Digital Commons Network^™

Development Of A Data-Driven Decision Support System For Intelligent Traffic Management At Intersections, B M Tazbiul Hassan Anik

Graduate Thesis and Dissertation post-2024

Intersections are complex traffic environments where frequent vehicle and pedestrian interactions can lead to safety- and mobility-critical (SMC) conditions. Identifying these conditions promptly is crucial, as delays may result in missed interventions, thereby heightening crash risk, and diminishing operational efficiency. This dissertation introduces a data-driven, real-time decision support framework for the identification and mitigation of SMC conditions using High-Resolution Traffic Controller Data (HTCD) collected via Automated Traffic Signal Performance Measures (ATSPM) system and Connected Vehicle (CV) data from Signal Analytics. First, an end-to-end analytical pipeline was developed to transform HTCD into actionable safety and mobility measures. A Bayesian beta-binomial framework, …

Multi-Trait Convergent Evolution Correlated With Host Ecology Suggests Trait Syndromes In Parasitoid Wasps (Hymenoptera: Braconidae)., Christina L. Watkins

Graduate Thesis and Dissertation post-2024

Convergent evolution, the independent evolution of similar traits in different lineages under shared ecological pressures, provides information on patterns of adaptation and character evolution. In Braconidae (Hymenoptera), a diverse and abundant parasitoid wasp family, the assorted biological and ecological traits of their insect hosts have been frequently hypothesized to drive repeated morphological evolution. This study presented the first family-wide phylogenetically informed test of host-driven convergence in Braconidae, integrating morphological and ecological data across 74 genera representing 32 subfamilies on the most current and robust phylogeny. Analyses revealed multiple instances of correlated evolution between host traits and parasitoid morphology, suggesting trait …

Determining Essential Attributes For Experience And Satisfaction In The Customer 4.0 Era: A Kano Model Approach, Cintia Z. Buffon

Graduate Thesis and Dissertation post-2024

The world is constantly changing, with rapid technological advancements and increased digital connectivity shaping daily activities. In this evolving landscape, customers have transformed into what is known as Customer 4.0—a new generation of consumers who expect high levels of personalization, digital engagement, and greater transparency from organizations. These expectations extend to higher education, where students demand more customized learning experiences, interactive content, and digital accessibility. This research investigates engineering students' preferences regarding educational attributes aligned with Customer 4.0 characteristics using the Kano model. While the Kano model has been widely applied across industries, its use in higher education remains limited. …

Genomic Consequences Of Habitat Fragmentation In The Florida Scrub-Jay: Diversity, Adaptation, And Disease Susceptibility, Maryam Ghoojaei

Graduate Thesis and Dissertation post-2024

Habitat fragmentation is a severe threat to biodiversity by reducing population size, gene flow, and adaptive potential, especially in habitat specialists like the Florida Scrub-Jay (Aphelocoma coerulescens). This dissertation integrates whole-genome sequencing and immunogenetics to assess the genomic consequences of habitat fragmentation across three primary Florida Scrub-Jay strongholds: Archbold Biological Station, Ocala National Forest, and Florida’s East Coast. In Chapter 2, I focused on adaptive genetic diversity at the Major Histocompatibility Complex (MHC) class II genes. I characterized MHC allelic richness, evaluated signatures of selection, and examined associations with pathogen infections. Despite population declines, I found moderate MHC diversity, suggesting …

Towards Safe And Reliable Ai Models, Zihang Zou

Graduate Thesis and Dissertation post-2024

With the rapid advancement of artificial intelligence, its applications have become indispensable in our daily lives. Emerging advanced AI models, from general-purpose Large Language Models (LLMs) such as ChatGPT and Deepseek to domain-specific systems such as autopilot for autonomous vehicles, are changing the way we work and live by increasing efficiency and fostering innovation. However, these advancements also bring significant risks, as they may compromise data ownership or yield unreliable outcomes. In response, we focus on two critical areas: data safety and model reliability. To ensure data safety and promote lawful data processing, we investigate anti-neural watermarking techniques designed to …

Contagion Chronicles: A Spatiotemporal Analysis Of Armed Conflict And Infectious Disease, Devyn Escalanti

Graduate Thesis and Dissertation post-2024

This dissertation examines the relationship between armed conflict and infectious disease transmission through a novel theoretical framework that integrates ecological disturbance and health systems resilience theories into epidemiological contexts. The framework conceptualizes diseases along a continuum from "pulse" to "press." Pulse diseases, like Ebola, are characterized by sudden onset, high mortality, and short-term outbreaks. Press diseases, like HIV, progress gradually with lower mortality and long-term persistence. Grounded in this pulse–press disease typology and ecological and health systems resilience, the theory suggests that the absorptive capacity, adaptive capacity, and resilience of community health workers in conflict areas can influence disease transmission, …

Computation-Efficient And Scalable Robotic Motion Planning Techniques To Address Dynamic Environmental Constraints, Apan Dastider

Graduate Thesis and Dissertation post-2024

In modern robotics, effectively computing optimal robotic control policies under dynamically varying environmental constraints poses substantial challenges and remains a unique research endeavor. To compute efficient and safe robotic motion planning while achieving collective objectives, robotic agents have to satisfy two criteria. First, the working robotic entity must be capable of handling a non-stationary working environment with dynamic obstacles and system constraints. Second, to ensure the real-time response, the robotic agent has to compute an effective control policy that meets a real-time learning performance. Despite significant advancements in motion planning with the appearance of extensive computing resources and advanced deep …

Evaluating Pedestrian Behavior In Response To Innovative Detection Messages And Wait-Time Indicators At Signalized Intersections: A Virtual Reality Study, Nada K. Ahmed

Graduate Thesis and Dissertation post-2024

Pedestrian safety at signalized intersections remains a growing concern in urban environments, necessitating effective communication between pedestrians and traffic control devices. Traditional approaches often prove inadequate in addressing these challenges. This study utilized virtual reality simulation to examine how innovative detection systems and wait-time indicators affect pedestrian behavior under varying environmental conditions. Ninety participants evenly distributed across three age groups experienced 18 crossing scenarios that differed by detection type, wait indication, crossing time, crossing distance, visibility, traffic density, and time of day. Key behavioral measures including start-up lost time, crossing speed, and total crossing duration were analyzed using linear mixed-effects …

Gait Kinematics And Muscular Responses To Rhythmic Perturbations In Healthy Young Adults, Kayley A. Romero

Graduate Thesis and Dissertation post-2024

Falls pose a significant risk across all ages, leading to injury and loss of independence. Understanding gait adaptation to external perturbations provides insights into balance control and muscle activation, with implications for rehabilitation and fall prevention. While entrainment, the synchronization of biological rhythms to external stimuli, has been widely studied using auditory and visual cues, its effects in response to mechanical perturbations remain unclear. This study examines how young, healthy adults adapt to discrete mediolateral perturbations and whether entrainment occurs when mediolateral perturbations are applied at set timings determined by their natural stride period.

Participants walked on a self-paced treadmill …

E-Cskpd: A Frequentist Framework For Multi-Modal High-Order Tensor Regression, Joshua D. White

Graduate Thesis and Dissertation post-2024

Understanding which features drive the outputs of modern learning systems remains a core challenge for regression and classification. While deep neural networks can be highly accurate, their latent representations often obscure how inputs map to decisions, limiting statistical inference, stakeholder trust, and practical use in domain decision-making. Sparse Kronecker Product Decomposition (SKPD) is a frequentist, tensor-structured approach that offers interpretable regression/classification in high dimensions. However, standard SKPD restricts relationships among predictors and covariates and is typically limited to order-2 tensors. This dissertation introduces E-CSKPD, an ensemble framework that (i) revisualizes designs to control non-imaging covariates; (ii) applies invertible, CNN-inspired transforms …

Practical Applications For Biomechanical Modeling Methods, Joseph M. Dranetz

Graduate Thesis and Dissertation post-2024

Models are tools humans use to understand and assess the world around them. The field of biomechanics is one well suited to modeling pursuits. Physiological systems are complex, and modeling may serve as an appropriate method in understanding them. Recently, technologies enabling the collection of biomechanical data have improved greatly and continue to expand. This dissertation serves as an anthology of various attempts to collect and interpret biomechanical data with modeling.

The first study: Electromyographic, ultrasound, and knee torque data was collected from both legs of an individual with a unilateral transtibial amputation. These data were used to generate a …

The Effects Of Camera, Environmental, And Motion Properties On Simultaneous Localization And Mapping, Esteban Segarra Martinez

Graduate Thesis and Dissertation post-2024

Simultaneous localization and mapping (SLAM) is the process of estimating the pose of a robot within an unknown environment. A variant, visual SLAM (V-SLAM), estimates the pose only using visual information from a camera. In recent years, the importance of V-SLAM has increased and expanded to applications such as robotics, virtual reality, augmented reality, and autonomous driving. Accurate estimation of poses is critical as these applications need to quickly perceive an environment and update its pose under complex scenarios. V-SLAM is composed of algorithms that utilize robot vision to distinguish landmarks in the environment in order to estimate the geometric …

Integration Of System Dynamics And Agent-Based Simulation To Emulate Cyberattacks In An Iot-Based Smart Grid, Francisco Valdez

Graduate Thesis and Dissertation post-2024

The electric power grid is a cyber-physical system (CPS) that plays a fundamental role in modern society. With the integration of renewable energy sources and advanced communication technologies, Smart Grids (SGs) can enhance both the profitability and reliability of the electric power system. The communication network that interconnects numerous remotely distributed generators, devices, and controllers plays a vital role in grid control, and current trends favor the widespread adoption of Internet of Things (IoT) devices. However, this network is inherently vulnerable to cyberattacks. This dissertation presents a hybrid methodology to model and analyze the dynamic behavior of an electrical Smart …

Critical State-Based Study On Deformations Due To Construction Activities And Blast Densification, Jorge Enrique Orozco-Herrera

Graduate Thesis and Dissertation post-2024

The behavior of geo-structures and foundation systems in densely populated urban areas relies on the successful performance of the supporting soils when subjected to static (e.g., the weight of the structure or service loads) or dynamic (e.g., earthquake-induced ground motions, construction-induced vibrations, wind loads, or traffic) loading scenarios. Construction-related activities and ground improvement operations can also be responsible to alter the stress-strain-strength behavior of supporting soils by causing vibration-induced ground deformations, excessive vibrations, or mobilizing soil shear strength in relation to in situ or as-built conditions. Construction-related activities and ground improvement operations such as blast densification trigger complex dynamic input …

Fundamental Mechanisms For Flame Acceleration And Detonation Phenomena, Rachel Hytovick

Graduate Thesis and Dissertation post-2024

Hypersonic propulsion via scramjets and detonation engines is enabled through the acute understanding of deflagrations, detonations, and multiphase interactions. Deflagrations are explored to understand the compressible flame regime for scramjet unstart and detonation transition in reacting systems. Compressible flames are experimentally classified with a comprehensive diagnostic suite combining particle image velocimetry (PIV), chemiluminescence, schlieren, and pressure measurements in a turbulent shock tube. Several gaseous fuels are explored to identify the universal mechanism responsible for flame compressibility and acceleration. The ability to compress deflagrations and accelerate the system to a detonation directly relies in the successful conversion of energy available to …

Bim-Based Machine Learning Surrogate Models For Energy And Carbon Prediction To Support Leed Certification Evaluation, Liliane Magnavaca De Paula

Graduate Thesis and Dissertation post-2024

This research developed and validated a Building Information Modeling (BIM)-based Machine Learning (ML) framework for predicting the energy and carbon performance of office buildings during early design stages. The study addresses the need for reproducible data-driven methods supporting sustainable design decisions with reduced simulations time. The proposed approach integrates Revit and Insight with statistical modeling in Weka, creating an automated, transparent, and regionally adaptable workflow for energy and carbon prediction from a BIM-generated data. A reduced-factorial Design of Experiments (DOE) guided the generation of 260 parametric Insight simulations, including base, generalization, and stress-test models distributed across six U.S. climate zones. …

An Integrated Approach To Waste Management And Development Of Data Driven Decision Support Tools, Mert Gokgoz

Graduate Thesis and Dissertation post-2024

Municipal solid waste management requires balancing environmental performance, operational efficiency, and community participation. Proper recycling contamination in single-family units is a persistent barrier to material recovery, reducing the effectiveness of local programs by increasing recycling contamination. This dissertation develops an integrated, data-driven framework that combines behavioral modeling of communities via statistical analysis and machine learning (ML), and life cycle assessment (LCA) to support decision-making in residential waste management. The first component examines household recycling behavior using data from Orange County, Florida’s Recycling Quality Improvement Program (RQIP). Cart-tagging results were integrated with socio-demographic data from the U.S. Census to predict contamination …

Shock Tube Droplet Breakup Experiments Facilitated By Acoustic Levitation, Anthony J. Aguilera

Graduate Thesis and Dissertation post-2024

Droplet introduction methodology in a shock tube differs based on the experimental conditions. For experiments in which the objective is to image droplet breakup, acoustic levitation allows for a consistent droplet shape and for diagnostic equipment to be aligned to a droplet prior to an experiment. A levitator was designed and used in the University of Central Florida’s shock tube facility. Polystyrene doped droplets were levitated to increase the likelihood of cavitation occurring. Multiple droplets were impacted by a shock wave, and the breakup regime was captured using a high-speed camera.

Robust Low-Rank Tensor Discriminant Analysis With Optimal Scoring, Kyle Brinker

Graduate Thesis and Dissertation post-2024

Robust discriminant analysis with optimal scoring has been shown effective for classifying high-dimensional matrix data, particularly in the context of two-dimensional images. The proposed method presents an extension of the Multi-Projection Optimal Scoring Discriminant Analysis framework introduced by Huang \& Zhang (2020), incorporating low-rank Tucker decomposition and regularization techniques to address higher-dimensional cases. The enhanced method is designed to effectively handle tensor-structured data, which is prevalent in imaging applications. The model creates a sparse discriminant projection tensor B that captures class-specific features, such as ball-like patterns in image data and indicators of Alzheimer’s disease in Magnetic Resonance Imaging (MRI) scans. …

Design Tool For Ammonia Cracker Hardware, Andrew Menendez

Graduate Thesis and Dissertation post-2024

Ammonia is a promising carbon-free fuel for commercial aviation and represents a viable pathway toward net-zero emissions. However, direct ammonia combustion is inefficient, creating the need for onboard cracking to generate hydrogen for improved combustion characteristics. One pathway for onboard cracking is to have the hardware live around the combustor liner where heat generated from the combustion process can be used to sustain the reaction. This study aims to develop a design tool for the ammonia cracking reactor hardware. A simplified, single-step surface reaction mechanism was implemented using ANSYS FLUENT and CHEMKIN PRO R1, demonstrating strong agreement with experimental data …

Smart Urban Air Pollution Management Using Crowdsourced Big Data, Low-Cost Sensor Network And Aerial Remote Sensing, Md Hasibul Hasan

Graduate Thesis and Dissertation post-2024

Air pollution remains one of the most pressing environmental and public health challenges, especially in urban areas with dense populations and concentrated emission sources. Effective air quality management requires accurate information on emissions to guide control strategies and reliable exposure estimates to evaluate health impacts. This dissertation examines multiple sources of uncertainty in both emission and exposure assessments. Two major emission sectors were investigated: on-road motor vehicles and landfills. For on-road sources, detailed traffic information inferred from crowdsourced big data were leveraged to improve the estimation of traffic-related emissions. For landfills, the application of low-cost sensor and aerial remote sensing …

Towards Achieving General Video Understanding, Rohit Gupta

Graduate Thesis and Dissertation post-2024

This dissertation investigates the problem of general video understanding, aiming to develop models capable of robust, adaptable, and semantically rich interpretation of video data across diverse domains and tasks. Despite significant progress in video analysis, existing methods remain limited by restricted vocabularies, inadequate fine-grained discrimination, and vulnerability to noise and adversarial perturbations. To address these challenges, the research advances four complementary directions encompassing multimodal fusion, open-vocabulary recognition, robustness enhancement, and unified video–language modeling. First, a multi-modal prototype contrastive framework is proposed for fine-grained video classification, integrating visual and auditory cues through a transformer-based architecture and supervised contrastive learning with class …

Analysis Of An Aero-Engine Using Ammonia As A Fuel Carrier, Kangana Patel

Graduate Thesis and Dissertation post-2024

Numerous studies in the literature have explored the decarbonization of aviation. Some opt for sustainable aviation fuels (SAF), while others propose Hydrogen(H₂). Recent studies have also investigated Ammonia (NH₃) as a potential hydrogen fuel carrier. This study of NH₃ as a viable alternative fuel could help reduce overall emissions and overcome the challenges of H_2, which is highly flammable and difficult to store. This study aims to develop a core-engine model in a system simulation software called Simcenter Amesim. Results show a reasonable similarity between jet-A fuel and NH₃ cracked H₂ within …

Large Language Models And Networks: Edge Proposal, Synthetic Corpora, And Adaptive Node Generation, Nathan Gonzalez

Graduate Thesis and Dissertation post-2024

Natural Language Processing (NLP) and Artificial Intelligence (AI) have evolved from brittle rule-based engines through data-driven statistical models to today’s transformer architectures whose multi-head attention enables rich contextual reasoning. Against this backdrop, this dissertation unifies three complementary investigations that deepen our understanding of how large language models (LLMs) can reason over, generate, and strategically adapt language within networked settings. First, instruction-tuned LLMs are recast as latent-relationship detectors: by prompting models to hypothesize links between text-described entities, we recover edge sets that reconstruct social, thematic, and citation graphs with high precision, revealing how attention distributions encode topological cues. Recognizing the difficulty …

Performance Analysis Of A Turbofan Intergrated With An Entropy-Minimized Heat Exchanger For Ammonia-Powered Aviation, Lucas K. Cavalcante

Graduate Thesis and Dissertation post-2024

Ammonia has emerged as a promising aviation fuel due to its carbon-free emissions, well-established production methods, and transportation infrastructure. However, challenges with its slow chemical kinetics and low flame speed necessitate the partial cracking of Ammonia to introduce hydrogen as a combustion promoter. This study addresses these challenges by designing and optimizing a tube bank heat exchanger to transfer heat from the Cooled Cooling Air (CCA) of the High-Pressure Compressor (HPC) to the Ammonia stream, facilitating the necessary cracking. Preliminary heat transfer calculations indicate insufficient heat in the CCA to achieve cracking temperatures, prompting an alternative approach: splitting the Ammonia …

Learning-Based Ethereum Phishing Detection: Evaluation, Robustness, And Improvement, Ahod Alghuried

Graduate Thesis and Dissertation post-2024

Phishing attacks continue to pose a significant threat to the Ethereum ecosystem, accounting for a major share of Ethereum-related cybercrimes. To enhance the detection of such fraudulent transactions, this dissertation develops a comprehensive framework for machine learning-based phishing detection in Ethereum transactions. The framework addresses critical aspects such as feature selection, class imbalance, model robustness, and the vulnerability of detection models to adversarial attacks. By systematically evaluating these key practices, this work contributes to the development of more effective detection methods. The first part of the dissertation assesses the current state of phishing detection methods, identifying gaps in feature selection, …

High-Efficiency Dual Active Bridge Converter With Stacked Secondary Phase For Next-Generation Ai Data Center Power Delivery, Anirudh Ashok Pise

Graduate Thesis and Dissertation post-2024

The rapid electrification of hyperscale AI data centers is accelerating a shift from legacy 48 V distribution ladders to ±400 V high-voltage DC backbones, concentrating residual loss and dynamic stress on a single isolated bidirectional interface. This work presents a high-density Dual Active Bridge with Stacked Secondary Phasing (DAB-SSP) architecture as a scalable GaN-based solution for 48 V–800 V rack-level power conversion. By stacking 400 V secondaries, the topology leverages 650 V GaN devices while satisfying 800 V system requirements, reducing clearance and creepage distances, minimizing magnetic path lengths, and embedding leakage inductance necessary for wide-range zero-voltage switching.

A rigorous …

Advanced Large Language Model Approaches And Natural Language Processing Techniques To Improve Hate Speech Detection Using Ai, Saad Almohaimeed

Graduate Thesis and Dissertation post-2024

The proliferation of hate speech on social networks can create a significant negative social effect, making the development of AI-based classifiers that can identify and characterize different types of hateful speech in messages highly important for stakeholders. While this is a highly challenging problem, recent advances in language models promise to advance the state of the art such that even subtle and indirect forms of hate speech can be detected.

In this dissertation we present a series of contributions that improve different aspects of hate speech classification. We developed THOS, a hate speech dataset consisting of 8.3k tweets. Compared to …

Characterization Of Turbulent Flames In Confined Combustors, Max Fortin

Graduate Thesis and Dissertation post-2024

As industry transitions to a net-zero carbon future, turbulent premixed combustion will remain an integral process for power generating gas turbines and are also desired for aviation engines due to their ability to minimize pollutant emissions. However, accurately predicting the behavior of a turbulent reacting flow field remains a challenge. To better understand the dynamics of premixed reacting flows, this study experimentally investigates the evolution of turbulence in a high-speed bluff-body combustor. The combustor operates across a range of equivalence ratios from 0.7-1 to quantify the role of heat release and flame scales on the evolution of turbulence as the …

Towards An Intelligent Speculative Software-Defined Networking, Ahmad Hariri

Graduate Thesis and Dissertation post-2024

Software-Defined Networking (SDN) separates the control and data planes, enabling better programmability of the control plane to predict, route, and schedule traffic at the data plane. Reactive SDN dynamically installs flow rules when a new flow arrives, making it adaptable to application dynamics. This design is well-suited for emerging low-latency applications like online gaming and AR/VR, which require millisecond-level response times for an acceptable quality of experience. However, Reactive SDN faces limitations, as each new flow triggers a miss, sending a Packet-in message from the switch to the SDN controller, increasing latency. To reduce delay, it's crucial to predict flow …