov, Norbert Marwan, Ola Kwiecien, Sebastian Breitenbach

International Journal of Speleology

Speleothems from caves in near-permafrost regions can provide unique insights into the climatic history of temperature-sensitive environments. These regions are often remote and only seasonally accessible, which makes cave monitoring – a requirement for speleothem-based proxy interpretation – challenging. We document ventilation, the infiltration regime, and the isotopic composition of dripwater in Botovskaya Cave in southern Siberia, a host to speleothems previously used to reconstruct Quaternary permafrost dynamics in continental Eurasia. We explore surface and cave temperature records, cave air CO₂ concentration data and stable isotopes in precipitation and dripwater and evaluate infiltration conditions and the sensitivity of speleothems …

Sensing Negative-Cone Rotational Diffusion Of Dipole-Like Emitters, Yuanxin Qiu, Kaizhi A. Nie, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Fluorescence anisotropy and single-molecule orientation-localization microscopy (SMOLM) are powerful techniques that quantify the rotational diffusion of dipole-like emitters, which is important for sensing molecular interactions and chemical environments at the nanoscale. Numerous theoretical and experimental studies have thoroughly characterized single-molecule rotations even when those rotations are much faster than the detector integration time. Here, we extend the theory of measuring rotational diffusion to situations where a single dipole rotates uniformly everywhere outside of an isotropic cone of a certain size, termed a negative cone. This scenario corresponds to negative fluorescence anisotropy 𝑟 and has been observed in emitters exhibiting strong …

Tutoring Prompt For Personalized Self-Study Using Generative Ai, Vasiliy S. Znamenskiy

Open Educational Resources

This educational material presents a reusable tutoring prompt for general-purpose generative AI systems (e.g., ChatGPT, Gemini, Claude, Copilot, Perplexity) designed to support personalized self-study. The prompt turns a chatbot into an adaptive self-testing engine: it asks a sequence of multiple-choice questions, waits for the learner’s response, provides brief feedback, and adjusts difficulty based on performance. While the approach can be adapted to many disciplines, the current implementation is tuned to College Physics 2e, Chapter 21 (Circuits and DC Instruments) and uses “formulas only” answer choices to emphasize symbolic reasoning and reduce reliance on verbal cues. Shared in the spirit …

Impediments To Transforming The Healthcare Delivery System: Shifting The Paradigm From Provider Centric To Patient Centric, Elizabeth A. Regan, Manasa Devi Chinta

Faculty Publications

Introduction: 

Stated aims for digital healthcare transformation frequently cite goals for better coordinated patient-centric systems. However, despite advances in medical science, digital technologies, health policies, and billions of dollars invested over the past 25 years, most healthcare providers are far from fully realizing the demonstrated benefits of today's digital technologies for improving patient care. Sharing information across healthcare systems remains challenging. Problems with fragmentation, quality, inequities, and rising costs of care delivery persist. A recent study of 1,026 U.S. hospital systems found that only 15.8 percent achieved a digital maturity level needed to provide digitally enabled healthcare services to better …

Clinical Validation Of Perfusion Imaging With Pulmonary Function Test Data Using Voronoi-Based Discretization, Jorge Cisneros, Caleb J. Herrera, Yi-Kuan Liu, Lisa V. Du, Yevgeniy Vinogradskiy, Richard Castillo, Girish Nair, Edward Castillo

Department of Medicine Faculty Papers

Objective.Accurate lung function assessment is essential for diagnosing and managing diseases like chronic obstructive pulmonary disorder, pulmonary emboli, and lung cancer. Single-photon emission computed tomography (SPECT) provides valuable 3D functional imaging of ventilation and perfusion, but is limited by low spatial resolution, availability, additional radiation, and cost. Alternative methods, including CT-based perfusion (CT-P) and deep learning models, require large datasets to validate results that are often scarce. Pulmonary function tests (PFTs) offer rapid and noninvasive global lung function measures and are clinically widely used. While ventilation correlates well with PFTs, perfusion imaging presents challenges due to complex blood flow and …