Digital Commons Network^™

Patterns Of Multimorbidity Among Low-Income Adults Who Smoke With Implications For Tailored Interventions: A Cluster Analysis Using A Mixture Of Bernoulli Model, Monique T. Cano, Michael R. Lindstrom, Oscar F. Rojas Perez, Ricardo F. Muñoz

School of Mathematical & Statistical Sciences Faculty Publications

Introduction: Smoking cigarettes remains a leading modifiable risk factor for preventable health conditions. In the United States, the health burden of smoking disproportionately impacts low-income individuals. Multimorbidity is common in this group, complicating treatment and worsening outcomes. Identifying multimorbidity clusters can support targeted, individualized interventions. This study aimed to identify multimorbidity clusters among individuals who smoke and experience economic hardship and provide clinical recommendations to enhance health outcomes.

Method: Individuals who smoke and experience economic hardship (N = 60) were recruited from the San Francisco Health Network (SFHN) and were assessed for physical and mental conditions. Cluster analysis was …

The Core-Modulation Architecture (Cma): A Structural Overview Of A 14-Paper Research Program (Preprint), Griselda Poe

Publications and Research

This document provides a structural overview of the Core-Modulation Architecture (CMA), a 14-paper research program on cognition, communication, and AI interaction.

The series specifies the conditions under which cognition operates, terminates, fails, and generates structure. Rather than describing cognition by its contents (beliefs, emotions, decisions), it defines cognition through its underlying architecture: constraint-governed processing across layers with distinct termination conditions.

The framework introduces a layered model consisting of Core processing (constraint preservation and structural coherence) and Modulation (affective calibration and social interface adjustment), extended by a Prior layer as the source of constraints. Across the series, phenomena such as miscommunication, …

Towards Understanding Robust Neural Coding Through Representational Geometry, Zeyuan Ye

Arts & Sciences Graduate Student Theses and Dissertations

Neural activity is high-dimensional and variable, yet animals represent information robustly. This thesis studies the origins of such robustness through the lens of representational geometry and deep neural network modeling. From a geometric perspective, population responses concentrate near smooth manifolds embedded in a high-dimensional state space. To infer these manifolds from noisy data and quantify their geometric properties, we develop a statistical method based on Gaussian processes and kernel regression (GKR). Applying GKR to simultaneously recorded grid-cell population activity during open-field navigation, we show that increasing running speed expands a torus-like representational manifold and improves spatial decodability. Thus, despite faster …

Pong Revised: Network-Based Competitions Through Secure Socket Services, Noah T. Jennings, Destiny D. Hale, Jared D. Williams, Michael J. Lively-Scholz

Knowledge and Creativity Expo

We aim to provide a safe, thrilling, locally hosted, and educational multiplayer experience that can be quickly replicated in modern Capture The Flag (CTF) events.

Being There For Mom: The Strengths Of Daughtering, Allison M. Alford, Kaitlin E. Phillips, Luke V. Stipanovic, Cayd A. Rocha-Barnette, Michelle Miller-Day

Communication Faculty Articles and Research

Objective

Daughters undertake daughtering, or the everyday role portrayal of contributing to a meaningful family relationship with their mothers, but the labor of it is often invisible.

Background

Using a strengths-based approach, we investigated what daughters do well in their relationships with mothers.

Method

We analyzed the responses of 1,444 women to the open-ended question, “What do you do well as a daughter?” to learn more about how women describe their daughtering. Utilizing the artificial intelligence of a large language model for data analysis, we supplied definitions and descriptions of 12 virtues and strengths from existing literature and created a …

Beyond The Survey: Performance Assessments For Higher-Order Sustainability Literacy In Higher Education, Gregory Vincent Massara, Courtney B. Ryan, Mary Ellen Mallia

Educational Policy & Leadership Other Graduate Student Scholarship

Purpose This study explores how a performance-based assessment measures students’ higher-order sustainability competencies in ways that multiple-choice or scaled-survey tools, common ways to assess sustainability literacy, may not.

Design/Methodology/Approach This exploratory case study created a performance task for a first-year honors writing and critical inquiry course. Students (n=9) analyzed a scenario across environmental, social, and economic domains. A rubric measured students’ abilities to address all three domains and articulate a feasible and balanced solution supported by credible evidence.

Findings Students demonstrated proficiency in defining the three sustainability domains but tended to struggle to balance them. Choosing scenarios in familiar local …

Enhancing Cyber Hygiene Among Communities Through Experiential Cyber-Security Awareness Programs, Dr Atul Bamrara, Partha Roy, Vishwanath Gargote, Khandu Thungon

Journal of Cybersecurity Education, Research and Practice

Human error remains the most frequently exploited vulnerability in the cyber-security ecosystem. Despite substantial investments in technical safeguards, cybercriminals increasingly rely on social engineering, misinformation, and emotionally manipulative tactics to compromise users. This study examines behavioral changes among participants who underwent structured cyber-security workshops addressing both conventional cyber hygiene practices and emerging digital threats. The training modules covered digital arrest scams, identity theft, sextortion, fake technical support fraud, fake social media profiles, online gaming related risks, and deep fake manipulation. The workshops were designed using interactive simulations, real world case studies, and hands on problem based exercises, with the explicit …

Addressing The Problems Of Data Variations, Quality, And Scarcity In Training Deep Neural Networks, Jian Sun

Electronic Theses and Dissertations

The performance of deep neural networks (DNNs) is strongly influenced by the characteristics and quality of the underlying datasets. This Ph.D. dissertation addresses three pervasive data challenges-imbalance, quality degradation, and scarcity-that commonly hinder the effectiveness of DNNs in computer vision (CV) and natural language processing (NLP) applications.

Class imbalance remains one of the most frequent causes of degraded model generalization. While Focal Loss effectively mitigates inter-class imbalance by assigning higher weights to minority classes, it struggles with intra-class imbalance, particularly in video datasets where longer clips dominate feature representation. To address this, I implement and utilize …

Health Effects Of Cadmium And Manganese Exposure On Luteinizing Hormone Levels: A Pilot Study Of Female Infertility, Shatha F. Alhous, Anees A. Al-Hamzawi, Murtadha Sh. Aswood

Al-Bahir

Female infertility occurs for many reasons, some unknown. Exposure to heavy metals is a risk factor that negatively impacts on female reproductive system. Therefore, this study aimed to evaluate the effect of cadmium Cd and manganese Mn on luteinizing hormone LH levels for females’ infertility. This study included a healthy group (n = 20), primary infertile females (n = 40), and secondary infertile females (n = 20) collected from Al-Zahraa hospital in Najaf center, Iraq. The Atomic Absorption Spectroscopy (AAS) was used to measure the cadmium and manganese concentrations and LH levels were measured by a Maglumi 800 from Snibe …

Clinical Prediction Of Posttreatment Migraine Recurrence Using Biofeedback Data: A Machine Learning Framework For Enhanced Patient Stratification And Treatment Monitoring, Shibbir Ahmed Arif, Ferdib-Al-Islam, Mehidy Hasan Sium

School of Computing Faculty Scholarship and Creative Works

Migraine is a complex neurological disorder with significant implications for individual well-being and public health. Predicting migraine occurrences after treatment is crucial for evaluating therapeutic efficacy and enabling personalized care, yet remains largely underexplored. This study proposes a robust machine learning framework to predict posttreatment migraine headache occurrences using real-world headache log data collected from 133 patients undergoing biofeedback therapy. The methodology includes rigorous data preprocessing, outlier removal via the interquartile range (IQR) method, and class imbalance correction through the synthetic minority oversampling technique (SMOTE). A total of 10 classical and a hybrid ensemble machine learning models were developed and …

Why General Ai Inherited The Body: A Structural Account Of Embodied Modulation Under Monolithic Imitation, Griselda Poe

Publications and Research

General AI has pursued the replication of human-level intelligence without first decomposing human cognition into structurally distinct components. Human cognition, however, is shaped by embodied constraints such as mortality, survival pressures, finite lifespan, and physiological states. This paper argues that when cognition is treated as a single undifferentiated whole, embodied modulation is not accidentally introduced into AI systems but structurally entailed. Any attempt to imitate “human intelligence” under a monolithic model necessarily incorporates variability shaped by mortal embodiment. The tensions observed in contemporary AI systems are better understood as consequences of copying an undecomposed target rather than isolated implementation errors. …

Termination Of Conceptual Search: A Structural Account Of Meaning Stabilization And Conversion, Griselda Poe

Publications and Research

Conceptual definitions form a recursive structure: words are defined by other words, which themselves require further definition. Traversing such definitions produces an open-ended branching process. Because the lexical system contains no intrinsic endpoint, conceptual understanding requires a termination operation that stabilizes meaning. This paper proposes that conceptual processing functions as an open-ended search over a definition graph and that stabilization occurs when this search is terminated. The termination mechanism depends on which cognitive layer is foregrounded. When Empathic modulation is foregrounded, termination occurs through contextual translation: concepts are stabilized once they can be mapped onto socially recognizable meanings. When Core …

Ecomusicological Representation Of Climate Change And Environmental Sustainability In Selected Bongo Fleva Lyrics In Tanzania, Auson N. Wincheslaus

Journal of Humanities and Social Sciences

This article critically explores how Bongo Fleva’s lyrics engage with environmental themes; and the interrelationship between nature and culture in the context of the Anthropocene. The data were purposively sampled from YouTube via content analysis, from which 50 Bongo Fleva songs were listened to, and only 10 environmentally-themed songs were analysed. Then, the selected songs were subjected to transcription (from oral to written form), translation (from Kiswahili into English), and close reading and textual analysis. The close reading of the selected songs focused on the employed aesthetic and rhetorical strategies, such as anthropomorphism, symbolism, antithesis, apocalyptic tones, solastalgia, rhetorical questions, …

Ecosystem-Based Flood Disaster Risk Reduction In The Little Ruaha River Basin, In The Southern Highlands Of Tanzania, Mawazo Ghambi, Tiemo Romward Haule

Journal of Humanities and Social Sciences

Ecosystem-based approach that integrates floods and environmental risk management is believed to reduce flood disaster risk while providing socio-economic and environmental benefits to floodplain occupants. This study examined the socio-economic and environmental benefits of the ecosystem-based approach in managing flood disaster risk in the Little Ruaha River Basin in the southern highlands of Tanzania. The study involved 157 participants and employed the mixed research design to collect quantitative and qualitative data. Semi-structured interviews, in-depth interviews, direct field observation and Focus Group Discussions were used to collect primary data, whereas documentary review was used to collect secondary data. Findings revealed that …

Designing Risk-Aware Mixed-Mode Evacuation Strategies For Tsunamis: Insights From İstanbul, Vedat Bayram, Doruk Ergez, Ada Arikanoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

​Tsunamis pose severe and time-critical risks to densely populated coastal cities, where limited warning times and infrastructure constraints demand carefully coordinated evacuation strategies. This study develops an integrated, risk-aware optimization framework that jointly considers vertical and horizontal sheltering options together with mixed pedestrian-vehicular evacuation dynamics. The proposed mixed-integer second-order cone programming (MISOCP) model simultaneously determines vertical shelter location, evacuee assignment, road-use designation for pedestrians and vehicles, and route selection under congestion, capacity, and budget constraints. Vehicle travel times incorporate congestion effects through a convex flow-dependent function, while pedestrian routing ensures convergent and conflict free evacuation paths. A risk-minimization objective accounts …

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 …

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 …

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 …

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 …

Redesigning Online Graduate Orientation To Foster Academic Resilience And Prevent Underperformance, Stella Michael-Makri, David E. Rodriguez

Journal of Academic Underperformance

Graduate students in fully online programs often begin their academic journey without adequate preparation for the emotional, structural, and cultural challenges of graduate-level work. For students who are first-generation, racially marginalized, international, or returning to education after time away, this lack of scaffolding can lead to early disengagement, underperformance, or attrition. Orientation, often treated as a checklist of logistical tasks, represents a missed opportunity for meaningful academic intervention. This manuscript proposes a five-module conceptual model for online graduate orientation designed to proactively support online graduate students in the domains of emotional regulation and academic identity, time management and executive functioning, …

Measuring What Matters: Specifications Grading And Latin* Students’ Mathematics Identity, Luis Miguel Fernández, Mayra Ortiz Galarza, Cristina Villalobos, Martha Asare

School of Mathematical & Statistical Sciences Faculty Publications

This study examined Specifications Grading, an alternative grading system emphasizing clearly defined learning outcomes and revision, and mathematics identity among 846 Latin* Calculus I students at a Hispanic-Serving Institution. Mathematics identity, comprising competence/performance, recognition, and interest, was measured at the beginning and end of the semester. Repeated-measures analyses indicated stable competence/performance and recognition alongside declines in interest. Specifications Grading was associated with increased mathematics identity, and multilingual students experienced smaller declines than their peers overall.

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Este estudio examinó la calificación por especificaciones, un sistema de evaluación alternativo que enfatiza resultados de aprendizaje claramente definidos y oportunidades estructuradas de revisión, …

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 …

Numerals And Other Means Of Expressing Plurality In Language And Thought: A Developmental Perspective Across Cultures., Giovanni Giuseppe G.G. Nicosia Prof.

Numeracy

Several ancient languages use linguistic structures to differentiate between singular, dual, and plural forms that are used when one, two, or many objects are referenced. (Some languages go further, adding a trial form to differentiate two from three.) In many modern languages, numerals are used to convey precise number. After reviewing a range of ways languages express number (including the interesting case of Pirahã which does not express precise number), the paper concludes with a proposed developmental path for the expression of quantity in languages.

Multiple Q-Dqn Algorithm Based Rumor Control In Social Networks, Zainab Hasan, Huda Naji Nawaf

Journal of Intelligent Informatics, Networking, and Cybersecurity

Malicious rumours on social media platforms like Facebook, Twitter, and others can be widely disseminated because of social problems. It is challenging to manage a rumour once it gains growth and quickly moves throughout a network. One of the main issues with information dissemination is figuring out how to reduce the propagation of rumours within a social network. A deep reinforcement learning technique might be an effective strategy to manage the rumour issue. Deep Q learning network (DQN) has been used in the literature to mitigate rumors by selecting a blocker at each time step. In this work, the proposed …

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 …

The Need For Developing Psychologically Informed Physical Therapy Education, Dustin Cox

Journal of Applied Disciplines

Chronic pain presents a multifaceted challenge for healthcare providers, often impacting patients' physical, emotional, and psychological well-being. Research indicates that individuals with chronic pain process pain through emotional and reward pathways in the brain, underscoring the need for integrative treatment approaches. Evidence supports the effectiveness of combining psychological interventions with physical rehabilitation to improve outcomes. Acceptance and Commitment Therapy (ACT), a mindfulness-based behavioral therapy, has shown promise in addressing the psychological dimensions of chronic pain. However, physical therapists often lack formal training in ACT. This study protocol was designed to explore effective methods for educating physical therapists in ACT principles …

The Illusion Of Causality In Llms: A Developmentally Grounded Analysis Of Semantic Scaffolding And Benchmark–Capability Mismatches, Daisuke Akiba

Publications and Research

Recent benchmarks increasingly report that large language models (LLMs) exhibit human-like causal reasoning abilities, including counterfactual inference and intervention planning. However, many such evaluations rely on domains that are heavily represented in training data and embed strong semantic cues, raising the possibility that apparent causal competence may reflect semantic pattern recombination rather than structure-sensitive causal reasoning. Drawing on human developmental theories of causal induction, this perspective argues that genuine causal understanding requires robustness to novelty and reliance on conditional structure rather than semantic familiarity. To illustrate the testability of this claim, the paper includes a pilot demonstration using synthetic causal …

Student Mental Health: Screening For Stress, Anxiety And Depression Using Fitbit Data, Rebecca Lopez, Avantika Shrestha, Ml Tlachac, Kevin Hickey, Xingting Guo, Shichao Liu, Elke Rundensteiner

Information Systems and Analytics Department Faculty Conference Proceedings

College students experience many stressors, resulting in high levels of anxiety and depression. Wearable technology provides unobtrusive sensor data that can be used for the early detection of mental illness. However, current research is limited concerning the variety of psychological instruments administered, physiological modalities, and time series parameters. In this research, we collect the Student Mental and Environmental Health (StudentMEH) Fitbit dataset from students at our institution during the pandemic. We assess the ability of predictive machine learning models to screen for depression, anxiety, and stress using different Fitbit modalities. Our findings indicate potential in physiological modalities such as heart …

Does Genetic Testing Influence Outcomes: Early Experience In Melanoma Case Series, Ryan Reyes, George Skenteris, Stella Coker Watson Self Ph.D., Ms, Christine Marie-Gilligan Schammel, Steven D. Trocha

Faculty Publications

Background:

The treatment and prognosis of melanoma have historically been based on histologic stage and Breslow depth; however, due to the increase in surveillance, melanomas are being identified at an earlier stage and lower Breslow depth. Advances in genetic testing, such as Decision Dx®, mean that melanoma diagnostic decisions and prognosis can now be directed by genetics. The purpose of this project was to assess the influence of a Decision Dx® high-grade (Class 2A/B) classification on the treatment of melanoma patients at a regional medical center, particularly those not deemed high risk by conventional classification methods, including Breslow depth and …

Diagnostic Trends And Service Enhancements In Deaf Mental Health: A Longitudinal Analysis, Kent Schafer, Steve Hamerdinger, Frank Wu, Rie Sakai Bizmark, Charlene Crump

JADARA

A longitudinal study investigated mental health diagnostic trends and patterns among Deaf and Hearing individuals using a comprehensive dataset from Alabama Department of Mental Health spanning 2004-2024 (Deaf=4,547; Hearing=2,056,188). Chi-square analysis revealed significant proportional differences in diagnoses between the groups. Deaf individuals showed significantly higher proportions of psychotic (12.93% vs. 8.33% Hearing), mood (20.76% vs. 18.84% Hearing), and personality (0.58% vs. 0.43% Hearing) disorders. These findings suggest that increased access to specialized and competent mental health services for individuals who are Deaf, particularly following the increase of staffing patterns for the Office of Deaf Services in 2015,contributed to more accurate …