Digital Commons Network^™

Reachability In Interactive Chemical Reaction Networks, Aberto Avila-Jimenez, Bin Fu, Elise Grizzell, Robert Schweller, Tim Wylie

Computer Science Faculty Publications

This paper studies the effects of interactivity on molecular computation, specifically in the Step Chemical Reaction Networks model (Step CRNs), by adding the ability for a user to interact with the system by selecting which species to add at each step, or by having some control over which reactions execute. The two proposed variants are Interactive CRNs and Randomized Interactive CRNs. We show that in Interactive CRNs, even when restricted to void (deletion-only) rules of relatively small size, if a user can decide which species to add at each step based on the configuration, reachability is PSPACE-complete when bounded and …

The Ai Academic Integrity Dichotomy: A Gender Lens, N. Sayegh, S. Larloulian, A. C. Elias, Leila Halawi

Publications

Using a gender lens, this research explores the intersection of artificial intelligence (AI) educational tools and academic integrity. Studies suggest gender differences exist in academic dishonesty rationalization, with males potentially exhibiting more opportunistic behaviors. This qualitative study collected data through 30-minute face-to-face semi-structured interviews, recorded with consent, and analyzed thematically. Four significant themes emerged: Gender Perspectives on AI Tool Usage, Patterns of AI Tool Utilization, Academic Integrity Concerns, and Implications of Academic Dishonesty. The results indicate that educational establishments should adopt responsible AI governance frameworks and ethical guidelines to uphold academic standards while promoting scholarly integrity and institutional trust.

Electronic Transport In Twisted Bilayer Tungsten Diselenides, Walid Zaher

Theses and Dissertations

This thesis investigates density waves (DWs) in twisted bilayer tungsten diselenide (tWSe₂) using a mean field approximation, with a focus on electron-electron interactions, and the system’s free energy at zero temperature. For the mean field approximation, the study initially employed same-spin nesting vectors, which were found insufficient to induce DWs formation. In contrast, when opposite-spin nesting vectors were used, DWs emerged, indicating that these vectors can support DWs formation. The plots were produced under perfect nesting conditions, where van Hove singularities (VHS) occur. At zero temperature, perfect nesting provides a complete picture of the upper limit of DWs formation, as …

A Virtual-Reality-Based Dental Simulator For Endodontics With Automated Formative Feedback, Yousef Salah Abo El Ela

Theses and Dissertations

Advancements in virtual reality (VR) and haptic technology are transforming the landscape of medical and dental education, offering new avenues for safe, immersive, and repeatable training experiences. Within dentistry, endodontics presents unique challenges for preclinical education due to anatomical complexity, limited access to extracted teeth, ethical concerns, and the shortcomings of conventional plastic models. Despite endodontics specific plastic teeth being available, they fall short of replicating the hardness of real extracted teeth, are relatively costly compared to generic plastic teeth, and are ultimately a disposable item which makes them inadequate as a sustainable long-term solution. Extracted teeth do a much …

A Computational Study Of The Chemomechanics Of Paracetamol Polymorphs: Towards Improving Drug Tabletability, Karim Nader Samra

Theses and Dissertations

The mechanical properties of pharmaceutical crystals are of great importance in tablet manufacturing. Also, knowing the mechanical properties of different polymorphs of the same crystal can reduce the overall production cost. In this study, computational work was done via performing density functional theory calculations on 3 paracetamol crystal polymorphs Form I, II, and III. Understanding the chemical bonding helps in predicting the key elastic properties for different crystallographic directions. In Form II, lattice vector C is dominated with the intermolecular hydrogen bond OH…O giving it the stiffest Young Modulus and the least Poisson’s ratio. Then comes the A-Axis which contains …

Evaluation Of Large Language Models As Decision Support Tools For Head And Neck Cancer Management: A Blinded Multidisciplinary Simulation Study, Sholem Hack, Ron J. Karni, Antonino Maniaci, Christopher E. Fundakowski, Luca Castellani, Fabiola Incandela, Remo Accorona, Miguel Mayo-Yanez, Martina Violati, Lorenzo Giannini, Niccolo' Mevio, Alberto Maria Saibene

Department of Otolaryngology - Head and Neck Surgery Faculty Papers

BACKGROUND: The management of head and neck cancer relies on multidisciplinary expertise; however, access to tumor boards remains variable. Large language models (LLMs) may support guideline-based decision-making, although performance in complex oncologic scenarios is not well defined.

METHODS: Fourteen synthetic cases based on real tumor board encounters were evaluated. Five blinded comparator arms produced recommendations: a human expert, Non-RAG-GPT-4, Non-RAG-GPT-5, RAG-GPT-4, and RAG-GPT-5. Eight head and neck oncologic surgeons scored each recommendation for appropriateness, clarity, specificity, and feasibility using 5-point Likert scales. Paired permutation testing and inter-rater reliability were assessed.

RESULTS: LLM outputs showed close alignment with expert recommendations. RAG-based …

Quantum Benchmarking Of High-Fidelity Noise-Biased Operations On A Detuned Kerr-Cat Qubit, Bingcheng Qing, Ahmed Hajr, Ke Wang, Gerwin Koolstra, Long B. Nguyen, Jordan Hines, Irwin Huang, Bibek Bhandari, Larry Chen, Ziqi Kang, Christian Jünger, Noah Goss, Nikitha Jain, Hyunseong Kim, Kan-Heng Lee, Akel Hashim, Nicholas E. Frattini, Zahra Pedramrazi, Justin Dressel, Andrew N. Jordan, David I. Santiago, Irfan Siddiqi

Mathematics, Physics, and Computer Science Faculty Articles and Research

Ubiquitous noise sources in quantum systems remain a key obstacle to building quantum computers, necessitating the use of quantum error correction codes. Recently, error-correcting codes tailored for noise-biased systems have been shown to offer high fault-tolerance thresholds and reduced hardware overhead, positioning noisebiased qubits as promising candidates for building universal quantum computers. However, quantum operations on these platforms remain challenging, and their noise structures have not yet been rigorously benchmarked to the same extent as those of conventional quantum hardware. In this work, we develop a comprehensive quantum control toolbox for a scalable noise-biased qubit, detuned Kerr-cat qubit, including initialization, …

Asymmetric Positioning Errors In Gnss Time-Series: A Study From Different World Regions, Francesca Silverii, Emilie Klein, Roberto Devoti, Walter Szeliga, Sylvain Michel, Adriano Gualandi, Elisa Trastatti

All Faculty Scholarship for the College of the Sciences

Global Navigation Satellite System (GNSS) plays a fundamental role in monitoring time-dependent ground displacement. However, GNSS daily position time-series can often contain significant outliers, reaching up to several centimetres. These are likely of non-tectonic origin, and, if not properly accounted for, they can significantly impact the accuracy and dependability of the estimation of key parameters for geophysical analyses, such as long-term velocities and transient deformations. Characterizing these outliers can provide information about their possible sources and help us implement mitigation strategies. Asymmetric outliers, that is, those characterized by a primary direction, therefore occurring on one side of the mean time-series, …

Novel R Shiny Tool For Survival Analysis With Time-Varying Covariate In Oncology Studies: Overcoming Biases And Enhancing Collaboration, Yimei Li, Yang Qiao, Fei Gao, Jordan Gauthier, Qiang Ed Zhang, Jenna Voutsinas, Wendy Leisenring, Ted Gooley, Corinne Summers, Alexandre Hirayama, Cameron Turtle, Rebecca Gardner, Jarcy Zee, Qian Vicky Wu

Wills Eye Hospital Papers

PURPOSE: Our study is motivated by evaluating the role of hematopoietic cell transplantation (HCT) after chimeric antigen receptor T-cell (CAR-T) therapy for ALL, a debated topic. Because patients may receive HCT at different times after CAR-T infusion or never, HCT post-CAR-T should be considered as a time-varying covariate (TVC).

METHODS: Standard Cox models and Kaplan-Meier (KM) curves (naïve method) assume that TVC status is known and fixed at baseline, which can yield biased estimates. Landmark analysis is a popular alternative but depends on a chosen landmark time. Time-dependent (TD) Cox model is better suited for TVC although visualizing survival curves …

A Systematic Literature Review On The Influence Of The El Niño Southern Oscillation On Rainfall Variability In Central Java, Indonesia, Sri Endah Ardhi Ningrum Abdullah, Trinah Wati, Wahyu Hardyanto

Jurnal Pendidikan Geografi: Kajian, Teori, dan Praktek dalam Bidang Pendidikan dan Ilmu Geografi

The El Niño-Southern Oscillation (ENSO) phenomenon greatly influences rainfall variability in tropical regions, including Central Java, Indonesia. This study employs a Systematic Literature Review (SLR) approach in accordance with the PRISMA guidelines. This study examines 50 articles from 2010 to 2025, as well as conducting a bibliometric analysis using VOSviewer, to identify trends and the evolution of themes. The results of the study indicate that El Niño generally reduces rainfall and increases the risk of drought, while La Niña tends to increase rainfall, potentially causing flooding. The impacts of ENSO extend to agriculture, food security, and water management, and are …

Black Hole Spectroscopy And Tests Of General Relativity With Gw250114, The Ligo Scientific Collaboration, The Virgo Collaboration, The Kagra Collaboration

Department of Physics and Astronomy Faculty Scholarship and Creative Works

The binary black hole signal GW250114, the loudest gravitational wave detected to date, offers a unique opportunity to test Einstein’s general relativity (GR) in the high-velocity, strong-gravity regime and probe whether the remnant conforms to the Kerr metric. Upon perturbation, black holes emit a spectrum of damped sinusoids with specific, complex frequencies. Our analysis of the postmerger signal shows that at least two quasinormal modes are required to explain the data, with the most damped remaining statistically significant for about one cycle. We probe the remnant’s Kerr nature by constraining the spectroscopic pattern of the dominant quadrupolar (ℓ =𝑚 =2) …

Black Hole Spectroscopy And Tests Of General Relativity With Gw250114, A. G. Abac, I. Abouelfettouh, Teviet Creighton, Mario C. Diaz, Raul Alberto Espinosa Perez, J. Lawrence, Francisco Llamas Villarreal, Soma Mukherjee, Volker Quetschke, Miriam Ramos Arevalo, Wenhui Wang

Physics & Astronomy Faculty Publications

The binary black hole signal GW250114, the loudest gravitational wave detected to date, offers a unique opportunity to test Einstein’s general relativity (GR) in the high-velocity, strong-gravity regime and probe whether the remnant conforms to the Kerr metric. Upon perturbation, black holes emit a spectrum of damped sinusoids with specific, complex frequencies. Our analysis of the postmerger signal shows that at least two quasinormal modes are required to explain the data, with the most damped remaining statistically significant for about one cycle. We probe the remnant’s Kerr nature by constraining the spectroscopic pattern of the dominant quadrupolar (ℓ =𝑚 =2) …

Artificial Intelligence In Higher Education, Opportunities, And Challenges: A Review, Sharifa Alblooshi

All Works

Artificial intelligence (AI) is a growing force of change in higher education, providing assistance to students, teachers, and administrators in teaching, learning, and administration. As AI technologies advance rapidly, they present a combination of significant opportunities and complex challenges. In this study, we examine the role of AI in higher education, highlighting both its positive and negative impacts, as well as current policy gaps and issues arising from its deployment. The literature on the topic was reviewed to determine how AI decisively impacts teaching and learning, the role of AI in assessments and academic integrity, as well as ethics, psychological …

A High-Resolution Daily Precipitation Fusion Framework Integrating Radar, Satellite, And Nwp Data Using Machine Learning Over South Korea, Hyoju Park, Hiroyuki Miyazaki, Menas Kafatos, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Accurate precipitation mapping is essential for effective disaster management; however, individual radar, satellite, and numerical weather prediction products often struggle in the topographically complex terrain of South Korea. This study proposes a high-resolution (~500 m) daily precipitation fusion framework that integrates Korea Meteorological Administration (KMA) radar, Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), and Local Data Assimilation and Prediction System (LDAPS) data. The framework employs a Random Forest model augmented with a monthly Empirical Cumulative Distribution Function (ECDF) correction. Auxiliary predictors are incorporated to enhance physical interpretability and stability, including terrain attributes to represent orographic effects, land-cover …

Interpretable Linear Models For Heart Disease Prediction: A Comparative Study, Dipok Deb, Emran Hossain

Data Science and Data Mining

Heart disease remains a leading cause of mortality worldwide, underscoring the importance of accurate and transparent methods for early diagnosis. While many machine learning and artificial intelligence models have demonstrated strong predictive performance, their limited interpretability poses challenges for clinical adoption. In this study, we evaluate three interpretable linear classification models—Generalized Linear Model (GLM) logistic regression, L1-regularized (Lasso) logistic regression, and Linear Discriminant Analysis (LDA)—for heart disease prediction using the Cleveland Heart Disease dataset. Following comprehensive data preprocessing, the models are assessed on a held-out test set using standard evaluation metrics, including accuracy, precision, recall, F1-score, and the area under …

Leveraging Blockchain Technology In Mining Supply Chain Management: Vietnam Coal Mining Case Study, Thu Hang Nguyen, Nguyen Trung Tuan, Hong Anh Le

Journal of Sustainable Mining

The global economy heavily relies on the mining industry for essential resources such as coal, oil, gas, and metal ores. However, the intricate nature of mining operations poses significant challenges in supply chain management (SCM). This research investigates how blockchain technology can address these challenges within mining supply chain management (MSCM). Through a systematic review of existing research and projects, a conceptual blockchain model is proposed to improve mining supply chains’ transparency, traceability, efficiency, and sustainability, specifically focusing on coal supply chain management in Vietnam. The model integrates distributed ledgers, smart contracts, IoT devices, identity management, and consensus mechanisms to …

Characterization Of Ru(Ii) Polypyridyl Photosensitizers Bound To Tio2 Supports Through A New Covalent Metal-Ester Bonding Motif, D. M. S. C. Dissanayake, Christopher T. Lebarron, Laura C. Maybach, Joseph J. Kuchta Iii, Seyyedamirhossein Hosseini, Aaron K. Vannucci

Faculty Publications

The development of photoelectrochemical (PEC) devices involves the attachment of molecular photosensitizers onto solid supports. Particularly significant to this work are PECs that rely on ruthenium-based photosensitizers on high band gap metal oxide (MOx) supports. In this study, we have explored a new ester binding motif (MOx-ester) to covalently attach a ruthenium polypyridyl photosensitizer to TiO₂. This new MOx-ester photoanode is compared to the traditional photoanodes that utilize acid/base binding groups to attach the molecular Ru sensitizers to TiO₂. The new MOx-ester binding motif is characterized by cyclic voltammetry UV-Vis, solid state ¹³C NMR, and …

Adaptive Multi-Agent Intelligence: A Dynamic Data Management System For Enhanced Data Quality And Reconciliation, Anthony Taeyang Vigil

USF Tampa Graduate Theses and Dissertations

Organizations struggle with data quality problems that cost them millions of dollars annually. Manual data cleanup and standardizing, also known as reconciliation, can take skilled analysts days to complete and can still contain human errors. This dissertation addresses this challenge by developing and validating the Origin Data Management System, which automates processes that traditionally require days of manual work. The system achieved up to 100% accuracy in standardizing vendor names and identifying CEO information across real-world test datasets from Jefferson County Public Schools, Forbes, and Fortune Magazines. Processing time dropped from days to minutes—a 96-fold improvement over manual methods. These …

Iron-Involved Orr Electrocatalysts Under The Lens Of In-Situ/Operando Mössbauer Spectroscopy, Sumbal Farid, Jun-Hu Wang

Journal of Electrochemistry

Exploring cost-effective and efficient catalysts for oxygen reduction reaction (ORR) poses a significant challenge, especially in the pursuit of alternatives to precious metals like platinum. Significant advancements have driven electrochemists to develop efficient ORR catalysts using abundant materials, particularly iron (Fe)-based, known for their exceptional performance in ORR. While the crucial function of Fe in boosting ORR catalytic activity is recognized, the connection between material attributes and catalytic performance remains enigmatic. Understanding the dynamic processes involved in oxygen electrocatalysis is paramount for designing precious-metals-free ORR electrocatalysts. Mössbauer spectroscopy stands out as a powerful technique for deciphering the structural characteristics of …

Carbon Supported Octahedral Ptni Nanoparticles (Oct-Ptni/C) As A Cathode Catalyst For Proton Exchange Membrane Fuel Cells (Pemfcs) With Improved Activity And Durability, Zi-Wei Feng, Hai-Zhong Chen, Xiao Duan, Ling Tang, Yun-Kun Zhao, Long Huang

Journal of Electrochemistry

Proton exchange membrane fuel cells (PEMFCs) are considered as a promising renewable power source. However, the massive commercial application of PEMFCs has been greatly hindered by their high expense and less-satisfied performance mainly due to the sluggish oxygen reduction reaction (ORR) kinetics even on state-of-the-art Pt catalyst. Octahedral PtNi nanoparticles (oct-PtNi NPs) with excellent ORR activity in a half-cell have been widely studied, while their performance in membrane electrode assembly (MEA) has much less reported. Herein, we investigated the MEA performance using the carbon supported oct-PtNi NPs (oct-PtNi/C) as the cathode catalyst. Under the mild acid washing condition, the surface …

From Latent Manifolds To Targeted Molecular Probes: An Interpretable, Kinome-Scale Generative Machine Learning Framework For Family-Based Kinase Ligand Design, Gennady M. Verkhivker, Ryan Kassab, Keerthi Krishnan

Mathematics, Physics, and Computer Science Faculty Articles and Research

Scaffold-aware artificial intelligence (AI) models enable systematic exploration of chemical space conditioned on protein-interacting ligands, yet the representational principles governing their behavior remain poorly understood. The computational representation of structurally complex kinase small molecules remains a formidable challenge due to the high conservation of ATP active site architecture across the kinome and the topological complexity of structural scaffolds in current generative AI frameworks. In this study, we present a diagnostic, modular and chemistry-first generative framework for design of targeted SRC kinase ligands by integrating ChemVAE-based latent space modeling, a chemically interpretable structural similarity metric (Kinase Likelihood Score), Bayesian optimization, and …

Observation-Based Reconstruction Of High-Resolution Daily Temperature Field Using Lapse-Rate-Constrained Kriging In Complex Terrain: A Nationwide Dataset For South Korea, Youjeong Youn, Menas Kafatos, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

High-resolution air-temperature fields are essential for climate, hydrologic, and ecological applications in complex terrain, yet operational products often lack the spatial detail to resolve topographic effects. We develop an observation-driven reconstruction of daily air temperature fields for South Korea (2024) using ordinary kriging with lapse-rate correction (OKLR), integrating a dense network of over 500 stations from the Automatic Mountain Meteorology Observation System (AMOS) and the Automated Surface Observing System (ASOS). The OKLR framework systematically removes elevation-driven trends using a physically based fixed lapse rate (–6.5 °C km−1), performs kriging on detrended residuals, and reapplies Digital Elevation Model (DEM)-based corrections to …

Handling Missing Data In Copd Research, Chia-Ying Chiu

ETDs from 2020-2029

Chronic Obstructive Pulmonary Disease (COPD) remains a major global health concern and one of the leading causes of death in the United States, affecting approximately 4.6% of adults and reaching a prevalence of 9.4% in Alabama according to 2024 National Health Interview Survey. The disease imposes a substantial burden on quality of life and healthcare costs and contributes to increased disability-adjusted life years and years of life lost, as highlighted by the 2022 Lancet Commission report. Despite its im-pact, COPD is frequently diagnosed only after irreversible lung damage has occurred, largely due to under-recognized symptoms and limited diagnostic tools. Early …

Development Of Salinomycin Analogs As Potential Anti-Cancer Agents, Pavan Goud Juluri

Theses and Dissertations

Salinomycin, a poly-ionophore antibiotic originally isolated from Streptomyces albus, exhibits antimicrobial activity against Gram-positive bacteria. Over the years Salinomycin has been studied in the effects it has on cancer, specifically cancer stem cells. Poly(ADP-ribose) polymerase (PARP) is an enzyme involved in DNA repair. Inhibiting PARP has been explored as a strategy in cancer treatment, particularly in cancers with defective DNA repair mechanisms, such as those with BRCA mutations. The benzo-thiazoles, and -imidazoles are identified as inhibitor scaffolds, which compete with nicotinamide in the binding pocket of human poly- and mono-ADP-ribosylating enzymes. Benzothiazole, and benzimidazole based compounds particularly with modification at …

Ultrafast Terahertz Spectroscopy And The Suppression Of Superconductivity In Strained Iron Selenide Below 1 Kelvin, Tracy Glen Hastings

ETDs from 2020-2029

We report on terahertz time-domain spectroscopy measurements of iron selenide thin films of various thicknesses grown on a magnesium oxide substrate, focusing on the frequency-dependent complex conductivity in the range of 0.25 - 7 THz. By an- alyzing films with and without an approximately 100 nm thick Se capping layer, we examined the role of oxidation in the suppression of superconductivity. X-ray photo- electron spectroscopy data confirmed that oxidation was effectively mitigated, yet the films exhibited insulating behavior down to 200 mK, a temperature range that has not been extensively studied in this system. Our results indicate that the suppression …

Mapping Post-Rainfall Recovery In Arid Regions Using A Hierarchical U-Net, Xin Hong

All Works

The United Arab Emirates (UAE) experienced an extreme rainfall event between April 15 and 17, 2024, and that resulted in severe flooding in its coastal regions. Dubai was among the most affected regions. This study applies a hierarchical deep learning model on PlanetScope imagery to detect flood inundation, quantify flood extent by land cover, and examine short-term recovery dynamics. While earlier work detailed the methodological development of a hierarchical U-Net model (Hong et al., in press), here we emphasize its application for monitoring resilience trajectories in an arid urban environment. Results show that approximately 22 km² of land was …

Assessing Urban Flooding And Vegetation Impact In Dubai Creek Following The April 2024 Extreme Rainfall, Dana Alhammadi, Xin Hong

All Works

During April 2024, the United Arab Emirates experienced an unusual phenomenon of an intense rainfall episode between April 14 and 18 that resulted in massive flooding in urban environments, particularly low-lying areas such as Dubai Creek. As a tidal waterway with dense urban development and environmentally sensitive zones surrounding it, Dubai Creek is an ideal site for assessing environmental changes caused by to floods. The study employed pre-flood (14 April) and post-flood (18 April) high-resolution PlanetScope satellite images, in combination with QGIS analysis, to evaluate vegetation health and surface water changes. Quantification of affected areas from flooding was achieved through …

Recurrence Of Locally Perturbed Random Walks With Long Jumps, Tamer Oraby, András Telcs

School of Mathematical & Statistical Sciences Faculty Publications

We study the effect of local perturbations on the recurrence of random walks with long jumps. Such walks serve as discrete models for infinite-horizon Lorentz processes, in which a particle can take arbitrarily long steps in specific directions. Motivated by a question of Sinai in the finite-horizon case and its extension by Szász to the infinite-horizon setting, we give recurrence and transience criteria for long-jump walks on Z2 and certain classes of graphs, and we prove that local perturbations in a bounded region do not change the recurrence property. Our proofs combine the Markov chain approach with the electrical network …

Pitfalls Of Multiplied 3d Landforms Projection: Mapping Deep Multilevel Cave Systems In The Alps (Gamssteig Cave System, Göll Massif), Mateusz Golicz, Jacek Szczygieł

International Journal of Speleology

Cave mapping represents one of the most complex challenges in geomorphological cartography, as it must convey the true three-dimensional geometry of subterranean spaces such as overlapping passages, irregular cross-sections, and variable ceiling and floor morphologies, within a two-dimensional framework. This study examines the methodological and interpretive challenges of cave mapping, utilizing the Gamssteighöhle cave in the Austrian Alps as a case study. During the 11 years of exploration, over 10 km of passages were surveyed using the DistoX vector survey method. Yet, the complex morphological forms necessitated deviations from the standard symbology recommended by the International Union of Speleology. Several …

Correlation Analysis Of Factors Associated With Students’ Numeracy Skills Using Decision Tree Algorithms, Yenita Roza, Arisman Adnan, Zul Indra, Tuti Alawiyah

Numeracy

Numeracy is a critical competency for academic and everyday functioning. This study investigates the key factors associated with students’ numeracy skills by employing decision tree algorithms as a data mining technique. The dataset used in this study is educational assessment data from Indonesia. Utilizing a dataset comprising 6,953 entries and 60 variables from Education Report, the research adopts an exploratory approach involving data preprocessing, exploratory data analysis, and decision tree model construction. The findings reveal that students’ literacy skills serve as the most dominant predictor of numeracy proficiency, emerging as the root node in the decision tree structure. Additional associated …