Physical Sciences and Mathematics Commons^™

Electron Transport Layers In Thin-Film Solar Cells: Materials, Interfaces, And Device Performance, Mohammad Khairul Basher, Samiul Sadek, Tarek Abedin, Mohammad Nur-E-Alam, Mongi Amami, Rajesh Haldhar, M. Khalid Hossain

Research outputs 2022 to 2026

Thin-film photovoltaic technologies such as perovskite, CIGS, CdTe, and organic solar cells have gained considerable attention due to their potential for low-cost, flexible, and lightweight energy conversion solutions, necessitating advanced components to optimize device efficiency and stability. A complex component in these devices is the electron transport layer (ETL), which governs charge extraction and recombination dynamics, directly impacting overall performance. Despite numerous advances, there remains a lack of unified understanding of ETL materials and interface engineering, highlighting a research gap in cross-technology comparative studies and universal design principles. This review addresses this gap by systematically analyzing ETL materials, interface modification …

Use Of Electrocardiograms To Identify Coronary Artery Disease: Cross-Validation Of An Artificial Intelligence Model, Michael Leasure, Indu Poornima, Adam Butchy, Utkars Jain, Devin Vasoya, Michael Warnick, Brent Williams, John Rehder, Prahlad Menon, Veronica A. Covalesky, Gary S. Mintz

Department of Medicine Faculty Papers

BACKGROUND: The current gold standard for the diagnosis of coronary artery disease (CAD) is invasive angiography; however, it is an invasive procedure. Therefore, we developed an artificial intelligence model designed to predict significant CAD from a resting digital 12-lead electrocardiogram (ECG).

OBJECTIVES: This retrospective study assessed the model's ability to predict clinically significant CAD in a patient population presenting for coronary angiography.

METHODS: From 2019 to 2021, 16,476 patients had a resting 12-lead digital ECG recorded within 90 days prior to coronary angiography. The artificial intelligence model was developed using 10-fold cross-validation methodology. Clinically significant disease was defined as angiographic …

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) …

2026 January 29 - Tennessee Weekly Drought Summary, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Weekly Drought Summaries

No abstract provided.

Geometric Modeling Through Multiple Implicit Functions, Yiwen Ju

McKelvey School of Engineering Graduate Student Theses & Dissertations

Implicit representations have become a dominant paradigm in computational settings ranging from learning-based geometry generation to advanced manufacturing. While treating geometry as the level set of a black-box function provides significant modeling flexibility, converting these representations into explicit surface meshes remains a major challenge. Standard volumetric extraction methods are fundamentally designed for smooth manifolds and therefore struggle to capture sharp geometric features such as creases, corners, and non-manifold junctions that are critical for high-fidelity industrial design and engineering tasks. Many of these intricate features arise from modeling multiple implicit functions. Examples include Constructive Solid Geometry (CSG), material interfaces, and more …

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 …

Patient-Specific Lumped-Parameter Model For Quantifying Vessel-Specific Remodeling And Predicting Right Ventricular Function In Pulmonary Hypertension, Christopher G. Lechuga, Amirreza Kachabi, Mitchel J. Colebank, Claudia E. Korcarz, Farhan Raza

Faculty Publications

Purpose: Pulmonary hypertension (PH) is a heterogeneous disease with patient-specific variability and vessel-specific remodeling, which eventually lead to right ventricular (RV) failure. The gold standard for RV assessment—pressure–volume (PV) loop acquisition—is invasive and limited to specialized settings. This study aims to develop a patient-specific lumped-parameter model that quantifies vessel-specific remodeling and simulates RV PV loops across PH phenotypes using routine clinical data.

Methods: A lumped-parameter model was calibrated using right heart catheterization and echocardiography data. Model agreement was assessed by R2 values for pressure and flow goodness-of-fit, and model-derived hemodynamic metrics were comparedwith clinical values. A dimensionality reduction approach was …

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 …

Development Status And Existing Problems Of Ion-Solvation Membranes For Electrolysis Of Water, Zheng-Yuan Zhou, Yu-Tao Sun, Zheng-Bang Liu, Chuan-Zheng Wang, Yong-Nan Zhou, Xi Luo, Tian-Chi Zhou, Jin-Li Qiao

Journal of Electrochemistry

Ion-solvaing membranes (ISMs) have received extensive attention in recent years as a key component in electrochemical energy conversion and storage devices. This article provides an overview of structural composition, performance advantages, research progress, ion conduction mechanism and existing issues of ISMs, primarily classifying them according to the matrix structure. A detailed analysis of performance enhancement methods, key performance indicators of ISMs and performance influencing factors is also presented. The article contributes to further optimizing the design and application of ion-solvation membranes, providing theoretical support for the development of fields such as hydrogen production through electrolysis of water and electrochemical energy …

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 …

The Ntp Anode For Aqueous Sodium Ion Batteries: Recent Advances And Future Perspectives, Ming-Li Wang, Xue-Ying Su, Zheng-Xiang Shan, Shu-Zhe Yang, Heng-Rui Guo, Hao Luo, Dong-Liang Chao

Journal of Electrochemistry

Aqueous sodium-ion batteries (ASIBs) have attracted great attention in aqueous batteries due to their merit of high safety. However, the constrained work potential and insufficient chemical stability of anode materials in aqueous electrolytes hinder the large-scale application of ASIBs. Sodium titanium phosphate, NaTi₂(PO₄)₃ (NTP), is considered one of the most promising anode materials for ASIBs due to its excellent electrochemical performance and tunable structure. Recently, great achievements have been made in the development of NTP, however, a comprehensive review of existing studies is still lacking. This article firstly introduces the basic properties of NTP and …

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 …

Land Use And Sovereignty Along The Catawba River, Thomas C. Brugh, Lucile C. Rencher

Student Scholarship

This document-based case study explains how land-use change along the Catawba River Corridor (Lancaster and York Counties, South Carolina) has been produced through the interaction of property rights (dominium) and rule-setting authority (imperium), showing why sovereignty continues to shape development even after land disputes appear “settled.” Through analyzing legal records (Treaty of Nation Ford, the 1959 Catawba Division of Assets Act, the 1986 Supreme Court timing decision, and the 1993 Settlement Act), planning documents, parcel records, and field observations, we trace how shifting jurisdiction and title certainty structured what kinds of land uses were possible and when. We argue that …

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 …

Investigation Of The Phase Space Of Tungsten Oxides Nanostructures Via Resistive Hot Wire Oxidation For Efficient Electrochemical Energy Storage And Conversion Applications., Krishna Kc

Theses and Dissertations

The synthesis of tungsten oxide (WO3−δ, 0 ≤ δ ≤ 1) nanostructures with controlled phase and morphology remains a significant challenge, particularly for applications in energy conversion and storage. This dissertation presents an eco-friendly and time-efficient method for synthesizing tungsten oxide nanostructures with tailored stoichiometry and crystal phases through the Resistive Hot Wire Oxidation (RHWO) technique. In this method, a tungsten wire is heated via resistive heating in an oxygen environment, and by precisely controlling the growth conditions, we successfully synthesized WO2.72, WO2.76, WO2.80, WO2.83, WO2.90, and WO2.98 phases with controlled morphologies using simple instrumentation. This approach eliminates the need …

Clustering Of Temporal And Visual Data: Recent Advancements, Priyanka Mudgal

Computer Science Faculty Publications and Presentations

Clustering plays a central role in uncovering latent structure within both temporal and visual data. It enables critical insights in various domains including healthcare, finance, surveillance, autonomous systems, and many more. With the growing volume and complexity of time-series and image-based datasets, there is an increasing demand for robust, flexible, and scalable clustering algorithms. Although these modalities differ—time-series being inherently sequential and vision data being spatial—they exhibit common challenges such as high dimensionality, noise, variability in alignment and scale, and the need for interpretable groupings. This survey presents a comprehensive review of recent advancements in clustering methods that are adaptable …

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 …

Mapping Urban Vegetation Changes Using Planetscope Imagery And Gis: A Case Study Of The 2024 Dubai Flood, Sumayya Almansoori, Xin Hong

All Works

In April 2024, unexpected heavy rainfall triggered one of the most severe flooding events in Dubai’s recent history, causing widespread concern for urban infrastructure and green spaces. This study evaluates the flood’s impact on urban vegetation in South Dubai using high-resolution PlanetScope satellite imagery and the Normalized Difference Vegetation Index (NDVI). Vegetation conditions before and after the flood (April 14 and April 18–19, 2024) were quantified and compared using NDVI analysis within QGIS to assess changes in vegetation health and coverage. Results indicate significant declines in vegetation health in areas dominated by intensive turf management, such as Damac Hills and …

Generative Ai Use And Self-Learning In Higher Education: The Role Of Learning Difficulties, Dana Saleh, Areej Elsayary

All Works

The rapid development of GenAI tools and their adoption in education have shown promising potential to personalize learning experiences. However, their effectiveness is influenced by factors such as familiarity, frequency of use, and the impact on self-learning. This study investigates the undergraduate students' familiarity with Generative AI (GenAI) tools, their frequency of use, and the perceived impact of GenAI on self-learning, with particular consideration of differences between students with and without learning difficulties. Prompt engineering is also included as a secondary aspect of students' GenAI experience. The research employed a quantitative survey design, utilizing validated scales to measure familiarity, usage …

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 …