Engineering Commons^™

Reverse (Bio)Engineering: A Machine Learning Approach To Optimize Baseball Pitcher Health And Performance, Robert C. Moore

All Dissertations

Ball tracking systems are becoming ubiquitous in sport, creating an unprecedented opportunity for big data applications to optimize human health and performance. These applications are especially common in baseball, a sport known for analyzing ball flight data to quantify performance. Analysts routinely use ball flight data to identify the attributes of top performing pitchers, finding that the best pitchers throw with optimal combinations of release speed and spin to precise locations. However, for certain pitchers, the throwing motion required to produce optimal ball flight places exceedingly high biomechanical load on the elbow, and consequently injury rates continue to rise. This …

Hybrid Learning For Rough Terrain Navigation Of Actively Articulated Wheeled Vehicles, Dhruv Mehta

All Dissertations

Conventional wheeled ground vehicles have been used for rough terrain navigation in the recent years. They consist of a chassis connected to wheels through passive, semi-active, or active suspension systems. However, their fixed configurations limit mobility and maneuverability, constraining their ability to autonomously navigate diverse and rough terrains. Autonomous Ground Vehicles (AGVs) face significant challenges in this regard, including varying terrain roughness, soil hardness, and obstacle crossing.

To address these limitations, Actively Articulated Wheeled Vehicle (AAWV) architectures have recently emerged, offering real-time geometric adaptability. AAWVs have chassis and wheels connected via articulated serial or parallel linkages. However, increased articulation introduces …

Data-Driven Discovery Of Finite-Dimensional Koopman Operator For Modeling And Control Of Uncrewed Ground Vehicles, Ajinkya Joglekar

All Dissertations

This dissertation advances data-driven modeling and adaptive control techniques for Uncrewed Ground Vehicles (UGVs), with a focus on autonomy in mission-critical and safety sensitive environments. UGVs are deployed across a wide spectrum of domains, from structured manufacturing shop floors to unstructured off-road terrains, including planetary exploration, precision agriculture, and disaster response. These platforms, operating in dull, dirty, and dangerous conditions, demand autonomy that is both adaptable and robust. While traditional model-based control methods offer interpretability and robustness, they struggle with unmodeled dynamics, parameter variations, and integration of high-dimensional sensing. Conversely, modern machine learning approaches can directly exploit sensory data but …

Hydrodynamics Of Perforated Heave Plates, Muhammad Usman

All Dissertations

Ocean waves are a clean and renewable energy source that can help reduce dependence on fossil fuels and combat climate change. To capture this energy, researchers have developed devices known as wave energy converters (WECs). One common type, called a two-body point absorber WEC, uses a buoy floating on the ocean surface connected to a submerged reaction plate, known as the heave plate, which lies underwater. The relative up-and-down motion between the buoy and the heave plate, driven by ocean waves, generates usable power via a power take-off (PTO) system. Traditionally, these submerged heave plates are solid structures. However, this …

Opposed-Piston Two Stroke Engine As A High Power Density Solution In Heavy Duty Applications: An Experimental Evaluation With Diesel And Ethanol, Ankur Bhatt

All Dissertations

With an increasing demand to reduce greenhouse gas (GHG) emissions, the conventional powertrain technology is at a saturation point. There is reinvigorated research interest in alternative powertrain designs that offer potentially higher efficiencies and lower engine-out emissions compared to conventional powertrains. The opposed piston two-stroke (OP2S) engine is one such alternative engine architecture that has demonstrated a reduction in engine-out emissions and increased efficiency compared to conventional four-stroke diesel engines. Like any two-stroke engine, the scavenging process and the composition of the internal residuals are predominantly governed by the pressure differential between the intake and the exhaust ports. Without dedicated …

Unraveling Nucleation Mechanisms In Membrane Distillation: A Molecular Dynamics And Enhanced Sampling Approach, Nazanin Abbasi

Graduate Theses and Dissertations

Membrane distillation offers a promising route for desalination but remains limited by salt scaling, where mineral crystals nucleate and grow in concentrated brines. To inform strategies that mitigate scaling, this thesis investigates homogeneous nucleation in supersaturated aqueous NaCl solutions using molecular dynamics and enhanced sampling techniques. Several collective variables (CVs) are tested to capture the early stages of ion aggregation and crystal formation, including ion–ion coordination number, dehydration, and bond orientational order parameters Q₄ and Q₆. Enhanced sampling techniques, such as metadynamics and umbrella sampling, are employed to overcome the high free energy barriers associated with the formation of critical …

Extending Touch: Investigating Sensorimotor Integration, Cognitive Workload, And Presence With Neuro-Haptic Feedback In Virtual Reality, Aliyah Khadijah Shell

Graduate Theses and Dissertations

Sensory feedback from the hand is fundamental for the coordination of fine motor skills and overall sensorimotor function. Impaired somatosensation during task execution has been associated with the development of inaccurate motor plans, which can hinder motor learning. Virtual Reality (VR) has emerged as a promising tool for motor rehabilitation; however, most VR-based interventions overlook the critical role of tactile feedback in motor control. The absence of touch feedback, which is integral to conventional rehabilitation and training, may limit the full therapeutic potential of VR. Integrating functionally relevant neurostimulation based haptic feedback (neuro-haptic feedback) into VR-based interventions may enhance functional …

Design And Testing Of A Gallium Nitride Power Amplifier For High-Temperature Radar Applications, Walker Landry Harbison

Graduate Theses and Dissertations

This thesis offers the design, fabrication, and evaluation of a gallium nitride (GaN) power amplifier integrated circuit (IC) intended for high-temperature radar applications. Radar systems are used in many different applications, such as defense, aerospace, and weather. For their function, these systems require high power, efficiency, and reliability under a wide range of operating conditions. Taking advantage of the material properties of GaN, including its high breakdown voltage, wide bandgap, and thermal conductivity bolstered by the use of silicon carbide in the substrate, this work focuses on examining amplifier performance in both ambient and elevated temperature conditions. The PA was …

Developing Standardized Testing Datasets For Benchmarking Automated Quality Control Algorithm Performance With Aquatic Sensor Data, Ehsan Kahrizi

All Graduate Theses and Dissertations, Fall 2023 to Present

Advances in water monitoring technologies have led to a large increase in the amount of data collected from rivers, lakes, and other water systems. However, ensuring that these data are accurate and reliable remains a major challenge. Traditional data quality checks are done manually by a technician, which can be slow, inconsistent, and not practical for real-time monitoring. This research addresses these challenges by developing standardized datasets for testing computer-based methods that automatically detect and correct errors in water data. Using information from the Logan River Observatory in northern Utah, we created a step-by-step process to identify, categorize, and label …

Bim-To-Brick: Using Graph Modeling For Iot/Bms And Spatial Semantic Data Interoperability Within Digital Data Models Of Buildings, Filippo Vittori, Fu Chuan Tan, Laura Anna Pisello, Adrian Chong, Cristina Piselli, Clayton Miller

Research Collection College of Integrative Studies

The holistic management of a building requires data from heterogeneous sources such as building management systems (BMS), Internet-of-Things (IoT) sensor networks, and building information models (BIM), all aimed at environmental well-being. Data interoperability is a key component to eliminate silos of information, and using semantic web technologies like the BRICK schema, an effort to standardize semantic descriptions of the physical, logical, and virtual assets in buildings and the relationships between them, is a suitable approach. However, current data integration processes can involve significant manual interventions. This paper presents a methodology to automatically collect, assemble, and integrate information from a building …

Cross-Domain Disaggregation Of Electricity For Heating In All-Electric School Buildings – Learning From School Buildings With District Heating, Synne Krekling Lien, Ada Canaydin, Clayton Miller, Chun Fu, Hussain Kazmi, Jayaprakash Rajasekharan

Research Collection College of Integrative Studies

Electric heating is widespread in Norwegian buildings and significantly contributes to peak loads in the electricity grid. Non-residential buildings are typically heated either by district heating or a combination of electrical heating appliances. Despite its widespread use, most buildings lack sub-meters for electric heating. As a result, the true potential for energy efficiency and load flexibility from heating appliances in buildings remains unknown. Non-intrusive load monitoring and disaggregation techniques offer alternatives to sub-metering by using data-driven methods to extract electricity use for appliances from time-series data. However, little research has been conducted on disaggregating electrical heating loads from low-resolution data, …

Strategic Enhancement Of Biofuel Production Using Bacillus Subtilis As A Model Organism: A Dual Approach Of Gene Essentiality Mapping And Optimization, Angela A. Zebede

Electronic Theses and Dissertations

With rising global demand for renewable energy, enhancing microbial platforms for biofuel production is vital. This study engineered Bacillus subtilis to improve resilience to solvent stress by targeting membrane lipid biosynthesis pathways essential for integrity. Growth curve analysis (OD₆₀₀) showed that phosphatidylserine (PS) and phosphatidylethanolamine (PE) knockout strains grew significantly slower than wild type (168), indicating the importance of these lipids under stress. Laurdan fluorescence spectroscopy was used to assess membrane properties. Generalized polarization (GP) analysis revealed a concentration-dependent reduction in membrane order upon 1%–2% 1-butanol exposure. GP values declined progressively from control to 2% treatment, reflecting increased membrane fluidity …

Towards Trustworthy Federated Learning, Alina Basharat

Theses and Dissertations

Federated learning is a collaborative training model in which multiple clients optimize aglobal model by transmitting updates to a coordinating server while keeping raw data on-device, thereby reducing direct data exposure and enabling iterative global improvement. However, the iterative communication process is vulnerable to malicious attackers that either deliberately destroy the model or curious to infer raw data. Moreover, learning from multiple agents may result in unfair results. To enhance trustworthiness within this setting, we employ two-sided norm-based screening (TNBS) that removes both abnormally large and abnormally small updates, pair it with a q-fair objective to emphasize high-loss (disadvantaged) clients, …

When Textures Trim Drag: Micro-Structures For Adaptive Drag Reduction And Flow Authority, Miguel Angel Olvera

Theses and Dissertations

Turbulent boundary layers are a major source of drag and performance loss. This thesis investigates Triangular Porous Texturing (TPT), apex-forward microstructures with optional bleed channels, as a passive concept for simultaneously reducing skin friction and strengthening boundary layer momentum near separation. Low-speed wind tunnel tests are conducted over tripped turbulent boundary layers on flat plates with three surfaces: a smooth baseline, a solid-crest TPT array isolating the knife-edge “slip-cut” effect, and a porous-bleed TPT array whose paired micro-channels vent a small fraction of the free stream. Time-resolved PIV provides wall-normal velocity fields; skin friction is inferred from near-wall shear, and …

Modeling Flood Risks For Small-Scale Coastal Watershed Across Shared Socioeconomic Pathway Scenarios, Sebastian Loschner

Graduate Theses and Dissertations (2019 - present)

Coastal flooding, driven by the mixture of natural processes and human activities such as precipitation, waves, sea level rise, and urbanization, remains a growing concern. While recent flood-risk studies use IPCC's Shared Socioeconomic Pathways with climate projections to assess future flood exposure, smaller coastal watersheds, < 120,000 acres, remain insufficiently studied. Projected sea level rise is expected to intensify these impacts in smaller systems, potentially causing more severe flooding than reported for larger watersheds. To address this gap, this study used the SRH-2D model integrated with Aquaveo's Surface-water Modeling System to simulate flooding in the lower Fish River basin, Alabama. A coastal watershed sensitive to both riverine and tidal influences. Simulations tested different sea levels based on SSP scenarios corresponding to low, moderate, and high emissions for the years 2050 and 2100. Results indicate that the midcentury scenarios remained even in inundation extent and volume, between 9.9-10.4% compared to the 2020 baseline, the latter scenarios had a wider range in results of between 25.9-50.3% compared to the same baseline. Across scenarios, the most vulnerable and inundated areas of the smaller coastal watershed were the narrow points where upstream flow convene with downstream backwater. These findings highlight the need for futuristic location-specific flood mitigation strategies.

Learning To Accelerate Tightening Of Convex Relaxations Of The Ac Optimal Power Flow Problem, Faith Cengil, Harsha Nagarajan, Russell Bent, Sandra Eksioglu, Burak Eksioglu

Industrial Engineering Faculty Publications and Presentations

We propose a novel machine learning (ML)-based approach to significantly reduce the run times of the optimality-based bound tightening (OBBT) algorithm for strengthening the convex relaxations of the non-convex Alternating Current Optimal Power Flow (AC-OPF) problem. While OBBT can yield near-global solutions via tight convex relaxations, its runtime remains a critical bottleneck on large-scale power grids. Our key contribution is a dynamic policy that selects smaller subsets of voltage magnitude and phase-angle difference variables for sequential bound tightening at every iteration of the OBBT algorithm. This ensures that the bound-tightening process remains adaptive, thereby circumventing the stalling in the optimality …

Wearable Triboelectric Nanogenerators Based Sensors For Human Cardiovascular Monitoring: Progress And Perspectives, Mashrufa Akther, Andrea K. Quezada, Md. Arafat Hossain, Julia I. Salas, Md. Mahmud Alam, Mohammed Jasim Uddin

Mechanical Engineering Faculty Publications

Cardiovascular conditions remain the leading cause of death worldwide, driving a critical need for non-invasive, continuous, and dependable health monitoring results. Triboelectric nanogenerators have emerged as a groundbreaking technology enabling self-powered wearable sensors that convert natural biomechanical energy, such as heartbeat, pulse wave, and body motion, into electrical signals. The paper reviews the recent progress and development in TENG-based wearable sensors for cardiovascular monitoring, with a focus on monitoring particular and clinical healthcare. The working principles, advanced materials, structural designs, and integration with wireless data transmission, machine literacy, and bio-signal processing technologies are explored in this article. Operations, including heart …

Favorability Mapping For Hydrothermal Power Resource Assessments Of The Great Basin, Usa, Stanley P. Mordensky, Erick R. Burns, John Lipor, Jacob Deangelo

Electrical and Computer Engineering Faculty Publications and Presentations

Highlights

• • The new approach to predict hydrothermal resource favorability for the U.S. Great Basin is a synthesis of modern data-driven machine learning improvements from the last several years and predicts 85 % of power-producing systems with operating power plants in the most favorable 10 % of the total area with over half of the power-producing systems (10 of the 19 exposed power-producing systems and 5 of the 9 hidden power-producing systems) in the 99th percentile.
• • The new hydrothermal favorability map predicts both hidden and exposed power-producing hydrothermal systems equally well.
• • The new hydrothermal favorability map preferentially predicts …

Realization Of Deterministic Quantum Circuits For Non-Deterministic Or Incompletely Specified Quantum State Machines, Manjith Kumar, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

In classical logic design, there are machine learning methods based on converting a set of input-output traces to non-deterministic automata that are then converted to deterministic automata and synthesized using logic gates. This approach has not yet been extended to quantum automata. In this paper, we present a method to convert a set of input-output traces to a non-deterministic automaton, which is then converted to an incompletely specified multi-output Boolean function. The existing logic synthesis approaches for designing quantum circuits are insufficient to handle incompletely specified functions. So, we present a novel algorithm to synthesize logic functions with don’t cares …

The Road To Regulation: A Literature Review Of The Influences On The Updated Silica Standards, Skye M. Medcraft

Capstone Experience: Master of Public Health

Silica is a hazardous compound that poses a risk to human health resulting from dust creating activities across various industries. Several agencies, including OSHA (Occupational Health and Safety Administration) and MSHA (Mine Safety and Health Administration) have adopted regulatory oversight of silica exposure in the workplace. As more research has been conducted on silica exposure, the health effects are becoming better described, which has led to the creation and updating of exposure regulations to protect worker health. The objective of this literature study is to review the data presented to the Department of Labor and discuss how the factors that …

Optimizing Pv Solar Array Design By Analysis Of The Industrial And Training Assessment Center (Itac) Database, Angel Samuel Fernandez

Theses and Dissertations

Since 2017, the Industrial Training and Assessment Center (ITAC) at UTRGV has included photovoltaic (PV) system evaluations in its industrial energy assessments. A review of these reports showed inconsistencies in PV system design, including variations in sizing methods, performance assumptions, and application of NEC requirements. To address these issues, this thesis develops a standardized and NEC-compliant methodology for designing PV systems, incorporating demand-based sizing, solar-geometry principles, and electrical calculations guided by NEC Articles 690 and 705.

This thesis focuses on demand-based PV system sizing and incorporates NEC-guided electrical design to ensure technical compliance and safety. System performance is evaluated using …

Performance Assessment Of Reconditioned Freight Rail Bearings Using Eddy Current Array For Enhanced Nondestructive Inspection, Eduardo Miranda

Theses and Dissertations

In North America, freight bearings removed from service must undergo a reconditioning process in accordance with Association of American Railroads recommended guidelines before reuse, with approximately 80% having been reconditioned at least once. This process includes bearing inspection, where repairable defects may be refurbished and returned to service with an extended performance range of 50,000 to 250,000 miles. Motivated by this extensive range in performance, researchers at the University Transportation Center for Railway Safety, in collaboration with MxV Rail, utilized a nondestructive evaluation method to enhance the reconditioning inspection process. An Eddy Current Array (ECA) was applied to bearing cup …

Examining The Relationship Between Title 14 Code Of Federal Regulations (Cfr) Part 147 Maintenance School Aviation Maintenance Technician (Amt) Instructor Experience And Instructor Pass Rates For Airmen Certification Standards (Acs) Element Subject Areas, Richard Lyman Johnson

Theses and Dissertations

This quantitative, correlational study examined the relationship between Aviation Maintenance Technician (AMT) instructor experience and student pass rates in 14 CFR Part 147 maintenance schools. Data were collected from February to August 2025 using purposive and snowball sampling, which yielded 67 complete responses. Instructor experience, measured in months of A&P-rated maintenance work prior to teaching, ranged from 1 to 684 months (M = 253.54, SD = 197.97). Student pass rates, defined as the proportion of students passing ACS element subject area courses, ranged from .52 to 1.00 (M = .95, SD = .07). Most instructors were male, White, over 50 …

Reinforcement Learning Based Intelligent Optimisation For Bin Packing Problems: A Review, Nadia Dahmani, Amril Nazir, Ikbal Taleb, Syed M.Salman Bukhari

All Works

The convergence of Reinforcement Learning (RL) and Bin Packing Problems (BPP) is a critical field of study that has profound ramifications in logistics, manufacturing, computer, and retail industries. This paper thoroughly examines the progression from simple rule-based tactics to advanced Deep Reinforcement Learning (DRL) techniques in solving BPPs. By conducting a thorough review of 231 papers conducted between 2019 and 2024, we address and provide answers to important research inquiries, such as “To what extent has academic research explored the use of RL for BPP during this time frame?” and “Which specific areas of application and methodologies have been predominantly …

Artificial Intelligence In Waste Management Systems: Applications, Challenges, And Prospects, Imane Belyamani

All Works

Despite global recognition of the climate crisis, greenhouse gas emissions are projected to rise by 8.8 % by 2030, primarily due to inadequate planning, poor implementation, and insufficient financial support. While international initiatives such as the ’Waste to Zero’ coalition launched at the 28th Conference of the Parties to the UNFCCC (COP 28) highlight the urgency of advancing decarbonization and the circularity of waste systems, this review focuses on how artificial intelligence (AI) can accelerate that transformation. It systematically explores the role of AI in advancing waste management practices, with a focus on predictive analytics, route optimization, and machine learning-based …

Lstm Network-Based Scheme For Automatic Characterization Of Power Quality Disturbances, Akram Elmitwally, Mohamed Nader

Mansoura Engineering Journal

Recognition of power quality (PQ) troubles is a critical task in the electrical power industry. Most previous works solve the classification problem using separate feature extraction phase and classification phase. Each phase has its own techniques, and consumes a computation time. This study proposes to utilize the long short-term memory (LSTM) network as a deep learning model to classify the PQ events in one shot. The LSTM network uses its particular processing to classify a PQ event signal directly by reading its time-sequence data. Then, a dedicated post-classification algorithm (PCA) extracts start time, end time, duration, amplitude, and total harmonic …

Bio-Inspired Computational Intelligence Metaheuristic-Based Optimization And Sensitivity Analysis Approach To Determine Techno-Economic Feasibility Of Hydrogen Refueling Stations For Fuel Cell Vehicles, Paul C. Okonkwo, Samuel Chukwujindu Nwokolo, Saad S. Alarifi, Stephen E. Ekwok, Rita Orji, Sunday O. Udo, Ahmed M. Eldosouky, El Manaa Barhoumi, Barun Kumar Das, David Gomez-Ortiz, Kamal Abdelrahman, Anthony E. Akpan

Research outputs 2022 to 2026

This study presents a comprehensive economic and technological evaluation of renewable hybrid power systems for hydrogen refueling stations (HRS) in Nizwa, Oman, leveraging cutting-edge optimization algorithms to determine the most cost-effective and efficient hybrid energy system configurations. Three hybrid energy systems of photovoltaic-wind turbine-battery (PV-WT-B), photovoltaic-wind-fuel cell-battery (PV-WT-FC-B), and wind turbine-battery (WT-B) were evaluated based on net present cost (NPC), levelized cost of energy (LCOE), and levelized cost of hydrogen (LCOH). The study employs advanced optimization techniques, including the Mayfly Algorithm, Genetic Algorithm, CUKO Search, Gray Wolf Optimizer (GWO), Constrained Particle Swarm Optimization (CPSO), Harmony Search (HS), and Flower Pollination …

Piezoelectric Energy Harvesting From Roadways: Challenges, Advances, And Future Directions, Heba Mohamed Gaber, Mohamed Abdel-Raheem

Civil Engineering Faculty Publications

As the global demand for renewable energy intensifies, piezoelectric energy harvesting from roadways has emerged as a promising avenue for sustainable power generation. This systematic literature review analyzes 61 peer-reviewed studies to assess the feasibility, performance, and potential of integrating piezoelectric systems into roadway infrastructure. While technology faces challenges, such as high installation costs, limited energy output, and a scarcity of thorough economic evaluations, findings suggest it holds considerable promise as a supplementary renewable energy source. The review analyzes the operational characteristics and efficiencies of various piezoelectric transducers, identifies key factors influencing system performance, and evaluates recent technological advances. It …

The Equity Implications Of Pecuniary Externalities On An Electric Grid, Charles Sims, Gasser G. Ali, J Scott Holladay, Tim Roberson, Chien-Fei Chen, Islam H. El-Haddad

Civil Engineering Faculty Publications

The adoption of rooftop photovoltaic (PV) systems can create upward pressure on retail electricity rates as utilities are forced to spread their fixed costs of generation and transmission across a smaller customer base. Since high-income households are more likely to purchase PV systems, low-income households may be disproportionately impacted by these rate increases. Using a novel combination of agent-based computational economic modeling and a choice experiment of rooftop solar adoption, we show how this pecuniary externality between low- and high-income customers increases low-income electricity bills by 10% in an area with some of the highest poverty rates in the United …

Advancements In Sinkhole Remediation: Field Data-Driven Sinkhole Grout Volume Prediction Model Via Machine Learning-Based Regression Analysis, Bubryur Kim, Yuvaraj Natarajan, K. R. Sri Preethaa, V. Danushkumar, Ryan Shamet, Jiannan Chen, Rui Xie, Timothy Copeland, Boo Hyun Nam, Jinwoo An

Civil Engineering Faculty Publications

Sinkhole formation poses a significant geohazard in karst regions, where unpredictable subsurface erosion often necessitates costly grouting for stabilization. Accurate estimation of grout volume remains a persistent challenge due to spatial variability, site-specific conditions, and the limitations of traditional empirical methods. This study introduces a novel machine learning-based regression model for grout volume prediction that integrates cone penetration test (CPT)-derived Sinkhole Resistance Ratio (SRR) values, spatial correlations between CPT and grouting points (GPs), and field-recorded grout volumes from six sinkhole sites in Florida. Three data transformation methods, the Proximal Allocation Method (PAM), the Equitable Distribution Method (EDM), and the Threshold-based …