Physical Sciences and Mathematics Commons^™

Mapping Seafloor Habitats And Characterizing Demersal Reef Fish Communities On The West Florida Shelf Across Multiple Spatial Scales Using Multibeam Sonar And Underwater Video, Alexander R. Ilich

USF Tampa Graduate Theses and Dissertations

In this dissertation, I examine the role of spatial scale in deriving seafloor terrain attributes, modeling benthic habitat, and analyzing ecological patterns of the demersal reef fish communities on the West Florida Shelf (WFS), with implications for improving predictive habitat mapping and understanding fish-environment relationships. Spatial scale plays a central role in benthic habitat mapping and ecological inference, influencing how terrain attributes are derived, how habitats are modeled, and how ecological responses are manifested. This work integrates multiscale terrain analysis, acoustic remote sensing, and community ecology to evaluate how spatial scale influences both physical habitat representation and ecological analysis.

Chapter …

Frequency And Radiative Analysis Of Random Yagi-Uhf/Vhf Phased Array, Luis M. Bres, Luis A. Hernandez, Teviet Creighton

Physics & Astronomy Faculty Publications

This study investigates a phased array ground station capable of tracking multiple sources, multi-beamforming, electronic steering, easy scaling, and low cost. The project will develop a 20-pair dual-polarized Yagi-UHF/VHF phased array, comparing the parameters of random and uniform layouts. This study will be divided into two domains: frequency, and radiative domain. The frequency domain will explore the reception and transmission spectra analysis under several scenarios for our antennas. The radiative domain will explore the following: general side lobe analysis across both elevation and azimuth, antenna element sweep analysis, electronic beam steering analysis, electro-mechanical beam steering analysis, array density analysis, and …

Laminarin-Loaded Solid-In-Oil Nanodispersion For Enhanced Non-Invasive Transdermal Immunization, Md. Shahin Ekka Sarker, Yoshirou Kawaguchi, Rie C. Wakabayashi, Noriho J. Kamiya, Muhammad Moniruzzaman, Masahiro Goto

Publications and Research

Simple and non-invasive transdermal vaccination is an attractive alternative to conventional injection-based immunization. However, the effectiveness of transdermal vaccines is often constrained by the stratum corneum barrier. Although the use of solid-in-oil (S/O) nanodispersion technology has successfully facilitated skin permeation to induce an immunological response, the antibody titers remain suboptimal. Herein, a dectin-1 selective ligand, laminarin, was used as an immunostimulatory adjuvant to enhance the immune response. S/O nanodispersions loaded with laminarin and ovalbumin (OVA) were systematically developed and characterized in terms of particle size, in vitro OVA release behavior, and skin permeation performance using excised mouse skin. In vivo …

A Mathematical Model Of The Interactions Between A Collagen Lattice, Fibroblasts, And Fixed Surfaces With Varying Topographies, Mary Jenkins

Theses and Dissertations

Collagen is an important structural protein in the body, which plays a role in wound healing, particularly the contraction process. Collagen lattices have been studied for nearly 50 years to provide insight into wound contraction. In some cases, collagen interacts with surfaces that have fixed shapes, such as medical implants. Our model focuses on the interactions of a collagen lattice with such a fixed surface. We mathematically model a collagen lattice as a network of nodes connected by springs. We also model fixed surfaces that have various topographies. Our model includes fibroblast cells that connect to both surfaces and remodel …

Anticodon Loop Remodeling And D-Stem Shape Drive The Specific Recognition Of Ann-Decoding Trnas For T6a Modification, Ana Kutchuashvili, Erik Scheleen, George Guynes, Junzhou Wu, Chi-Kong Chan, Peter Dedon, Dirk Iwata-Reuyl, Manal A. Swairjo

Chemistry Faculty Publications and Presentations

N6-threonylcarbamoyladenosine (t6A) is a universal transfer RNA (tRNA) modification essential for translational fidelity. The modification is installed at position 37 of ANN-decoding tRNAs (N is A, U, G, or C) by transfer of a threonylcarbamoyl moiety from a pathway intermediate, catalyzed in bacteria by the TsaBD complex. Despite the strict requirement for the 36-UAA-38 sequence in substrate tRNAs, the structural basis for this specificity has remained unclear. We determined cryo-EM structures of the Thermotoga maritima t 6A synthase bound to unmodified and to natively modified tRNA carrying the t 6A37 modification, at a nominal resolution of 3.2 A˚ . In …

An Analysis Of Heat Spread Through Smoldering Pine Needles, Timothy Keith

Theses and Dissertations

The goal of this thesis is to calculate the average speed of flame spread through smoldering pine needles. We present two different models for doing this: the first is a very large system of ODEs meant to represent the spread of flame through individual needles, the second is a macroscopic model based on the porous medium PDE. Simulations are run for each of these models and compared to each other as well as to existing models and experimental results.

Qualitative Analysis Of Solutions To A General Class Of Nonlinear Difference Equations With Applications, Osama Moaaz, Mohamed F. Abouelenein, Mona Anis

Mathematical Modelling and Numerical Simulation with Applications

This work examines the qualitative behavior of a general class of difference equations. We establish criteria guaranteeing the stability, periodicity, and boundedness of the solutions of the equation under consideration. In addition, we identify its invariant intervals. The theoretical results are subsequently applied to various special cases, among them the May--Host model. Numerical simulations are presented to demonstrate the dynamics of the solutions and to validate the theoretical analysis.

Portable Trace Gas Analysis With Multipass Cavity Raman Scattering, Charuka Muktha Arachchige

USF Tampa Graduate Theses and Dissertations

The continual increase in the production and utilization of chemicals across a growing range of industrial, environmental, and medical applications necessitates accurate and efficient measurement of molecular composition. Trace gas detection is particularly critical in industrialized settings, where the identification of colorless and odorless gases is essential for ensuring operational safety and regulatory compliance. Recent advances in optical instrumentation have positioned spontaneous Raman scattering as a promising analytical technique for trace gas analysis, with multipass cavity enhancement providing a cost-effective pathway to high detection sensitivity. However, existing implementations remain confined to laboratory-scale optical table setups, limiting their applicability in field …

Designing For Fitness And Resilience In Human Artificial Intelligence Systems, Thomas R. Gill

USF Tampa Graduate Theses and Dissertations

Over the last decade, new artificial intelligence models, frameworks, and applications have emerged that have radically changed the socio-technical landscape. These new technologies represent both great opportunities and great potential risks. Due to the recency of their emergence, there is little known about the longevity and long-term repercussions of artifacts powered by new artificial intelligence technology. In this dissertation, I evaluate A.I. and A.I.-based artifacts through the lens of the fitness-utility model, a tool developed by Gill and Hevner (2013) for the evaluation of an artifact's ability to survive and reproduce over long time horizons. In so doing, I also …

The Soil Crisis In Modern Food Systems: Rethinking Agricultural Land Use, Antonia Moure Richard

Journal of Food Law & Policy

Feeding a larger world while preserving the resource that makes agriculture possible—soil—poses a governance problem. By 2050, food systems must support 9.8 billion people even as prevailing practices continue to degrade soils that are non-renewable on human timescales. Technological fixes (e.g., vertical farming, hydroponics) may complement production, but they cannot substitute for soil at scale. The question that follows is simple: are current uses of soil compatible with the future needs of food systems? This article argues that without a shift in governance, short-run productivity gains are achieved by drawing down the soil asset, thereby undermining long-run food security and …

Supersymmetric Quantum Fields Via Quantum Probability, Radhakrishnan Balu

Journal of Stochastic Analysis

The super version of imprimitivity theorem is available now to describe global supersymmetry of systems using the representations of super Lie groups (SLG). This result uses the equivalence between super Harish- Chandra pairs and super Lie groups, at the categorical level, and is applicable to super Poincaré group and generalizes a smooth SI to super context. We apply the result to build supersymmetric quantum fields. Towards this end, we set up a super Fock space of a disjoint union of super Hilbert spaces which is equivalent to super tensoring of boson (even) part symmetrically and that of fermion (odd) part …

Convergence To Fractional Brownian Motion For Weighted Random Sums In Besov Space, Ibrahima Mendy

Journal of Stochastic Analysis

We consider infinite sums of weighted i.i.d. random variables, with finite variance and arbitrary distribution, and we derives conditions for the weak convergence in Besov space of normalized sums to fractional Brownian motion (fBm).

Pricing Variance Swaps Using Extended Heston Model, Semere Gebresilasie, Mulue Gebreslasie, Indranil Sengupta

Journal of Stochastic Analysis

Abstract. In this study, we introduce a variance swap for the underlying asset utilizing the Heston model, incorporating a long-term variance that is treated as a stochastic function of time. We develop a closed-form solution for the variance swap under this framework, where the log returns are driven by a compound Poisson process. Our analysis of historical data reveals that long-term variance is not constant; instead, it fluctuates over time, reflecting market dynamics more accurately. By integrating this time-varying long-term variance into the model, we achieve an improvement in prediction performance of approximately 60%. Furthermore, we perform model calibration using …

Non-Euclidean Geometries And Fairness Constraints In Advanced Clustering, Arnab Seal

Master’s Dissertations

A fundamental challenge in modern unsupervised learning is adapting classical clustering algorithms to handle complex, real-world data constraints. Traditional models often assume data resides in a flat, Euclidean space and optimize strictly for cluster cohesion, thereby failing to capture intrinsic hierarchical structures and ignoring sociotechnical demographic biases. This thesis addresses these critical limitations by extending generalized mean-shift dynamics into two novel clustering frameworks. First, to natively accommodate data with tree-like structures (e.g., taxonomies and social networks), we propose Hyperbolic Gaussian Blurring Mean Shift (HypeGBMS). By projecting data into the Poincar´e ball model and utilizing M¨obius vector space operations, HypeGBMS successfully …

Leveraging Spatial Statistics For Domain Adaptation Of Vision Language Models In Medical Vqa, Himanshu Raj

Master’s Dissertations

Recent advances in Vision–Language Models (VLMs) have demonstrated strong performance in Medical Visual Question Answering (Medical VQA) task. Although they perform very well within their domains, these models often experience issues with their generalization ability on unknown clinical distribution data because of different imaging technologies and patient groups used in various medical facilities. Generalization problems faced by these models make their practical application in the field of VLM-based medical VQA systems rather difficult. To overcome this limitation we proposed our method named Spatial Semantics Aware Domain Adaptation (SSADA), which is an integrated framework that combines both finetuning and prompt-based in-context …

Re: Conditional Approval Letter For Butte Priority Soils Operable Unit Draft Final Leak Detection Monitoring Plan (Dated May 13, 2026), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Movement Ecology And Population Dynamics Of Box-Nesting Black-Bellied Whistling-Ducks (Dendrocygna Autumnalis) In Louisiana, Katie E. Miranda

LSU Master's Theses

Understanding demographic and behavioral processes underlying population dynamics is essential for effective wildlife management, especially for species subject to harvest pressure or experiencing population shifts. Black-bellied Whistling-Ducks (Dendrocygna autumnalis) are currently undergoing a notable northward range expansion and are increasing in abundance across the southeastern United States, raising management concerns for both ecological and economic reasons. As a result, there is growing public support for a special Black-bellied Whistling-Duck-specific harvest season, yet little research has been conducted on this species, particularly within newly-established populations. To address this gap, I conducted this study with the goal of evaluating seasonal …

Predictive Importance Sampling Based Coverage Verification For Multi Uav Trajectory Planning, Snehashish Ghosh

Master’s Dissertations

In next-generation wireless networks, unmanned aerial vehicle (UAV) networks are emerging as a promising solution for ultra-reliable low-latency communication (URLLC). A key challenge in millimeter-wave UAV networks is ensuring that mobile users are always in line-of-sight (LoS) coverage, since the current snapshot-based trajectory planning approach does not consider the mobility of the users during the decision interval, resulting in disastrous LoS gaps. For continuous coverage verification, standard uniform sampling is too computationally expensive, as it would need a large number of samples to estimate rare failure events that have latencies that are not suitable for real-time requirements. In this work, …

Photoluminescent Studies Of Metal-Organic Frameworks For Small Molecule Sensing, Julia T. Dematteo

USF Tampa Graduate Theses and Dissertations

Metal–organic frameworks (MOFs) are highly tunable nanoporous materials with broad applications in gas separation, drug delivery, light harvesting, and chemical sensing. The incorporation of photoactive components into MOFs enables the development of optical sensors capable of detecting a wide range of analytes. Porphyrins and metalloporphyrins are particularly attractive for such applications due to their high molar absorptivity, strong emission quantum yields, and versatile interactions with chemical species.

In this work, two porphyrinic MOFs, PCN-222 and MOF1(Cd), are investigated. Both frameworks utilize tetrakis(4-carboxyphenyl)porphyrin (TCPP) as the organic linker, imparting optical functionality to the materials. Integration of TCPP into the framework induces …

Risk-Aware Deep Reinforcement Learning In Portfolio Optimization, Shimin Zhang

USF Tampa Graduate Theses and Dissertations

Portfolio optimization is important in investment, with the purpose of maximizing profit whilemanaging risk. The traditional portfolio optimization method, Modern Portfolio Theory, is based on the assumption that returns are normally distributed, which is always violated in practical financial market settings. In addition, traditional approaches to portfolio optimization are mostly designed as static optimizations and fail to account for the nonlinearity and non-stationarity of contemporary financial markets. Recent developments in deep reinforcement learning have attracted considerable attention for treating portfolio optimization as a sequential decision-making process. Traditional portfolio optimization methods usually evaluate risk based on the variance of the returns. …

Learning Trajectories Of Online Batch Selection Methods, Luke Green

Theses and Dissertations

Modern deep neural networks achieve strong performance on large-scale datasets, but often require substantial training time. Online batch selection methods seek to reduce this cost by updating models on informative subsets of each batch rather than on all available examples. Recently introduced methods leverage teacher models and report substantial speedups, particularly in noisy-label settings. However, comparisons are often based on the number of epochs required to reach a target test accuracy, a coarse metric that is sensitive to implementation details and may obscure important differences in learning dynamics. In this thesis, we implement several online batch selection methods in a …

Advancing Data Usability, Activity Modeling, And Stability Optimization In Computational Enzyme Design, Spencer Gardiner

Theses and Dissertations

A grand challenge of computational biology is to computationally design, in a single pass, a protein sequence that catalyzes an arbitrary chemical reaction at a high rate under specified conditions [1]. This work details advances in three essential areas on the path to that goal: data quality, activity prediction and modeling, and stability optimization. The structure and implementation of the Allotrope Simple Model (a FAIR data format for many scientific instruments) was examined in [2], setting the stage for training deep learning models on high-quality experimental datasets from diverse sources. In [3], the limits of physics-based and deep learning tools …

Beyond Single Images: A Comprehensive Benchmark For Album-Level Vision-Language Understanding, Shawn Huang

Theses and Dissertations

Automatic album organization has been studied extensively over the past decades due to significant progress in digital photography. Recent Vision-Language Models (VLMs) have shown strong performance on multi-image understanding, making them natural candidates for automating album organization workflows. While VLMs’ abilities in multi-image understanding have been widely studied, their performance on album organization remains underexplored. To bridge this gap, we introduce AlbumBench, the first comprehensive benchmark for automatic album organization. Specifically, we (1) define album organization tasks as photo selection for album-specific user objectives, photo rating according to how well user intents are fulfilled, and album-specific photo grouping given a …

Long-Term Monitoring Of The Optical Performance Of The Primary Mirrors At The Coihueco And Loma Amarilla Sites Of The Pierre Auger Observatory, A. Abdul Halim, P. Abreu, M. Aglietta, M. Ahmed, I. Allekotte, K. Almeida Cheminant, B. Fick, K. Nguyen, D. Nitz, Et Al.

Michigan Tech Publications

This paper presents the results of an optical performance monitoring campaign for the primary mirrors installed in fluorescence telescopes at the Coihueco and Loma Amarilla sites at the Pierre Auger Observatory in Argentina. Since the end of 2003, this effort has developed into a unique long-term dataset that addresses the performance of the primary mirrors. We have focused on the scattering characteristics and specular reflectance of mirror segments, as well as their evolution over several years of operation. Despite being housed in climate-controlled buildings, dust accumulation, impurities, and natural aging — such as oxidation or other chemical or physical degradation …

Generation Of Khz-Rate Complex-Structured Liquid Targets For Relativistic Laser–Plasma Interactions, Michael L. Dexter, Stephen J. Hageman, Gregory Ngirmang, Kyle D. Frische, Joseph Snyder, John T. Morrison, Enam A. Chowdury, Anil K. Patnaik

Faculty Publications

With the rise of high repetition rate ultra-intense laser systems, there is a need for solid density targets to study relativistic laser–plasma interactions that can operate at the same repetition rate. Flowing liquid targets are attractive because they are self-replenished, debris free, cost effective and easy to use. Liquid targets have been used for high-repetition rate (up to kHz rate) generation of electrons, protons, x rays, and neutrons by our group and elsewhere. In this Letter, we demonstrate a kHz-rate generation of a variety of dynamically shaped complex-structured targets from the interaction of a 10¹⁶ W/cm² focused laser …

To What Extent Could Quantum Computing Pose A Threat To Global Modern Data Security?, Aniket Maheshwari

Journal of Cybersecurity Education, Research and Practice

Quantum computing has emerged as a transformative technology with the potential to fundamentally disrupt modern cryptographic systems that underpin global data security. This paper examines the extent to which quantum computing could pose a threat to modern global data security by synthesising existing technical, institutional, and policy-oriented literature. Drawing on a narrative review of scholarly research, industry reports, and government frameworks, the analysis focuses on the implications of quantum algorithms such as Shor’s and Grover’s, which challenge the mathematical foundations of widely used cryptographic schemes. The findings suggest that while quantum computing presents a credible long-term threat to asymmetric encryption …

Editorial: Plant Responses To Abiotic Stress: Unraveling Complex Mechanisms Through Genomics And Physiology, Mizanur Rahman, Takashi Asaeda, Md Harun Rashid

School of Earth, Environmental, & Marine Sciences Faculty Publications

No abstract provided.

Ecological Insights From Field And Laboratory Studies Of Akashiwo Sanguinea, Alexis L. Pasulka, Matthew J. Harke, Jaden Hansen, Ryan K. Walter, Eva Kokkino

Physics

Blooms of the dinoflagellate Akashiwo sanguinea occur in coastal ecosystems worldwide and can have significant ecological consequences. Along the central California coast, long-term oceanographic and harmful algal bloom observations indicate that A. sanguinea has exhibited seasonal blooms since approximately 2017, coinciding with negative upwelling anomalies and warm, stratified conditions characteristic of late summer and early fall. In laboratory experiments, three A. sanguinea strains isolated from different sites and/or environmental conditions along the central California coast exhibited variation in growth rates, temperature sensitivity, and transcriptional responses. Under the conditions tested in this study, one strain exhibiting higher maximum growth rates and …

Label-Flip Attack Detection Via Trust-Weighted Aggregation In Federated Learning For Underground Mine Security, Md Sazedur Rahman, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining operations are increasingly dependent on autonomous vehicles, robotic drilling systems, and intelligent inspection platforms operating in confined, GPS-denied tunnel environments. These systems rely on distributed perception models to interpret navigation cues, hazard warnings, and environmental signals in real time. While centralized deep learning can enhance model performance, transferring raw operational data across mining sites introduces serious confidentiality and security risks. Federated Learning (FL) offers a privacy-preserving alternative by enabling collaborative model training without sharing local datasets. However, deploying FL in underground mining introduces several critical challenges: (i) Training labels may be modified either maliciously by compromised clients or …

Bayesian Subgroup Learning Of Spatially Resolved Transcriptomics Data, Hou-Cheng Yang, Huimin Li, Guanyu Hu, Qiwei Li

School of Mathematical & Statistical Sciences Faculty Publications

Recent advancements in spatially resolved transcriptomics (SRT) technologies have enabled the comprehensive molecular and spatial characterization of single cells, providing valuable insights into the cellular organization of tissues. SRT techniques, such as single-molecule fluorescence in situ hybridization (FISH)-based methods (e.g., seqFISH, STARmap) and next-generation sequencing (NGS)-based methods (e.g., spatial transcriptomics, 10x Visium), allow for the measurement of gene expression across large populations of cells or tissue spots. These approaches generate high-dimensional data that integrate both molecular profiles and spatial context, which is crucial for understanding tissue structure and function in areas like development, neuroscience, and cancer biology. Identifying spatially variable …