Digital Commons Network^™

Entropy-Gated Probabilistic Inference For Optical Financial Identifier Recognition, Jack Ngare

Defensive Publications Series

Recognizing financial identifiers from variable or degraded optical inputs can be challenging, as conventional deterministic scanners may fail and general-purpose optical character recognition systems may lack semantic logic. A system can employ a cascaded inference pipeline that first assesses the visual entropy of an optical input. Based on this assessment, the input can be routed to either a fast, deterministic decoder for low-entropy signals or a probabilistic inference engine for high-entropy signals. The probabilistic engine could generate multiple candidate identifiers and use a reasoning model, constrained by financial domain logic, to evaluate a plausible candidate. A validation layer can then …

Promise P R O G R A M Providing Opportunities For Mathematics And Science Enrichment, Illinois Math And Science Academy

Promise Programs

IMSA’s PROMISE Program aims to address the challenges of culturally, linguistically, and economically diverse (CLED) students. PROMISE is a proven model of success for realizing the vision of a highly diverse and creative STEM workforce. Eligible students will be in 7th-9th grade in Illinois and demonstrate high academic achievement. This grantfunded program is open to all students, especially those from culturally, linguistically, and economically diverse backgrounds, including Black or African American students, Latinx students, students who qualify for the National Free or Reduced-Price Meal Program, and students from rural Illinois counties.

Cv: Seung Kim (Literacy-Instructional Technology), Seung Kim

Education Department Faculty Curricula Vitae

No abstract provided.

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 …

2026 March 11 - Undergraduate Curriculum Council Minutes, Undergraduate Curriculum Council, East Tennessee State University

Undergraduate Curriculum Council Minutes

No abstract provided.

An Ambient Acoustic Ice-Fracturing Dataset Taken In Shallow Freshwater, John Case, Andrew Barnard, Daniel Brown

Michigan Tech Publications

This paper describes an acoustic dataset collected on a frozen shallow freshwater lake between February and March of 2024. This collection took place over one full week on Portage Lake in the Upper Peninsula of Michigan, USA. The first sub-dataset consists of ambient ice and environmental noises collected by an array of hydrophones, microphones and geophones placed below, above and on the ice respectively. The second sub-dataset consists of instrumented force hammer impacts at a series of locations on the the ice with the corresponding response at each acoustic sensor. All acoustic data were recorded at a sample rate f …

Rewriting The Rules: Game-Based Learning For Affective And Cognitive Student Engagement In An Undergraduate History Course, Rachel Hoke, James Risk

Faculty Publications

Constructivist psychology has long demonstrated the potential for play to offer various benefits to learning. This study introduced active game-based learning experiences, in the form of role-playing games, into an undergraduate history course that also included passive lecture and discussion elements. Mixed methods student feedback surveys were used to examine the impacts of game-based learning on students’ affective and cognitive engagement in the course and offer a comparison between the lecture and game-based learning experiences. Qualitative data were analyzed through the paradigm of Mohsen, Abdollahi, and Omar’s (2019, 518-520) framework for educational value, including affective evaluation, conceptual understanding, skill development, …

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 …

Congruence Properties Modulo Prime Powers For A Class Of Partition Functions, Matthew Boylan, Swati .

Faculty Publications

Let p be prime, and let p_[1,p](n) denote the function whose generating function is

∏ _n≥1 (1 − qⁿ)⁻¹ (1 − q^pn)⁻¹.

This function and its generalizations p_[c^r,d^m](n) are the subject of study in several recent papers. Let ℓ ≥ 5, let j ≥ 1, and let p ∈ {2, 3, 5}. In this paper, we prove that the generating function for p_[1,p](n) in the progression β_{p,ℓ,j …}

Quantifying And Integrating Community Hardship Into Two-Stage Stochastic Grid Reliability Optimization With Battery Storage, Fredrica Arthur

LSU Master's Theses

Traditional reliability planning for conventional distribution systems is largely utility-oriented, with a focus on collective system performance metrics like Expected Energy Not Supplied (EENS), where implicitly all unserved energy is considered of equal weight in terms of post-outage economic hardship. Yet, it is well understood that extended outage durations cause an uneven level of hardship to socioeconomically disadvantaged communities. This thesis proposes a community-informed reliability planning framework where the hardship caused by outages is explicitly considered in the battery energy storage system (BESS) location and sizing problem. First, a hardship-weighted Energy Not Supplied (WENS) measure is proposed, where income, education, …

Defining A Multi-Omic, Ai-Enabled Stool Screening Paradigm For Colorectal Cancer: A Consensus Framework For Clinical Translation, Arturo Loaiza-Bonilla, Yan Leyfman, Viviana Cortiana, Rhys Crawford, Shivani Modi

Einstein Health Papers

Colorectal cancer (CRC) develops through both conventional adenoma-carcinoma and serrated neoplasia pathways, yet noninvasive screening still under-detects the advanced precursor lesions that enable true cancer prevention. Stool-based screening reduces CRC mortality, but its preventive impact remains constrained by limited detection of advanced precancerous lesions (APLs), including advanced adenomas and sessile serrated lesions. Next-generation multitarget stool DNA assays (mt-sDNA; e.g., Cologuard Plus) have established high sensitivity for CRC and specificity approaching 94%, leaving improved APL detection as the principal opportunity for innovation. This review presents a consensus framework for a multi-omic stool screening paradigm that integrates host epigenetic markers (DNA methylation) …

Norm-Variation Of Triple Ergodic Averages For Commuting Transformations, Polona Durcik, Lenka Slavíková, Christoph Thiele

Mathematics, Physics, and Computer Science Faculty Articles and Research

We prove an r-variation estimate, r>4, in the norm for ergodic averages with respect to three commuting transformations. It is not known whether such estimates hold for all r≥2 as in the analogous cases for one or two commuting transformations, or whether such estimates hold for any r< ∞ for more than three commuting transformations.

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 …

The Host Galaxies Of Active Galactic Nuclei With Direct Black Hole Mass Measurements, Vardha N. Bennert, Nico Winkel, Tommaso Treu, Xuheng Ding, Vivian U, Raymond P. Remigio, Aaron J. Barth, Matthew A. Malkan, Lizvette Villafaña, Samantha Allen

Physics

Reverberation mapping (RM) determines the mass of black holes (BHs) in active galactic nuclei (AGNs) by resolving the BH gravitational sphere of influence in the time domain. Recent RM campaigns have yielded direct BH masses through dynamical modeling for a sample of 32 objects, spanning a wide range of AGN luminosities and BH masses. In addition, accurate BH masses have been determined by spatially resolving the broad-line region with GRAVITY for a handful of AGNs. Here, we present a detailed analysis of Hubble Space Telescope images using surface-brightness profile fitting with state-of-the-art programs. We derive AGN luminosity and host-galaxy properties, …

Understanding Math-Anxious Teachers' Stories Of Comfort, Competence, And Confidence With Learning And Teaching Mathematics, Deborah Peart Crayton

USF Tampa Graduate Theses and Dissertations

Math anxiety is a complex issue that affects students and teachers. It has been researched for decades, but the focus has often been on addressing math anxiety in students, while finding solutions to support elementary teachers experiencing math anxiety has been overlooked. This study fills a gap in existing research by shedding light on math anxiety in elementary education and highlighting ways to address it by leveraging existing structures that support professional development. This qualitative narrative inquiry explores the experiences of four elementary educators who identify as math-anxious and how they report that math anxiety impacts their confidence in teaching …

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 …

A Unified Non-Visual Explainable Ai Framework For Neonatal Pain Assessment, Md Imran Hossain

USF Tampa Graduate Theses and Dissertations

With the power of parallel processing, large datasets, and fast computational resources,deep neural networks (DNNs) have outperformed highly trained and experienced human experts in medical applications. However, the large global community of healthcare professionals, many of whom routinely face potentially life-or-death outcomes with complex medicolegal consequences, have yet to embrace this powerful technology. The major problem is that most current AI solutions function as a metaphorical black-box positioned between input data and output decisions without a rigorous explanation for their internal processes. With the goal of enhancing trust and improving acceptance of artificial intelligence– (AI) based technology in clinical medicine, …

Primordial Recognition And The Structure Of Human Cognition: Toward A Quranic Epistemology, Jana El-Boustani

USF Tampa Graduate Theses and Dissertations

This thesis reconstructs a Qur’anic epistemological architecture grounded in narrative rather than abstract system-building. It argues that the Qur’an articulates a unified account of human cognition in which intelligibility is grounded in divine instruction, recognition is affirmed through primordial testimony, and knowledge is exercised within finite yet accountable faculties.

Through close analysis of the story of Adam in Surat al-Baqarah, the primordial covenant in Surat al-Aʿrāf, and the verse of the Amanah in Surat al-Aḥzāb, the study demonstrates that the Qur’an presents human knowing as structured, oriented, and morally conditioned. Revelation functions not as the introduction of wholly new knowledge, …

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] …

Predicting House Prices Using Machine Learning, Pejal Rath, Robert Slater

SMU Data Science Review

This study explores the use of Machine Learning methods to predict housing prices, a problem of significant interest in real estate economics, and data science. Traditional hedonic pricing models have long been used to evaluate the impact of housing attributes on property values, but they often lack the flexibility to capture nonlinear relationships and complex feature interactions. Recent advancements in regression based and Machine Learning approaches provide promising alternatives that may improve prediction of accuracy and market insights. This research will investigate how models such as linear regression, random forest, and gradient boosting can be applied to publicly available housing …

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 …