Digital Commons Network^™

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 …

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, …

Brain-Based Mechanisms Of Behavioral Impairment In Fetal Alcohol Spectrum Disorder (Fasd): The Neuroimaging Biomarkers Of Inhibitory Control, Zinia Pervin

Biomedical Engineering ETDs

The developing brain is highly susceptible to alcohol-induced toxicity, often resulting in long-term deficits in executive function and behavioral regulation. Inhibitory control impairments are among the most prominent deficits observed in Fetal Alcohol Spectrum Disorder (FASD). This study investigated the neural mechanisms of inhibitory dysfunction using a multimodal MEG–DTI approach in 67 children aged 6–8 years (34 with FASD, 33 controls) who performed a Go/No-Go task. Source-level MEG analyses revealed reduced stimulus-locked cortical activation in the anterior cingulate cortex and significant group-by-hemisphere interactions in the superior parietal cortex and cuneus. Time–frequency analyses showed diminished response-locked beta power in the sensory-motor …

Technical Advancements In Single-Molecule Spectroscopy For High-Throughput Measurement Of Long-Time Dynamics, Quyen B. Le

Chemical and Biological Engineering ETDs

Single-molecule fluorescence spectroscopy is a powerful technique for resolving transient biomolecular dynamics and quantifying free energy landscapes, kinetics, and binding interactions. However, two fundamental limitations restrict its broader application: slow, labor-intensive data acquisition and the limited observation time imposed by fluorophore photobleaching. These limitations hinder its use in drug discovery and biomedical engineering applications that require both high-throughput and access to long-time dynamics. In this work, both challenges are addressed through technical advancements. First, a simple, generalizable approach is introduced to automate data acquisition, eliminating manual intervention during experiments. This increases the acquisition rate by more than an order of …

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 …

Design And Analysis Of An Intermediate Heat Exchanger And Passive Decay Heat Removal System For A Generic Fluoride Salt-Cooled High-Temperature Reactor Application, Bao Hoang Nguyen

Nuclear Engineering ETDs

Generation IV reactors, such as the Fluoride salt-cooled High-temperature Reactor (FHR), feature efficient heat transfer and passive heat removal systems. However, accurately predicting thermal behavior under steady-state and transient conditions remains challenging. This work designs and evaluates a twisted tube heat exchanger (TTHX) serving as an intermediate heat exchanger (IHX) and a reactor cavity cooling system (RCCS) serving as a passive decay heat removal system for a generic FHR (gFHR), integrating both systems into a system-level MELCOR model. The TTHX was first designed and assessed using a MATLAB-based framework, followed by detailed MELCOR simulations. The results demonstrate effective heat transfer …

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 …

Representativeness And Sensitivity Analysis Of Accident Tolerant Fuel Cladding Behavior In Reactivity-Initiated Accidents, Melissa Andrea Moreno

Nuclear Engineering ETDs

Accident Tolerant Fuels (ATFs) such as FeCrAl are being developed to enhance safety margins in light water reactors during transients like Reactivity Initiated Accidents (RIAs). This dissertation evaluates the accuracy and representativeness of TRACE thermal hydraulic models for predicting FeCrAl C36M cladding behavior under steady state and transient flow boiling conditions. Benchmarking against University of New Mexico separate effects experiments shows that TRACE reproduces key thermal trends but underpredicts cladding temperatures near CHF by approximately 25 to 30%. A calibrated uncertainty and global sensitivity analysis, using Sobol indices, demonstrates that CHF and cladding temperature variability are dominated by mass flux, …

Nuclear Deterrence: Enhancing The Mission Through Smart Factory Predictive Solutions, Jarrod Matthew Ronquillo

Chemical and Biological Engineering ETDs

To meet the stewardship and modernization initiatives set by the Department of Energy new technologies must enter the manufacturing facilities within the nuclear deterrent complex. Predictive solutions begin with collecting data. An equipment health monitoring device was created to streamline data collection and organization for equipment and processes. A predictive and process performance dashboard was developed for a deionized water system. The dashboard used Western Electric statistical process rules and a machine learning regression algorithm to predict when the resistivity would fall out of specification. Lastly, a remaining useful life calculation was developed for all equipment related to nuclear deterrent …

Kinetic Study Of Oil–Water Separation In A Dual Port Inlet Cyclone Separator, Ikhwanul Qiram, Agung Nugroho

Journal of Mechanical Engineering Science and Technology (JMEST)

In this study, a Computational Fluid Dynamics method is used to investigate the oil–water separation kinetics in a dual-port inlet cyclone separator. This was achieved using the Reynolds Stress Model coupled to an Eulerian multiphase framework. Three Reynolds numbers were studied (Re = 1.41×10⁵, 1.94×10⁵ and 2.52×10⁵) to analyse the flow; axial velocity distribution, vortex stability, radial migration velocity and separation efficiency were examined individually. Results indicate that both the radial migration velocity (vᵣ) and separation probability (p_remove) grow with Reynolds number, especially for larger oil droplets (10–100 µm). The best condition concerned is that …

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 …

Novel Algorithmic Methods For Random Telegraph Noise Detection And Characterization In Electronic Devices, Victor Darie Pepel

Electrical and Computer Engineering ETDs

Random telegraph noise (RTN) produces discrete stochastic fluctuations in nanoscale semiconductor devices and increasingly limits performance and reliability as dimensions scale. This dissertation introduces three algorithmic contributions enabling automated and accurate RTN characterization across diverse devices and operating conditions. First, a computationally efficient histogram-based detection algorithm enables rapid identification of RTN in large focal plane array datasets for statistically robust defect analysis. Second, a frequency decomposition framework separates slow and fast RTN components, extending the range of extractable time constants and reducing estimation error in multi-trap signals obscured by background noise. Third, to address the lack of standardized RTN metrics, …

Applications And Comparisons Of Machine Learning Methods In Ultra-Fast Laser Control, Aasma Aslam

Electrical and Computer Engineering ETDs

This dissertation demonstrates the applications and comparative analyses of machine learning methods in ultrafast laser control. By learning the relationship between the system’s input parameters and output pulse characteristics, the performance of a laser can be significantly improved. In this work, the results are presented in two stages by utilizing data from the femtosecond laser system. The first stage concerns two neural networks, named NN1 (fitrnet) and NN2 (feedforwardnet). The second stage, which extended with five different models, namely the linear regression (fitlm), the support vector machine (SVM), the Gaussian process regression (GPR), the boosted tree (fitrensemble), and LASSO (fitrlinear), …

Millimeter-Wave Antenna Gain Enhancement Through Stacked Planar Substrates, Zachary Bergstedt

Electrical and Computer Engineering ETDs

This work presents a new wideband millimeter-wave (mmWave) and sub-terahertz antenna design with flexible directivity through the integration of stepped horn antennas and transverse substrate integrated waveguide (SIW) slots for radar and communication applications. The work gives a theoretical and analytical basis for this filter-inspired approach to improving bandwidth and directivity, and presents design and results for a standalone stepped horn, a Ka-band antenna with a solid stepped horn and SIW feed, and W-band antennas with empty SIW feeds and stepped horns manufactured out of multiple planar layers. The realized antennas show bandwidth up to 40% and gain up to …

Calibration Methodology, Diagnostic Performance, And Redesign Of The Current Monitors On The Z Machine At Sandia National Laboratories, Stacie Hernandez

Electrical and Computer Engineering ETDs

A proper evaluation of the current diagnostics fielded in the inner-MITL region of the Z facility in 3D simulation models had not been performed until now. The evaluation of the current monitors has brought insight to their performance in a new view that has led to discoveries. The B-dot probe was the current diagnostic-of-choice since before the refurbishment of the Z facility [1] and until the development of the Inductively Driven Transmission Line (IDTL) current diagnostic [2]. Experimental data has shown that the IDTL can produce cleaner signals and it is more robust than conventional B-dots. Simulation (modeled using COMSOL …

Hierarchical Safe Reinforcement-Learning Framework For Mission-Aware, Edge-Enabled Multi-Uav Iot Networks, Abee F. Alazzwi

Electrical and Computer Engineering ETDs

This Ph.D. dissertation presents a unified Hierarchical Safe Reinforcement Learning (HSRL) framework for mission-aware, edge-enabled multi-UAV Internet of Things (IoT) networks. The work addresses the need for autonomous aerial infrastructures capable of delivering low-latency communication, scalable edge computation, and provably safe operation in dynamic environments. The dissertation develops three primary contributions. First, it formulates longhorizon drone base station placement and load balancing as a strategic actor–critic learning problem, enabling proactive adaptation to spatiotemporal demand variations. Second, it introduces a mission-aware multi-agent reinforcement learning controller for coordinated mobility, sensing, and computation offloading under latency and energy constraints. Third, it integrates a …

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 …

Liquid-Phase Chemical Melting Deposition For Anchored Nanoparticle–Nanofiber Architectures, Hiep Pham, Kiernan O'Boyle, Gracie Boyer, Jonghyun Park

Mechanical and Aerospace Engineering Faculty Research & Creative Works

We report chemical melting deposition (CMD), a manufacturing strategy designed to overcome the low mass loading and weak interfacial bonding inherent to vapor-based synthesis. Unlike conventional vapor routes, CMD leverages a transient liquid-phase transfer (TLPT) mechanism driven by the differential thermal degradation of carrier fibers to transfer and anchor nanoparticles directly onto target fibers. This process thermodynamically drives the wetting and interfacial fusion of nanoparticles, establishing a liquid-phase contact pathway that enables markedly higher active material loading. To validate the structural resilience of this fused architecture against extreme volumetric stress, we utilized lead oxide (PbO) as a model system, which …

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 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, …

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 …

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, …

Cortical Bone Density And Thickness Assessment Of Intraradicular Sites In Adolescent Patients Using Cbct Imaging, Sara Endo

UNLV Theses, Dissertations, Professional Papers, and Capstones

Background: The indications for the use of cone beam computed tomography (CBCT) in orthodontics has grown since it was first introduced and dentists are discovering new ways that 3D images enhance diagnosis and treatment planning. Evaluating bone quality and quantity can be measured as bone density and thickness in CBCT imaging and is helpful for temporary anchorage device (TAD) placement in orthodontic treatment. TADs rely on primary stability and are commonly used in orthodontic treatment to increase anchorage and expand the limit that nonsurgical orthodontics can provide.

Objectives: This study aims to assess the bone thickness and density at different …

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, …

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, …

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 …

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 …

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 …