Engineering Commons^™

Modeling Rank Distribution And The Relative Importance Factor Index In Discrete Power-Law Models: Application To Social Resilience Using The Scopus Database, Brian Llinas, Jose Padilla, Humberto Llinas, Erika Frydenlund, Katherine Palacio

VMASC Publications

Prior research on power-law distributions has primarily focused on modeling frequency patterns, with less attention given to rank distributions and how ranked positions reflect relative importance among elements. In discrete power-law distributions, frequency-based metrics often provide limited discrimination in the tail, where elements may exhibit similar counts but differ in relative dominance. These patterns are especially evident, for instance, in academic publishing, where keywords, affiliations, and citations commonly exhibit power-law behavior. To address this limitation, we introduce the Relative Importance Factor (RIF) Index, a statistical measure derived from the estimated discrete power-law rank distribution rather than an additional independent parameter. …

Social Safety And Social Security: Validating Context-Specific Instruments For Slums, Liss D. Romero, Katherine Palacio, Zacheous Ako Abang, Valeria Silgado, Humberto Llinas, Leidy González, Erika Frydenlund, Daniel Bolivar, Jose J. Padilla

VMASC Publications

Social safety and social security are concepts that help explain a community's well-being by assessing how it manages and mitigate existing, or perceived, risks. However, these terms are often conflated, which can limit understanding, particularly in areas with unstable living conditions. This study explores both concepts in the context of residents living in slums. As the number of displaced people grows, slums and informal settlements are becoming increasingly common worldwide, making it essential to clarify these concepts. Residents of slums face numerous hazards, including crime, violence, inadequate housing, overcrowding, and limited access to essential services. To measure social safety and …

Standardization Of Neuromuscular Reflex Analysis—Role Of Fine-Tuned Vision-Language Model Consortium And Openai Gpt-Oss Reasoning Llm-Enabled Decision Support System, Eranga Bandara, Ross Gore, Sachin Shetty, Ravi Mukkamala, Christopher K. Rhea, Brittany S. Samulski, Amin Hass, Atmaram Yarlagadda, Shaifali Kaushik, Malith De Silva, Andriy Maznychenko, Inna Sokolowska, Kasun De Zoysa

VMASC Publications

Background/Objectives: Accurate assessment of neuromuscular reflexes, such as the Hoffmann reflex (H-reflex), plays a critical role in sports science, rehabilitation, and clinical neurology. Conventional interpretation of H-reflex electromyography (EMG) waveforms is subject to inter-rater variability and interpretive bias, limiting reliability and standardization. This study aims to develop an automated, interpretable, and robust agentic AI–driven framework for H-reflex waveform analysis. Methods: We propose a fine-tuned Vision–Language Model (VLM) consortium combined with a reasoning Large Language Model (LLM)–enabled decision support system for automated H-reflex interpretation. Multiple VLMs were fine-tuned on curated datasets of H-reflex EMG waveform images annotated with expert clinical observations, …

Distilling The Complexity Of Agent-Based Simulations Into Textual Explanations Via Large Language Models, Noé Y. Flandre, Philippe J. Giabbanelli

VMASC Publications

Communicating the design and results of agent-based models (ABMs) to subject matter experts is challenging, which hinders participation and limits trust in simulation-based decision support. Large language models (LLMs) can communicate ABMs as textual summaries, thus complementing traditional disclosure through statistical and visualization techniques. While prior work translated the structure of conceptual models into narratives via LLMs, our extension covers the dynamics of simulation models via an automated simulation-to-text method that extracts contextual information from NetLogo ABMs, performs repeated simulations, and generates narrative descriptions (including the model’s purpose, parameters, and simulation dynamics) using mutimodal LLMs. Furthermore, four summarization algorithms spanning …

Sex Differences In Freezing Behavior And Extinction Learning Among Wistar Rats Exposed To Social Isolation And 15cgy Gcrsim Space Radiation Both Alone And In Combination, Zachary N. M. Luyo, Riley S. Heerbrandt, Alea F. Boden, Frederique E. E. Keumeni, Richard A. Britten, Laurie L. Wellman, Larry D. Sanford

Department of Biomedical and Translational Sciences Faculty Publications

Long-term space missions traveling beyond the Earth’s magnetosphere will expose astronauts to prolonged periods of social isolation (SI) and exposure to space radiation (SR). Throughout these missions, astronauts will have to cope with these stressors and adapt to their environment to carry out mission objectives. It is known that SI and SR can impact mission performance and overall health. Additionally, behavioral stress responses can differ between sexes. However, the independent and interactive effects of SI and SR on fear behavior and extinction learning between sexes is unknown. This study compared freezing behavior and stress-induced hyperthermia (SIH) in female and male …

Toward Neurosymbolic Reinforcement Learning Via Editable Specifications, Vedant Khandelwal, Hong Yung Yip, Amit Sheth

Publications

Reinforcement learning systems are commonly adapted to new settings by retraining or fine-tuning policies. This default is costly, difficult to audit, and poorly aligned with structured requirement changes such as revised safety rules, new operational constraints, or updated user preferences. We argue for an alternative abstraction: adaptation via edits to an external, human-readable specification that the agent consults at execution time. We propose conditioning decision-making on an editable knowledge graph encoding (i) rules capturing action applicability and high-level effects, (ii) hard constraints defining feasibility, and (iii) soft preferences shaping tradeoffs among feasible behaviors. Requirement changes become graph edits, not policy …

A Dataset-Centric Analysis Of Evaluation Protocols In Face Morph Attack Detection, Sheerko R. Hmasalah, Abdulbasit K. Al-Talabani, Aras T. Asaad

Polytechnic Journal

Face morph attack detection (MAD) is often evaluated using datasets collected from diverse sources, created for different purposes, and acquired under varying conditions. This heterogeneity raises questions about how evaluation protocols and dataset characteristics influence reported performance. This work adopts a dataset-centric perspective to analyze the effects of evaluation protocol and dataset composition, including visual quality and morph-to-genuine imbalance, on MAD performance. Experiments are conducted on eight datasets and four morphing techniques using a fixed pipeline evaluated across six backbones, including Vision Transformer (ViT) and five CNN architectures, and ten evaluation scenarios covering homogeneous and heterogeneous cross-morph generalization. Performance is …

Evaluating The Use Of Machine Learning For Road Maintenance Cost Estimation, Shahla Shirinzad, David Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

Accurate estimation of highway maintenance costs is essential for efficient resource allocation and long-term infrastructure sustainability. This study evaluates the effectiveness of advanced machine learning models, including ResNet, Transformer, and other neural network architectures, for forecasting maintenance costs using historical data from the Highway Maintenance Improvement Program (HMIP) provided by the North Carolina Department of Transportation (NC DOT). A comprehensive comparative analysis is conducted across multiple models using standard performance metrics, including R², MAE, RMSE, and MSE. The results demonstrate that advanced architectures, particularly ResNet and Transformer, consistently outperform traditional statistical approaches and baseline machine learning models, achieving …

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 …

Age-Related Hearing Loss Involves Mitochondrial Dna Instability And Copy Number Depletion In The Cochlea: Insights From In Vivo And In Vitro Models, Akil Turner, Bo Ding, Xiaoxia Zhu, Tam Nguyen, Parveen Bazard, Robert D. Frisina

Chemical and Biochemical Engineering Faculty Research & Creative Works

Age-related hearing loss (ARHL) is a highly prevalent sensory neurodegenerative disorder that involves various molecular mechanisms. The present study investigated if age-related oxidative stress (OS) induces alterations in mitochondrial DNA (mtDNA) copy number and heteroplasmy, using complementary in vivo and in vitro models. Aged (30-month) CBA/CaJ mice have elevated auditory brainstem response (ABR) thresholds amplitudes vs. young (3-month) CBA/CaJ mice, a key physiological characteristic of ARHL The in vitro experiments employed the strial SV-K1 cochlear cell line treated with hydrogen peroxide (H₂O₂). Both the aged cochleae and H₂O₂-treated cells exhibited a significant …

Insights Of Quantitative Imaging And Instantaneous Heat Transfer Coefficient Measurement In Spouted Bed Column, Hasan A. Abdulwahab, Abbas J. Sultan, Amer A. Abdulrahman, Haydar A.S. Aljaafari, Ali A. Yahya, Zahraa W. Hasan, Malik M. Mohammed, Laith S. Sabri, Bashar J. Kadhim, Jamal M. Ali, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

In this study, the heat transfer behavior of a conical spouted bed column was imaged for the first time across its entire cross-sectional area and at multiple axial heights. To achieve this, a custom-built quantitative imaging approach was developed. This method combined FluxTeq heat flux sensors with an Arduino-based data acquisition system. The experimental setup enabled instantaneous, spatially resolved measurements of surface temperature, heat flux, and local heat transfer coefficients (LHTC) under various operating conditions. Measurements at different radial locations, angles, and heights provided a comprehensive view of heat transfer behavior throughout the column. The obtained cross-sectional images show that …

Probability Of Detection For Corrosion-Induced Mass Loss And Yield Strain In Metal Structures Using Fe-C Coated Long Period Fiber Gratings Sensors And Electroresistive Strain Gauges, Ying Zhuo

Doctoral Dissertations

"Graphene-based, Fe-C coated long-period fiber grating (LPFG) sensors individually reveal a causal relation between LPFG wavelength change and Fe-C mass loss. Their correlation parameters vary significantly between the sensors mainly because hydrophobic multilayer graphene and graphene oxide make sensor fabrication inconsistent. This study explores MXene-based, Fe-C coated LPFG sensors due to MXene’s inherent hydrophilic property in the presence of surface functional groups such as hydroxyl and oxygen and the probability of detection (POD) for mass loss from Fe-C coated LPFG sensors when deployed at a fixed location. A novel fabrication process incorporating Piranha solution pretreatment is proposed to ensure uniform …

Computational Fluid Dynamics Simulation Of In-Situ Plasma- Assisted Cold Spray, Lucas Degen

Electronic Theses and Dissertations

Cold spray is a solid-state form of additive manufacturing with high versatility and widespread applicability, especially in relation to metal coatings. Many applications exist within the fields of aerospace, defense and energy systems to obtain sustainable, temperature and corrosion resistant, robust coatings. High-strength alloys and refractory metals generally encounter challenges with regards to cold spray coating adhesion and overall deposition efficiency. Heat induced methods of post-processing or in-situ treatment have been explored to encourage particle softening in the cold spray plume, as well as coating recrystallization to enhance material properties. These approaches have primarily been limited to an experimental approach, …

Fast And Sustainable Video Anomaly Detection With Continual Learning, Preethi Amasa

Electronic Theses and Dissertations

Real-time video anomaly detection systems deployed in surveillance, healthcare, and industrial environments face continuous distribution shifts in lighting, viewpoint, and activity patterns. Existing models often experience performance degradation under these conditions and may suffer catastrophic forgetting when adapting to new environments. This thesis proposes RegiGrow, a parameter-efficient continual adaptation framework built on the Flashback retrieval pipeline. RegiGrow integrates Mixture-of-Experts Low-Rank Adaptation into a frozen ImageBind encoder, enabling sequential domain adaptation without modifying the pretrained backbone. A lightweight router maps visual regime features to a distribution over LoRA experts, each specializing in a distinct normal operating regime. The central contribution is …

Bridging Language And Game Worlds: Semantic Representations And Text-Driven Terrain Generation For Procedural Content, Zhongyu Xie

Electronic Theses and Dissertations

Procedural Content Generation (PCG) systems produce vast quantities of game levels, terrain, and environments, but lack semantic interfaces: no shared vocabulary exists between natural language, designer intent, and the structured representations generators operate on. This thesis addresses the language-content grounding gap in PCG through two complementary studies spanning semantic analysis and semantic synthesis. The first study introduces a group-supervised contrastive learning framework for semantic representation of symbolic PCG maps under many-to-one semantics, where visually distinct maps may share the same design intent. The framework combines parameter-guided semantic grouping, LLM-based caption augmentation, and a multi-positive contrastive objective that aligns language with …

Texels: A Programmable Textile Interface For Replicating Textures, Maya E. Eusebio

Honors Undergraduate Theses

Self-moving fabric interfaces have massive potential for applications in fields ranging from art to haptic feedback to deployable space structures. However, current systems of implementation face the impracticalities of bulkiness, burnout, and energy consumption on top of limiting designs that can only contract uniformly or create one pre-programmed shape. For this technology to bring the change that it promises, we must break the barriers of usability and sustainability to make it a practical choice. This thesis aims to develop a scalable model of fabric that designers, programmers, and anyone else can acquire or create with accessible materials, integrate into design …

Performance Analysis Of Sparse Neural Networks In Brain Abnormality Detection, Megan Danh

Honors Undergraduate Theses

Neuroimages have held the capability of revealing to medical professionals patterns for brain abnormalities since their development. However, more recently, these professionals and researchers are looking to use neural networks to identify these brain abnormalities through neuroimages for early detection that would allow more effective treatment. Neuroimage datasets, specifically functional magnetic resonance imaging (fMRI), are extremely large in size. This would result in their processing and training to be computationally expensive, even with smaller neural networks. Fortunately, recent pruning methods have recently emerged, where network weights and neurons are pruned to reduce computational cost without compromising too much accuracy. By …

Effects Of Hydrogen Enrichment And Premixing On Flame Stability In A High-Pressure Axial-Stage Combustor, Alexandre Fernandes C. Santiago Filho

Honors Undergraduate Theses

The addition of hydrogen to conventional fuels enhances reactivity and can increase the risk of flashback in premixed gas turbine combustors. This study investigates the effects of premixing length and hydrogen concentration on flame stability in a high-pressure axial-stage combustor. Experiments were conducted at the UCF Propulsion and Energy Research Laboratory using a two-stage configuration, where the primary stage provided a high-temperature vitiated crossflow using a hydrogen-piloted, premixed methane–air mixture, and hydrogen-enriched methane–air mixtures were injected through a concentric fuel-tube injector in the axial stage. Premixing length was varied for pure hydrogen flames, and hydrogen concentration was varied at a …

Evaluation Of The Long-Term Sealability Performance Of Aramid Fiber Nitrile-Based Gaskets, Salome Ramirez

Honors Undergraduate Theses

The reliability of sealed, oil-filled containers is critical for maintaining the operation of high-voltage energy systems, in which the sensitive electronics need to be isolated from the cyclic conditions associated with outdoor environments. New materials bring the possibility of extending service lifetimes and maintenance intervals. Aramid fiber nitrile-based gaskets are an attractive alternative to conventional gasket choices due to their long-term stability in various environments and compatibility with porcelain, metals, and plastics. For newly designed porcelain-to-metal fittings, understanding the behavior of the candidate gasket material under realistic loading conditions is essential to ensure long-term seal integrity and equipment safety. Traditional …

Temperature Compensation In Loop And Patch Fss Strain Sensors: Analysis And Experimental Validation, Swathi Muthyala Ramesh, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Frequency selective surfaces (FSSs) are arrays of conductive elements or apertures that exhibit frequency-dependent reflection and transmission properties. Their electromagnetic response is influenced by geometry and environmental conditions, making them attractive for wireless strain-sensing applications. However, temperature variations can produce frequency shifts similar to those caused by strain, reducing measurement accuracy. This work investigates the effects of intrinsic temperature compensation on two common FSS unit cell geometries—loop and patch—through comprehensive simulation analysis. The results show that loop-based cells offer superior thermal stability, while patch-based cells provide greater strain sensitivity, illustrating the trade-off between thermal robustness and mechanical responsiveness. A patch-type …

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 …

Corrections To: Enhancing Measurement Accuracy: The Impact Of Missing Data On Parameter Estimation In Mass-Spring-Damper Systems (Ieee Transactions On Instrumentation And Measurement (2026) 75 (1–12) Doi: 10.1109/Tim.2026.3676091), Michkath Omanda Bouraima, Steven Thompson, Maciej J. Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

In the above article [1], a wording ambiguity appears in Proposition 4 regarding the description of the missing at random (MAR) mechanism. The published sentence states that the probability of observing the kth sample depends on the realized measurement value. This wording may be interpreted as dependence on the current unobserved value y[tk], which could suggest a missing not at random (MNAR) mechanism. The intended MAR mechanism is that the observation probability for the kth sample depends only on previously observed measurement information, such as y[tk-1], and not on the current unobserved value y[tk]. Therefore, the corrected wording clarifies that …

Increasing Actuation Authority And Bandwidth For Aeromechanics Testing: A Piezoelectric-Based Approach, Sydney A. Giannuzzi

Graduate Studies Theses and Dissertations 2026

Flutter in turbomachinery poses significant risks to aerospace systems, requiring accurate identification of flutter boundaries to ensure operational safety. Traditional binary “flutter / no flutter” testing methods provide limited data without error margins. Aeromechanical Identification Methodology (AIM) offers a safer alternative by mapping aeromechanical damping across operational conditions using non-contact excitation. However, current AIM actuators are constrained to excitation frequencies below 500 Hz, limiting their effectiveness and applicability. To bridge this gap, this research analyzed and validated performance of a novel piezoelectric-based oscillatory flow injection valve (NOVA) designed to deliver high-bandwidth, high-authority actuation for expanded flutter boundary mapping.

NOVA was …

Evaluating The Impact Of Cognitive Distraction On Spaceflight-Relevant Task Performance Using Surface Electromyography And Motion Capture, Allyson K. Mitchell

UNF Graduate Theses and Dissertations

Cognitive distraction poses a risk to astronaut performance during complex, multitasking operations in spaceflight environments. This study examined the effects of cognitive load on neuromuscular coordination and task execution using surface electromyography (sEMG) and motion capture. Thirteen participants performed spaceflight-relevant tasks under undistracted and distracted conditions, with distraction induced through verbal questioning. EMG signals from eight upper-extremity muscles were processed using envelope filtering, peak normalization, and time normalization to enable inter-subject comparison, and group-level mean activation with standard deviation was analyzed. While overall muscle activation was similar between conditions, phase-dependent differences were observed, with undistracted trials showing higher activation during …

Regulation And Localization Of Urease In Sporosarcina Pasteurii: Implications For Coastal Protection, Amar Kosovac

UNF Graduate Theses and Dissertations

Coastal erosion is a persistent problem exacerbated by global warming. As the climate changes, the southeastern coast is likely to experience an increased frequency and intensity of storms, which can significantly damage the coastline. The coast is crucial as it serves as a habitat for numerous animals, supports tourism, and underpins infrastructure. Microbially induced calcite precipitation (MICP) is a novel method to enhance erosion resistance in sandy soils using the non-pathogenic bacterium Sporosarcina pasteurii. S. pasteurii produces urease to catalyze urea into bicarbonate, which drives the precipitation of calcium carbonate. Maximal expression of urease is a limiting factor for …

Multi-Objective Optimization Strategy For Component Sizing In Solar-Hydrogen Microgrids Using An Advanced Hybrid Genetic Algorithm, Dylan Jones

UNF Graduate Theses and Dissertations

This thesis presents the development of a genetic algorithm (GA) optimization framework for the design and component sizing of hybrid solar-hydrogen microgrids. The framework addresses a critical gap in research and existing commercial tools by unifying performance maximization and cost minimization objectives across both grid-tied and islanded configurations. Integrating solar photovoltaics, electrolyzers, hydrogen storage, fuel cells, and batteries, the GA employs adaptive weighting and dynamic boundary constraints to balance technical feasibility with economic efficiency. To ensure real-world viability, the algorithm relies on a novel Daylight Sun Factor (DSF) for localized solar assessment and was rigorously validated against multi-year, high-fidelity irradiance …

Mechanical Behavior Of Additively Manufactured Ti-6al-4v Eli Parts: Effects Of Laser Parameter Selection, Samuel Lopez

UNF Graduate Theses and Dissertations

Additive manufacturing (AM) of TI-6AL‑4V Extra‑Low Interstitial (ELI) enables complex geometries for fatigue‑critical medical device applications, yet fatigue performance remains sensitive to process‑induced defects. This work investigates the effect of laser process parameter selection on the microstructure, mechanical properties, and fatigue behavior of TI- 6AL‑4V ELI fabricated via laser powder bed fusion (L‑PBF) using a Renishaw RenAM system. The influence of laser parameters was isolated by holding powder chemistry, build orientation, scan strategy, sub‑transus annealing, and post‑processing constant between a non‑optimized baseline and an optimized parameter set selected based on tensile performance.

Optical microscopy showed the optimized condition exhibited improved …

Development Of A Flexible In Vitro Model System For Studying Biomineralization In Humans And Marine Organisms, Reed J. Harper

UNF Graduate Theses and Dissertations

Biomineralization is a process where cells control the supersaturation of ions and spatiotemporal deposition of numerous proteins to guide biomineralization, resulting in hard mineralized tissues with complex structures and novel properties. The mechanisms of this process are not well understood but key components have been identified as critical for biomineralization to occur. The cellular expression of intrinsically disordered proteins (IDPs) and the subsequent post-translational modification (PTM) play a role in stabilization of mineral precursors, control of morphology and growth, phase/polymorph selection, and composition of mineral formed. Currently in vitro model systems use either synthetic polymers, native proteins extracted from mineralized …

Experimental Validation Of Optical Wireless Communication And Power Transfer For Uav Applications, Fnu Dhruv

UNF Graduate Theses and Dissertations

Unmanned Aerial Vehicles (UAVs) have revolutionized emergency response, disaster assessment, and search-and-rescue operations. However, their operational efficacy is fundamentally constrained by limited battery endurance and the susceptibility of traditional radio-frequency communication to disruption in adverse weather. To address these limitations, this thesis proposes and experimentally validates a novel architecture integrating Free-Space Optical (FSO) communication with Simultaneous Lightweight Information and Power Transfer (SLIPT). This system utilizes a split-beam configuration to concurrently enable high-bandwidth data transmission and optical energy harvesting to replenish the UAV's battery pack. The research was conducted in three progressive phases. Initially, system feasibility was established through rigorous optical …

The Effects Of Weathering On The Very High Cycle Fatigue Behavior Of Aluminum 6061-T6, Rama Albatesh

UNF Graduate Theses and Dissertations

Aluminum 6061-T6 is commonly used in structural and transportation applications due to its strength-to-weight ratio, corrosion resistance, and ease of manufacturing. In many practical situations, components made from this alloy experience cyclic loading while exposed to environmental conditions such as humidity, temperature changes, and ultraviolet radiation. These factors can affect surface integrity and fatigue performance, especially in the very high-cycle fatigue (VHCF) regime.

This study aims to explore how accelerated weathering impacts the ultrasonic fatigue behavior of Aluminum 6061-T6. First, assess the material through tensile testing to determine its mechanical properties. Then conducted microstructural analysis using optical microscopy, scanning electron …