Physical Sciences and Mathematics Commons^™

Solving High-Dimensional Differential Equations Using Recurrent And Residual Neural Network Architectures, Hind Khaled Kolaib

Knowledge Engineering and Data Science

High-dimensional Partial Differential Equations (PDEs) form the foundation of complex process modeling in various scientific and engineering applications, including finance, physics, and optimal control. However, classical numerical methods are adversely affected by the curse of dimensionality, making them inapplicable for large-scale problems. Recently, however, deep learning-based approaches have provided a new toolbox for these high-dimensional PDEs, including methods such as the Deep Backward Stochastic Differential Equation (Deep BSDE) method. Our approach draws on a more sophisticated deep learning backbone, using neural networks (in our case, a Residual Neural Network and a Long Short-Term Memory network (LSTM) integrated into the Deep …

Volatile Organic Compound Analysis Of Humboldt Penguin (Spheniscus Humboldti) Preen Oil: A Pilot Study, Dante E. Rojas, Mitchell M. Mccartney, Eva Borras, Michael O. Eze, Abigail Pietrow, Jennifer J. Valvo, Cristina E. Davis

Chemistry Faculty Research & Creative Works

Avian olfaction has gained prominence in recent decades for its roles in social communication and behavior. In penguins, the chemical characterization of preen oil remains limited. In this exploratory study, we characterized the volatile organic compound (VOC) profile of preen oil from Humboldt Penguins (Spheniscus humboldti). Preen oil from 12 captive individuals was analyzed using headspace sorptive extraction (HSSE) coupled to gas chromatography–mass spectrometry (GC–MS). Chemometric analyses examined variation in VOC profiles by sex, age class, and breeding activity. We detected 54 compounds (20 tentatively identified), including linear alcohols, methyl ketones, carboxylic acids, saturated hydrocarbons, oxygenated and nitrogen-containing organics, prenolipids, …

Soft-Chemical Scalable One-Pot Aqueous-Medium Synthesis Of Na3(Vo)2(Po4)2f-Based Cathodes: Compositional Tuning And Electrochemical Performance In Na- And Li-Ion Batteries, Prashanth Sandineni, Subal Chandra Manna, Sutapa Bhattacharya, Santhoshkumar Sundaramoorthy, Ramesh Deokate, Milad Aghayi-Anaraki, George E. Sterbinsky, Kartik Ghosh, Amitava Choudhury

Chemistry Faculty Research & Creative Works

One of the most important cathode materials for Na-ion batteries, Na₃(VO)₂(PO₄)₂F, and its compositional variants have been synthesized using four different and facile one-step soft chemical routes. The as-synthesized compounds, Na_2.95(VO)₂(PO₄)₂F (I), Na_2.93(VO)₂(PO₄)₂F (II), and Na_2.58(VO)₂(PO₄)₂F (IV), crystallize in the tetragonal crystal system in the P4₂/mnm space group, while Na₃(VO)₂(PO₄)₂F (III) crystallizes in the orthorhombic crystal system in …

Do Comments And Expertise Still Matter? An Experiment On Programmers’ Adoption Of Ai-Generated Javascript Code, Changwen Li, Christoph Treude, Ofir Turel

Research Collection School Of Computing and Information Systems

This paper investigates the factors influencing programmers’ adoption of AI-generated JavaScript code recommendations within the context of lightweight, function-level programming tasks. It extends prior research by (1) utilizing objective (as opposed to the typically self-reported) measurements for programmers’ adoption of AI-generated code and (2) examining whether AI-generated comments added to code recommendations and development expertise drive AI-generated code adoption. We tested these potential drivers in an online experiment with 173 programmers. Participants were asked to answer some questions to demonstrate their level of development expertise. Then, they were asked to solve a LeetCode problem without AI support. After attempting to …

Innovative Methods In Intact Rock Deformation Detection, Ali Abdullah M Alzahrani

Doctoral Dissertations

"An accurate assessment of intact rock deformation is imperative in engineering activities carried out either in or above rock masses. However, instruments used for post-peak deformation are easily debonded and cannot record the whole process of post-peak deformation required in the failure modeling of rocks. This dissertation focuses on the investigation of the performance of two non-contact measuring systems (Laser Displacement Sensor (LDS) and Inductive Proximity Sensor (IPS)) compared to the contact type instrument (conventional Strain Gauge System (SGS)) in monitoring intact rock deformations. Subsequently, an IPS was installed in a high-pressure triaxial testing system before investigating its performance in …

Lessons On Generative Artificial Intelligence From The American Association Of Dental Editors And Journalists (Aadej), Christopher J. Smiley

Journal of the American College of Dentists

The American Association of Dental Editors and Journalists (AADEJ) recently released "Guidance for Authors, Editors and Publishers on the Use of Generative AI". Developed by an 11-member stakeholder panel, this guidance paper serves a dual purpose: It provides practical strategies for mitigating the risks that generative artificial intelligence (GAI) poses to professional writing and explains how GAI creates these risks. Understanding both the underlying vulnerabilities of GAI and how to address them is critical for users at all levels, from authors and reviewers to editors, publishers, and general users, to maintain the validity and reliability of their written work. This …

Improving Human Dimensions Of Conservation Planning: Challenges And Opportunities For Sustainable Conservation Outcomes, Vivian Hulugh

Electronic Theses and Dissertations

Conservation planning is important to ensure both ecological and social benefits of natural resources, including the maintenance of functional ecosystems and stable wildlife populations as well as the provision of resources that communities rely on. Yet, the integration of the human dimensions of conservation planning remains limited in practice. This thesis examines conservation planning in South Dakota and within Joint Venture (JV) partnerships, using qualitative methods to identify challenges and opportunities for more effective and coordinated planning. In chapter one, I examine how collaboration, public participation, and the use of climate information are integrated conservation planning in South Dakota and …

The Spacetime Finite Element Method For Investigations Into Physics Ghost Systems And Time Parallel Preconditioning, Jax Wysong

Electronic Theses and Dissertations

This work operates on two fronts, focusing on interesting physical phenomena before turning our attention to an interesting numerical math problem. First, using the spacetime finite element method (FEM), we investigate a PDE system consisting of two Klein Gordon equations, which are coupled nonlinearly through the potential energy. The system contains a ghost (negative kinetic energy term). Systems such as these are generally deemed physically unstable, resulting in infinite energy in finite time. However, recent work has shown that this is not always the case. We investigate multiple scenarios arising from different initial conditions to characterize if/when a ghost system …

Chemical Load: An Unseen Herbicide Filter In Restored Prairies?, Rylie Franklin

Electronic Theses and Dissertations

Community Assembly Theory is a framework used to describe the process by which species from a regional species pool disperse and encounter various abiotic and biotic conditions that act as filters, limiting which species will populate a specific local community. Prairie restoration success can be improved by understanding abiotic and biotic filters. As agricultural practices have adopted integrated weed management plans, that include the use of both chemical and non-chemical techniques, another filter needs to be considered during restoration: the chemical load of soils, i.e., herbicide residue from recent on-site applications and herbicide drift from adjacent areas. To examine the …

The Role Of Resistance Training In Managing Menopause-Related Changes, Alyson K. Strauss

Honors Undergraduate Theses

Resistance training serves as a critical non-pharmacological strategy for mitigating the physiological and psychological declines associated with the menopausal transition. This natural biological phase, marked by declining estrogen levels, typically leads to accelerated bone mineral density loss, sarcopenia, metabolic regulation disturbance, and heightened mood disturbances. To evaluate the efficacy of targeted exercise interventions, a systematic review was conducted following PRISMA guidelines. From an initial pool of 60 relevant articles, 13 studies met the specific inclusion criteria, focusing on structured resistance training protocols for perimenopausal, menopausal, and postmenopausal cohorts.

The synthesis of these studies indicates that resistance training is a highly …

To Print A Remake: An Analysis Of Hollywood Remakes And Their Cultural Value, Connor F. Seaton

Honors Undergraduate Theses

In recent years, audiences and movie critics have expressed concern that Hollywood’s growing reliance on remakes, sequels, franchises, and similar adaptations has led to a broader worry that originality is fading from modern cinema and that the industry is instead focused on using adaptations to maximize profits. Although adaptation is often seen as a commercially driven framework for reproducing existing intellectual property in a new media format, this thesis argues that it should be recognized as an autonomous cultural category with its own artistic, historical, and social significance and merit. By analyzing adaptation scholarship and reviewing its complex historical development, …

Safety Aware Continual Reinforcement Learning-Based Output Tracking Control Of Nonlinear Continuous-Time Systems, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

An output feedback (OF)-based control scheme utilizing both a scalable multilayer neural network (MNN) observer and actor–critic MNN via integral reinforcement learning (IRL)/adaptive dynamics programming (ADP) approach for a class of nonlinear systems with output constraints is introduced. The proposed observer, critic, and actor MNN weight updates are derived using a singular value decomposition (SVD) of MNN activation function gradient along with output error, Bellman and control input errors, respectively. Next, the approach incorporates continual learning (CL), utilizing a penalty function in the weight update laws for both actor–critic MNNs to consolidate knowledge from previous tasks and enhance learning in …

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 …