Digital Commons Network^™

Site Specific Reliability-Targeted Snow Loads And Winter Wind Parameters Across The World, Brennan Bean, Nicholas Brimhall, Bikram Bhusal, Marc Maguire, Maha Moussa

Mathematics and Statistics Faculty Publications

This report presents a framework for estimating Reliability Targeted Snow Loads (RTSLs) and Winter Wind Parameters (WWPs) at locations outside the Conterminous United States (OCONUS). The methodology integrates ground-based in-situmeasurements with gridded global climate products to generate spatially continuous RTSL estimates for nearly any location in OCONUS. RTSLs are computed at qualifying in-situ stations, and predictive models relating gridded climate products to in-situRTSLs enable estimation at locations lacking direct measurements. This report describes the development of the in-situ annual maximum snow load database, the construction of global gridded climate summaries, the estimation of site-specific RTSLs and Service Targeted …

Studies Of Seismicity In The Alaska Subduction Zone And Antarctica, Patrick Matulka

Arts & Sciences Graduate Student Theses and Dissertations

This dissertation utilizes multiple methods of earthquake detection, relocation, and characterization to investigate the patterns of seismicity along the Alaska subduction zone and Antarctica. The studies of the Alaska subduction zone are built on the data collected during the Alaska Amphibious Community Seismic Experiment (AACSE) that deployed temporary land and ocean-bottom seismometers along the Alaska Peninsula and offshore regions from May 2018 to September 2019. Using seismic arrivals at those stations, as well as a combination of other land-based networks, I relocated 1307 earthquakes in the incoming plate and trench slope regions of the Alaska subduction zone with an absolute …

Geographic Patterns Of Educational Achievement: A Methodological Case Study Of Regression Modeling Approaches, Leokadia Lipien

USF Tampa Graduate Theses and Dissertations

This methodological case study introduces a novel approach to investigate the impact of student, teacher, and geographic factors on the academic achievement of virtual school students in Florida. Traditional linear regression models, and even multilevel models, often overlook spatial dependencies, which can lead to biased estimates and incomplete inferences. This research addresses this limitation by comparing a cross-classified multilevel model (CCMM) with a novel cross-classified spatial model (CCSM) that incorporates coefficients from geographically weighted regression (GWR). Utilizing secondary data from a statewide virtual school, including student demographics, teacher characteristics, and standardized test scores, along with U.S. Census Bureau data, the …

Circulating Levels Of Insulin-Like Growth Factor I (Igf-I) And Risk Ofmultiple Myeloma: An Observational And Mendelian Randomisation Study, Yolanda Benavente, Sara Hermosa, Nikos Papadimitriou, Alyssa I. Clay-Gilmour Ph.D., Elizabeth E. Brown, Jonathan N. Hofmann, Nathaniel Rothman, Qing Lan, Sonja I. Berndt, Demetrius Albanes, Mark Purdue, Mitchell J. Machiela, Stephen J. Chanock, Parveen Bhatti, Wendy Cozen, Aaron Norman, Susan L. Slager, James R. Cerhan, Vincent Rajkumar, Shaji J. Kumar, Et. Al.

Faculty Publications

Evidence for an association between insulin-like growth factors (IGF) and multiple myeloma (MM) is inconsistent. We examined total IGF-I concentrations and risk of MM by combining baseline serological data among UK Biobank participants (n = 444 187; 732 incident MM) with a two-sample Mendelian randomisation (MR) analysis using identified genetic variants associated with circulating total IGF-I and IGF-binding protein 3 (IGFBP-3) in the InterLymph consortium (2434 MM and their 2567 controls). Finally, additional lymphoid neoplasm (LN) subtypes were included for comparison with the main hypothesis. Circulating IGF-I level was positively associated with MM risk Hazard ratio-HR-per one standard deviation-SD-increase …

Elucidating The Effects Of Water Activity On The Hydration Kinetics And Thermodynamics Of Ye’Elimite–Calcium Sulfate Hydrate Systems, Godwin I. Ogbuehi, Rupack R. Halder, Aditya Kumar, Gaurav Sant, Monday U. Okoronkwo

Materials Science and Engineering Faculty Research & Creative Works

Relative humidity and water activity (aH) reductions have been known to suppress the hydration of anhydrous cement clinker phases. However, the relationship between ye'elimite (C4A3$) hydration kinetics and water activity remain unclear. This study employs experimental and thermodynamic modeling approaches to investigate the influence of water activity on ye'elimite hydration in the "C4A3$ + water", and "C4A3$ + gypsum + water" systems. Experimental findings indicate that as water activity decreases, C4A3$ hydration diminishes until the reaction is brought to a halt at a threshold aH. The critical aHand corresponding solubility constant of C4A3$ (KC4A3S¯) estimated from thermodynamic analysis are 0.46 …

Engaging With Ai In A Technical Writing Course: A Collaboration Between A Writing Instructor And A Librarian, Isabel Baca, Joy Urbina

HIIT 2025

After describing our collaboration (a Technical Writing Instructor and a Librarian) on teaching students how to use artificial intelligence (AI) to strengthen their writing, we will engage attendees by having them reflect and practice with AI. For our workshop presentation, attendees will:

• Learn how a librarian and a writing instructor collaborated to teach students to use AI effectively and ethically in their writing.
• Reflect on how they can incorporate AI in their classroom or workplace.
• Learn how a librarian can help them incorporate AI into their courses.
• Practice using AI and developing their prompt engineering skills.

Our workshop presentation will …

Control And Thermodynamics Of Superconducting Qubits With Non-Hermitian Dynamics, Serra Erdamar

McKelvey School of Engineering Graduate Student Theses & Dissertations

As quantum systems are understood better from a fundamental physics perspective, we are gaining more insight and ideas for their many applications. Among them are quantum computation, sensing, and materials. Some difficulties that have been encountered are short lifetimes and coupling to unwanted modes while attempting to control these systems, resulting in error propagation. Previously, physicists have pursued Hermitian descriptions of systems to preserve unitary evolution and real-valued measurements. However, non-Hermitian systems not only provide a realistic description of physical systems within larger environments, but also a rich topological landscape from their complex energy spectrum. In this thesis, we will …

Validation Of A Risk Score For Cancer-Associated Thrombosis Using Nationwide Ehr Data, Ang Li, Omid Jafari, Barbara D Lam, Jun Y Jiang, Rock Bum Kim, Shengling Ma, Emily Zhou, Joyce W Tiong, Elizabeth C Chiang, Justine Ryu, Christopher I Amos, Jennifer La, Nathanael R Fillmore

Faculty, Staff and Student Publications

Importance: Venous thromboembolism (VTE) is associated with increased mortality and morbidity in patients with cancer. Existing risk prediction models are typically validated within individual sites, a fragmented approach that limits clinical adoption.

Objective: To validate the electronic health record cancer-associated thrombosis (EHR-CAT) score compared with the benchmark Khorana score in a contemporary cohort of patients with cancer across the nation, before and after treatment, excluding those at high risk of bleeding.

Design, setting, and participants: This prognostic study included patients in a nationwide longitudinal EHR database from January 2018 to December 2023 with follow-up continuing to April 2025. Patients with …

Systematic Review Of Elementary Cybersecurity Education: Curriculum, Pedagogy, And Barriers, Na Liu, Siyu Long, Florence Martin

Journal of Cybersecurity Education, Research and Practice

Abstract -As children increasingly engage with digital platforms, the need for effective cybersecurity education has become urgent. This systematic review synthesizes 81 studies published between 2017 and 2024 to examine global curricula research focus and topics, pedagogical approaches and assessment methods, and key challenges in elementary cybersecurity education. The findings reveal six major thematic categories: student awareness, parental mediation, teacher engagement, curriculum design, community and policy support, and pedagogical innovation. Among instructional strategies, game-based learning and narrative storytelling emerge as the most frequently explored. Despite this growth, major gaps remain in curriculum consistency, teacher preparation, assessment rigor, and stakeholder coordination. …

Electrochemical Breath Sensors In Medical Diagnostics: Emerging Trends And Future Directions, Natalie E. Strom, Courtney J. Weber, Olja Simoska

Faculty Publications

With the rising prevalence of metabolic diseases, infections, and mental health disorders, there is a growing demand for noninvasive diagnostic tools that enable early detection and continuous health monitoring. In this context, exhaled breath biomarkers provide insight into physiological and pathological processes. Electrochemical breath sensors (EBSs) have emerged as a promising platform for rapid, real-time, and cost-effective disease tracking via the detection of volatile breath biomarkers, such as NH₃, NO, and CO₂. Recent advancements in electrode materials, biological recognition elements, and sensor architectures—spanning nanomaterials, enzymes, aptamers, and molecularly imprinted polymers—have enhanced the analytical performance of EBSs. …

Electrochemical Breath Sensors In Medical Diagnostics: Emerging Trends And Future Directions, Natalie E. Strom, Courtney J. Weber, Olja Simoska

Faculty Publications

With the rising prevalence of metabolic diseases, infections, and mental health disorders, there is a growing demand for noninvasive diagnostic tools that enable early detection and continuous health monitoring. In this context, exhaled breath biomarkers provide insight into physiological and pathological processes. Electrochemical breath sensors (EBSs) have emerged as a promising platform for rapid, real-time, and cost-effective disease tracking via the detection of volatile breath biomarkers, such as NH₃, NO, and CO₂. Recent advancements in electrode materials, biological recognition elements, and sensor architectures—spanning nanomaterials, enzymes, aptamers, and molecularly imprinted polymers—have enhanced the analytical performance of EBSs. …

Advancing Power System Reliability And Security With Efficient And Resilient Graph Neural Network Frameworks, Seyed Hamed Haghshenas

USF Tampa Graduate Theses and Dissertations

Enhancing the reliability and security of smart grids is critical for ensuring their seamless operation and resilience against disruptions. The increasing integration of distributed energy resources, advanced measurement devices, and cyber-physical elements introduces both opportunities and challenges for grid management. While these advancements provide enhanced visibility and operational control, they also expose the grid to vulnerabilities from cyber-physical stresses, such as cyber-attacks, equipment failures, and fluctuating power demands. Traditional methods for reliability assessment and threat detection often rely on model-based approaches that struggle to adapt to the complexity and dynamic nature of modern smart grids. These limitations necessitate novel data-driven …

Electric-Field Tunable Magnetoexcitons In Xenes/Hbn/Tmdc, Xenes/Hbn/Bp, And Xenes/Hbn/Tmtc Heterostructures, Roman Ya. Kezerashvili, Anastasia Spiridonova, Klaus Ziegler

Publications and Research

In this work, we propose novel van der Waals heterostructures composed of Xenes, transition metal dichalcogenides (TMDCs), phosphor ene, and transition metal trichalcogenides (TMTCs), which are separated by insulating hexagonal boron nitride (hBN) layers. We theoreti cally investigate the behavior of Rydberg indirect excitons in Xenes/hBN/TMDC, Xenes/hBN/phosphorene and Xenes/hBN/TMTC heterostructures subjected to parallel external electric and magnetic fields that are oriented perpendicular to the layers. By incorporating both isotropic and anisotropic materials, we demonstrate that excitonic properties can be effectively tuned through the external field strengths and the heterostructure design. Our results show that the exciton reduced mass and the …

Quantum Phase Coherent Tunneling In A Mixed-Morphology Superlattice Heterostructure, Qing Shao, Can C. Aygen, Xianyu Chen, Woongkyu Lee, Robert P.H. Chang, Julia E. Medvedeva, Matthew A. Grayson

Physics Faculty Research & Creative Works

Multilayer phase coherence is demonstrated in a mixed morphology superlattice heterostructure, generalizing the paradigm for superlattice miniband engineering. Thirty years since the original proposal by Tsu, a superlattice has been realized with alternating amorphous (a-MoO₃) and polycrystalline (pc-In2O₃) layers, whose high-quality exhibits quantum phase coherence across multiple periods of amorphous barriers. Cross-plane phase coherence is quantified via weak localization signatures in magnetic field under the diffusive Fermi-surface model.

Ireland Blossoms Through Multiculturalism - Designing For Community Connection, Julia Kuczera, Shirley Casey, Hilary Kenna

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

The Impact Of Chronic Pain On Income And Employment, Jordan Vaarsi, Amber Jolly, Karim S. Ladha

SURE Journal: Science Undergraduate Research Experience Journal

No abstract provided.

Temporal And Heterogeneous Graph Neural Network For Remaining Useful Life Prediction, Zhihao Wen, Yuan Fang, Pengcheng Wei, Fayao Liu, Zhenghua Chen, Min Wu

Research Collection School Of Computing and Information Systems

Predicting remaining useful life (RUL) plays a crucial role in the prognostics and health management of industrial systems that involve a variety of interrelated sensors. Given a constant stream of time-series sensory data from such systems, deep learning (DL) models have risen to prominence at identifying complex, nonlinear temporal dependencies in these data. In addition to the temporal dependencies of individual sensors, spatial dependencies emerge as important correlations among these sensors, which can be naturally modeled by a temporal graph that describes time-varying spatial relationships. However, the majority of existing studies have relied on capturing discrete snapshots of this temporal …

Composition Pedagogy As Ai‑Native Coding: From Design Kit To Scholarly Framework, Daniel Plate, James Hutson

Faculty Scholarship

This article advances a field-ready framework that reconceives first-year composition as AI-native coding, translating a complete “design kit” into scholarly method, evaluative protocol, and curriculum architecture. Background: Contemporary composition pedagogy emphasizes process, genre awareness, and collaborative revision; meanwhile, modern software practice operationalizes iteration through version control, test-driven development, and continuous integration. The uploaded kit demonstrates that these cultures are isomorphic: writing stages align with SDLC phases, and automated pipelines can lint prose, execute argument “tests,” and publish artifacts with auditable histories. Approach: The study systematizes that kit into (1) a conceptual map that recasts authorship as orchestration and verification, (2) …

Living Shoreline Prioritization And Hydrodynamic Habitat Suitability Models For Restoration In The Indian River Lagoon And Lake Worth Lagoon, Florida, Kelly M. Kibler, Melinda J. Donnelly, Vincent Encomio, Madison Giuntoli, Manisha Thenuwara, Brice Bennett, Laura Andrade Barron, Mariam Sonbol, Wynter Raine Seppala, Namritha Ramakrishnan, Jennifer Jordan Báez, Beth Orlando, Irene Arpayoglou, Melissa Meisenburg

Indian River Lagoon Shorelines

The spatial dataset: UCF_shorelinemodel_IRL_LWL.shp, was created with support from the Indian River Lagoon National Estuaries Program and Florida Sea Grant. These projects cumulatively led to creation of a living shoreline restoration prioritization model and hydrodynamic habitat suitability models for 1,550 km (963 miles) of estuarine shorelines in Indian River Lagoon and Lake Worth Lagoon.

The dataset presented herein includes:

1. shoreline data from >15,000 transects collected in the field,
2. shoreline wave climate data created through hydrodynamic modeling and frequency analysis of historic data,
3. categorization of potential risk for shoreline boat wake impact,
4. persistence of seagrass within 210 m of the shoreline, …

Explainable Sentiment Analysis With Deepseek-R1: Performance, Efficiency, And Few-Shot Learning, Donghao Huang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have transformed sentiment analysis, yet balancing accuracy, efficiency, and explainability remains a critical challenge. This study presents the first comprehensive evaluation of DeepSeek-R1—an open-source reasoning model—against OpenAI’s GPT-4o and GPT-4o-mini. We test the full 671B model and its distilled variants, systematically documenting few-shot learning curves. Our experiments show DeepSeek-R1 achieves a 91.39% F1 score on 5-class sentiment and 99.31% accuracy on binary tasks with just 5 shots, an eightfold improvement in few-shot efficiency over GPT-4o. Architecture-specific distillation effects emerge, where a 32B Qwen2.5-based model outperforms the 70B Llama-based variant by 6.69 percentage points. While its reasoning …

Does Generative Ai Facilitate Investor Trading? Early Evidence From Chatgpt Outages, Qiang Cheng, Pengkai Lin, Yue Zhao

Research Collection School Of Accountancy

In this paper, we use ChatGPT outages to provide early evidence on whether investors rely on generative artificial intelligence (GenAI) to perform professional tasks and the associated impact on stock price informativeness. We document a significant decline in stock trading volume during ChatGPT outages. The effect is stronger for firms with corporate news released immediately before or during the outages and for firms with higher ownership held by transient institutional investors. We then document declines in short-run price impact and return variance during the outage periods, consistent with reduced informed trading. Lastly, we document a positive effect of GenAI-assisted trading …

Chain Of Strategy Optimization Makes Large Language Models Better Emotional Supporter, Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu

Research Collection School Of Computing and Information Systems

The growing emotional stress in modern society has increased the demand for Emotional Support Conversations (ESC). While Large Language Models (LLMs) show promise for ESC, they face two key challenges: (1) low strategy selection accuracy, and (2) preference bias, limiting their adaptability to users’ emotional needs. Existing supervised fine-tuning (SFT) struggles to address these issues, as it rigidly trains models on single gold-standard responses without modeling nuanced strategy trade-offs. To overcome these limitations, we propose a novel two-stage framework that optimizes strategy selection preferences at each dialogue turn. We first leverage Monte Carlo Tree Search to construct ESC-Pro, a high-quality …

Envisioning Future Interactive Web Development: Editing Webpage With Natural Language, Truong Hai Dang, Jingyu Xiao, Yintong Huo

Research Collection School Of Computing and Information Systems

The evolution of web applications relies on iterative code modifications, a process that is traditionally manual and time-consuming. While Large Language Models (LLMs) can generate UI code, their ability to edit existing code from new design requirements (e.g., ”center the logo”) remains a challenge. This is largely due to the absence of large-scale, high-quality tuning data to align model performance with human expectations. In this paper, we introduce a novel, automated data generation pipeline that uses LLMs to synthesize a high-quality fine-tuning dataset for web editing, named Instruct4Edit. Our approach generates diverse instructions, applies the corresponding code modifications, and performs …

Intentionframe: A Semi-Structured, Multi-Aspect Framework For Fine-Grained Conversational Intention Understanding, Zailong Tian, Zhuoheng Han, Lizi Liao, Lizi Liao

Research Collection School Of Computing and Information Systems

Understanding user intentions in multi-turn dialogues is critical for conversational AI, yet existing approaches—relying on rigid slot-value structures or unstructured free-text—fail to fully capture conversational complexity. In this paper, we propose IntentionFrame, a semi-structured framework inspired by psychological and cognitive intention theories, which organizes conversational intents into four interrelated aspects: situation, emotion, action, and knowledge. This design not only retains interpretability but also provides LLMs with a rich context to accurately parse and respond to nuanced user inputs. To efficiently scale IntentionFrame annotations, we introduce a Weakly-supervised Reinforced Generation (WeRG) method that leverages a small set of high-quality human annotations …

Context-Aware Hierarchical Taxonomy Generation For Scientific Papers Via Llm-Guided Multi-Aspect Clustering, Kun Zhu, Lizi Liao, Yuxuan Gu, Lei Huang, Xiaocheng Feng, Bing Qin

Research Collection School Of Computing and Information Systems

The rapid growth of scientific literature demands efficient methods to organize and synthesize research findings. Existing taxonomy construction methods, leveraging unsupervised clustering or direct prompting of large language models (LLMs), often lack coherence and granularity. We propose a novel context-aware hierarchical taxonomy generation framework that integrates LLM-guided multi-aspect encoding with dynamic clustering. Our method leverages LLMs to identify key aspects of each paper (e.g., methodology, dataset, evaluation) and generates aspect-specific paper summaries, which are then encoded and clustered along each aspect to form a coherent hierarchy. In addition, we introduce a new evaluation benchmark of 156 expert-crafted taxonomies encompassing 11.6k …

Security Modelling For Cyber-Physical Systems: A Systematic Literature Review, Shao Fei Huang, Christopher M. Poskitt, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Cyber-physical systems are at the intersection of digital technology and engineering domains, rendering them high-value targets of sophisticated and well-funded cybersecurity threat actors. Prominent cybersecurity attacks on CPS have brought attention to the vulnerability of these systems and the inherent weaknesses of critical infrastructure reliant on them. Security modelling for CPS is an important mechanism to systematically identify and assess vulnerabilities, threats, and risks throughout system life cycles, and to ultimately ensure system resilience, safety, and reliability. This survey delves into state-of-the-art research on CPS security modelling, encompassing both threat and attack modelling. While these terms are sometimes used interchangeably, …

Disc: Decentralized Identity System With Self-Sovereign Credential Aggregation, Yang Yang, Wai Keung Ching, Minming Huang, Supachate Innet, Guomin Yang, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

The evolution of decentralized identity (DID) and self-sovereign identity (SSI) frameworks, as endorsed by W3C Verifiable Credentials (VC) and eIDAS 2.0, underscores the need for secure, efficient, and privacy-preserving credential management. However, existing credential systems often depend on centralized issuers, lack efficient aggregation mechanisms, or fail to ensure unlinkability across authentication sessions. To address these challenges, we propose DISC (Decentralized Identity System with Self-Sovereign Credential Aggregation), a novel credential system that enables multi-authority credential issuance, user-controlled credential aggregation, and unlinkable authentication. DISC allows users to aggregate credentials from multiple issuers while maintaining constant-size authentication tokens and supporting batch verification for …

Towards Goal-Oriented Intelligent Tutoring Systems In Online Education, Yang Deng, Zifeng Ren, An Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Interactive Intelligent Tutoring Systems (ITSs) enhance the learning experience in online education byfostering effective learning through interactive problem-solving. However, many current ITS models do notfully incorporate proactive engagement strategies that optimize educational resources through thoughtfulplanning and assessment. In this work, we propose a novel and practical task of Goal-oriented IntelligentTutoring Systems (GITS), designed to help students achieve proficiency in specific concepts through a tailoredsequence of exercises and evaluations. We introduce a novel graph-based reinforcement learning framework,named Planning-Assessment-Interaction (PAI), to tackle the challenges of goal-oriented policy learningwithin GITS. This framework utilizes cognitive structure information to refine state representation andguide the selection …

Stable Isotope Paleoecology In The Bridgerian (Early Eocene): Investigation Of The Bridgerian Crash And The Canopy Effect, Aaron M. English

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

The Early Eocene Climatic Optimum (EECO) was the warmest sustained interval of the Cenozoic. Its waning stages coincided with a marked decline in mammalian biodiversity known as the Bridgerian Crash, which was likely driven by increasingly cooler and drier conditions. Paleobotanical evidence suggests the early Eocene supported wet, subtropical to tropical forests at mid-latitudes, however the structure of these forests at the close of the EECO remains poorly understood. In this study, I measure carbon isotope values (d¹³C_E) from the enamel of early Bridgerian (~52.2 – 48.3 Ma) herbivorous mammals from the Greater Green River …

Gerunds In Irish Sign Language: An Exploratory Analysis, Zaid Mohammed

Doctoral

In sign languages (SLs), e.g., American Sign Language (ASL), gerunds help express the meaning of ongoing actions and activities conveyed by verbs (Klima & Bellugi, 1979, p. 295). In spoken languages, a gerund is a form of the verb that functions as a noun and expresses the state of being or an ongoing action (Maekelberghe, 2020). Gerunds are complex constructions as they have nominal-verbal nature (Travis, 2005, p. 321) and morphological similarity with present participle or progressive aspect (Siegel, 1998). This research is motivated by the challenging nature of distinguishing gerund constructions at the morphological level, and research in sign …