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Articles 2641 - 2670 of 197948

Full-Text Articles in Engineering

Brain-Based Mechanisms Of Behavioral Impairment In Fetal Alcohol Spectrum Disorder (Fasd): The Neuroimaging Biomarkers Of Inhibitory Control, Zinia Pervin May 2026

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 …

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Nuclear Deterrence: Enhancing The Mission Through Smart Factory Predictive Solutions, Jarrod Matthew Ronquillo May 2026

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 …

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Assessing Uranyl-Specific Dnazymes And Dna-Aptamers For Uranium Binding In Environmentally Relevant Waters, Ashley R. Apodaca-Sparks May 2026

Assessing Uranyl-Specific Dnazymes And Dna-Aptamers For Uranium Binding In Environmentally Relevant Waters, Ashley R. Apodaca-Sparks

Civil Engineering ETDs

The legacy of uranium mining affects many communities in the western United States, resulting in elevated levels of uranium in surface water. The ability to quickly and accurately detect uranium on site in affected communities can lead to real-time water quality analysis that informs risk assessment and remediation efforts. The objective of this work is to advance the application of biosensing technologies for the measurement of uranium in waters affected by mining legacy. The third chapter of this thesis compares ANDalyze by AlpHa Instruments, a commercially available uranium biosensor, and inductively coupled plasma mass spectrometry (an EPA-verified method for uranium …

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Investigating Workforce Development Gaps In Tribal Transportation Agencies, Benson Long Jr May 2026

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 …

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Assessing The Impact Of Multi-Physics Effects On Photovoltaic Module Degradation Using Computational Modeling, James Yuan Hartley May 2026

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, …

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Technical Advancements In Single-Molecule Spectroscopy For High-Throughput Measurement Of Long-Time Dynamics, Quyen B. Le May 2026

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 …

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Helium Effects In High Entropy Alloys For Fusion Reactor Plasma Facing Components, Shane Evans May 2026

Helium Effects In High Entropy Alloys For Fusion Reactor Plasma Facing Components, Shane Evans

Nuclear Engineering ETDs

The plasma facing materials (PFMs) within fusion reactors, such as the divertor, must be able to withstand high temperature (>1000 K) for extended durations of time, in addition to extreme flux from neutrons, helium (He) ash, and neutral species such as deuterium and tritium. These materials must withstand these conditions while maintaining their selected mechanical properties, minimal sputter yield, and low fuel retention. In recent years refractory high entropy alloys (RHEAs) have been investigated due to their improved mechanical and irradiation-resistant properties when compared to pure W, the current material for PFMs. In this work RHEAs have undergone fusion …

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Development Of A Lead-Lithium Eutectic Magnetohydrodynamic Loop For Corrosion Studies, Xavier S. Angus May 2026

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 …

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Cfd Simulation Analyses Of The Blowdown Phase Of A Depressurized Loss Of Forced Cooling Accident In A Htgr, Keenan Kresl-Hotz May 2026

Cfd Simulation Analyses Of The Blowdown Phase Of A Depressurized Loss Of Forced Cooling Accident In A Htgr, Keenan Kresl-Hotz

Nuclear Engineering ETDs

This research numerically investigates the helium-air mixing in HTGR containment cavities during the blowdown phase of a simulated DLOFC accident. The results of the performed CFD simulation analyses, using the commercial code STAR-CCM+, are compared with reported measurements from an experiment conducted at CCNY. The analyses investigate the effects of the RANS and LES turbulence models, numerical mesh refinement, time-step size, and flow rate and temperature of the injected hot helium into the scaled reactor cavity. Calculated parameters analyzed include the pressure and spatial distributions of temperature and oxygen concentration in the simulated reactor and steam generator cavities. CFD oxygen …

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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 May 2026

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 …

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Sensitivity-Informed Resonance Parameter Cross Section Adjustments, Matthew Juan Lazaric May 2026

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 …

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Representativeness And Sensitivity Analysis Of Accident Tolerant Fuel Cladding Behavior In Reactivity-Initiated Accidents, Melissa Andrea Moreno May 2026

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, …

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In-Situ Measurement Of Dynamic Deuterium Retention In Tungsten-Based Alloys Under Plasma Exposure, Ethan Gabriel Rashap May 2026

In-Situ Measurement Of Dynamic Deuterium Retention In Tungsten-Based Alloys Under Plasma Exposure, Ethan Gabriel Rashap

Nuclear Engineering ETDs

Nuclear fusion is a promising pathway for sustainable energy production, but the performance of plasma-facing materials remains a key challenge for reactor operation. In particular, hydrogen isotope retention in tungsten—the leading candidate material for divertor components—affects tritium inventory, fuel recycling, and overall material lifetime. In this work, the temperature-dependent deuterium retention behavior of W–Ti and W–TiTa alloys was experimentally investigated under fusion-relevant ion irradiation conditions between 400 and 700 K. In-situ nuclear reaction analysis (NRA) was used to track retention during plasma exposure and subsequent cooldown, providing depth-resolved insight into deuterium accumulation and release. The results show that deuterium retention …

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Self-Supervised Spoofing Detection, David S. Choi May 2026

Self-Supervised Spoofing Detection, David S. Choi

Electrical and Computer Engineering ETDs

Global Navigation Satellite Systems (GNSS) are vulnerable to spoofing attacks that can mislead receivers with counterfeit signals. Traditional detection techniques, such as antenna-based, encryption based, and signal processing approaches, often face limitations in adaptability, computational cost, or reliance on predefined thresholds. Supervised machine learning models, while powerful, require large labeled datasets and struggle to generalize to unseen spoofing scenarios. In this work, we propose a self-supervised spoofing detection framework based on Adaptive Sparse Gaussian Processes (ASGP). The method predicts incoming GNSS features using past observations and identifies spoofing as anomalous deviations in the prediction residuals. Unlike supervised approaches, ASGP adapts …

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Hierarchical Safe Reinforcement-Learning Framework For Mission-Aware, Edge-Enabled Multi-Uav Iot Networks, Abee F. Alazzwi May 2026

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 …

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Novel Algorithmic Methods For Random Telegraph Noise Detection And Characterization In Electronic Devices, Victor Darie Pepel May 2026

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, …

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Statistical And Spectral Theory For Spatially Correlated Random Aperiodic Antenna Arrays, Thomas Edward Christian Jr May 2026

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 …

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Quantum Control Protocols For Robust Quantum Computing, Leeseok Kim May 2026

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 …

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Fault Tolerant Quantum Computing With Lower Overhead, Benjamin E. Anker May 2026

Fault Tolerant Quantum Computing With Lower Overhead, Benjamin E. Anker

Electrical and Computer Engineering ETDs

Quantum computation promises asymptotic speedups over classical algorithms, but realizing these advantages requires overcoming the noisiness of quantum hardware. Although fault-tolerant error correction can allow for reliable quantum computation even using unreliable components, the resource overheads required can substantially erode the asymptotic performance gains. This dissertation focuses on constructing and optimizing fault-tolerant procedures with lower overhead than previous methods were capable of. We present new frameworks for fault-tolerant syndrome extraction using flag gadgets with exponentially reduced ancilla requirements, explicit measurement schedules that achieve asymptotically fewer measurements than stabilizer generators, and a general method for making arbitrary Clifford circuits fault tolerant. …

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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 May 2026

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, …

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Complex-Valued Convolutional Neural Network With Time-Frequency Representation For Electrocardiogram-Based Arrhythmia Detection, Kajeeth Kumar Gurusamy, Muthurajkumar Sannasy May 2026

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 …

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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 May 2026

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 …

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Adapting Independent Large-Scale Pretrained Models For Human Action Recognition, Selen Pehli̇van May 2026

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 …

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Adaptive Backstepping Nonsingular Fast Terminal Sliding Mode Control For Robotic Manipulators Based On Disturbance Observer, Xin Zhang, Xu Wang May 2026

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 …

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Chaotic Artificial Bee Colony-Optimized Stacking Ensemble For Robust Multifault Diagnosis Of Wind Turbines, Veilraj Revathi, Solaimalai Jeyadevi, Madasamy Sudalaimani May 2026

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 …

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Machine Learning Approaches For Predicting Biochemical Oxygen Demand And Ammonium Nitrogen: A Decade-Long Weekly Field Study At A Full-Scale Water Resource Recovery Facility, Hoda Khoshvaght, Ratish Ramyad Permala, Amir Razmjou, Mehdi Khiadani May 2026

Machine Learning Approaches For Predicting Biochemical Oxygen Demand And Ammonium Nitrogen: A Decade-Long Weekly Field Study At A Full-Scale Water Resource Recovery Facility, Hoda Khoshvaght, Ratish Ramyad Permala, Amir Razmjou, Mehdi Khiadani

Research outputs 2022 to 2026

Unlike previous studies that rely on high-frequency (15-min or hourly) datasets, this study is among the first to use low-frequency (weekly) data to evaluate the performance of linear and nonlinear machine learning (ML) algorithms for predicting biochemical oxygen demand (BOD) and ammonium nitrogen (NH4+-N) in the primary and secondary treatment effluents from the Subiaco Water Resource Recovery Facility (WRRF) in Western Australia. Various feature selection methods, including filters, wrappers, and embedded methods, were employed to identify the most effective approach that achieves the highest model performance while enhancing computational efficiency. The results demonstrate that a reduced set of …

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Surface Directed Microwave Assisted Fabrication Of Znmn2o4 And Its Composite As Anode Materials For Lithium Ion Battery, Anirudh Singh May 2026

Surface Directed Microwave Assisted Fabrication Of Znmn2o4 And Its Composite As Anode Materials For Lithium Ion Battery, Anirudh Singh

Electronic Theses & Dissertations

The Lithium-ion batteries (LIBs) are widely utilized in portable electronics and electric/hybrid electric vehicles. However, the limited theoretical capacity of graphite (372 mA h g⁻¹) restricts further advancements. In our recent work, a series of ZnMn2O4 materials with various additives were synthesized using the microwave reaction method and evaluated as anodes for LIBs. Additives such as 2-methylimidazole (2MIM), polyvinyl alcohol (PVA), and cetyltrimethylammonium bromide (CTAB) were employed to prepare ZnMn2O4 with distinct morphologies. Our findings revealed that ZnMn2O4  with pure phases was achieved when 2MIM and CTAB were used as additives. …

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Liquid-Phase Chemical Melting Deposition For Anchored Nanoparticle–Nanofiber Architectures, Hiep Pham, Kiernan O'Boyle, Gracie Boyer, Jonghyun Park May 2026

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 …

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Machine Learning-Based Decision Support Models With Applications In Postsecondary Education, Marco Paolo Anglesio May 2026

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. …

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Developing And Evaluating U.S. Army Bridging For Ship Simulation, Jacob C. Hodges May 2026

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 …

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