Digital Commons Network^™

Quantum Control Protocols For Robust Quantum Computing, Leeseok Kim

Electrical and Computer Engineering ETDs

The fundamental goal of quantum computing is to precisely control quantum systems to perform meaningful tasks, including implementing high-fidelity quantum gates for reliable quantum computation and accurately simulating complex quantum many- body dynamics. In this dissertation, we develop improved quantum control protocols for three distinct objectives, quantum error suppression, quantum optimal control, and analog quantum algorithms, achieving performance beyond standard approaches. First, we introduce new dynamical decoupling protocols, including both determin- istic and randomized constructions, that can substantially outperform conventional deterministic sequences. We then extend the randomized approach to dynamically corrected gates. Second, we propose a randomized quantum optimal control …

Assessing The Impact Of Multi-Physics Effects On Photovoltaic Module Degradation Using Computational Modeling, James Yuan Hartley

Mechanical Engineering ETDs

This dissertation describes research to develop and apply multi-physics simulation capabilities using finite element methods to analyze photovoltaic module damage mechanisms. Analyses of full-scale solar modules under mechanical load are presented, including experimental validation against measurements of external deflection and internal strain. Module damage by solar cell breakage and interconnection fatigue are discussed, and the applicability of simplifying analyses using mathematical plate theory is assessed. Detailed sub-module component models undergoing thermal-mechanical stressors are also presented, to identify the design features and materials most influential to stress generation and to assess the representativeness of using sub-module assemblies in accelerated testing. Finally, …

Development Of A Lead-Lithium Eutectic Magnetohydrodynamic Loop For Corrosion Studies, Xavier S. Angus

Nuclear Engineering ETDs

Lead–lithium eutectic (LLE) is a leading candidate coolant and tritium breeder for fusion reactor blankets due to its favorable heat transfer properties and high tritium breeding ratio. However, LLE is highly corrosive and, in the strong magnetic fields present in fusion reactors, experiences magnetohydrodynamic (MHD) effects that can produce significant pressure drops and flow instabilities. Understanding corrosion behavior in these extreme environments is essential for assessing the viability of LLE blanket systems. This work presents the design and construction of a forced-convection LLE corrosion loop to study corrosion behavior at temperatures up to 425 °C and flow velocities up to …

Sensitivity-Informed Resonance Parameter Cross Section Adjustments, Matthew Juan Lazaric

Nuclear Engineering ETDs

This work details the process of developing several new features into the MCNP6.3 Monte Carlo code for use in uncertainty quantification and reduction. These new capabilities, which include the CLUTCH method of calculating k-eigenvalue cross section sensitivities and the Windowed Multipole method of calculating cross sections, are implemented and verified against previous implementations and existing cross section data, respectively. These capabilities are then combined to produce sensitivities of k-eigenvalue to resonance parameters, which are verified against direct perturbation sensitivity estimates. The resonance parameters are calibrated using linear Bayesian methods and the accuracy of the resulting cross section is evaluated via …

Statistical And Spectral Theory For Spatially Correlated Random Aperiodic Antenna Arrays, Thomas Edward Christian Jr

Electrical and Computer Engineering ETDs

Aperiodic phased arrays enable beam steering, interference suppression, and spectrum efficiency for 6G, radar, biomedical imaging, and distributed sensing. Minimum inter-element spacing and keep out zones induce spatial correlation, violating the i.i.d. element-position assumption behind classical probabilistic random array theory. This dissertation develops a unified probabilistic spectral framework for correlated (non-i.i.d.) arrays. Second moment power pattern analysis incorporates the pair-correlation function  and structure factor , recovering the i.i.d. limit when  and accommodating unequal excitations. Side lobe and main lobe fields deviate from Rayleigh/Exponential and are modeled by weighted Nakagami and Gamma-mixture distributions, parameterized via Monte Carlo. The blue noise spectral …

Investigating Workforce Development Gaps In Tribal Transportation Agencies, Benson Long Jr

Civil Engineering ETDs

Tribal transportation programs face challenges in recruiting and retaining qualified staff, prompting reliance on external consultants for key responsibilities. This study explores how workforce constraints and organizational structures influence tribal outsourcing decisions. Survey data from 57 tribes informed a logistic regression model examining how geographic isolation, population size, training access, and the presence of a transportation department affect preferences for internal work, actual task completion, and instances where tribes intended to perform tasks in-house but outsourced instead. Findings indicate that outsourcing is driven by limited internal capacity, including staffing shortages, lack of certified personnel, and training opportunities. Geography also plays …

Classification Of Hif Detection In Nev Profile Using Wavelet Transform And Convolution Neural Network, Abdul Hafiz Kassim, Mohd Abdul Talib Mat Yusoh, Aster Smith Valentinie Wilson Nottelmarc, Ahmad Farid Abidin, Sim Sy Yi, Daw Saleh Sasi Mohammed

Turkish Journal of Electrical Engineering and Computer Sciences

High impedance faults (HIFs) present a critical challenge in power systems due to their subtle signal characteristics, which often remain undetected by conventional protection methods. These faults typically do not produce significant phase disturbances, making reliable detection difficult. However, analysis of the neutral-to-earth voltage (NEV) profile under fault conditions provides a promising alternative for fault identification. Existing approaches for detecting and classifying HIFs using NEV signals remain limited and may result in inaccurate maintenance decisions. This paper proposes a fault classification framework for multiple fault types, including HIF, three-phase fault, three-phase fault to ground, double line, double line to ground, …

Complex-Valued Convolutional Neural Network With Time-Frequency Representation For Electrocardiogram-Based Arrhythmia Detection, Kajeeth Kumar Gurusamy, Muthurajkumar Sannasy

Turkish Journal of Electrical Engineering and Computer Sciences

This research proposes an end-to-end procedure for arrhythmia detection based on electrocardiogram (ECG) signals using complex-valued convolutional neural network (CVCNN) incorporated with time-frequency representation. The proposed model leverages complex numbers to capture amplitude and phase information that enhances the ability of the model for detecting time-frequency variation in cardiac signals. First, signal preprocessing techniques---including normalization, wavelet denoising, and R-peak detection---are applied. Subsequently, the model extracts complex features from raw ECG data by employing the Hilbert transform to derive the analytic signal and the short-time Fourier transform (STFT) to generate a time–frequency representation. The proposed CVCNN framework effectively learns spatial-temporal features …

Accurate Diagnosis Of Diseases By A Novel Ai Pipeline Based On Feature Extraction, Feature Ranking, And Feature Selection From Medical Images, Tuğba Nur Bozkurt, Mehmet Emi̇n Yüksel

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of the global population has led to a substantial increase in the number of patients, while the availability of healthcare professionals has not expanded at a comparable rate. This imbalance highlights the urgent need for efficient and reliable computer-aided decision support systems that can reduce clinical workload while maintaining high diagnostic accuracy. In this study, a novel and systematically integrated artificial intelligence-based pipeline is proposed for medical image classification, combining statistical significance-driven feature ranking with evolutionary feature selection in a unified framework. The proposed pipeline consists of four sequential stages: feature extraction, ranking, selection, and classification. Features …

Adapting Independent Large-Scale Pretrained Models For Human Action Recognition, Selen Pehli̇van

Turkish Journal of Electrical Engineering and Computer Sciences

Transferring knowledge from large-scale, independently pretrained image and text models to video understanding requires addressing several challenges, including maintaining generalization capabilities of models, integrating them into multimodal architectures, and fine-tuning with temporal dynamics. This study evaluates the effectiveness of parameter-efficient fine-tuning (PEFT) techniques in transferring pretrained knowledge from two independent models for video action recognition within a simple, streamlined multimodal fusion pipeline. Specifically, we adapt CLIP as the text branch and DINOv2 as the image branch, keeping both backbones frozen to preserve their pretrained robustness, while introducing lightweight, task-specific modules to adapt and fuse the branches with temporal dynamics. A …

Adaptive Backstepping Nonsingular Fast Terminal Sliding Mode Control For Robotic Manipulators Based On Disturbance Observer, Xin Zhang, Xu Wang

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents an adaptive backstepping nonsingular fast terminal sliding mode controller integrated with a nonlinear disturbance observer to achieve precise trajectory tracking of robotic manipulators subject to model uncertainties and unknown time-varying disturbances. A dead-zone–based adaptive gain mechanism is introduced to dynamically adjust the control gain according to the deviation of the sliding surface, thereby enhancing robustness and reducing chattering. The proposed reaching law ensures fast, nonsingular, and adaptive convergence, suppressing high-frequency oscillations without compromising stability and the nonlinear disturbance observer enables real-time estimation and compensation of modeling errors, friction, and external disturbances for superior rejection. The semiglobal uniform …

Chaotic Artificial Bee Colony-Optimized Stacking Ensemble For Robust Multifault Diagnosis Of Wind Turbines, Veilraj Revathi, Solaimalai Jeyadevi, Madasamy Sudalaimani

Turkish Journal of Electrical Engineering and Computer Sciences

The complex electromechanical structure of wind turbines, along with harsh operating conditions, poses significant challenges for precise and robust fault diagnosis. To address this challenge, an ensemble multifault diagnostic framework based on an adaptive chaotic artificial bee colony (C-ABC)-optimized support vector machine (SVM) and gradient boosting machine (GBM) is proposed. In the proposed framework, data redundancy and overfitting are reduced through a two-stage hybrid filter-transformer-based feature reduction approach using ReliefF, followed by Principal Component Analysis. The chaos function of the proposed C-ABC maintains an adaptive balance between the exploration and exploitation phases, thereby preventing premature convergence, which is a common …

Exploring Ai-Driven Scaffolding For Critical Questioning In Argument Evaluation, Ebenezer A. Belete

UNLV Theses, Dissertations, Professional Papers, and Capstones

The fast-paced changes caused by generative AI (GenAI) innovations call for exploring the potential benefits of GenAI in empowering 21st-century pedagogical strategies. Previous studies in the field of argumentation have shown how students can benefit from using critical questions. However, scaffolding argument evaluation through custom GenAI using critical questions has not been systematically investigated. This study involved two components: (1) designing and determining the usability of a GPT-powered conversational assistant (CQMAA Conversational Assistant) and (2) testing its impact on participants' efficacy for argument evaluation and their acceptance of GenAI as a learning tool through a pretest–posttest experiment. A convergent mixed-methods …

Visual Interpretability Of Multimodal Tissue Perfusion Classification Using Grad-Cam And Saliency Maps, Metehan Zorluoglu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate identification of the tissue perfusion phase from hand images can aid doctors in decision-making with non-invasive techniques. The present study proposes a multimodal deep learning model for classifying the tissue perfusion phase using infrared, thermal, and visible spectrum images of the human hand. The proposed model consists of various preprocessing techniques such as manipulation, homography alignments, and masking. The significant contribution of this thesis is the interpretability analysis of deep learning models, achieved through the analysis of saliency maps and the Gradient-weighted Class Activation Mapping (Grad-CAM) methods. The purpose of this method is to find out how the convolutional …

A Descriptive Analysis Of Plant Leaf Disease Detection Using Machine Learning And Deep Learning Models: A Systematic Review, Arzoo Chamoli, Anuj Kumar

Turkish Journal of Electrical Engineering and Computer Sciences

Plant leaf disease detection (PLDD) is a growing active research area with burgeoning practical applications across various sectors such as agricultural monitoring, food security, and environmental conservation. Accurate segmentation and classification of plant leaf diseases remains a key challenge in the field of plant leaf disease prediction. The challenge demands automated methods for the plant disease identification because it needs to develop better crop management systems, which will boost agricultural production. In this article, we provide a systematic review of various machine learning (ML) and deep learning (DL) methods extensively used for PLDD. The review strategy follows a formal protocol, …

Multiobjective Optimization Framework For Renewable Energy Iot Networks Balancing Security, Energy Efficiency, And Communication Reliability, Muhammad Hjouj Btoush, Ashraf S. Mashaleh, Amjad Gawanmeh

Turkish Journal of Electrical Engineering and Computer Sciences

The deployment of Internet of things (IoT) networks powered by renewable energy sources presents unique challenges in balancing security requirements, energy efficiency, and communication reliability. This paper presents a comprehensive multiobjective optimization framework for secure renewable energy IoT nodes that addresses fundamental trade-offs between these competing objectives. We develop a mathematical model incorporating energy harvesting dynamics, security protocols, and communication performance metrics across various environmental scenarios. The proposed framework employs a modified NSGA-II algorithm to identify Pareto-optimal configurations for different deployment contexts. Through extensive simulation analysis, we demonstrate that hybrid energy sources (solar-wind combinations) with lightweight security protocols achieve optimal …

Predictive Current Control Approach For Grid-Integrated Multifunctional Converter Under Source And Load Disturbances, Ravi Kumar Majji, Tirumalasetty Chiranjeevi, Chilukoti Varaha Narasimha Raja, Nagulapati Kiran

Turkish Journal of Electrical Engineering and Computer Sciences

This paper discusses and presents a model predictive control (MPC)-based predictive current control technique for a solar photovoltaic (PV)-integrated grid system during dynamic operation. This control technique employs extension pq (EPQ) theory to estimate reference currents and utilizes an MPC framework for tracking reference currents. Various MATLAB/Simulink simulations were conducted for solar PV generation (source disturbances) and dynamic loading. The results of the OPAL-RT OP4510 real-time simulation are also presented. A multifunctional grid-integrated converter (MFGC) integrates solar active power into the utility grid while achieving unity power factor, reactive power compensation, current balancing, and harmonic suppression. EPQ optimizes mathematical calculations, …

Machine Learning Assisted Development Of Al0.2cufemnni High Entropy Alloy Through Selective Laser Melting, Hareharen K Mr

Theses and Dissertations

High Entropy Alloys (HEAs) are an emerging class of advanced materials that have gained significant attention due to their exceptional mechanical strength, thermal stability, and structural performance. Unlike conventional alloys based on a single principal element, HEAs are composed of multiple elements in a near-equiatomic ratio. Despite these advantages, designing HEAs with tailored properties is difficult because of the enormous number of possible combinations and the limitations of traditional trial-and-error methods. To overcome these challenges, this study presents a machine learning (ML) based approach to accelerate the design and development of an HEA.

In this work, a newly designed composition, …

Feasibility-Aware Deep Reinforcement Learning For Sustainable Timber Procurement Under Hurricane Demand Uncertainty, Jarod Wright

Theses and Dissertations

The timber supply chain connects landowners and mills to provide wood products but faces challenges from stochastic demand, seasonal variations, and disruptions such as hurricanes. Fur- thermore, sustainability concerns like transportation emissions create trade-offs in procurement. This study proposes a feasibility-aware Deep Reinforcement Learning framework for sustainable timber procurement and inventory control under joint demand–hurricane uncertainty. We develop a stochastic mathematical model capturing mill-landowner interactions, seasonal demand, hurricane- driven pricing, and carbon emissions. The problem is formulated as a constrained Markov decision process and solved using Proximal Policy Optimization with a feasibility-enforcing layer. A Mississippi-based case study with 2,100 landowners …

Machine Learning-Based Decision Support Models With Applications In Postsecondary Education, Marco Paolo Anglesio

Theses and Dissertations

This dissertation investigates the deployment of machine learning methodologies in an industrial engineering framework for the development of advanced decision support systems in the context of enrollment management. Drawing on techniques from educational data mining, the research addresses three key phases in the lifecycle of traditional and non-traditional students. First, it analyzes student retention using predictive classification models designed to identify individuals at elevated risk of attrition. Second, it employs temporal convolutional networks for time series forecasting, estimating aggregate enrollment levels over highly variable, finite planning horizons on the basis of partially observed data and using an asymmetric loss function. …

Developing And Evaluating U.S. Army Bridging For Ship Simulation, Jacob C. Hodges

Theses and Dissertations

This research investigates the intricacies of ship simulation and developmental processes for integrating wet gap crossing capabilities into a simulated environment. The US Army Mark III Bridge Erection Boat (BEB) and Improved Ribbon Bridge (IRB) were modeled in commercial off the shelf (COTS) and Government Co-Owned (GCO) navigation software suites to determine their performance capabilities and limitations. Each model was constructed using naval architecture concepts to accurately capture their hydrostatic and hydrodynamic effects before being validated against available performance data and expert elicitation from expert US Army watercraft operators. Models were configured and tested in simulated river flows with current …

Harmonic Response Analysis Of Asymmetric Double-Wishbone Suspension Systems For Amphibious Vehicle Stability, Harper K. Mccraw

Theses and Dissertations

Amphibious vehicles in the surf zone face severe hydrodynamic forcing, yet their internal suspension dynamics remain under-characterized. This research quantifies the non-linear dynamic response of an asymmetric double-wishbone suspension under regular wave impact. Using a physical model in a wave flume and a Qualisys motion-capture system, global rigid-body roll angles were processed through a decoupled kinematic numerical framework to isolate time-domain strut displacements. To evaluate stability, the data was transformed into the frequency domain to calculate Total Harmonic Distortion (THD). Results indicate that all test configurations exhibited significant non-linearity, primarily driven by mechanical clipping as struts reached their physical travel …

The Effects Of Experiential Learning On Engineering Self-Efficacy: A Study Of The Impact Of Inperson And Virtual Internships, Co-Ops, And Research Experiences, Sunny Bharat Patel

Theses and Dissertations

This dissertation examined the relationship between participation in Engineering Learning Experiences (ELEs) and engineering self-efficacy, with particular attention to differences among ELE types (internships, cooperative education, and undergraduate research) and delivery modalities (in-person versus hybrid/virtual). Grounded in social cognitive theory, this study sought to better understand how work-related experiential formats influence students’ confidence in their ability to succeed in engineering tasks and professional contexts. As engineering programs and industry increasingly adopt flexible and technology-mediated environments, examining how these experiences shape beliefs about engineering capability is both timely and necessary. Using a mixed-methods approach, quantitative analyses compared self-efficacy outcomes across students …

A Cradle To Grave Life Cycle Assessment Of Captured Landfill Methane Abatement Strategies, Joshua Caleb Richard

Theses and Dissertations

Landfill methane mitigation strategies are commonly evaluated using percentage-based leakage assumptions and linear scaling models. These approaches may misrepresent system performance at varying throughput levels and real-world operating scenarios. This thesis develops a system-level life cycle assessment framework that incorporates both fixed infrastructure losses and employs sensitivity analysis to more accurately characterize emissions from landfill gas management pathways. Three scenarios are evaluated: methane capture and flare, methane capture and combustion for electricity generation, and methane upgrading with pipeline injection as renewable natural gas. Results demonstrate that emissions models over- dependance on static factors may inadvertently lead to under-reporting and provide …

Vegetated Canopy Heterogeneity Footprints In The Roughness Sublayer, Giulia Salmaso, Raul Bayoan Cal, Marc Calaf

Mechanical and Materials Engineering Faculty Publications and Presentations

Turbulent flows over horizontally homogeneous rough surfaces are categorized as rough‐wall boundary layer flows, while flows over homogeneous vegetated canopies are better described through a mixing‐layer analogy. At present, numerous studies have investigated canopy density as a transition mechanism between rough‐wall and mixing‐layer‐type flows. Yet, most considered canopies have been spatially homogeneous, with few exceptions investigating agricultural arrangements. However, most vegetated canopies are not homogeneously distributed, but instead contain gaps and spatial heterogeneities of different scales. In these cases, it remains unclear which are the dominant flow traits, and how spatial heterogeneity affects them. To help overcome these knowledge gaps, …

A Novel Machine-Learning Based Method For Resolving Secondary Structure Topology In Medium-Resolution Cryo-Em Density Maps, Bahareh Behkamal, Mohammad Parsa Etemadheravi, Ali Mahmoodjanloo, Amin Mansoori, Mahmoud Naghibzadeh, Kamal Al Nasr, Mohammad Reza Saberi

Computer Science Faculty Research

Medium-resolution cryo-electron microscopy (cryo-EM) density maps preserve substantial information about protein secondary-structure organization; however, accurately recovering the topology and connectivity of α-helices and β-strands remains challenging due to noise, structural heterogeneity, and the intrinsic resolution limitations that obscure residue-level detail. Topology determination is a key intermediate step toward building atomic protein models from medium-resolution cryo-EM density maps. It requires identifying the correct correspondence and orientation between secondary-structure elements (SSEs), i.e., α-helices and β-strands, predicted from the amino-acid sequence and those detected in the three dimensional (3D) density map. Despite significant advances in cryo-EM reconstruction and molecular modelling, this correspondence problem …

Agentic Scientific Machine Learning For Autonomous Model Discovery In Systems Pharmacology, Nazanin Ahmadi, George Karniadakis

Biology and Medicine Through Mathematics Conference

No abstract provided.

A Study & Comparison Of Lift Boosters For An Unmanned Aerial Vehicle, Trevor Sean Dady

Theses and Dissertations

Unmanned aerial vehicles are becoming increasingly prevalent in modern society for tasks, such as package delivery, defense, and cinematography. Due to this surge in applications, many researchers have begun to search for improvements to unmanned aerial vehicles for their lift and thrust properties. Potential solutions to magnify an unmanned aerial vehicle’s thrust capability are the implementation of Coanda surfaces, nozzles, or bowl-shaped structures on each propeller. Coanda surfaces provide additional lift force by creating a vacuum in response to the adhesion of fluid to the smooth surface. Nozzles can constrict the flow of air, while also increasing its velocity. The …

Thermal Transient Analysis Of Mahogany Wood Biomass Pyrolysis: The Role Of Zeolite Catalyst Variations On Temperature Distribution, Yusri Yusri, Widya Wijayanti, Mega Nur Sasongko

Journal of Mechanical Engineering Science and Technology (JMEST)

Biomass pyrolysis is a thermochemical process that converts waste into fuel. The use of zeolite catalysts can enhance efficiency by accelerating biomass decomposition. To demonstrate its effect, thermal simulations were conducted using ANSYS Thermal Transient to analyze temperature distribution, reaction rates, and catalyst effectiveness in the pyrolysis process. This study aims to improve thermal efficiency and temperature uniformity by analyzing the effect of 10% and 20% zeolite mixtures on heat distribution and heating rates at input temperatures of 650 K, 750 K, and 850 K, using the 2D axial symmetry method. The simulation was performed under specific boundary conditions: input …

Experimental Measurements Of The Forces Acting On A Submerged Beam Using High-Speed Videography, Madelyn Burrell

Masters Theses

The interaction of structures and fluids is highly relevant in many fields but difficult to accurately and reliably characterize. This study demonstrates a novel method of simplifying such problems by analyzing the structural deformations directly, without needing to explicitly solve for the fluid aspect. Six cantilever beams of varying properties were submerged in still water and released from an initial deflection at the free end: the cases consisted of three beam shapes (one of uniform width, one with a narrower free end, and one with a wider free end), with an end mass attached or detached. High-speed videography was utilized …