Physical Sciences and Mathematics Commons^™

Structured Decomposition And Efficient Adaptation For Scientific Data Movement: From Transfer Concurrency To Genomic Data Acquisition, Rasman Mubtasim Swargo

Masters Theses

"The end-to-end performance of scientific data movement is increasingly limited not by raw network capacity but by whichever resource along the path is momentarily slowest, a bottleneck that no single static configuration can track. This thesis argues that the right response is to decompose each problem along the resource or workflow axis where bottlenecks vary, adapt only the genuinely dynamic control variables at runtime, and make that adaptation efficient through utility-guided or offline-learned control rather than expensive online search. The thesis develops this principle through four systems of widening scope. AutoMDT decomposes a transfer by operation, assigning separate concurrency levels …

Ecological And Evolutionary Divergence Of A Shared Sympatric Color Polymorphism In Paracirrhites Hawkfish, Samuel Greaves

Graduate Studies Theses and Dissertations 2026

Color polymorphism provides a tractable window into how selection, gene flow, and ecology interact to maintain phenotypic diversity and initiate divergence. I investigate the ecological and genomic basis of sympatric light and melanistic morphs in two reef hawkfishes, Paracirrhites arcatus and P. hemistictus, across Guam and Hawai‘i. This study aims to determine whether the color morphs of these hawkfishes are a stable polymorphism or have begun the process of ecological speciation. For P. arcatus revealed substantially lower density on Guam (0.086 ind/m²) than reported for Hawai‘i (0.292 ind/m²) and a strong skew away from the melanistic morph on Guam …

Performance Of Numerical Methods Applied To The Black–Scholes Model, Scott Cameron Williams

UNF Graduate Theses and Dissertations

We compare five numerical approaches for approximating solutions to the Black–Scholes partial differential equation for pricing European call options: FTCS, BTCS, Crank– Nicolson, Monte Carlo simulation, and a physics–informed neural network (PINN). These methods span finite difference techniques, probabilistic simulation, and machine learning. Performance is evaluated based on computational efficiency and accuracy relative to the analytical Black–Scholes solution.

Among the methods, Crank–Nicolson and the PINN demonstrated the strongest overall performance. Crank–Nicolson achieved the highest accuracy but exhibited increased runtime as the number of underlying stock price grid points grew. In contrast, the PINN produced slightly less accurate results but with …

A Comparative Evaluation Of Data Imbalance Handling Techniques In Machine Learning Models For One-Year Mortality Prediction In Liver Cirrhosis, Sumiya Hasan Trisha

UNF Graduate Theses and Dissertations

Liver cirrhosis is associated with substantial morbidity and mortality, making one-year mortality prediction a clinically relevant problem. Using a liver cirrhosis dataset as the motivating application, this thesis evaluates five machine learning classifiers—Logistic Regression, Random Forest, XGBoost, LightGBM, and CatBoost—under five class-imbalance handling strategies: Baseline learning, Random Oversampling, SMOTE-NC, ADASYN, and Cost-Sensitive Learning. Hyperparameter tuning was conducted using randomized search, and predictive performance was assessed over 200 iterations of Monte Carlo Cross-Validation using Accuracy, Precision, Recall, Fl-score, and ROC-AUC.

The results suggest that imbalance-handling strategies can materially affect predictive performance, particularly recall. Because the outcome of interest is death within …

Variance Shrinkage In Dunnett-Type Multiple Comparisons With Missing Data, Md Habibullah

UNF Graduate Theses and Dissertations

Dunnett’s procedure is widely used for comparing multiple treatments with a control, but its application becomes challenging in the presence of missing data and multiple com- parisons. An improved Dunnett-type procedure addresses this by using multiple imputation under Rubin’s framework and constructing unified confidence intervals based on a multi- variate t distribution, allowing valid simultaneous inference while controlling the family-wise error rate (FWER). This work further extends the method by incorporating shrinkage-based variance estimation. Specifically, individual group variances are shrunk toward a common value to improve stability. This approach is particularly effective when group variances are similar or moderately different, …

Ridit-Based Adaptive Allocation In Two-Stage Clinical Trials With Binary Outcomes, Dewan Fahim

UNF Graduate Theses and Dissertations

This study explores better ways to assign patients to treatments in clinical trials with binary outcomes, such as success or failure. Adaptive methods are used to learn from early results and adjust treatment assignments during the trial, helping more patients receive better performing treatments while maintaining reliable conclusions. We focus on trials comparing multiple treatments using a two-stage design. In the first stage, several treatments are tested to identify the most promising one; in the second stage, that treatment is compared with a control. Unlike traditional equal assignment, we use adaptive allocation in the second stage to make better use …

A Comparative Study Of Classification Methods For Healthcare Analytics, Xueting Zhao

UNF Graduate Theses and Dissertations

This thesis presents a comparative study of logistic regression, Linear Discriminant Analy- sis (LDA), and Quadratic Discriminant Analysis (QDA) for binary classification in healthcare analytics, integrating theoretical derivation, simulation, and real-data application. A facto- rial simulation study crosses the covariance structure (equal vs. unequal), predictor correla- tion (ρ ∈ {0, 0.5, 0.9}), dimensionality (p ∈ {2, 5, 10}) and sample size (n ∈ {50, 100, 200}) across 54 scenarios with 1,000 Monte Carlo replicates each. Three main findings emerge. Logistic regression and LDA are nearly interchangeable when the assumption of equal-covariance holds. QDA achieves substantially better discrimi- nation when class-specific …

Two-Stage Response-Adaptive Randomization Designs For Multi-Arm Trials With Normal Outcome, Tanjin Tamanna Happy

UNF Graduate Theses and Dissertations

This study focuses on improving how clinical trials compare new treatments with a standard treatment, when the response is quantitative (normally distributed). In a common two-stage design, several new treatments are first evaluated, and the best-performing one is selected if it appears better than the standard. In the second stage, this selected treatment is compared again with the standard using additional data to confirm its effectiveness. This approach is known to be efficient in terms of accuracy and sample size savings. We extend this design by introducing an adaptive method for assigning patients to treatments in the second stage. Instead …

A Stability Analysis Of The Phase-Lock Equations, Brian M. Sunguza

UNF Graduate Theses and Dissertations

Ginzburg and Landau have provided a set of equations that relate superconductivity to magnetic fields. Through a transformation process, Zhan has derived what are now called the phase-lock equations. A stability analysis of the spatially-independent phase-lock equations is the purpose of this presentation. This simplification is significant since it allowed for the analytical determination of equilibria, their stability, and the influence of a periodic forcing function. Through the use of an original code, numerical simulations are shown to corroborate the analytical results described above.

This analysis includes novel Lyapunov functions that allowed for the analytical determination of the instability region. …

Energy-Efficient Security For Narrowband Iot Using Blockchain And Ep-Cumac, Hafizullah Kakar

UNF Graduate Theses and Dissertations

The Narrowband Internet of Things (NB-IoT) continues to expand but faces challenges such as cryptographic overhead and energy consumption. Security frameworks such as blockchain and Energy-Performance Cumulative Message Authentication Codes (EP-CuMAC) rely heavily on SHA-256, which is not optimized for energy-limited devices.

This work unifies two complementary approaches, a hybrid blockchain-based NB-IoT framework and an EP-CuMAC-based framework, by engineering their cryptographic core with an Energy Complexity Model-optimized SHA-256 (ECM-SHA256). ECM applies parallel memory-bank mapping and block-level access optimization to reduce redundant power usage while preserving algorithmic integrity.

Experimental evaluation on identical Intel DDR3 systems using pyRAPL shows energy savings of …

Growth And Dielectric Characterization Of Epitaxial Sr1-Xmgxtio3 Thin Films, Richard Badu

UNF Graduate Theses and Dissertations

Complex oxide materials with ferroelectric ground states remain promising candi dates for next-generation high-capacitance and nonlinear microelectronic applica tions. One of those materials is SrTiO3 (STO). However, STO is an incipient fer roelectric with anomalously high dielectric permittivity at low temperatures. STO crystals possess a highly polarizable lattice that is sensitive to even small amounts of impurities. Substituting Mg into the STO matrix is predicted to induce a corre lation between ferroelectric and relaxor behavior. In this thesis, we demonstrated the growth of a series of epitaxial STO and Sr1-xMgxTiO3 thin films using molec ular beam epitaxy. The surface evolution …

Advancing Multi-Modal Learning, Swetha Sirnam

Graduate Studies Theses and Dissertations 2026

Multimodal learning aims to develop systems that understand and reason across multiple sources of information such as images, videos, audio, and text. These capabilities are central to applications including visual search, digital assistants, robotics, and visual question answering. Despite recent progress in multimodal large language models, current approaches struggle to capture the structured complexity of real-world visual data. Fine-grained visual details are often lost during multimodal alignment, models frequently fail to capture nuanced human intent, and existing methods remain limited in understanding long-form videos with complex temporal dynamics. Furthermore, many evaluation benchmarks rely heavily on explicit visual cues, which can …

Federated Learning In The Era Of Foundation Models, Guangyu Sun

Graduate Studies Theses and Dissertations 2026

The emergence of large-scale foundation models has reshaped computer vision and multi-modal learning, but their substantial computational and communication demands pose significant challenges for deployment in federated learning (FL) systems. Federated learning enables collaborative model training across decentralized clients without sharing raw data, offering strong privacy guarantees. However, adapting foundation models in FL remains difficult due to communication bottlenecks, heterogeneous data distributions, limited device resources, and scarce labeled data. This dissertation presents a unified line of research that addresses these challenges through four complementary frameworks, progressively advancing the efficiency, personalization, and practicality of foundation model adaptation in federated settings.

First, …

High Intensity Ablation By Laser Filaments At Standoff Distances, Owen Thome

Graduate Studies Theses and Dissertations 2026

The high-power densities delivered to a target material by a laser allow for melting and vaporization to occur, a process called laser ablation. At higher intensities, plasma formation occurs and more complex material removal mechanisms take place. As laser power increases, nonlinear effects occur during propagation. One such effect, Kerr self-focusing, leads to beam collapse and ionization, resulting in a channel of plasma and region of high intensity light referred to as a laser filament. Filaments carry intensities on the order of 10¹³ W/cm² to significant distances without the use of large-scale focusing optics. Laser filament ablation is …

Watch Your Step, Mind The Crowd: Socially Compliant Navigation In Unstructured Environments, Jack Vice

Graduate Studies Theses and Dissertations 2026

Autonomous mobile robots increasingly operate in complex real-world environments characterized by uneven terrain, dynamic obstacles, and human presence. This dissertation addresses the fundamental challenge of developing adaptive navigation strategies that enable robots to safely and effectively traverse unstructured outdoor environments including construction sites, agricultural fields, and disaster areas while maintaining social compliance around people. We advance this capability through five complementary contributions that collectively target dynamic, unstructured, human-occupied environments. First, we demonstrate evolutionary optimization of legged mobility for uneven terrain. Second, we propose using a spatiotemporal autoencoder to learn the temporal dynamics of moving people and objects. Third, we introduce …

Using Inferences From Natural Language Narratives To Improve Video Question Answering, Hoang Bao Truong

Graduate Studies Theses and Dissertations 2026

The purpose of this thesis is to examine how structured narrative representation and reasoning strategies jointly affect the performance of Video Question Answering (VideoQA). Although recent advances in large multimodal and language models have improved video description and retrieval, causal and counterfactual reasoning remain challenging. This thesis studies an inference-only pipeline in which videos are converted into natural-language narratives, and those narratives may optionally be transformed into a structured NarrativeML format before multiple-choice questions are answered without task-specific fine-tuning. NarrativeML is a machine-readable narrative representation that explicitly encodes entities, events, and their causal and temporal relations. Three representational contexts are …

Charge Transport Modeling Of Multifunctional Molecular Transistors, Romena Akter

Graduate Studies Theses and Dissertations 2026

Distinguishing between the current–voltage characteristic curves associated with different transport mechanisms in a molecular electronic device and an artifact arising from device-level disorder is a central challenge in the study of molecular electronics. Characterizing these mechanisms and connecting them qualitatively to molecular-level orbital alignment, electrode coupling, voltage division, energetic disorder, and reorganization energy requires not only experimental data but also a rigorous theoretical framework for extracting physically meaningful parameters from measured transfer characteristics. This dissertation addresses this challenge in Chapters 2, 3, and 4 by developing and applying four complementary transport models —Simmons tunneling, single-level Landauer, Marcus hopping, and McConnell …

Learning Weibull Loss Severity Models From Truncated And Censored Data, Majed Alkhasha

Graduate Studies Theses and Dissertations 2026

In modern actuarial science and risk management, due to various loss control mechanisms, observed severity losses are typically left-truncated at the deductible, right-censored at the policy limit, and scaled by a pre-specified co-insurance factor. This results in two types of actuarial payment random variables: payment-per-payment (PPP) and payment-per-loss (PPL). To learn ground-up Weibull loss severity models from PPP and PPL sample data, we implement two estimation techniques: Maximum Likelihood Estimation (MLE) and the dynamic Method of Trimmed Moments (MTM). MLE is employed to obtain efficient estimates of the Weibull shape and scale parameters. However, MLE may assign unnecessarily large point …

Robust Deep Learning One-Class Classification, Shahd Alnofaie

Graduate Studies Theses and Dissertations 2026

One-Class Classification (OCC) focuses on learning the characteristics of normal data and identifying observations that deviate from this learned pattern as anomalies. It is commonly used in applications such as medical diagnosis, cybersecurity, industrial monitoring, and fraud detection, where abnormal examples are often rare or unavailable during training. Classical approaches such as SVDD and LS-SVDD describe normal data using a hypersphere. While effective in some settings, these methods rely on shallow representations and can be sensitive to noise and contaminated observations. To address these limitations, this dissertation introduces a Deep LS-SVDD framework that combines hypersphere-based data description with deep neural …

Making Unsafe Sequences Unexecutable: Formal Protocol Enforcement For Cyber-Physical Systems, Arthur Amorim

Graduate Studies Theses and Dissertations 2026

Cyber-physical systems execute physical actions in response to software commands, making their communication protocols a primary attack surface. A stealthy attack is a sequence of individually valid messages that violates a required ordering, driving the system into an unsafe state without malware or protocol violation. Existing defenses examine messages or physical state in isolation, not protocol level sequences, and cannot prevent them. Preventing them requires enforcement that makes unsafe sequences unexecutable at the communication boundary.

Formal methods offer a principled path to enforcement, but no tool spans specification to safe deployed hardware. Model checking automates proofs but has no certified …

Connecting The Existing Fiber Infrastructure To The Future With Antiresonant Hollow Core Fibers, Timothy Bate

Graduate Studies Theses and Dissertations 2026

Optical fiber systems based on solid-core silica waveguides underpin modern telecommunications, high-power laser delivery, precision sensing, and coherent optical systems. However, nonlinear effects, material absorption, and thermal limitations within silica increasingly constrain further scaling in both optical power and transmission performance. Antiresonant hollow-core fibers provide a promising alternative by guiding light predominantly in air, substantially reducing nonlinear interactions, latency, and optical damage while enabling transmission regimes inaccessible to conventional solid-core fibers. Despite rapid advances in antiresonant hollow-core fiber attenuation and power handling, one of the largest remaining barriers to widespread adoption is reliable integration with the existing solid-core fiber ecosystem. …

A Systematic Review And Characterization Of Privacy Noncompliance In Real-World Applications, Alexander E. Charkiewicz

Graduate Studies Theses and Dissertations 2026

Software applications increasingly rely on user data to provide their functionality, but improper handling of such data can lead to serious privacy noncompliance with applicable regulations and policies. A prominent example is the Facebook–Cambridge Analytica scandal, in which a third-party application collected the personal data of approximately 87 million Facebook users without users' consent. Despite growing attention to privacy compliance, two key challenges hinder the systematic understanding and analysis of privacy noncompliance. First, unlike security vulnerabilities, which have been systematically categorized through taxonomies such as the Common Weakness Enumeration (CWE), privacy noncompliance lacks a technical taxonomy describing how it manifests …

Design, Fabrication Modeling, And Optical Metrology Of Mwir Silicon Metalenses, Weiyu Chen

Graduate Studies Theses and Dissertations 2026

Mid-wave infrared (MWIR) optical systems require compact, broadband, manufacturable components whose performance can be predicted and measured reliably. Silicon metalenses are attractive because silicon combines high refractive index, MWIR transparency, and semiconductor-compatible fabrication. This dissertation develops a scale-bridging design–fabrication-modeling–metrology framework for MWIR silicon metalenses.

A shared full-aperture framework connects meta-atom libraries, physical layout generation, angular-spectrum propagation, point-spread-function calculation, and focal-plane energy metrics. Building on this foundation, the Dispersive Sweatt Model (DSM) incorporates meta-atom dispersion into conventional ray-tracing software, enabling broadband co-optimization of metasurfaces and refractive elements. A process-aware design framework then incorporates scanning electron microscopy (SEM)-measured height–radius relationships produced by …

An Empirical Comparison Of K-Nearest-Neighbors And Logistic Regression Classification Models, Jackson Cushing

Graduate Studies Theses and Dissertations 2026

This thesis presents an empirical comparison of two classification methods: Logistic Regression and K Nearest Neighbors (KNN). The primary objective of this research is to evaluate the strengths and limitations of each method when applied to real-world datasets. Several publicly available datasets on diabetes, breast cancer, heart attack risk, and cardiovascular disease, were analyzed. For each dataset, K Nearest Neighbors models were implemented in the same way logistic regression had already been applied. The results demonstrate that while logistic regression offers interpretable parameter estimates and performs well when the underlying predictor and outcome relationship is approximately linear, however KNN can …

Non-Gaussian Phenomena In Light Scattering And Applications, Shubham Atul Dawda

Graduate Studies Theses and Dissertations 2026

Physical reality is rarely deterministic; which, when probed by electromagnetic fields that also fluctuate, leads to observables that are most efficiently modelled as statistical processes. At equilibrium, this is usually achieved by invoking the Gaussian statistics of underlying physical processes, however, in practice, one often encounters out-of-equilibrium conditions where Gaussian descriptions may not suffice. Such situations are plentiful in nature– from biology to astronomy. This dissertation addresses several non-Gaussian phenomena associated with light scattering, and includes specific models’ derivations, experimental techniques developments, and demonstrations of potential applications. The systematic presentation will consider circumstances that infringe upon specific assumptions of the …

Deciphering Ultrafast Photoinduced And Photocatalytic Reaction Dynamics On Oxide Surfaces: Direct Detection Of Radical Intermediates, Fragment Trapping, And Carbon–Carbon, Carbon–Oxygen, And Carbon–Hydrogen Bond Formation Pathways, Aakash Gupta

Graduate Studies Theses and Dissertations 2026

Understanding photoinduced reactions at solid interfaces is essential for advancing heterogeneous photocatalysis, surface photochemistry, and energy conversion technologies. This dissertation investigates the ultrafast dynamics of reactive intermediates, fragment trapping, and radical-mediated bond formation on oxide surfaces using time-of-flight mass spectrometry in conjunction with femtosecond pump-probe spectroscopy. Various experimental findings are validated through collaborations via density functional theory (DFT). By combining temporal, mass, and energy-resolved measurements, this work provides molecular-level insight into the elementary processes governing light-driven surface reactions. The photodissociation dynamics of CH₃I adsorbed on TiO₂(110), TiO₂(100), and amorphous silicon oxide surfaces are examined …

Integrated Optical Probes For Confocal Scanning Imaging And Adjustable Coherent-Gated Dynamic Sensing, Yonglin Huang

Graduate Studies Theses and Dissertations 2026

Optics and photonics have been one of the most important sciences and technologies that impact modern human life in a big way. For example, fiber-optics for communications and artificial intelligence. Optical probes are critical components for optical imaging and optical sensing technologies that have been actively researched and developed in the past decades. Advanced fiber-optic sensor probes with smaller size, better performance, lower noise, higher photon efficiency, rapid sensing time, and lower cost are needed in many applications, such as nanoscale material science, chemistry, and biomedical fields, etc.  In this project, new fiber-optic sensor probe technologies and integrated micro-optic devices …

Advances In Active And Passive Integrated Photonic Devices On Thin-Film Lithium Niobate, Pooja S. Kulkarni

Graduate Studies Theses and Dissertations 2026

Thin-film lithium niobate (TFLN) has emerged as a powerful platform for integrated photonics, combining the exceptional electro-optic, nonlinear, and optical properties of bulk lithium niobate with the scalability and compactness of planar nanophotonic technologies. Building on these principles, advanced device architectures such as adiabatic dichroic filters have demonstrated exceptional spectral performance spanning over two octaves of bandwidth on the TFLN platform. Beyond reciprocal devices, it also offers promising pathways toward integrated nonreciprocal components. By leveraging broadband filtering structures and advanced nonlinear photonic design strategies, compact and monolithic optical isolators can be envisioned without relying on traditional magneto-optic materials. In the …

Modeling And Mitigating Atmospheric Degradation In Computer Vision With Application In Renewable Energy Prediction, Sumit Laha

Graduate Studies Theses and Dissertations 2026

Weather-induced variability poses significant challenges to the reliability and performance of modern computational systems, particularly those relying on visual perception and environmental prediction. This dissertation focuses on enhancing computer vision and machine learning based predictive models that operate under varying atmospheric conditions. Two representative weather-impacted applications are investigated: image dehazing and solar photovoltaic (PV) power output forecasting. Image dehazing focuses on the restoration of clear, unobstructed visuals from hazy or foggy images, a task that is vital for various applications. On the other hand, photovoltaic (PV) power forecasting aims to predict future solar energy generation based on historical sky images …

Multi-Scale Drivers Of Beetle Biodiversity In A Rapidly Urbanizing Region, Alessandra Pandolfi

Graduate Studies Theses and Dissertations 2026

As cities continue to expand, designing urban landscapes that balance ecological resilience with biodiversity conservation has emerged as a pressing challenge. This dissertation examines urban ecosystems as complex and dynamic environments shaping plant and animal communities, with a particular focus on ground-dwelling beetles as ecologically significant indicators of environmental change. Conducted across the Orlando metropolitan area, one of the fastest-growing regions in the United States, this dissertation integrates experimental and observational approaches. Specifically, it aims to (1) test sustainable landscaping practices, (2) compare the ecological outcomes of conventional and alternative residential landscape designs, and (3) assess how gradients of urbanization …