Engineering Commons^™

Impact Of Student Beliefs And Self-Efficacy On Performance In Higher Education Stem Courses, Lauren Nicole Fogg

Doctoral Dissertations

In engineering education, students often face feelings of inadequacy, leading to academic struggles and potential dropout. This dissertation investigates the impact of interactive course materials on students' confidence and self-efficacy in problem-solving, focusing on an Engineering Materials class at Louisiana Tech University. Over four quarters, involving seven sections and 218 students, a 13-question Likert scale survey was administered repeatedly, alongside demographic data and textbook usage surveys. The study aims to compare students’ attitudes and beliefs when not using a textbook versus when using an interactive web-native book. Hypotheses suggest that the interactive book will enhance problem-solving beliefs, confidence, and grades. …

Phase Interface Dynamics And Heat Transfer Mechanisms In Evaporating Droplet And Pool Boiling Processes, Md Tanbin Hasan Mondal

Doctoral Dissertations

Despite the significant importance and widespread use of phase-change cooling techniques, there are still fundamental questions about the microscopic processes that govern the heat transfer mechanisms. In order to gain a better understanding of the underlying physics involved, it is essential to have information at the microscale regarding the surface temperature distribution with time as well as the location and speed of the moving contact line (MCL). A comprehensive understanding of heat transfer mechanisms and phase-interface behavior during phase-change cooling is crucial for improving heat transfer models, optimizing surface engineering, and maximizing overall effectiveness. Firstly, this dissertation presents a capacitance-based …

Milling Stability Map Identification And Machining Parameter Optimization Using Bayesian Inference, Aaron William Cornelius

Doctoral Dissertations

This dissertation describes a physics-guided Bayesian learning approach for statistically modelling and optimizing machining processes under a state of uncertainty. This approach uses a series of automatically-selected cutting tests to refine uncertainties about the machining system's dynamics and cutting force and identify higher productivity cutting parameters. The algorithm is evaluated experimentally and compared to the cutting tool manufacturer’s recommendations, both in laboratory conditions and in an industrial setting to optimize the machining process for a large aluminum component. These results show that the proposed Bayesian model can quickly identify both highly-productive machining parameters and accurate information about the underlying system …

Power System Electromagnetic Transient Simulation Using A Semi-Analytical Approach, Min Xiong

Doctoral Dissertations

This dissertation investigates efficient power system electromagnetic transient (EMT) simulations using a semi-analytical approach.

First, based on state-space equations of system EMT models, a semi-analytical solution (SAS) is acquired using the Differential Transformation Method (DTM). The DTM can efficiently derive the SAS of any linear or nonlinear system as a power series in time in a recursive manner using well-developed transformation rules. A high-order SAS allows a large time step to speed up the simulation while maintaining the same level of accuracy. Also, a variable time step approach is proposed to further improve its efficiency. Case studies on multiple systems …

Characterization Of Ceramic Powders Through Powder Rheology, Samuel Weimer

Doctoral Dissertations

Frequently in the literature, it has been shown that any single characterization technique is incapable of sufficiently describing the overall rheology properties of a powder. Consequently, much work has gone into exploring multivariate relationships between common rheological tests. However, such efforts have been primarily focused on powders used in pharmaceutical and food industries. Much less rheology work has been conducted for powders of other industries, such as ceramic powders used in making grinding media and crucibles. Yet, it has been shown that supplementing particle size distribution and chemical composition measurements of ceramic powders with powder rheology techniques can greatly increase …

Design And Develop Lignin Based Recyclable Copolymers For Hydrophobic Coatings, Di Xie

Doctoral Dissertations

Due to the abundance, renewability, biodegradability, overall hydrophobicity, good compatibility with cellulose, and anti-UV/oxidant abilities, lignin has great application potentials in hydrophobic coatings on cellulose-based substrates. However, lignin's structural heterogeneity and rigidity challenge its value-added utilization. Herein, Kraft lignin (KL), from paper mills, is fractionated into more homogeneous fractions (FL), nanosized into lignin micro-nanospheres (LMNS), chemically modified and copolymerized with other constituents to fabricate hydrophobic coating materials with improved coating performances.

To investigate structure-property relationships of lignin-based copolymers, solvent fractionation is conducted to obtain FLs with different molecular weight (MW) and hydroxyl (OH) contents to prepare copolymers by integrating with …

Development And Feasibility Studies Of Ai-Powered Socially Assistive Robotics To Promote Wellbeing Of Persons With Alzheimer’S Disease And Related Dementias, Fengpei Yuan

Doctoral Dissertations

The number of persons living with Alzheimer's Disease and Related Dementias (PLWDs) has been keeping growing. In 2024, it is estimated that there will be approximately 6.7 million individuals living with Alzheimer's Dementia. This number will increase to about 14 million in 2060. Due to the damage in neurons, the capabilities of memory, thinking, and language will decline as the disease progress. As a result, persons with dementia will gradually withdraw from their social activities and become more dependent on others during their activities of daily living. Making it worse, our society is not ready for the increasing requirements of …

Feature Interaction Selection For High-Dimensional Experimental Data, Di Bo

Doctoral Dissertations

In a material development process, discerning the effect of material properties and their interactions on material behaviors is critical to achieving the desired functionality of a material. This causal analysis often involves a small experimental dataset arranged in a high dimension and is challenged by the curse of dimensionality. Feature selection can alleviate such a challenge by producing a short list of features that are significant, but identifying significant feature interactions is very challenging. In this proposal, we propose a couple of approaches that can evaluate and determine important interactions, including a randomized subspace-based model (RSM), feature subspace selection (FSS), …

Cmos-Memristive Neuromorphic Architecture For Nonlinear Signal Processing, Manu Rathore

Doctoral Dissertations

Neuromorphic computing mimics the functional components and structure of the human brain to achieve highly efficient computing with minimal resources and power consumption. Creating neuromorphic systems in Complementary Metal-Oxide-Semiconductor (CMOS) technology offers an alternative computing paradigm to Von neumann computing. However, implementing these systems on an CMOS Integrated Circuit (IC) poses major challenges. These challenges include implementing synaptic weight multiplication and weight tuning operation that conserves energy and occupies minimal area. Additionally, designing a network-on-chip architecture that is reconfigurable and offers a full-connectivity design space is crucial. Furthermore, implementing a complete architecture for nonlinear data processing and, specifically, online learning …

Multi-Objective Radiological Analysis In Real Environments, David Raji

Doctoral Dissertations

Designing systems to solve problems arising in real-world radiological scenarios is a highly challenging task due to the contextual complexities that arise. Among these are emergency response, environmental exploration, and radiological threat detection. An approach to handling problems for these applications with explicitly multi-objective formulations is advanced. This is brought into focus with investigation of a number of case studies in both natural and urban environments. These include node placement in and path planning through radioactivity-contaminated areas, radiation detection sensor network measurement update sensitivity, control schemes for multi-robot radioactive exploration in unknown environments, and adversarial analysis for an urban nuclear …

Exploration Of Event-Based Camera Data With Spiking Neural Networks, Charles Peter Rizzo

Doctoral Dissertations

Neuromorphic computing is a novel, non-von Neumann computing architecture that employs power efficient spiking neural networks on specialized hardware. Taking inspiration from the human brain, spiking neural networks are temporal computation units that propagate information throughout the network via binary spikes. Compared to conventional artificial neural networks, these networks can be more sparse, smaller in size, and more efficient power-wise when realized on neuromorphic hardware. Event-based cameras are novel vision sensors that capture visual information through a temporal stream of events instead of as a conventional RGB frame. These cameras are low-power collections of pixels that asynchronously emit events over …

Development Of Novel Cesium Chloride-Based Ultrafast Inorganic Scintillators For Fast Timing Radiation Detection Applications, Daniel Rutstrom

Doctoral Dissertations

Cs₂ZnCl₄ [dicesium zinc tetrachloride] and Cs₃ZnCl₅ [tricesium zinc pentachloride] are relatively new scintillator materials that appear to be promising for use in fast-timing radiation detection applications owing to their 1 to 2 nanosecond decay times. Moreover, they offer several advantages over the state-of-the-art ultrafast inorganic scintillator BaF₂ [barium fluoride]. To fully realize the potential of these novel materials, growth of crystals having improved optical quality must be demonstrated. The mechanism responsible for the ultrafast decay times, core valence luminescence (CVL), in cesium zinc chloride crystals can also be observed in other compounds containing …

A Pattern Matching Algorithm For Self-Adjusting Basal Rates In Insulin Pump Systems, Lauren Smith

Doctoral Dissertations

In a Type 1 Diabetic, Insulin can be administered in a pump system. There are two types of insulin that must be given: basal and bolus. Basal insulin is a long-acting form of insulin that works in the background while fasting, while Bolus insulin is rapid/short acting given in response to food to immediately begin working to lower blood sugar.

Modeling in Diabetes can be represented by algorithmic approaches ranging from simple autoregressive models of the Continuous Glucose Monitor time series to multivariate nonlinear regression techniques of machine learning. Other examples of modeling in Diabetes include prediction models of hypoglycemia …

The Role Of Grain Morphology And Individual Grain Fracture On Granular Material Behavior, Anne Katherine Turner

Doctoral Dissertations

Granular materials, such as sand, are multi-scale materials combining an immense number of crushable particles to create an assembly. Numerical approaches to modeling sand’s mechanical behavior often use simplifications, such as treating the volume as a continuum or representing the particles with rigid, idealized spheres using the discrete element method. These approaches overlook crucial nonlinear phenomena, such as individual grain morphology and fracture, which have been shown to influence sand’s strength and plasticity behavior. This research investigated methods to incorporate such phenomena , focusing on simulating single grain crushing tests and 1D confined compression of Ottawa sand using finite element …

Discrete Time State-Space Modeling Framework For Switched-Mode Power Supplies, Jared Baxter

Doctoral Dissertations

Electrical power consumption has become ever prominent in modern society. Switch mode power supplies, now more than ever, have become a foundation for residential, commercial, and industrial electrical needs. These demands require numerous advanced power converters, and modeling plays a vital role in the design of these converters. Commonly, modeling is completed using either dedicated hand analysis, which must be completed individually for each topology, or time-stepping circuit simulations, which are insufficiently rapid for broad analysis considering a wide range of potential designs or operating points. Discrete time state-space modeling of switching converters has shown merits in rapid analysis and …

The Development And Enhancement Of A Forward Mathematical Model Of The Human Knee Joint, Seth Coomer

Doctoral Dissertations

Degenerative joint disease, or osteoarthritis, is a common occurrence in the knee joint. This can often result in joint pain, decrease in range of motion, and ultimately disability. One way to counteract osteoarthritis is the incorporation of a total knee arthroplasty (TKA). TKAs replace the damaged bone and soft tissue surrounding the knee with metal and polyethylene components. Ideally this will improve the joint’s performance and reduce pain. However, there is still a number of TKA patients who remain dissatisfied. In order to investigate this, in depth research must be done on the design and performance of TKAs.

One such …

Development Of Supg And Stabilized Finite Element Method Solvers For The Two Fluid Plasma Model, Kenneth A. Croft

Doctoral Dissertations

This dissertation describes efforts to employ stabilized finite element method approaches to simulate ideal two fluid plasma dynamics. First, the streamline-upwind/Petrov-Galerkin (SUPG) finite element method, which is well developed and known to be applicable to models containing terms like those in the ideal two fluid plasma model, is employed. Then, in an attempt to address some shortcomings found in that approach, another stabilized finite element method is developed along similar lines, starting from a steady state advection-reaction equation rather than a steady state advection-diffusion equation as was done in the development of the SUPG method. The performance of the SUPG …

Stability Of Quantum Computers, Samudra Dasgupta

Doctoral Dissertations

Quantum computing's potential is immense, promising super-polynomial reductions in execution time, energy use, and memory requirements compared to classical computers. This technology has the power to revolutionize scientific applications such as simulating many-body quantum systems for molecular structure understanding, factorization of large integers, enhance machine learning, and in the process, disrupt industries like telecommunications, material science, pharmaceuticals and artificial intelligence. However, quantum computing's potential is curtailed by noise, further complicated by non-stationary noise parameter distributions across time and qubits. This dissertation focuses on the persistent issue of noise in quantum computing, particularly non-stationarity of noise parameters in transmon processors. It …

Understanding The Impact Of Divertor And Main Chamber Ion Fluxes On Divertor Closure In The Diii-D Tokamak, Kirtan M. Davda

Doctoral Dissertations

The diverted tokamak redirects extreme heat and particles to targets, a plasma-facing component designed for such loads. Here, the local fluxes produce strong particle recycling and sputtering. Recycled neutrals can “leak” into the region between the core and wall, the scrape-off-layer (SOL), impacting plasma performance. Increasing divertor closure can reduce leakage by containing neutrals within the divertor. However, there exists a need to quantify divertor baffle restrictions and understand closure directly from empirical data as opposed to indirectly through modeling.

Our study introduces the Geometric Restriction Parameter (GRP) based on simplifying neutral transport to ballistic pathways. Specifically, it considers the …

Tunable Catalysts And Electrodes With High Material Utilization And Durability For Hydrogen Production, Lei Ding

Doctoral Dissertations

Hydrogen, the future energy carrier, has gained extensive attention due to its advantages of high energy density, low weight, and zero-carbon emission. So far, hydrogen has been widely used in various fields including transportation, oil upgrading, metal refining, etc. Most hydrogen is currently produced from fossil fuels, which can cause serious environmental problems. Water electrolysis was proposed to produce clean hydrogen with carbon-free emission and only byproduct of oxygen. Among various water electrolyzer devices, the proton exchange membrane electrolyzer cell (PEMEC) shows great potential to produce green hydrogen by integrating renewable sources (solar, wind, etc.) due to its advantages of …

Controlling Complex Dynamic Transportation Systems: Development And Adaptation Of A Novel Distributed Cooperative Multi-Agent Learning Technique, Russell Thomas Graves

Doctoral Dissertations

Intelligent transportation systems continue to increase complexity, scale, and scope as more devices contain embedded compute. Cooperation among vehicles, intersections, and other members of the greater traffic ecosystem at a system-of-systems level is critical to improving the efficiency of the multi-billion-dollar asset that is the U.S. roadway infrastructure. This work introduces a negotiations strategy among multi-agent reinforcement learning agents and applies this to both traffic signal control and supervisory control of vehicle platooning. The traffic signal control implementation builds off of many prior research thrusts, and was shown to improve vehicle throughput by an average of 671veh/hr over actuated traffic …

Multimodal Data Fusion And Machine Learning For Advancing Biomedical Applications, Md Inzamam Ul Haque

Doctoral Dissertations

This dissertation delves into the intricate landscape of biomedical imaging, examining the transformative potential of data fusion techniques to refine our understanding and diagnosis of health conditions. Daily influxes of diverse biomedical data prompt an exploration into the challenges arising from relying solely on individual imaging modalities. The central premise revolves around the imperative to combine information from varied sources to achieve a holistic comprehension of complex health issues.

The chapters included here contain articles and excerpts from published works. The study unfolds through an examination of four distinct applications of data fusion techniques. It commences with refining clinical task …

Experimental Quantum Key Distribution In Turbulent Channels, Kazi Mh Reaz

Doctoral Dissertations

Quantum Key Distribution (QKD) ensures security by relying on the laws of quantum physics rather than the mathematical intricacy of encryption algorithms. The transmission medium is a critical restricting factor for any quantum communication protocol. Fiber-based optical networks suffer great losses, making quantum communication impossible beyond metropolitan scales. Here free-space quantum communication can be a great alternative for long-distance communication. Even though modern Communications are mostly wireless the atmosphere poses a challenge for QKD. So QKD must be resistant to both atmospheric loss and variations in transmittance. In this thesis we conduct an experiment to strengthen the BB84 protocol's resistance …

Urban Ecohydrology And The Watershed Microbial Continuum, Victoria C. Rexhausen

Doctoral Dissertations

A rapidly urbanizing world is adding unprecedented stress to water resources. Urbanization results in high rates of stream impairment, including but not limited to ecological stress, pollution, and flooding. A fully integrated and holistic perspective is necessary for effective remediation strategies for these wicked problems. This monitoring study from a heterogeneous urban watershed investigated the geospatial and seasonal interactions between urban hydrology and biological processes using stable isotope compositions, anions, and microbial.

This research found that nitrate concentrations in an urban watershed were ecologically driven. NO₃^- concentrations ranged from 0.4 to 12.7 mg/L throughout the watershed. Nitrate concentrations …

Computational Study Of Confined Cytoskeletal Assemblies: Simple Rules, Complex Behavior, Oghosa Honor Akenuwa

Doctoral Dissertations

The actin cytoskeleton is crucial for cellular processes and proper organization in cells. Physical regulators like actin crosslinking proteins, molecular motors, and physical confinement significantly impact the organization of the actin cytoskeleton. Despite advances, much remains unknown about how these physical regulators affect actin organization. In this thesis, we employ coarse-grained computer simulations to investigate the effect of physical regulators on the dynamics and organization of semiflexible actin filaments. First, we explore the role of crosslinker properties and confinement shape on actin organization by varying the system shape, the number and type of crosslinking proteins, and the length of filaments. …

Motor Control Quantification And Necessary Improvements For Individuals With Post-Stroke Gait: Implications For Future Customizable Rehabilitation Approaches, Azarang Asadi

Doctoral Dissertations

Although often taken for granted, walking is an extremely complex motor skill that requires sensory inputs, neural communication, advanced control strategies, and coordination of the muscles and joints. Electrical signals traveling from the brain to the muscles are transformed to mechanical forces to achieve desired motion. A stroke damages the central nervous system and neural pathways, limiting the ability of survivors to walk. Walking speed is significantly decreased and asymmetrical walking patterns emerge. A crucial component of stroke rehabilitation is gait training, a therapeutic intervention to help individuals to improve their walking ability, as walking is essential for functional independence …

Discrete-Event Simulation Process Model For The Pyrochemical Processing Of Plutonium At Los Alamos National Laboratory, Devin C. Kimball

Doctoral Dissertations

The pyrochemical metal production operations that occur in the Plutonium Facility at Los Alamos National Laboratory perform plutonium purification with the aim to provide plutonium metal for a variety of defense- and non-defense missions within the National Nuclear Security Administration. The demands and constraints associated with the pyrochemical processing of plutonium are complex, making decision analyses challenging for program managers who require plutonium production for their mission applications. A full description is provided of the development of a discrete-event simulation process model, constructed in the ExtendSim™ [trademark] software, to measure and report the process capacity, material throughput, equipment requirements, and …

Integrated Approaches In Wastewater Surveillance Of Sars-Cov-2: Monitoring, Persistence, Sampling Optimization, And Impacts On Microbial Community Structure, Ye Li

Doctoral Dissertations

Wastewater-based epidemiology (WBE) serves as a crucial tool for monitoring SARS-CoV-2 prevalence on university campuses, yet concerns persist regarding its effectiveness as an early warning system for COVID-19 outbreaks. This study aimed to address these concerns through a comprehensive field trial conducted at the University of Tennessee, Knoxville. We investigated correlations between SARS-CoV-2 concentrations, both with and without normalization using Pepper Mild Mottle Virus (PMMoV), and various parameters. Significant positive correlations were observed between SARS-CoV-2 concentrations and active cases in the weeks prior to, during, and following the monitoring period, unaffected by PMMoV normalization. Concurrently, the persistence of SARS-CoV-2 RNA …

Uniting Cognitive Models And Ai: Early Alzheimer’S Screening Through Language Analysis, Ziming Liu

Doctoral Dissertations

Alzheimer's Disease (AD) and related dementias (PwADRD) often lead to memory issues, language difficulties, and cognitive impairments. These symptoms can cause social isolation, anxiety, depression, accelerating cognitive decline, and negatively impacting quality of life. Traditional diagnostic and rehabilitation methods, relying on Artificial Intelligence (AI) or clinical observations, sometimes lack transparency and explainability for the growing population affected by AD. My research aims to address these challenges by developing AI systems that analyze affective states and cognitive deficits in PwADRD interactions. This involves creating more precise diagnostic tools and rehabilitation methods, as well as trustworthy AI agents to improve PwADRD's quality …

Engineering Of Functional Hybrid Nanocomposites For Renewable Energy Applications Via Laser Ablation, Mahshid Mokhtarnejad

Doctoral Dissertations

Carbon-based hybrid nanocomposites (HNCs) have increasingly gained prominence in the materials science community due to their widespread applications in advanced energy storage and conversion devices. Yet, current, and conventional methods to synthesize tailored HNC structures involve complex and multi-step processes that often require harsh chemical reagents. To address the majority of these shortcomings, this thesis proposes Laser Ablation Synthesis in Solution (LASiS) as a rapid, inexpensive, and facile technique for synthesis of Metal-Organic Framework (MOF)–derived and Metal Oxide/reduced Graphene Oxide (rGO) HNCs in an environmentally friendly fashion. These engineered composite nanomaterials show superior properties as (1) non-precious medal based electrocatalysts …