Digital Commons Network^™

Analysis Of Dynamic And Final Surface Subsidence Movements Over Subcritical Panels In The Eastern Us, Mauricio Parra Valencia Sr

Theses and Dissertations--Mining Engineering

Subsidence prediction is a critical field in mining engineering, focusing on anticipating ground movements caused by underground coal extraction. Accurately forecasting these movements is essential for protecting surface structures and infrastructure from damage. However, despite extensive research by mining professionals worldwide, reliable prediction remains challenging, especially in complex environments like subcritical mining areas and locations characterized by highly variable topography. These conditions introduce non-linear rock mechanics that often complicate the application of traditional predictive models. This thesis utilizes measured subsidence data related to the extraction of two adjacent longwall panels in a deep coal mine in the eastern US to …

Analysis Of Ptfe Radial Lip Seals For Wheel Bearing Applications, Madison Vogt

Theses and Dissertations--Mechanical and Aerospace Engineering

A numerical analysis was conducted to investigate the viscoelastic effects of nonlinear materials on the sealing zone and contact pressure distribution of radial lip seals in a conventional geometric topology. The use of nonlinear materials in radial lip seal applications is desirable to improve operation and reliability of rotating machinery due to their high operating temperatures, chemical compatibility, and low friction coefficients. Examples include, but are not limited to, seals within industrial air compressors, turbine engines, aircraft landing systems, chemical processing equipment, and Freon pumps. This work focuses on analyzing the nonlinear viscoelastic response of pure Polytetrafluoroethylene (PTFE) and PTFE …

Analysis Of Closed-Loop Dynamical Systems Using Relu Nns, Pouya Samanipour

Theses and Dissertations--Mechanical and Aerospace Engineering

This thesis develops a framework for analyzing the stability and invariance properties of closed-loop dynamical systems whose dynamics are derived from neural networks with rectified linear units (ReLUs). The ReLU neural network defines continuous piecewise affine (PWA) functions, which makes them ideal for analysis using polyhedral geometry and optimization techniques. To begin, we introduce two exact algorithms for enumerating polytopic regions of affine behavior induced by ReLU networks: a half-space-based method that checks the feasibility of activation patterns and a geometric vertex-based method. Using these methods, we are able to perform precise region-wise analysis, and these methods serve as the …

Machining-Induced Residual Stress Modeling Using Physics-Informed Neural Networks, Md Mehedi Hasan

Theses and Dissertations--Mechanical and Aerospace Engineering

Process-induced residual stress (RS) plays a critical role in determining the structural integrity, fatigue life, corrosion resistance, and dimensional stability of manufactured components. Tensile residual stress can be harmful to the machined workpiece by lowering fatigue strength, while compressive residual stress helps to prevent such issues. Therefore, accurate prediction and control of machining-induced residual stresses are essential for optimizing manufacturing processes and enhancing component performance. However, existing modeling approaches—empirical, analytical, and numerical—often have limited predictive accuracy, demand extensive calibration, or involve high computational costs. Traditional finite element analysis (FEA) provides modeling insights but is constrained by its high computational cost …

Development Of An Immersed Boundary Method-Wall Modeled Large Eddy Simulation Approach For Incompressible Flows And Aeroacoustic Predictions, John C. Higgins

Theses and Dissertations--Mechanical and Aerospace Engineering

The performance capabilities of rotorcraft have improved significantly in the past decade as both manned and unmanned rotorcraft become a larger part of future transportation systems. Private companies as well as various government offices have worked to develop capabilities ranging from transportation/package delivery to intelligence/surveillance to livestock monitoring. Their practicality in both rural and urban areas, however, is questionable due to the significant tonal and broadband noise which is produced by the rotors. Public acceptance of rotorcraft, namely small Unmanned Aerial Vehicles (sUAVs), in transportation systems is an issue largely associated with the noise generation and it's effect on nearby …

Direct Numerical Simulation Of Pulsating Flow In Curved Pipes: Insights Into Aortic Dissection In Humans, Gokul Anugrah Gopakumar

Theses and Dissertations--Mechanical and Aerospace Engineering

This dissertation investigates the hemodynamics of pulsating blood flow in curved vessels through direct numerical simulations (DNS), with the overarching aim of uncovering fluid dynamic mechanisms linked to the onset and progression of aortic dissection in humans. The work is structured in two parts: (i) fundamental studies in idealized geometries and (ii) preliminary investigations in patient-specific anatomies. In the first part, curved pipe models representing the aortic arch are used to isolate the effects of pulsation frequency, amplitude ratio, and curvature ratio on transitional and turbulent flow dynamics. The simulations reveal that curvature-driven centrifugal forces shift the peak velocity toward …

High-Altitude Balloon-Launched Uncrewed Aircraft System Measurements Of Atmospheric Turbulence And Qualitative Comparison With Infrasound Microphone Response, Anisa Haghighi

Theses and Dissertations--Mechanical and Aerospace Engineering

This study explores the use of a balloon-launched uncrewed aircraft system (UAS) to measure atmospheric turbulence in the troposphere and lower stratosphere using both wind velocity measurements and infrasonic acoustic energy. The UAS, a glider configured for autonomous descent along a predefined trajectory, had on board, in situ sensors to capture thermodynamic and kinematic atmospheric parameters. Additionally, it carried an infrasonic microphone to evaluate its potential for remotely detecting clear-air turbulence by capturing infrasonic waves. The system’s performance was assessed over the course of three test flights conducted in New Mexico, USA, in 2021. The descent enabled high-resolution profiling, with …

Simulation-To-Hardware Validation Of Mpc For Binary Thruster 2d Cubesat Control, Aevar Amundinusarson Oefjoerd

Theses and Dissertations--Mechanical and Aerospace Engineering

Small satellites must execute precise, fuel-limited maneuvers under strict actuation and operational constraints. Model Predictive Control (MPC) is a suitable approach because it optimizes future actions while explicitly enforcing these limits. This work evaluates (1) whether MPC can operate in real time using binary (on/off), asymmetric thrusters, and (2) whether a high-fidelity digital twin can be used to tune and validate the controller prior to hardware testing.

An MPC framework was implemented on a planar satellite prototype that floats on air bearings, operates at 16.7 Hz (60 ms period), and uses eight binary thrusters exhibiting up to 13.2% variation in …

Enhancing Public Health Surveillance: Development And Validation Of Machine Learning Models For Suspected Opioid Overdose Detection In Emergency Medical Services Data, Peter J. Rock

Theses and Dissertations--Clinical and Translational Science

The ongoing opioid overdose crisis in the United States requires timely and accurate surveillance systems to inform public health responses. Traditional public health surveillance methods rely on hospital discharge data and death certificates, which suffer from significant reporting delays and miss cases where patients refuse hospital transportation. Emergency Medical Services (EMS) data presents a promising alternative with advantages in timeliness and case ascertainment but lacks validated definitions for suspected opioid overdose (SOO).

This dissertation addresses this critical gap through the development, validation, and fairness assessment of machine learning models with natural language processing (ML-NLP) for identifying SOOs in EMS data. …

The Relationship Between Perceived Racial Stress And Academic Achievement Of African American Students, Jodie E. Felder

PCOM Psychology Dissertations

Educational institutions are not only places of learning, but they can be places of refuge and support for students. Academic engagement and performance can be negatively affected for ethnic students of color. This can lead to a disconnection between the education system of teachers, administrators, and students. Studies suggest racial stress contributes to negative environmental factors, to which African American (Black) students have been found to be especially vulnerable. Overlooked, ignored, and misunderstood environmental factors for African American students exacerbate educational obstacles, widening achievement gaps and increasing attrition rates. A lack of culturally guided knowledge among stakeholders lessens the opportunity …

Personal Jurisdiction And Web3: How We Don’T Need Zipp2.0 For Blockchain Technologies, Alexander Walton

Student Works

No abstract provided.

The National Basketball Association’S Collective Bargaining Agreements New Guardrails: Apron Rules, Mid-Level Exceptions, And Superstar Trades Within A Restructured Cap System, Justin Orsini

Student Works

No abstract provided.

“To See Or Not To See: The Supreme Court’S Constant Struggle With The Constitutionality Of Race-Conscious Affirmation Action Policies Post-Brown.”, Emme Anderson

Student Works

No abstract provided.

Extremely Wicked, Shockingly Evil, And Vile Minds: Insanity And Competency In Courts, Sarah Elsakhawy

Student Works

No abstract provided.

Chemical Compositions Of Initial And Residual Silicate Source Glasses From Degassing Experiments: Implications For Mars, Natalie Zimmermann

Geosciences Research Data

These files contain supporting data for the JGR manuscript Trace Element Transport and Deposition during Magmatic Degassing: The Effect on Martian Rocks and Fines. They report the chemical compositions (major oxides and volatiles) of initial and residual silicate source glasses from degassing and vapor-transport experiments. Composition (wt%) of major oxides calculated from EMPA (renormalized on a volatile-free basis); Cl and SO2 from EMPA (wt% based on total of oxides+Cl+SO2); wt% H2O calculated from micro-FTIR. Files include the raw EMPA and FTIR data, processing, and calculations used to obtain the chemical compositions presented in Table 5 of the manuscript.

Sem Images And Eds Spectra Of Vapor Deposited Phases, Natalie Zimmermann

Geosciences Research Data

This file contains supplemental data for the JGR manuscript Trace Element Transport and Deposition during Magmatic Degassing: The Effect on Martian Rocks and Fines (Zimmermann et al., in review). It includes SEM images and associated EDS spectra of vapor deposited phases produced in experiments.

Integrated Compliant Structure For A Hand Exoskeleton, Tristan R. Koopman

Honors Undergraduate Theses

This thesis presents the design and prototyping of a wearable hand exoskeleton that integrates a flexible structural framework to assist with hand movement while maintaining comfort and anatomical conformity. The goal was to create a device that supports tendon-driven actuation through a compliant structure, combining elements of rigidity and flexibility to match the natural geometry and motion of the human hand. Traditional hand exoskeletons often trade off motion for structure or vice versa. This project aims to bridge that gap with a hybrid compliant design that balances flexibility and support. The design process followed an iterative approach involving rapid prototyping …

High-Performance Computational Kernels For Algorithm-System Co-Design In Machine Learning: Enabling Efficient Gcn Training And Llm Inference Serving., Shakya D. Jayakody Arachchige

Graduate Thesis and Dissertation post-2024

The rapid advancement of machine learning, from Graph Convolutional Networks (GCNs) to transformer-based Large Language Models (LLMs), continues to expose fundamental limitations in memory efficiency, scalability, and system reliability. GCNs are critical for domains such as biomedical modeling, social networks, and recommendation systems, yet their reliance on sparse general matrix–matrix multiplication (SpGEMM) makes them highly sensitive to GPU memory constraints and irregular access patterns. As graph data scales, out-of-core computation becomes inevitable, but existing systems are hindered by I/O bottlenecks and underutilized GPU resources. Our contribution with AIRES demonstrates how algorithm–system co-design can alleviate these challenges: by introducing block-wise data …

Development Of Generalizable Data-Driven Traffic State Prediction Solutions: A Comprehensive Approach, Md Mobasshir Rashid

Graduate Thesis and Dissertation post-2024

Increased urbanization and population growth lead to more traffic in the transportation system, causing severe congestion. Particularly during disruptive events such as hurricanes or traffic incidents, the traffic flow in the network worsens significantly. As a result, there is a need for data-driven traffic prediction models to reduce congestion in real-time, and the prediction model should be generalizable to be applicable across different dynamic scenarios. Additionally, a robust and adaptable traffic prediction model can help traffic managers to take proactive actions to optimize traffic flow. However, the majority of data-driven traffic prediction models suffer from limited applicability as they are …

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 …

On The Dynamic Connectivity And Phase Transition In River Networks, Juthika Roy

Graduate Thesis and Dissertation post-2024

River network (RN) structure has a significant impact on flux transport, aggregation and ecosystem connectivity. In this dissertation, the structural control of RN on flux transport was investigated by the application of integrated framework of dynamic clusters and network graph theory. This new framework has been proposed to determine sub-catchment connectivity using minimum and maximum flow criteria. Our analysis on synthetic and natural RNs across United States demonstrates that basin scale connectivity at low flow conditions is efficiently governed by the abundance of side branching junctions whereas this characteristic slows down the flux convergence rate at maximum flow condition.

Furthermore, …

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 …

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 …

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. …

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. …

Optimizing Emissions And Sustainability Of Alternative Fuel Buses Using Machine Learning And Life-Cycle Modeling, Md Rezwan Hossain

Graduate Thesis and Dissertation post-2024

Public transportation is undergoing a critical transition as agencies seek to balance environmental responsibility, operational efficiency, and economic viability. Alternative fuel buses (AFBs), including electric, compressed natural gas (CNG), propane, and biodiesel, offer opportunities to reduce emissions and advance sustainability, yet their adoption raises complex trade-offs. High upfront costs, infrastructure constraints, range limitations, and variable real-world performance complicate decisions for fleet managers and policymakers. This dissertation addresses these challenges by integrating real-world operational data, life-cycle modeling, and machine learning within a multi-objective optimization framework to evaluate and optimize AFB deployment. Using detailed telematics and GPS datasets from Central Florida's transit …