Engineering Commons^™

Deep Brain Stimulation In Early-Stage Alzheimer's Disease: A Systematic Review Of Neuropsychological Outcomes, Nora Huey

Doctoral Dissertations

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and disruption of neural networks involved in cognitive functioning. Although existing treatments may provide symptomatic benefit, there remains a need for interventions capable of slowing or stabilizing disease progression. Deep brain stimulation (DBS) has emerged as a potential neuromodulatory intervention for early-stage AD through stimulation of memory- and cognition-related neural circuitry. This dissertation employed a systematic review research methodology to examine teh neuropsychological and neurobiological outcomes associated with DBS targeting the fornix (DBS-f) and nucleus basalis of Meynert (DBS-NBM) in individuals with early-stage AD over 12–24 …

Investigations Of Carbon Dioxide Storage In Low-Temperature Reservoirs For Non-Leaking Storage, Md Nahin Mahmood

Doctoral Dissertations

Carbon dioxide (CO₂) injection into subsea or low-temperature water zones has emerged as a promising strategy for long-term, non-leaking CO₂ storage through the formation of solid hydrates. This research investigates CO₂ injection into Berea sandstone cores saturated with water under simulated subsea conditions, focusing on hydrate formation behavior across varying temperatures, pressures, and injection flow rates. Experimental results demonstrate that CO₂ hydrate formation under dynamic (flowing) conditions occurs at significantly higher pressure compared to static conditions reported previously. At temperatures ranging from 0 °C to 5 °C, dynamic hydrate formation pressures were observed to be approximately double. This can be …

Multidomain Modeling And Ramp-Aware Forecasting Of Floating Photovoltaic Systems For Water-Energy Nexus Applications, Md Atiqur Rahaman

Doctoral Dissertations

Floating photovoltaic (FPV) systems have become a transformative renewable energy technology because of their cooling effects on PV performance and ability to prevent water evaporation in land-constrained areas. Although FPV systems have the potential to become a commercially viable technology, their large-scale deployment remains constrained by uncertainties in thermal behavior, sustainability, and grid-operational variability. This dissertation identifies and characterizes these three key issues and presents an integrated, measurement-based evaluation of a 130 kW FPV installation located at the Passaúna reservoir in Brazil. In the first contribution, four temperature models, including physical and empirical models, were developed and comparatively evaluated to …

Incremental Cluster Validity Indices And Their Role In Interpreting Lifelong Learning Systems, Niklas Max Melton

Doctoral Dissertations

Clustering and supervised learning are often treated as distinct paradigms, yet both rely on structure in feature space. This dissertation investigates the relationship between cluster validity indices (CVIs) and supervised learning in real-time and lifelong learning settings where data arrive incrementally and cannot be revisited. Across four studies, it develops methods for online cluster validation, uses supervised learning to improve their interpretability, and applies these ideas to evaluating performance degradation in continual learning.

The first study extends incremental cluster validity indices (iCVIs), enabling widely used validation metrics to operate in streaming environments. Experiments on synthetic and real-world datasets show systematic …

Architecting A Complex Adaptive System Model For Selecting Policies To Reduce Kidney Discard, Lirim Ashiku

Doctoral Dissertations

"The kidney allocation system is a complex, evolving system involving multiple heterogeneous agents. Each agent exhibits emergent behavior that may not fully align with the complex system goals. Therefore, there is a need for a transdisciplinary systems approach to visualize the interdependency among agents and understand the dominant patterns that shape the kidney allocation system.

First, this research presented an incremental hierarchical system engineering approach in identifying the agents’ needs and behaviors toward the complex systems’ goal of maximizing deceased donor kidney utilization and reducing kidney discard. The hierarchical systems approach linked with model-based system engineering aided in eliciting agents’ …

Strontium Titanate For Capacitor And Energy Storage Applications At Cryogenic Temperatures, Hung Trinh

Doctoral Dissertations

This study investigates the dielectric properties of single crystal and ceramic strontium titanate (SrTiO₃) for cryogenic capacitor applications from room temperature to 4 K. Permittivity (k) and loss tangent are dependent on temperature, frequency, mechanical stress, and applied DC electric field. Accordingly, the dielectric constant and loss tangent were measured at various frequencies and DC bias levels. Loss tangent data are also presented as equivalent series resistance (ESR). For single crystal SrTiO₃, an impurity level of ≈500 ppm barium resulted in an increase of the maximum permittivity to approximately 50,000 at 6 K. This relatively high …

Critical Parameters Controlling Oxide Scale Formation And Hydro-Descaling Efficiency During Steelmaking, Tochukwu Princewill Ojiako

Doctoral Dissertations

In modern steelmaking, cast slabs are exposed to high-temperature oxidizing environments during secondary cooling, reheating, and hot rolling, resulting in the formation of multilayer oxide scales on the steel surface. These scales interact with mold-flux residues originating from the casting process (CC). The morphology, chemistry, and adhesion of oxide scale strongly influence its removability during high-pressure hydraulic descaling and ultimately determine the surface quality of hot-rolled products. However, the mechanistic relationship between oxide scale evolution, scale-steel interfacial structure, and hydraulic descaling performance remains poorly understood.

This dissertation investigates oxide scale formation, modification, and removal in low-carbon thin-slab steels produced by …

Fabrication And Characterization Of A High Strength Novel Aluminum Alloy For Implementation With Directed Energy Deposition, Rachel Boillat-Newport

Doctoral Dissertations

"Additive manufacturing (AM) is of significant interest due to the ability to fabricate 3D components layer-by-layer. Compared with traditional manufacturing methods, AM allows for the direct fabrication of complex, intricate parts without the need for extensive machining, reducing manufacturing time and tooling costs and minimizing material waste. Aluminum alloys are highly desired for implementation via AM due to their high strength-to-weight ratios; however, there are several challenges that complicate the situation and hinder the use of aluminum alloys. One of the most challenging issues is that the desired high-strength alloys are not compatible with AM processing methods. A method to …

Integrating Stakeholder Priorities Into Resilience Planning And Investment Decision-Making, Victoria Jeaneen Kraemer

Doctoral Dissertations

"Resilience is a system’s ability to bounce back from a disruption. However, resilience needs may vary across different stakeholders. To better integrate stakeholder input into resilience planning and investment decision-making, existing frameworks were combined to develop the Integrated Stakeholder-Centered Resilience Framework. This broad framework is tailorable across contexts and stakeholder types. Research results demonstrate the use of this framework to evaluate the effects of disruptions and represent stakeholder preferences for investments. In the first study, participants were randomly assigned to power outage scenarios, which did not affect their criteria priorities. An exploratory factor analysis revealed two main priorities, (1) Absorption …

Quantifying, Forecasting, And Mitigating Construction Labor And Material Challenges In A Dynamic Global Economy Using Econometrics And Deep Learning, Ahmed Gamal Elsayed Mohamed Shiha

Doctoral Dissertations

"The construction industry contributes to the global economy, yet its cost management practices remain constrained by labor shortages and material price volatility. These challenges are intensified by economic disruptions, geopolitical tensions, and trade policy shifts. Despite a growing body of literature, five knowledge gaps remain unaddressed: (1) the absence of dynamic, and localized measures of construction labor shortages; (2) limited empirical investigation of macroeconomic leading indicators of labor shortages; (3) underutilization of deep learning (DL) algorithms in forecasting local construction labor earnings; (4) the limited treatment of structural breaks in existing construction material price forecasting models; and (5) the lack …

Manufacturing And Performance Evaluation Of Carbon/Epoxy Laminated Composites Cured Using Single- And Six-Magnetron Microwave Applications, Nayan Pundhir

Doctoral Dissertations

Microwave curing is fast, energy-efficient, and a viable alternative to conventional thermal curing processes. It has been widely adopted for processing carbon fiber-reinforced polymer (CFRP) composites because the high electrical conductivity of carbon fibers enables strong microwave coupling, leading to rapid volumetric heating and reduced energy consumption. The aim of this study is to investigate the use of microwave curing for manufacturing CFRP composites. IM7/Cycom 5320-1 unidirectional prepreg was utilized to fabricate laminated composites in two thickness ranges: 16-layer laminates (2.5 mm thickness) and 64-layer laminates (9.2 mm thickness). Two lay-up configurations were examined: symmetric cross-ply and quasi-isotropic. Different curing …

Zirconium Carbide Based Materials For Extreme Aerospace Environments, Nathaniel Hyman Blatt

Doctoral Dissertations

This research focuses on the processing and properties of zirconium carbide-based materials to promote their use in extreme environment aerospace applications, including nuclear thermal propulsion and hyper sonics. Several carbide systems including ZrC, ZrC-Mo cermets, (Zr, Nb)C, and a high entropy carbide were developed. The ZrC-Mo cermet was studied extensively to understand the effect of starting carbide grain size on the final microstructure, composition, elastic moduli, hardness, fracture toughness, room and elevated temperature flexural strength, thermal diffusivity, electrical resistivity, thermal expansion coefficient, and thermal conductivity. It was shown that heat transport in the cermets was dominated by the ZrC phase …

Investigations In Hydrogen Ironmaking, Joseph William Govro

Doctoral Dissertations

The purpose of this research is to contribute to the Grid Interactive Steelmaking with Hydrogen (GISH) project. This research investigates the viability of both producing and melting Direct-Reduced Iron (DRI) utilizing hydrogen. Conventional CO reduced DRI will be referred to as “C-DRI” and DRI produced using hydrogen gas will be referred to as “H-DRI”.

An H-DRI pilot plant was constructed in Golden Colorado. The pilot plant was commissioned and successfully operated four campaigns. Process improvements were made throughout the campaigns and the process was optimized. In addition to running the pilot plant in a pure hydrogen condition, the pilot plant …

Machine Learning Based Automation Of Pcb Pdn Design And Optimization, Haran Manoharan

Doctoral Dissertations

The rapid increase in power density and stringent power-integrity requirements in modern System-on-Chip (SoC) platforms have made Power Delivery Network (PDN) design an increasingly complex, multi-stage challenge. Critical decisions must be made both during pre-layout planning, such as stackup configuration, power-plane geometry, and early decoupling capacitor (decap) budgeting, and during post-layout refinements. Traditional heuristic and evolutionary optimization techniques struggle with scalability, require extensive manual iteration, leading to long runtimes and limited adaptability across varying board configurations. To address these challenges, this work proposes a unified reinforcement-learning-driven framework for automated PDN synthesis and decap optimization that spans both pre-layout and post-layout …

Analytical And Empirical Data-Driven Risk-Governance Design Across The Different Phases: Creating Informed Policy Adjustments For The Transportation Project Lifecycle, Mariam A. Elazhary

Doctoral Dissertations

"Transportation infrastructure is a critical foundation for economic productivity, yet agencies still face challenges in translating the expanding transportation datasets into defensible, phase-specific risk-governance tools. This dissertation addresses this gap by developing analytical and empirical, data-driven approaches to support risk governance across the transportation project lifecycle. Module 1 uses a panel model with bid-letting data to examine how out-of-state contractor participation affects pre-award competition and to derive entry-governance rules. Module 2 analyzes nonfatal injury data through iterative multiple linear regression and fatality data through association-rule change mining to identify multi-factor safety-risk configurations and develop adaptive safety-governance provisions. Module 3 develops …

Advancing Management Strategies For Airport Infrastructure Development: A Holistic Project Delivery And Contractual Perspective, Ramy Ahmed Anwar Ahmed Khalef

Doctoral Dissertations

The airport sector plays a vital role in the US infrastructure. Neglecting the maintenance and improvement of such critical domain poses serious repercussions. Current initiatives within the aviation sector have fallen short in providing substantial quantitative or qualitative support for comprehensive project and risk management processes. This research fills this knowledge gap. The associated objectives include: (1) identifying a holistic set of Design-Build risk factors and providing associated research recommendations; (2) enhancing the understanding of Design-Build (DB) with respect to Design-Bid-Build (DBB) on key risks impacting schedule/cost performance in airport projects; (3) establishing a risk-based benchmarking framework to evaluate schedule/cost …

Probability Of Detection For Corrosion-Induced Mass Loss And Yield Strain In Metal Structures Using Fe-C Coated Long Period Fiber Gratings Sensors And Electroresistive Strain Gauges, Ying Zhuo

Doctoral Dissertations

"Graphene-based, Fe-C coated long-period fiber grating (LPFG) sensors individually reveal a causal relation between LPFG wavelength change and Fe-C mass loss. Their correlation parameters vary significantly between the sensors mainly because hydrophobic multilayer graphene and graphene oxide make sensor fabrication inconsistent. This study explores MXene-based, Fe-C coated LPFG sensors due to MXene’s inherent hydrophilic property in the presence of surface functional groups such as hydroxyl and oxygen and the probability of detection (POD) for mass loss from Fe-C coated LPFG sensors when deployed at a fixed location. A novel fabrication process incorporating Piranha solution pretreatment is proposed to ensure uniform …

Adaptive Machine Learning Framework For Microstructural Optimization And Mechanical Performance Prediction In Steels, Henry Adekola Haffner

Doctoral Dissertations

Evolution of microstructures, such as polygonal ferrite, acicular ferrite, bainite, and martensite, plays a pivotal role in determining the final microstructural and mechanical properties of steel products. Given the established inter-relationship between processing parameters, microstructure, properties, and performance, precise control of phase transformation is essential to achieve pre-determined properties. To understand transformation routes in different steel grades, time-temperature-transformation (TTT) and continuous-cooling-transformation (CCT) diagrams are necessary and can be described using the Johnson-Mehl-Avrami-Kolmogorov equation and Scheil’s additivity rule. This study presents a comprehensive computational framework for predicting and optimizing microstructure and mechanical properties in advanced high-strength steels (AHSS) using adaptive machine …

Advancing Coal Rib Support Design Through The Integration Of Field Studies And Numerical Simulations, Alper Kirmaci

Doctoral Dissertations

Coal rib stability remains a major safety concern in U.S. underground coal mines, with rib failure-related injuries and fatalities still occurring. A key challenge is the lack of a standardized methodology for designing rib support systems that can address varying geological conditions. As a result, many mines rely on trial-and-error or traditional practices, leading to inconsistent designs. This research aims to develop a systematic methodology for rib support design to improve coal rib stability in U.S. mining operations.

The study consists of: i) field monitoring in active room-and-pillar coal mines, ii) in-situ pull-out tests on coal ribs, iii) numerical model …

Accelerating Circular Economy Transition For A Sustainable Built Environment: A Systems-Based Framework, Radwa Walid Yassin Eissa

Doctoral Dissertations

"The Circular Economy (CE) has gained momentum as a transformative framework for steering the construction value chain towards greater sustainability. However, this transition requires shifts in production and consumption patterns, adoption of circular business models, and the replacement of linear practices. Despite growing interest, CE adoption in construction faces persistent challenges, including sectoral fragmentation, lack of integrated governance frameworks, and limited coordination among stakeholders. This dissertation aims to accelerate the CE transition in the built environment through a multi-scale, interdisciplinary approach spanning five modules: (1) Module 1 develops a construction value chain-structured portfolio of CE strategies and assesses their implementation; …

Bond Graph And Extended Generalized Average Method With Applications In Cyber-Physical Systems And Power Electronics, Arnold Anthony Fernandes

Doctoral Dissertations

"This research examines two applications of control theory. The first application considers the bond graph (BG) modeling technique, which is used to develop the MATLAB structural analysis toolbox (MATSAT), an open-source toolbox for sensor placement and qualitative system analysis that considers the observability and fault-detection capabilities of multi-domain cyberphysical systems. The toolbox provides information on redundant sensors, guiding the system designer in cost and security trade-offs. The toolbox uses traditional BG causality assignment procedures. Additionally, MATSAT provides optimal causality assignment methods that perform significantly better at assigning causality to BGs with increased junctions, sources, and simple meshes, without encountering causality …

Lidar-Based Point-Cloud Human Modeling: Pose Estimation, Body-Parts Segmentation, 6g Localization, And Applications, Omar Rinchi

Doctoral Dissertations

"The human body represents a rich source of physiological and behavioral information, where precise analysis of anatomy, body parts, 3D pose, and motion enables cross-disciplinary precision applications. This dissertation formulates this challenge as a human modeling problem, where proposed algorithms convert the human body into a three-dimensional digital twin capturing anatomical structure and pose. These representations are further analyzed using additional algorithms to enable precision applications. To realize this vision, LiDAR sensing is adopted due to its privacy-preserving nature, robustness to lighting conditions, color-blind sensing characteristics, and decreasing cost.

Human modeling using LiDAR is challenging due to sparse, irregular, and …

Geometric Effects Of Warfighters On Body Armor Fit And Protective Capabilities Against Shock Wave Energy, Melissa Lynn Sutter

Doctoral Dissertations

Body armor is a vital piece of protective equipment for warfighters to defend against threats, necessitating continued development to improve comfort, weight, and protection. However, female warfighters often wear unisex body armor, designed primarily for the male torso. Current research has evaluated the short- and long-term detriments of female warfighters wearing these armors, focusing on comfort and performance. However, these studies do not comprehensively consider how a non-form-fitting armor compromises warfighter safety from battlefield threats. This research examines the geometric effects of female warfighters on armor protection level when defending against shock threats by evaluating the energy distribution on a …

Secret Key Generation Based On The Physical Layer Characteristics For Iot Networks, Abdullah Dakhlallah Alshamdayn

Doctoral Dissertations

The rapid expansion of low-resource devices, coupled with advances in telecommunications, has significantly increased the number of connected devices and enabled the development of affordable, energy-efficient, portable, and high-performance sensors for diverse applications. However, this convenience comes with security and privacy concerns related to the reliability of hardware, software, and communication infrastructure. The extensive interconnectivity of limited-resource devices and the transmission of large data volumes pose significant security challenges in wireless networks. The future wireless technologies, such as 5G, will enable the transfer of critical data, including personal, financial, military, and industrial information, necessitating secure communication in wireless networks. Generally, …

Advanced Plant Growth Using Halloysite Nanotubes (Hnts) And Waste Extraction For Medical Applications, Zeinab Jabbari Velisdeh

Doctoral Dissertations

This dissertation presents an integrated research framework that bridges nanotechnology, green chemistry, and sustainable agriculture, aiming to address two critical global challenges: enhancing plant growth under resource-limited conditions and valorizing agricultural waste for bioactive compound extraction. The study is divided into three major projects that together highlight the innovative application of magnesium oxide-coated halloysite nanotubes (MgO-HNTs) and the development of environmentally conscious extraction methods for high-value phytochemicals. The first component of this work investigates the design, fabrication, and functional evaluation of MgO-HNTs as advanced nanocarriers for promoting seed germination and early root development in tomato plants. MgO-HNTs were synthesized via …

Towards Robust Autonomous Systems: Handling Multi-Modal Uncertainties In Gps-Denied Environments, Vivya Kalidindi

Doctoral Dissertations

This dissertation focuses on designing a robust and uncertainty-aware framework for autonomous systems operating in GPS-denied environments, such as indoor infrastructures, underground tunnels, and lunar surfaces. The proposed framework addresses the challenges posed by multi-modal uncertainties, including sensor noise, distributional shifts under adverse conditions, and conflicting decision-making preferences. These challenges compromise the reliability and adaptability of autonomous platforms. To overcome these challenges, the proposed framework adopts a layered architecture that integrates advanced methodologies across the sensing, perception, and decision-making layers. At the sensing layer, an Edge-Kalman Filter combined with a density ratio-based update mechanism is employed to reduce aleatoric uncertainty …

Investigation Of Electrochemical Corrosion Fatigue Mitigation Strategies For Mild Carbon Steel, Joel Andrew Hudson

Doctoral Dissertations

Corrosion fatigue remains a critical durability challenge for steels used in boiler tubes and other safety-critical components, where the interaction of cyclic stresses and corrosive environments accelerates material degradation. This dissertation comprises three experimental studies aimed at advancing mitigation strategies and test methodologies: (1) enhancing corrosion resistance through electrodeposited Fe-Ni anodic coatings, (2) inducing synthetic crack closure to suppress fatigue crack growth, and (3) developing an accessible, low-cost, automated testing system to investigate environmentally assisted cracking (EAC) under controlled laboratory conditions. The first study examined the corrosion behavior of Fe and Fe-Ni electrodeposits synthesized from sulfate-based baths and applied to …

Posture - A Framework With Measures And Mediating Effects In Support Of A Structural Model For Attitude In Identity Formation, Christopher Kyle Prather

Doctoral Dissertations

This dissertation develops a framework and new, valid, abbreviated instruments for researching engineering identity formation as an evolving relational and attitudinal development process rather than a fixed outcome. The framework centers on “posture”, a mediating effect analogous to physical posture in human factors engineering. The investigation was unique, and developed evidence for a structural model using an interdisciplinary, systems-oriented approach. Methodologically, a clinical measure of internal emotional states from the Marriage and Family Therapy literature was administered alongside existing scales for perseverance, perceived workload, and psychological ownership. This study used the broad coverage of the clinical instrument to search for …

Advancing Rfid Systems: From Head Orientation To Robotic Localization Using Passive Tags, Guilherme Ricardo Mendes Da Silva Barreto De Figueiredo

Doctoral Dissertations

No abstract provided.

An Envisioned And Efficient Design Of Next Generation Decentralized Iot Bot Detection Model, Ahmed Abdullah Almalki

Doctoral Dissertations

The Industrial Internet of Things (IIoT) and Internet of Medical Things (IoMT) are revolutionizing critical infrastructures, but their expansion has also introduced severe cybersecurity vulnerabilities. Traditional IoT Bot Detection Systems (IBDS) struggle to scale in environments characterized by high-dimensional, large-scale, and redundant network traffic. These challenges hinder the development of reliable cloud-based intrusion detection systems. The limitations of static and rulebased methods in detecting evolving IoT botnet attacks—such as those launched by Mirai and Gafgyt—underscore the need for intelligent, adaptive approaches. To address this, the present study proposes a machine learning and deep learning-driven IoT Botnet Detection Model, validated through …