Digital Commons Network^™

Statistical Analysis Of Interstitial Impacts On Shock-Driven Fuel Droplet Atomization, Miguel L. Moran

Graduate Thesis and Dissertation post-2024

Understanding the deformation and aerobreakup of liquid droplets is critical for designing fuel injectors for pressure gain combustion systems. Previous literature has observed the morphology and timescales of individual liquid droplets exposed to the convective flow fields behind moving shocks propagating between Mach 1 and Mach 10. However, the liquid droplets produced by an injection system are not isolated and have many close neighbor droplets of similar diameter. Understanding interactions between these droplets is necessary to reconcile existing theory with droplet behavior observed in liquid jets and droplet clouds. The present study uses a simple injector to create vertically oriented …

Application Of Industry 5.0 Attributes For Successful Implementation: Framework Of Manufacturing Excellence In Aerospace And Defense Industries, Rena Lewis

Graduate Thesis and Dissertation post-2024

As a novel concept, Industry 5.0 is making its rounds around the academic community and various industries. Despite its introduction over five years ago, definitions and perceptions are still under deliberation. Applications, methods of implementation and values are still in the conceptual phase. This study introduces a framework inclusive of human-centricity, sustainability and resilience, the pillars associated with Industry 5.0. It aims to differentiate the revolution from its predecessor, Industry 4.0, as it dives into the technologies associated with Industry 4.0 including but not limited to the Internet of Things (IoT), autonomous robots, and artificial intelligence. As those technologies evolve …

Mitigation Of Transverse Gusts On Morphing Wings Via Span-Wise Twisting, Alex Ruiz

Graduate Thesis and Dissertation post-2024

Wind gust encounters create a highly unstable and unpredictable environment for air vehicles. Small aircraft are especially susceptible to gust-induced disturbances due to their limited size and weight, leading to significant fluctuations during flight. This study introduces a novel concept involving a twisting mechanism along the wingspan, allowing for span wise variations in pitch. Using a towing tank and a gust generator apparatus, we demonstrate the effectiveness of the proposed mechanism. Force data and flow field data obtained from Particle Image Velocimetry (PIV) provide insights into the vortex mechanisms at play. A Modified Discrete Vortex Method using the leading-edge separation …

Dynamic Ground Response Of Sandy Soils Under Vibratory Roller Compaction, Jorge Eliecer Ballesteros Ortega

Graduate Thesis and Dissertation post-2024

Vibratory roller compaction is a common technique for densifying granular materials in road construction, utilizing a combination of static and dynamic loads for greater efficiency. However, the vibrations generated during compaction activities can cause structural damage and human discomfort. This thesis investigates the dynamic ground response of sandy soils under vibratory roller compaction, aiming to predict the induced ground vibrations and deformations. Field tests were performed at different road construction sites in Central Florida by using geophones and settlement transducers to provide empirical support for the analysis. Finite element and finite difference models, implementing Hypoplasticity Sand and PM4Sand as constitutive …

Emission Spectroscopy Measurements And Post-Processing Methods For Nitromethane And Aluminum In A Post Detonation Enviroment, Hamil Patel

Graduate Thesis and Dissertation post-2024

The doping of conventional liquid fuels has been a topic of interest in the research community due to the potential for increased energy density and improved performance in oxygen-deficient environments. One of these proposed fuels is nitromethane doped with micron-sized aluminum powder. This work is focused on early spectroscopic measurements of each of these constituent fuel parts in a post-detonation environment that aims to mimic the real-world temperatures and pressures seen in explosives that are not typically replicated in experimental facilities. Spectroscopy was chosen as the primary diagnostic technique for these experiments because it can non-invasively probe the relatively harsh …

Time Resolved Shock Tube Speciation Studies Of Solid Fuel Decomposition Products At Ramjet Conditions, Juan Cruz Pellegrini

Graduate Thesis and Dissertation post-2024

Solid fuel Ramjets (SFRJs) are of interest to the navy and other defense applications because of their high specific impulse, ease of fuel transportation, and low relative cost. Hydroxyl-terminated polybutadiene (HTPB) is one of the main solid fuels of choice for SFRJs, and there is a need for updated chemical kinetic models for the decomposition products of HTPB which will aid in CFD simulations of SFRJs. This work is focused on collecting experimental data for HTPB decomposition products for SFRJ combustor conditions ranging from temperatures of 1000 – 1600 K, pressures of 5 ± 0.5 atm, and rich equivalence ratios …

Benchmarking Robustness Of Gait Recognition Models, Reeshoon Sayera

Graduate Thesis and Dissertation post-2024

This study investigates the robustness of gait recognition systems under realistic perturbations, with a focus on both silhouette parsing and gait recognition components. Experiments are conducted on three diverse datasets—CASIA-B, CCPG, and SUSTech1K—using five state-of-the-art parsing models and six gait recognition architectures. To simulate real-world degradations, we introduce 15 types of noise across five severity levels, resulting in 75 distinct corrupted scenarios. Our findings reveal that no parsing model performs optimally under all conditions, and transformer-based gait models exhibit greater resilience than CNN-based counterparts. However, all models remain highly sensitive to digital noise and occlusions. Interestingly, performance under temporal noise …

Advancing Responsible Ai: Disparity Mitigation Strategies For Human-Centered Ai Systems, Aida Tayebi

Graduate Thesis and Dissertation post-2024

In recent years, the widespread adoption of machine learning (ML) has driven the expansion of automated decision-making across various real-world applications. While ML models improve efficiency and predictive accuracy in domains such as healthcare, finance, and criminal justice, biases inherited from training data can potentially lead to unintended disparities. Growing awareness of these biases has raised concerns within the human-centered AI and responsible AI communities, highlighting the importance of promoting fairness in AI systems. Aiming for equitable outcomes in automated decisions is not only a technical goal but also an ethical consideration, as models influenced by biases may inadvertently reinforce …

Laminar Burning Speed Of Dme-Hydrocarbon Mixtures, Ahmed F. Safdari

Graduate Thesis and Dissertation post-2024

Fossil fuels, when burned in the air, release large amounts of carbon dioxide (CO₂) and greenhouse gases (GHG) into the air, causing significant pollution. To reduce greenhouse gas emissions (CO₂, CO, and NOₓ), alternative fuels such as Dimethyl Ether (DME) are being explored for their potential to offer cleaner combustion solutions. This study investigates the laminar burning speed (LBS) DME blends with several alkanes and alkenes in air at atmospheric pressure (1 atm) across a wide range of equivalence ratios (Φ). LBS is essential for understanding combustion characteristics, including combustion efficiency, heat release rates, and chemical kinetics. Accurate …

Framework For A Successful Startup Using Lean Startup Methodology Within Ecosystem, Mohamed Elsalamy

Graduate Thesis and Dissertation post-2024

Entrepreneurs want to change the world by converting their business visions to reality. Their dreams are to provide and invent a solution for the society they created to fill a specific market gap that has an opportunity to profit, scale, and grow fast. Startup businesses are booming very progressive due to the boom in technology. However, what is the definition of a startup? What is this business, and what are its business and revenue models? What are the parameters for success and failure? Different thoughts and concepts for a startup are being designated by business professionals and through the media. …

Examination Of Users’ Privacy Issues In Live Streaming Information Disclosure, Yanlai Wu

Graduate Thesis and Dissertation post-2024

Live streaming has gained global popularity, raising significant privacy concerns due to its performance-driven, real-time, on-camera, and public nature. Increasing reports highlight privacy violations in live streaming, including streamers facing harassment from malicious viewers and bystanders being inadvertently exposed to audiences. Despite these challenges, research on privacy in live streaming remains limited. This dissertation addresses this gap by examining users' privacy concerns and strategies in information disclosure and exploring design solutions to enhance privacy management on live streaming platforms.

To achieve these objectives, I conducted three qualitative studies: two interview studies (Study 1: Chapter 4 and Study 2: Chapter 5) …

A Deep Learning Framework For Last-Mile Delivery Enhancement Using Social Media, Valeria Laynes Fiascunari

Graduate Thesis and Dissertation post-2024

Over the past decade, people have been spending more time online. Almost anything can be done from a laptop or cellphone. This is one of the reasons why e-commerce has been in a constant boom, as it is easier to buy something online and have it delivered to the front door than to go to the store. As more people engage in this activity, e-commerce platforms' challenges are more complicated and need to be addressed faster. However, these challenges escape the company's scope when external factors influence the objective of optimized deliveries, for example, traffic issues or bad weather during …

Examining The Feasibility And Influencing Factors Of Large Language Models In Source Code Security Analysis, Jie Lin

Graduate Thesis and Dissertation post-2024

Large Language Models (LLMs) have recently emerged as promising tools for analyzing source code; however, their capabilities for automated vulnerability detection and localization remain underexplored, particularly given the widespread reliance on closed-source models which introduce significant privacy, security, and transparency risks. This dissertation rigorously examines the feasibility and key influencing factors determining the efficacy of open-source LLMs in source code security analysis. Initially, we investigate the impact of tokenized input length on vulnerability detection accuracy and explicitness across Java vulnerability datasets using ten distinct LLM architectures, identifying robust performance in specific models while others demonstrate considerable accuracy deterioration. Subsequently, we …

The Impact Of Visualization Styles On Movement Imitation Accuracy In Virtual Reality, Gabriela R. Shamblin

Graduate Thesis and Dissertation post-2024

Virtual reality (VR) has become a powerful tool for motor learning and skill acquisition, offering immersive environments for users to practice and refine movements. This thesis investigates how different visualization styles in VR affect movement imitation accuracy, specifically focusing on hand movements. While prior research has explored precise alignment and visualization individually, few studies have examined their combined impact. This study addresses that gap by evaluating the effectiveness of various visualization methods in relation to offset, animation, and manual type.

We developed an application to ensure all participants experienced each visualization factor as 12 combinations in varied sequences. The user …

Multiscale Modeling And Experimental Evaluation Of Droplet Evaporation In High-Pressure Pulsed Spray Cooling For Thermal Management Applications, Fernando Soria Proano

Graduate Thesis and Dissertation post-2024

Droplet evaporation plays a vital role in spray cooling applications, helping to maintain safe operating temperatures for high-powered devices such as lasers and supercomputers. There exists an ongoing debate regarding the appropriateness of diffusion-limited versus kinetically limited models for describing this complex process. This work seeks to bridge the macro-scale evaporation, governed primarily by capillary forces, with the nano-scale interactions among the vapor, liquid, and solid phases through a comprehensive description of disjoining pressure.

By integrating principles from lubrication theory, heat conduction, diffusion, and statistical methods, a detailed model for evaporative mass flux is developed. This model incorporates various interactions, …

Computational Methods For Population Genetic Inference Using Identity By Descent And Local Ancestry, Yuan Wei

Graduate Thesis and Dissertation post-2024

Identity by descent (IBD) and local ancestry are essential for population genetic inference, such as understanding genealogical relationships and demographic history through genomic data. With the availability of large and high-resolution datasets, new opportunities and computational challenges are brought for efficient and accurate methods to infer IBD segments and local ancestry. This dissertation presents three computational methods designed to assist in population genetic inference. First, RaPID-Query is introduced to efficiently query IBD segments for individual haplotypes over a large genotype dataset. This method is based on the positional Burrows-Wheeler transform (PBWT) algorithm and utilizes a random projection approach. RaPID-Query is …

Algorithms And Benchmarking For Parallel Identity-By-Descent Segment Detection, Kecong Tang

Graduate Thesis and Dissertation post-2024

As genomic biobank initiatives continue to grow, the availability of large-scale genotype datasets, encompassing hundreds of thousands to millions of individuals, has transformed genetic research and biomedical discovery. However, the sheer volume of this data presents major computational barriers. Efficient and scalable methods are urgently needed to process and extract meaningful signals from biobank-scale data using modern multi-core architectures. One central task in this domain is the detection of identity-by-descent (IBD) segments, which underpins a range of applications including genealogical inference, disease mapping, phasing, and population structure analysis.

This dissertation addresses these challenges by presenting a sequence of contributions that …

Beam-Clip: Multimodal Alignment For Mmwave Beam Pattern Learning, Andrew P. El Kommos

Graduate Thesis and Dissertation post-2024

This thesis presents a novel approach to beam prediction in wireless communication systems using a multimodal masked CLIP (Contrastive Language-Image Pre-training). We introduce a two-phase training methodology that first aligns representations across multiple sensor modalities—GPS, Radar, LiDAR, and RGB images—through masked contrastive learning, followed by task-specific fine-tuning for channel power reconstruction. Our approach adapts CLIP’s pre-training strategy to the domain of wireless signal modeling, enabling the model to learn rich, transferable features that capture the spatial and contextual dependencies of the beam distribution. Notably, the pre-training stage provides a substantial boost to overall performance, significantly improving the model's ability to …

Stress-Strain-Strength Behavior Of Florida Sands And Silty Sands, Sergio A. Marin Savatier

Graduate Thesis and Dissertation post-2024

Understanding the mechanical response of granular materials is fundamental to geotechnical engineering design. The presence of fines significantly influences the stress-strain-strength behavior, particularly when the material response is controlled by fines content. In this study, Florida sands were initially characterized by their grain distribution and particle morphology, while natural fines were evaluated according to their plasticity characteristics. Two materials were selected for detailed investigation: clean sands and a sand-fines mixture containing 20% fines, representing the threshold between coarse- and fine-dominated behavior. Constant rate of strain (CRS) consolidation tests were performed in both materials to study compressibility and effect of strain …

Improving Vulnerable Road Users' Safety Through Computer Vision-Based Crossing Direction Prediction And Multimodal Large Language Model-Based Accident Scene Description, Younggun Kim

Graduate Thesis and Dissertation post-2024

Vulnerable Road Users (VRUs), such as pedestrians and cyclists, are among the most at-risk participants in traffic, making their safety a key priority for intelligent transportation systems (ITS). Accurate perception and understanding of VRU behavior are essential for proactive accident prevention and the design of human-centric mobility systems. Computer vision has become a core component of ITS, enhancing VRU safety. This thesis introduces a human behavior-aware transformer-based framework for VRU intention prediction and presents VRU-Accident, a large-scale benchmark enabling systematic assessment of multimodal large language models (MLLMs) in understanding accident scenarios involving VRUs.

First, the proposed framework leverages multi-modal cues, …

Robust Actor-Critic Trajectory Optimization And Autonomous Harvesting Robot Integration, Luis R. Tituana

Graduate Thesis and Dissertation post-2024

This dissertation is organized into two parts. First, we develop a neural network–based constrained trajectory optimization algorithm with stability guarantees, along with a neural network–driven numerical error correction. The framework reformulates the structure of an optimal control problem as a neural network optimization problem, where the solution space lies on a subspace manifold generated by a bio-inspired motion rule that produces open-loop control commands. To address changing conditions, real-world disturbances, and numerical errors, we propose an Actor–Critic–like architecture. In this setup, the Actor network outputs the optimal open-loop control for the optimized trajectory, while the Critic network compensates for disturbances …

Towards Label-Efficient Approaches For Dense Video Tasks, Akash Kumar

Graduate Thesis and Dissertation post-2024

Although deep learning has advanced video understanding, its deployment is limited by two challenges: reliance on manual annotations and restricted ability to operate beyond closed-world settings. Spatio-temporal labeling is costly, while real-world applications demand models that interpret novel, open-ended queries. This dissertation develops methods to improve label efficiency and enable flexible, open-world video analysis.

To alleviate the annotation bottleneck, we establish a semi-supervised framework for video action detection using consistency regularization, where a model learns stable predictions across augmented views of the same video. This is challenging, generic augmentations often affect only static regions, providing limited information about dynamic actions. …

An Exploration And Evaluation Of Biologically Informed Variational Autoencoders For Scrna-Seq Data Analysis, Marjorie R. Principato

Graduate Thesis and Dissertation post-2024

Deep learning has been widely applied to the analysis of high-dimensional biological omics data, especially single-cell RNA sequencing (scRNA-seq). Incorporating biological information into the architecture of variational autoencoders (VAEs) has been shown to enhance model interpretability, allowing the model’s behavior to reflect the underlying mechanisms of the biological system used for its architecture. In recent years, several studies have employed these biologically informed VAEs to model large-scale single-cell transcriptomics data, with models correctly differentiating between cell states and identifying active pathways. However, systematic benchmarking and comparison of different biologically informed VAE architectures remain limited.

In this study, I evaluated and …

When Sediment Meets Strategy: Linking Shoaling Dynamics, Vessel Behavior, And Dredging Optimization, Matthew P. Davies

Graduate Thesis and Dissertation post-2024

Navigation channels are the economic arteries of U.S. maritime trade, yet their efficiency and safety are continually threatened by sedimentation and the escalating costs of dredging. Traditional tools used by the U.S. Army Corps of Engineers (USACE) to forecast shoaling and estimate dredging needs often fall short of capturing the complex interplay between channel condition, vessel behavior, and maintenance decisions. This dissertation develops and integrates three complementary innovations to address this challenge. First, a Parametric Linear Regression Methodology (PLRM) is introduced to improve shoaling predictions by blending long-term and short-term sedimentation rates with tunable weighting factors. This method achieves forecasting …

Cross-Layer Optimization In C-V2x Towards Level-5 Vehicular Automation, Mahdi Zaman

Graduate Thesis and Dissertation post-2024

Connected and autonomous vehicles (CAVs) necessitate highly reliable, scalable, and responsive Vehicle-to-Everything (V2X) communication systems. Despite recent advancements, Cellular-V2X (C-V2X) in its state-of-the-art form exhibits limitations in managing resource allocation and communication robustness, particularly in dense vehicular scenarios. Additionally, C-V2X standards have been tailored to particularly support periodic broadcasts; whereas true vehicular autonomy demands service-focused communication which differs in transmission behavior compared to periodic safety messages. This document addresses critical bottlenecks within C-V2X by developing cross-layer optimizations across physical, MAC, and application layers. This thesis proposes enhancements on C-V2X resource scheduling protocol with proposing novel adaptive and selective power control …

Examining Information Flows Within Transformer Models Using Transfer Entropy, Clayton Barham

Graduate Thesis and Dissertation post-2024

The rapid growth of artificial intelligence, in terms of both power and prevalence, motivates the need for new ways to study explainability in deep neural networks. This is especially true for transformer architectures and large language models, which are the driving force behind the current surge in artificial intelligence. The complexity of transformer architecture foils many pre-existing methods for studying explainability in deep neural networks. To address this gap in knowledge, this dissertation proposes a new method of studying explainability in transformer architectures that leverages insights from neuroscience, employing transfer entropy to map information flows between components of the transformer …

Study Of Injector Performance From An Accumulator-Based Plumbing System, Michael R. Benedict

Graduate Thesis and Dissertation post-2024

Accumulators are an alternative solution to pumps as they do not require energy in flight. This means power generated does not have to be diverted to an accumulator, boosting overall efficiency. Additionally, accumulators do not have pressure oscillations or cavitation risks typically associated with pump-driven systems. Further, they are lightweight systems with lower points of failure than traditional pumps. This makes them an interesting candidate for use in hypersonic flight. However, prior to flight data, accumulator-based systems should be tested statically on the ground to determine its feasibility. In this study, a piston accumulator is used in a plumbing system …

Exploring Segmentation, Detection And Tracking In Videos, Jyoti Kini

Graduate Thesis and Dissertation post-2024

Perception is the cornerstone of autonomy, enabling agents to interpret complex environments through segmentation, detection, and tracking. These perception capabilities are fundamental to intelligent systems, whether guiding self-driving vehicles along urban roads, aerial robots mapping diverse terrains, or embodied AI agents interacting naturally with people and objects. Despite sustained research efforts, current perception models remain constrained by fragmented architectures, heavy annotation dependence, and poor generalization to unseen viewpoints, which hinder scalability and reliability. This dissertation advances scene understanding through innovations in segmentation, detection, and tracking, with contributions spanning four key directions that emphasize scalability through end-to-end formulations in 2D and …

A Predictive Model Of Adsorption Of Cryogenic Liquids In Aerogel-Based Materials, Julie E. Foroosh

Graduate Thesis and Dissertation post-2024

Commodities that are gaseous at standard temperature and pressure are generally stored in thick-walled pressure vessels, or as cryogenic liquids in vacuum-jacketed tanks. An advantage of cryogenic storage is that more mass can be stored per unit volume. However, cryogenic tanks are often bulky and cannot be made into conformal geometries. Adsorption storage in porous and flexible materials, such as aerogel blankets, is a promising method for storing these commodities. Previous studies have shown that when the commodity is adsorbed from the liquid cryogen, instead of from a gaseous state, the mass adsorbed per unit volume of aerogel blanket is …

Autonomous Navigation And Real-Time 3d Reconstruction Of Interior Spaces Using A Quadruped Robot, Steve Wilhelm

Graduate Thesis and Dissertation post-2024

This thesis develops and evaluates a ROS2-based application that enables the quadruped robot Unitree Go2 to simultaneously perform autonomous navigation, real-time mapping, and 3D reconstruction in indoor environments using Simultaneous Localization and Mapping (SLAM). The robot system, equipped with onboard sensors, aims to generate high-resolution point clouds that provide detailed 3D scans. The assessment focuses on the platform’s practical feasibility, operational reliability, and accuracy. The hardware setup combines the Unitree Go2 robot with its built-in sensors, a high-resolution 3D LiDAR, and an RGB-D camera. Software components include ROS2, the Unitree SDK, a navigation framework, and SLAM approaches. The application was …