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Experimental And Numerical Investigation Of Flow Coefficients In Three-Way, T-Port, Ball Valve, Tyler A. Mindrum

All Graduate Theses and Dissertations, Fall 2023 to Present

Many systems that move fluids, like water distribution networks and heating, ventilation, and air conditioning (HVAC) systems, rely on valves to control flow. This study evaluates a three-way valve, which can direct flow in multiple directions, but has not been extensively researched. Testing included physical data collection and computer simulations to compare how the valves behaved under different valve openings and flow conditions. The results showed that computer simulations can predict the general flow behavior of three-way valves, but that physical testing is still important, especially for valves with complex internal geometries.

A Hybrid Machine Learning-Based Feasibility Prediction Of 3d Mechanical Designs Using Scalar And Geometric Features, Md Mohsin Uddin Fahim

Open Access Theses & Dissertations

The computational bottleneck of structural feasibility screening frequently hinders the transition from a digital 3D model to a physically manufactured component. Traditionally, engineers have relied on either overly rigid heuristic constraint checks or computationally exhaustive finite element simulations. To address this inefficiency, this thesis proposes and validates a hybrid machine-learning framework to predict the structural manufacturability of 3D mechanical designs. Moving beyond the conventional reliance on isolated scalar parameters, the proposed methodology extracts and integrates both scalar manufacturing constraints (e.g., tolerance, minimum feature thickness) and spatial geometric descriptors (e.g., bounding volume, aspect ratio) directly from STL mesh data. Utilizing a …

Ai-Assisted Import Of Static Requirements Documents Into Sysml Models Using A Vibe-Coded Plugin Approach, Joshua Larson

Open Access Theses & Dissertations

Model-based systems engineering (MBSE) promises a single authoritative model in place of static documents, but the translation of requirements documents into that model remains manual, slow, and dependent on scarce modeling expertise. This thesis asks whether an AI-assisted workflow can automate that translation, and whether the tooling itself can be built by vibe coding, in which a practitioner directs an AI in natural language rather than writing code directly. The result is a plugin for Magic Systems of Systems Architect (MSoSA) 2022x that reads a PDF requirements document, extracts requirements through the Anthropic Claude API, and generates a SysML model …

Prevention Vs Detection: A Discrete-Event Simulation Assessment Of Inspection Staffing, Pick-To-Light, And Ai-Based Inspection In Aerospace Wire Harness Kitting, Jorge Mares

Open Access Theses & Dissertations

Aerospace wire harness manufacturing operates under conditions that standard industrial engineering methods were not designed to address. Volumes are low, configurations change between orders, assembly is performed manually by certified labor that cannot be expanded quickly, and IPC/WHMA-A-620 Class 3 acceptance requires that every unit be verified rather than sampled. Kit assembly concentrates the resulting risk, because each item retrieved from the rack is an independent selection decision, and the attributes distinguishing a correct item from an incorrect one are frequently not visible to the operator. Errors introduced there are discovered downstream, after substantial certified labor has been invested. A …

Electrical Characterization Of Wide-Bandgap Devices Via Laboratory Measurements, Tcad Simulation, And Analytical Modeling, Sebastian Alessandro Mimbela

Open Access Theses & Dissertations

Gallium nitride (GaN) devices are important for high-power, high-frequency, and harsh-environment applications because of their wide bandgap, high breakdown capability, and electrical performance. This thesis focuses on the electrical characterization of GaN-based devices using laboratory measurements, analytical modeling, and TCAD simulation. Current-voltage (I-V) and capacitance-voltage (C-V) measurements were performed on a GaN Schottky diode. The measured data were analyzed to extract key parameters such as ideality factor, Schottky barrier height, series resistance, donor concentration, built-in potential, depletion width, and maximum electric field. A TCAD process was also developed in Synopsys Sentaurus to recreate the main geometry, layer structure, contacts, and …

A Hybrid Vsm-Des Approach For Bottleneck Identification And Reduction In A Continous One-Piece Flow Assembly System, Ana Sofia Rey Nevarez

Open Access Theses & Dissertations

Identifying bottlenecks in a production line is rarely as easy as pointing to the slowest station and assuming that's where the problem is. The real question is which stations moves the system when you change them. Lean manufacturing has been one of the initiatives that major companies in the U.S. have been trying to adopt to remain competitive. This thesis aims to understand the results of applying a combination of a Discrete-Event Simulation (DES) with a Value Stream Mapping (VSM), to find the bottleneck of a continuous one-piece flow assembly line of a tier-1 power management manufacturer from the El …

From Screening To Risk: Assessment Of Potentially Toxic Elements In Children's Toys Along The El Paso, Tx, U.S.A.-Ciudad Juarez, Chih, Mex Border Region, Jesus Rodriguez-Loya

Open Access Theses & Dissertations

Children are uniquely vulnerable to potentially toxic elements (PTEs), absorbing them more readily than adults and encountering them through the hand-to-mouth behaviors of early childhood. Exposure carries element-specific consequences: lead (Pb) impairs cognitive development with no established safe threshold; cadmium (Cd) damages the kidneys and reduces bone mineralization; arsenic (As) is a recognized carcinogen and developmental neurotoxicant; hexavalent chromium (Cr) is carcinogenic and induces allergic dermatitis; nickel (Ni) is the leading cause of allergic contact dermatitis in children; and excess zinc (Zn) disrupts copper absorption and causes gastrointestinal distress. Toys are a distinctive source of such exposure, yet none had …

Integrated Framework For Tsn-Enabled Ot Networks And Scalable Edge Computing To Enable Real-Time Feedback Loop, Taposh Kumer Sarker

Open Access Theses & Dissertations

The advent of Industry 5.0 envisions smart manufacturing characterized by human centricity, sustainability, and systemic resilience. Realizing this vision requires the seamless convergence of Information Technology (IT) and Operational Technology (OT) networks. However, integrating massive, stochastic IT edge computing workloads with deterministic physical control loops introduces severe architectural friction, inherently threatening the safety guarantees required by industrial machinery. To resolve this fundamental incompatibility, this dissertation proposes the Edge-Augmented Real-Time Industrial Control System (EA-RICS).

EA-RICS is a comprehensive, multi-layered architecture designed to dismantle systemic bottlenecks across the physical data plane, the centralized control plane, and the edge operating system. First, the …

Laponite Nanosilicate As A Platform For Antigen–Adjuvant Co-Delivery In Vaccination, Haoyu Qi

McKelvey School of Engineering Graduate Student Theses & Dissertations

Nanosilicates are two-dimensional, charged nanomaterials with tunable surface chemistry that enable electrostatic interactions with proteins and nucleic acids. These properties make Laponite nanosilicate a promising platform for vaccine delivery, where coordinated antigen–adjuvant presentation and lymph node delivery are important for shaping immune responses. This thesis investigated nanosilicates as scaffolds for lysozyme-based antigen delivery and co-delivery with CpG.

The preparation method reduced the apparent size of the NS–Lys–CpG complex at pH 10, although neutralization to pH 7 promoted larger aggregate formation. In vivo IVIS imaging after footpad injection showed persistent Cy7-associated fluorescence at the injection site for up to three weeks …

Balancing War And Peace In Taekwondo Athlete Development: A Multi-Criteria Coaching Decision Support Framework Using Leadership Scale For Sports And Fuzzy Ahp, King Harold A. Recto, Hazel Jade L. Antonio, Jhyrald Anthony P. Dalida

Electronics, Computer, and Communications Engineering Faculty Publications

Coaching effectiveness is frequently evaluated through competitive outcomes, with the assumption that coaching behaviors directly influence athlete performance. This study examined the relationship between perceived coaching behaviors and individual win percentage among collegiate Taekwondo athletes using the Leadership Scale for Sports (LSS). Descriptive statistics, correlation analysis, and regression analysis were conducted across five coaching dimensions: Training and Instruction, Democratic Behavior, Autocratic Behavior, Social Support, and Positive Feedback. Results showed that none of the coaching dimensions demonstrated a statistically significant relationship with win percentage. Although coaching behaviors did not significantly predict win percentage, the findings suggest that athlete development is shaped …

Validated Near-Infrared Spectroscopy And Chemometric Modelling For Rapid Quantification Of Essential Oil Yield And Α/Β-Santalol In Santalum Album L., Muhammad Hassnain, Muhammad Rizwan Azhar

Research outputs 2022 to 2026

The sandalwood industry remains constrained by destructive, time-intensive assays for essential oil (EO) yield, composition, moisture content, and wood fraction, which limit real-time decision-making. We report a unified near-infrared spectroscopy-artificial intelligence (NIRS-AI) platform for non-destructive analytics across the Santalum album L. value chain. Reflectance and transmittance spectra from solid matrices (disks, logs, chips, and powders), oils, ethanol extracts, and CID-derived emulsions were acquired using benchtop (400–2500 nm) and portable (900–1700 nm) spectrometers and calibrated against hydrodistillation, gas chromatography, extraction, and moisture assays. Advanced chemometric modelling using AI-based machine learning techniques, including regularised regression, ensemble learning, boosting algorithms, and neural networks, …

Optimizing Few-Shot Learning In Pruned Large Language Models With Task-Specific Prompts, Danyal Aftab

Dissertations

Few-shot learning enables large language models to efficiently perform tasks given only a limited number of labeled examples. However, training these models entirely from scratch requires substantial computational resources, making it challenging for many organizations to fully leverage their potential. This thesis explores how structured pruning, task-specific prompting, and parameter-efficient fine-tuning can be combined to preserve few-shot learning capabilities in compressed LLMs, while also extending their utility to real-world recommendation systems.

In this research, we propose the Tailored LLM framework, which first reduces model size through structured pruning and then enhances few-shot learning performance using carefully designed prompts. We experiment …

Extended Adaptive Oscillator Framework For Gait Phase Estimation With Adaptive Coupling And Transition Detection Across Locomotion Tasks, Nikhila Radha Krishnan

All Theses

Gait phase is a human-inspired physical variable that describes the instantaneous position of a person within a gait cycle. Adaptive oscillators have been widely used for gait phase estimation due to their ability to synchronize with periodic and pseudo-periodic biomechanical signals and generate monotonically increasing cyclic phase values. Typically, the performance of adaptive oscillators is strongly influenced by coupling strength, a parameter that bridges a trade-off between convergence speed and steady-state variability of the estimated stride frequency. In gait assistive applications, stride frequency estimation must be sufficiently fast to enable phase-based assistance while achieving a stable stride frequency estimate. The …

Enhancing Stem Education With Modeling, Simulation, And Ai Technologies: From Virtual Laboratories To Intelligent Teaching Assistants, Yiyang Li

Electrical & Computer Engineering Theses & Dissertations

Rapid advancements in modeling and simulation (M&S) and artificial intelligence (AI) present new opportunities to enhance various aspects of STEM education, from virtual laboratories that simulate physical lab environments in software to intelligent teaching assistants that provide on-demand, curriculum-aligned instructional support. Virtual laboratories offer a potential solution to the access and scalability challenges of laboratory courses by allowing students to conduct experiments without physical equipment or geographical constraints. AI-powered teaching assistants, particularly those grounded in course-specific materials, can help mitigate the instructional support gap that arises when students work independently in digital learning environments. This dissertation presents three-phase research into …

Experimental Investigation Of The Discharge Modes Of Nanosecond Pulsed Plasmas At Atmospheric Pressure, Md Ziaur Rahman

Electrical & Computer Engineering Theses & Dissertations

The generation of repeatable and stable nanosecond pulsed atmospheric pressure plasmas is important to non-thermal plasma applications in various fields including medicine, material processing, food processing, and plasma ignition for combustion. This dissertation investigates the atmospheric pressure plasma initiation and formation under 10 – 200 ns pulsed power for electrode configurations applicable for transient plasma ignition (TPI) for combustion. The impacts of pulsed power parameters, gas condition, and electrode geometry on the discharge initiation and modes (i.e., streamer, transient spark and spark) are systematically evaluated for applying TPI for combustion. We evaluated the discharge modes driven by both longer and …

Physics-Guided Deep Learning For Predictive Modeling Of Spatiotemporal Dynamical Systems, Niharika Deshpande

Engineering Management & Systems Engineering Theses & Dissertations

Many physical and networked systems evolve under continuously changing spatial and temporal conditions. Transportation networks respond to fluctuating demand, atmospheric fields reorganize as storms intensify, and coastal response depends on localized forcing pathways. Modeling such systems requires learning formulations that adapt to evolving structure, operate on irregular geometries, and provide interpretable measures of predictive uncertainty. This dissertation develops a physics-guided spatiotemporal learning framework designed for structured dynamical systems whose governing interactions are neither static nor Euclidean. The central premise is that spatial relationships in these systems are dynamic and geometry-dependent. To represent this behavior, system states are modeled on time-varying …

Computational And Ai Tools For Understanding Telomere-Associated Cancer Mechanisms, Eleni Adam

Computer Science Theses & Dissertations

Telomeres are the protective caps of the human chromosomes and are critical for genome stability. Dysfunctional telomeres caused by their erosion with age and cell proliferation as well as by defects in their maintenance is a major early event leading to genome changes and cancer. Subtelomeres possess the critical role of regulating adjacent telomeres. Due to their complex repeat structure and high variance from one person to another, these areas have not been analyzed in detail. We present a set of computational and machine learning tools to aid in the understanding of subtelomere structure and its rearrangements in cancer.

Initially, …

Macroscopic Classical And Quantum Models Of Inverse Compton Scattering, Emerson Penn Rogers

Physics Theses & Dissertations

Inverse Compton sources — in which a relativistic electron beam scatters a laser pulse to produce tunable, collimated, high-energy radiation—have emerged as among the most promising compact radiation sources, with applications ranging from nuclear photonics to medical and nanoscale imaging and metrology. The most viable current tabletop configuration couples laser-wakefield acceleration with inverse Compton scattering, producing GeV-scale electron beams over millimeter distances. As laser intensities increase and electron energies grow, the interaction enters the radiation reaction regime, where the energy radiated by the electron becomes a significant fraction of its kinetic energy. Predicting the scattered electron energy spectrum — the …

Time-Frequency Analysis Of Arterial Pulse Signals Of Cardiovascular Disease Patients Measured By A Microfluidic-Based Tactile Sensor, Md Mahfuzur Rahman

Mechanical & Aerospace Engineering Theses & Dissertations

This dissertation presents a single-degree-of-freedom (SDOF) based time–frequency analysis framework that can simultaneously extract a comprehensive set of cardiovascular autonomic indices from arterial pulse wave recordings obtained at rest and post-exercise, supporting non-invasive monitoring of autonomic recovery during cardiac rehabilitation. The framework rests on three technical contributions.

First, an SDOF analytical model of motion artefacts quantifies two physically distinct distortion mechanisms: additive baseline drift and Time-Varying System Parameter (TVSP) generated distortion of the Tissue-Contact-Sensor (TCS) stack. Baseline drift is slowly varying and removable by filtering; TVSP distortion rides on all harmonics of the true pulse signal and cannot be removed …

Constitutive Behaviour, Failure Mechanisms, And Life Cycle Assessment Of Internally Cured Fiber-Reinforced High-Strength Concrete, Kastro Kiran V, Dhanya Sathyan, Sanjay Kumar Shukla

Research outputs 2022 to 2026

High-strength concrete (HSC) with low water-to-cement ratios undergoes self-desiccation, resulting in incomplete hydration, capillary porosity, and autogenous shrinkage that can compromise long-term performance. Polyethylene glycol (PEG) functions as an internal curing agent by releasing stored water as internal relative humidity decreases, thereby sustaining hydration within the cement matrix. However, the combined influence of PEG grade, dosage, and fibre type on the constitutive response, fracture behaviour, and environmental performance of M65 HSC remains insufficiently understood. This study investigated twelve concrete mixes incorporating three curing regimes viz. Conventional curing, spray curing, and PEG-based internal curing (IC) using PEG 4000 (1.5%) and PEG …

Reinforcement Learning For Cathode Material Design Through Sequential Decision-Making Frameworks, Taimoor Muzaffar Gondal, Muhammad Qasim, Yasir Arafat

Research outputs 2022 to 2026

The cathode material design is a persistent challenge in the development of next-generation rechargeable batteries. The cathode performance is critically influenced by certain key parameters, i.e., composition, crystal structures, ion transport, and degradation behaviour. Moreover, techno-economic and sustainable considerations also play a pivotal role in the viable cathode material design. In recent years, the integration of static machine learning models with conventional experimental techniques has significantly enhanced the cathode material design. However, the sequential nature of cathode discovery has not been fully captured by these techniques as they do not update their decision strategy based on prior outcomes. In this …

Validation Of A Portable Electrochemical Sensor For Real-Time Soil Nitrate Monitoring In Cotton Rhizosphere, Mohammad Solaiman, Elvis D. Sangmen, Ali Reza Galib, Tushar C. Sarker, Anil C. Somenahally, Shawana Tabassum

Electrical Engineering Faculty Publications and Presentations

Soil nitrate (NO3) exhibits rapid spatial and temporal variability that conventional point-sampling methods cannot effectively capture, limiting timely nutrient management and plant stress detection. This study aimed to validate a low-cost, threedimensional- printed electrochemical NO3 sensor for continuous in-soil monitoring and to evaluate whether sensor-derived NO3 dynamics reflect plant physiological responses. For this purpose, cotton (Gossypium hirsutum L.) plants were grown in pots under greenhouse conditions. The sensor was evaluated in two sequential phases: an initial calibration phase to characterize sensor response across a broad NO3 concentration range, followed by a validation phase in the cotton plant rhizosphere. In the …

Genwriter 2.0: A Hybrid Case-Based And Llm Rewriting Approach For Mitigating Implicit Gender Cues In Text, Shweta Soundararajan, Sarah Jane Delany

Conference papers

Gendered language is the use of words or phrases that indicate an individual's gender. Although useful in some contexts, gendered language can reinforce gender stereotypes and introduce bias, particularly in machine learning models for tasks involving people such as recruitment or occupation classification. When textual content about individuals, such as biographies, contain gender cues, models can learn spurious associations between gender and other characteristics of individuals, such as profession, potentially resulting in unfair outcomes such as reduced hiring opportunities for women.

To address this challenge, we propose GenWriter 2.0, a hybrid approach that integrates Case-Based Reasoning (CBR) with Large …

Storm Surge And Tides In Convergent Estuaries: The Case Of Extratropical Storm Merbok In Alaska, Steven Dykstra, Stefan Talke, David A. Jay, Matthew Lobo, Silvia Innocenti, Pascal Matte

Civil and Environmental Engineering Faculty Publications and Presentations

Coastal floods are composed of multiple drivers that interact with coastline geometries and are difficult to differentiate. To investigate the effects of basin shapes on storm surge and tides, we study the landfall of ex-Typhoon Merbok in the east Bering Sea, Alaska. Water levels at 34 stations are detided with a new tide wavelet tool, CWT_Multi (Lobo et al., 2024), and the spatial and temporal shifts in tides and surge are interpreted via long-wave theory. Results show nonstationary semiannual trends in tidal constituents due to ice effects and large interannual variability in mean-sea level (20-40 cm). After removing non-stationary tidal …

What Drives Blockchain Technology Adoption? A Meta-Analysis Across Tam, Tpb, And Utaut Frameworks, Amir Rahmani, Roohollah Ahmadi, Tugrul Unsal Daim, Mehdi Zamani, Dilek Ozdemir

Engineering and Technology Management Faculty Publications and Presentations

Research on blockchain adoption has expanded rapidly, with most studies conceptualizing adoption as behavioural intention. Technology adoption models, such as the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Unified Theory of Acceptance and Use of Technology (UTAUT), are commonly applied to explore blockchain technology adoption (BTA). However, empirical results from the 12 hypotheses associated with these models remain fragmented and, at times, inconsistent, with limited comprehensive quantitative integration. To address this gap, the present study performs a meta-analysis to evaluate the degree and direction of these hypotheses systematically. In accordance with PRISMA guidelines, 149 quantitative …

City Farm Slo - Market Stand Modernization, Juan Diaz

Construction Management

This paper analyzes the design, planning, and buildout of a custom multi-level stand, farm signage, and supporting equipment for the non-profit organization City Farm SLO. The overall goal of the project was to provide the farm with a modernized and functional market space. The project first addressed any owner design requirements, the approval of materials lists, and the assessment of existing conditions. The pre-construction consisted of developed drawings, materials lists, cost estimates, and a project schedule. Aside from setting up requirements, the owner provided specific milestones to be achieved during the construction phase of the project. A major risk found …

Integrating Ai-Based Electricity Demand Forecasting With Solar Grid Planning To Enhance Sustainability And Reliability, Anas Thamer Mustafa, Omar Sharaf Al-Deen Al-Yozbaky

AUIQ Technical Engineering Science

Proper electricity-demand forecasting is essential for reliable power-system planning, particularly in urban networks facing rapid demand growth and transformer overloading. However, many previous studies have treated load forecasting and renewable-energy integration as separate tasks, which limits their usefulness for practical planning. This study develops an integrated forecasting–planning framework that links AI-based electricity-demand forecasting with photovoltaic (PV) system design and transformer-loading assessment. The framework is applied to real daily data from the Al-Intisar 132/33 kV substation in Mosul, Iraq, covering electrical load, temperature, population, and date-related variables for the period 2022–2024. Fourteen forecasting models from four methodological categories were evaluated: machine-learning …

Radiation Testing And Failure Analysis Of A Complex Heterogeneous Soc, Garrett Smith

Theses and Dissertations

Heterogeneous System on Chips (SoCs) provide a robust platform that can flexibly handle different applications. Their small form factor and low power consumption for their performance have made SoCs an attractive option for space applications. However, SoCs are sensitive to the ionizing radiation found in space. We need to understand how an SoC will behave in the high radiation found in space. This requires us to perform radiation testing of the SoCs on Earth to understand their behavior. The radiation testing and analysis of a heterogeneous SoC is complex as it contains multiple different processing cores, memories, and other system …

Evaluating Machine Learning Models On Classification Of Novel Cyber Attacks In The Healthcare Domain, Promise Ehimen

Dissertations, Theses, and Projects

The increasing adoption of the Internet of Medical Things (IoMT) has improved healthcare delivery through connected medical devices while simultaneously expanding the cybersecurity risks facing healthcare organizations. Although machine learning based intrusion detection systems have demonstrated high detection accuracy, their ability to respond reliably to previously unseen cyberattacks remains uncertain. This study investigated how a Neural Network model and a Logistic Regression model classified novel cyberattacks within the IoMT environment. The Neural Network and Logistic Regression models were both trained and tested using a subset of the CICIoMT2024 benchmark dataset. The Neural Network achieved 99.82% test accuracy and a 0.94 …

Evaluating Generative Ai-Based User Interfaces Using An Integrated Neutrosophic Multi-Criteria Decision-Making Framework, Nada Mohamed, Alshaimaa A. Tantawy

Neutrosophic Systems with Applications

User Interface (UI) design can be seen as an essential aspect of human-computer interaction (HCI) and makes communication easier between people and technology. In today's digital economy, interface quality has become one of the most important business concerns, since it has a direct impact on customer satisfaction and retention while affecting revenue. Although creating user-centered and accessible interfaces is crucial, doing so is a difficult and time-consuming process, which leads to burnout for many usability professionals. Although conventional artificial intelligence (AI) was utilized for design assessment and automation, the arrival of generative AI technology has created new possibilities for automated …