Implementation And Clinical Utility Of Ultra-Low-Field Portable Magnetic Resonance Imaging For Postprocedural Neurological Evaluation In Ambulatory Neurosurgery: Illustrative Cases, Devan Patel, Vinay Jaikumar, Taysia P. T. Morioka, Laz Rifkin, Kenneth S. Jacoby, Jaims Lim, Anais Andrade, Aimee C. Degaetano, Pui Man Rosalind Lai, Elad I. Levy

EVMS School of Health Professions Faculty Publications

BACKGROUND

Elective endovascular neurosurgical procedures are increasingly performed in ambulatory neurosurgery centers, enabled by advances in catheter technology, safety of conscious sedation, and refined patient selection. Although complication rates are low, rapid evaluation of postprocedural neurological deficits remains critical. Conventional MRI is often impractical in outpatient or procedural settings, whereas ultra-low-field portable MRI (ULF-pMRI) systems (such as Swoop) allow bedside imaging with favorable diagnostic performance.

OBSERVATIONS

Two women in their 60s developed acute neurological deficits at an ambulatory neurosurgery center (ANSC) after diagnostic cerebral angiography in one case and elective internal carotid artery flow diversion in the other. In both …

Unified Bayesian And Machine Learning-Based State Of Charge Estimation In A Pv Battery System, Showrov Rahman

Open Access Dissertations

The state of charge (SOC) of a battery indicates the remaining charge in the battery relative to its nominal maximum charge capacity. The accurate estimation of SOC is of utmost importance for efficient energy management as it indicates the level of energy stored in the battery.

Bayesian and machine learning (ML) approaches represent two distinct estimation strategies for battery state estimation. Bayesian filters, such as the Kalman filter (KF) and particle filter (PF), operate sequentially by incorporating physical models of battery dynamics. In contrast, ML-based methods are data-driven, and their performance largely depends on the quantity and quality of training …

Computational Modeling And Machine Learning For The Design Of Polymer-Based Protective Systems Under Dynamic Loading, Jonathan Tate Villada

Open Access Dissertations

Computational modeling and machine learning offer powerful pathways for designing polymer-based protective systems subjected to dynamic loading, particularly for mitigating the early shock-dominated response generated by near-field underwater explosions (UNDEX). As naval, offshore, and submerged infrastructure systems continue to grow in strategic importance, there is an increasing need for lightweight, damage-tolerant protective solutions capable of reducing transmitted pressure, deformation, and energy transfer under extreme impulsive environments. Since large-scale experimental testing under such conditions are costly and limited, validated numerical frameworks provide an efficient means to evaluate polymeric coatings, architected metastructures, and data-driven predictive tools across broad design spaces. The first …

Formulation Development Of Topical Inserts Containing Doxycycline And Doxycycline Combined With Tenofovir Alafenamide And Elvitegravir For The Prevention Of Sexually Transmitted Infections, Vivek Agrahari, M. Melissa Peet, Jasmin Monpara, Rijo John, Sriramakamal Jonnalagadda, Pardeep K. Gupta, Meredith R. Clark, Gustavo F. Doncel

CONRAD Publications

Despite advances in oral and injectable HIV prevention options and oral prophylaxis for sexually transmitted infections (STIs) of bacterial origin, there remains a critical need for effective on-demand topical (vaginal and rectal) products for pre- and post-exposure prophylaxis (PrEP and PEP). To fill this gap, we have developed single and first-in-kind multi-active topical inserts for bacterial STIs and HIV/STIs prevention. We have formulated two different inserts, one containing doxycycline (DOX) at 10, 50, and 100 mg doses for bacterial STI prevention, and a multipurpose prevention product (TED insert) that combines DOX (10 mg) with the antiretrovirals tenofovir alafenamide (TAF; 20 …

Performance Assessment And Design Improvements For An Urban Coastal Detention Basin Under Intensifying Rainfall Extremes, Imiya Mudiyanselage Chathuranika, Agyare Asante, Faeghe Borhani, Xixi Wang, Mujde Erten-Unal, Dalya Ismael

Civil & Environmental Engineering Faculty Publications

Coastal urban areas are increasingly exposed to flooding driven by more frequent and intense rainfall events, rising sea levels, and expanding impervious surfaces. Norfolk, Virginia, a low-lying coastal city with aging stormwater infrastructure, faces heightened vulnerability to these hydrologic pressures. This study evaluates the hydraulic performance of an existing urban detention basin within the Edgewater–Larchmont catchment under 10-, 50-, and 100-year, 2-h design storms using the U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM). Simulations were conducted for both pre- and post-development conditions to assess changes in peak discharge, storage capacity, and water level dynamics. Results show that urbanization, …

Real-Time Lidar-Based Vehicle Detection Using Pointpillars For Connected And Automated Vehicle Applications, Jared Townsend Behr

Graduate Theses, Dissertations, and Problem Reports (ETD)

This work explores the implementation of a real-time LiDAR processing model for autonomous vehicle design. Automotive perception systems are becoming increasingly important as Advanced Driver Assistance Systems (ADAS) and Connected and Automated Vehicle (CAV) technologies continue to progress toward higher levels of autonomy. Reliable perception is paramount for autonomous features, such as adaptive cruise control (ACC), where identifying and tracking a lead vehicle is a safety critical task.

Many production ADAS perception systems primarily rely on radar and camera sensors for object detection and sensor fusion. These sensors work well to achieve classification and accurate distance detections; however, they are …

Smartbot V3: A Scalable Educational And Research Robotics Platform And Decentralized Construction Framework, Robert Tyler Cook

Graduate Theses, Dissertations, and Problem Reports (ETD)

Autonomous robotic systems are increasingly used in both engineering education and robotics research, creating a need for systems capable of supporting both within a common platform. This thesis presents SMARTbot V3, a scalable robotics platform developed to support undergraduate robotics education and multi-robot research.

SMARTbot V3 is a differential-drive mobile manipulator incorporating a 3-degree-of-freedom manipulator, LiDAR, RGB cameras, onboard computation, and Robot Operating System 2 (ROS2) software. The platform was designed to support scalable deployment through standardized hardware, modular construction, and common software. A fleet of twenty robots was manufactured and deployed for both educational and research activities. Educational validation …

Ai-Based Smart Proxy Models For Flow Assurance, Integrating Oil Rate Prediction, Pipeline Monitoring, And Leak Detection, Ali Sajedian

Graduate Theses, Dissertations, and Problem Reports (ETD)

Oil and gas pipeline networks operate under complex multiphase flow conditions and dynamic transient regimes that present significant challenges for traditional monitoring, anomaly detection, and operational decision-making methodologies. Many existing monitoring approaches rely on high-fidelity physics-based transient flow simulations to accurately represent system behavior. However, these models are computationally intensive and therefore difficult to implement in real-time operational environments.

This dissertation presents a physics-guided artificial intelligence framework for intelligent pipeline monitoring and integrity management based on the concept of Smart Proxy Modeling. By integrating first principles engineering knowledge with machine learning techniques, the proposed framework develops computationally efficient surrogate models …

Assessment Of Emissions Factors For Produced Water And Other Atmospheric Liquid Storage Tanks On Natural Gas Sites, David Glenn Childs

Graduate Theses, Dissertations, and Problem Reports (ETD)

As the US natural gas (NG) sector continues to grow, concerns have increased regarding the effects and increasing volume of greenhouse gases emitted into the atmosphere. While NG is a cleaner burning fuel than diesel and gasoline, unburned methane (CH₄) has a global warming potential (GWP) 27-30 times that of carbon dioxide (CO₂). To better quantify CH₄ emissions on NG sites, the US Department of Energy (DOE) granted West Virginia University (WVU) funding to collect direct measurements at well sites and compressor stations.

In this work, a total of 18 sites, including controlled and uncontrolled …

Evaluation Of Engineering Controls In Mitigation Of Fecal And Chloride Contamination To An Urban Stream, Gabriel J. Zenny

Graduate Theses, Dissertations, and Problem Reports (ETD)

Fecal coliform contamination is a major contributor to surface water pollution in the United States’ (U.S.) waterways, whether from combined sewer overflows (CSOs) (point source pollution), runoff from precipitation and snowmelt (nonpoint source pollution), or exfiltration from aging and failing infrastructure. In addition to fecal bacteria, chloride contamination is also a major, prevalent surface water contaminant impacting U.S. waterways. Chloride contamination is predominantly released into U.S. waterways as nonpoint source pollution, endemic to northern areas that use industrial brine and salt as a form of mitigation for ice accumulation on roadways. The purpose of this research was to investigate impairment …

Plant-Wide Process Modeling, Techno-Economic Optimization, And Bayesian Uncertainty Quantification For Manufacturing Value-Added Products From Lignocellulosic Biomass, Poulomi Das

Graduate Theses, Dissertations, and Problem Reports (ETD)

For decades, many chemicals have been produced from fossil resources, contributing to resource depletion, pollution from extraction and refining, and hazardous byproducts. Growing environmental concerns and declining fossil reserves have intensified the search for sustainable sources of fuels, energy, and chemicals. Lignocellulosic biomass, the only abundant renewable carbon source, is therefore being explored as a feedstock for second-generation biofuels and high-value chemicals.

Currently, global wood-panel industry heavily depends on petroleum-derived commercial adhesives like phenol-formaldehyde, urea-formaldehyde, etc. These formaldehyde-based chemicals cause hazards to both human health and environment through emission of toxic volatile organic compounds. Typically, lignin is considered as one …

Dataless Neural Networks For Boolean Satisfiability And Network Optimization, Andrew Evan Gautier

Graduate Theses, Dissertations, and Problem Reports (ETD)

Combinatorial optimization problems (COPs) require searching over a finite solution space subject to constraints, with the goal of satisfying an objective function. They arise in operations research, scheduling, resource allocation, circuit design, and many other fields. Many problems in combinatorial optimization (including satisfiability and network design) are NP-hard. Traditionally, researchers have built approximate solvers that return near- optimal solutions efficiently by developing increasingly sophisticated heuristics and meta- heuristics. Deep learning has provided new opportunities for improving combinatorial solvers by leveraging neural guidance to prune the search space. Traditional neural networks have distinct drawbacks in this context: separate training and …

Physics-Informed Machine Learning Methods To Predict Heavy-Duty Diesel Engine Performance And Emissions, Sandeep Guguloth

Graduate Theses, Dissertations, and Problem Reports (ETD)

The development of internal combustion engines for increased efficiency and reduced emissions demands improved design tools that are cost-effective, fast, and easy to integrate into existing engineering workflows. Low-dimensional physically based engine models serve this role by reducing the need for iterative experimental prototyping but historically rely on empirical correlations that sacrifice fidelity compared to CFD. Machine learning (ML) approaches, conversely, achieve high predictive accuracy but lack interpretability and generalize poorly beyond their training data. Physics-informed machine learning (PIML) bridges this gap by constraining the solution space to physically feasible predictions, offering a more reliable way to model in-cylinder phenomena. …

Multi-Timescale Monitoring And Modeling For Resilient Smart Grids: Pmu Anomaly Detection And Battery Digital Twins, Muhammad Imran Hossain

Graduate Theses, Dissertations, and Problem Reports (ETD)

Modern power systems face failures at very different timescales. Cyber-physical disturbances may emerge within seconds, while battery degradation develops over hundreds of operating cycles. Both monitoring problems share the same underlying difficulty: power systems produce measurement data in abundance, but reliably labeled examples of abnormal or degraded operation are scarce. Rare grid events are difficult to label, and battery degradation data are heterogeneous across cells, cycling protocols, and chemistries. This thesis addresses these challenges through two complementary domain-informed learning frameworks that constrain representation learning using information specific to each physical problem.

First, at the system level, T-BiGAN, a Transformer-augmented bidirectional …

Data Augmentation And The Reliability Of Conformal Prediction For Uncertainty Quantification In Medical Imaging, Rizwan Ahamed

Graduate Theses, Dissertations, and Problem Reports (ETD)

The safe clinical deployment of deep learning models for high-stakes medical imaging tasks requires more than high average accuracy; it requires demonstrable, per-case reliability. Uncertainty quantification (UQ) provides the missing signal that tells a clinician when a model prediction can be trusted and when a case should be escalated for expert review. Among UQ approaches, conformal prediction (CP) is especially attractive because it produces prediction sets that are guaranteed, under the assumption of exchangeability, to contain the true label with a user chosen probability, and it does so without assumptions about the model or the data distribution. This report first …

Student Support In Civil And Environmental Engineering, Heather Marie Rice

Graduate Theses, Dissertations, and Problem Reports (ETD)

Many student support services target first-year engineering students to help improve their transition from high school to college and to reach first-year retention goals. These opportunities are communicated to students through a variety of ways. The objectives of this work are to evaluate student perspectives of student services support factors in a civil and environmental engineering program and investigate how student support services and opportunities are communicated to students in a civil and environmental engineering program. The media identified for student support service outreach were the fundamentals of engineering program newsletters, academic advising newsletters, Statler eNews newsletters, and the bulletin …

Multi-Sensor Fusion For Manual Wheelchair State Estimation, Kathylee Pinnock Branford

Graduate Theses, Dissertations, and Problem Reports (ETD)

Kathylee Pinnock Branford Manual wheelchairs are essential to mobility and independence, yet accurately characterizing wheelchair motion in real-world environments remains challenging. Inertial measurement units (IMUs) provide a practical means of estimating wheelchair kinematics, but existing trajectory-reconstruction studies have largely been limited to short, controlled trials. This report developed and evaluated a four-IMU framework comprising one sensor on each rear wheel and two sensors on the wheelchair frame. Three orientation algorithms (VQF, Mahony AHRS, and Madgwick AHRS), two accelerometer ranges (±2 g and ± 4 g), and calibrated and uncalibrated sensor data were evaluated during indoor motion-capture trials and a outdoor …

Assessing Mobility And Upper Extremity Function Beyond Laboratory Settings In Manual Wheelchair Users With Spinal Cord Injury Using Inertial Measurement Units, Kathylee Pinnock Branford

Graduate Theses, Dissertations, and Problem Reports (ETD)

Manual wheelchair users with spinal cord injury (SCI) face chronic, repetitive shoulder demands that far exceed those of able-bodied daily life, placing them at high risk for rotator cuff pathology and long-term upper extremity dysfunction. Despite this burden, existing research has relied predominantly on brief laboratory assessments that capture only a narrow part of real-world mechanical demands, leaving critical gaps in the identification of true injury risk factors. This dissertation developed and applied inertial measurement unit (IMU)-based methods and novel algorithms to characterize arm use, mobility, and environmental context during a full week of free living behavior in 25 manual …

Sustainable Nutrient Management Opportunities For Small Communities In The U.S. With Lagoon Wastewater Treatment Systems, Denis Sigei Ruto

Graduate Theses, Dissertations, and Problem Reports (ETD)

Lagoon wastewater treatment systems are among the most widely used secondary treatment technologies serving small and rural communities in the United States due to their low capital and operating costs, minimal energy requirements, and operational simplicity. While effective for removal of organic matter and suspended solids, most lagoon systems were not designed to achieve advanced nutrient removal. As regulatory priorities increasingly emphasize control of total nitrogen and total phosphorus to protect receiving waters and public health, lagoon-dependent communities face growing compliance challenges. These challenges are exacerbated by aging infrastructure, limited technical capacity, financial constraints, and insufficient infrastructure data, highlighting the …

Supply Chain Optimization For Fertilizer Production From Wastewater, Ethan Clement Robey

Graduate Theses, Dissertations, and Problem Reports (ETD)

In West Virginia, approximately 27,000 tons of wastewater solids are generated annually, with nearly 75% being landfilled or alternatively disposed of in a non-beneficial manner. Over the past two decades, the amount of these solids being taken for beneficial uses, such as agricultural application, has declined by 25%, while landfilling has increased by more than 30%. This highlights issues related to eutrophication and disposal costs. This study aims to develop a supply chain optimization framework that addresses the conversion of wastewater solids into fertilizers to match local farm requirements, while determining processing facility locations that minimize transportation costs. To determine …

Computational Methods For Identification Of Molecular Signatures, Weijun Yi

Graduate Theses, Dissertations, and Problem Reports (ETD)

This work develops computational methods for identifying molecular signatures from high-throughput genomic data and for modeling long non-coding RNA (lncRNA) sub-cellular localization. The response of multiple myeloma to CB-6644, a selective RUVBL1/2 complex inhibitor with potential anti-tumor activity, is analyzed to identify drug-responsive pathways and molecular signatures. Conventional gene set enrichment analysis (GSEA) often excludes low-expression genes. Here, phenotype comparison is reformulated as a supervised machine learning problem: genes most informative for discrimination are first selected using a machine learning approach, and GSEA is then applied to these machine-learning derived gene sets. This framework improves detection of CB-6644-associated pathways. For …

Development Of Hydrogen Emissions Quantification Systems To Expand Understanding Of Leaks And Losses Associated With The Burgeoning Hydrogen Transportation Sector, Christopher Loomis

Graduate Theses, Dissertations, and Problem Reports (ETD)

To combat global warming, alternative fuels are being investigated. Hydrogen is at the forefront of this movement, advertised as having zero emissions due to the products of the energy reaction being only water. However, studies over recent decades indicate that hydrogen in the atmosphere acts as a form of greenhouse gas (GHG) by increasing the lifespan of methane in the atmosphere. Models are attempting to estimate the possible effects of a transition to a hydrogen-fueled economy but lack empirical data to provide confidence to these estimated values. There is little data that quantifies hydrogen from anthropogenic sources.

To address this …

Comprehensive Evaluation Of Co₂ Eor Numerical Modeling In The Clinton Sandstone Of The Appalachian Tri-State Region Using Compositional Reservoir Simulation (Cmg Gem), Bushra Aref Alqattan

Graduate Theses, Dissertations, and Problem Reports (ETD)

ABSTRACT

The Clinton Sandstone of the Appalachian Basin represents a mature hydrocarbon-producing formation with potential for carbon dioxide (CO₂) enhanced oil recovery (EOR). This study develops a compositional reservoir simulation model using the CMG software suite (WinProp, Builder, and GEM) to evaluate the performance of CO₂ injection in improving oil recovery within the Clinton formation.

A three-dimensional reservoir model was constructed using representative geological and petrophysical properties consistent with those of the Clinton Sandstone. Fluid behavior was modeled using a compositional equation-of-state (EOS) approach to accurately capture phase behavior, miscibility development, and CO₂–oil interactions under reservoir conditions. Injection scenarios were …

Development And Additive Manufacturing Of Uv And Dual-Curable Elastomers For Soft Robotic Actuators And Embedded Sensors Via Direct Ink Writing, Emrah Demirkal

Graduate Theses, Dissertations, and Problem Reports (ETD)

The advancement of soft robotics requires materials and manufacturing methods that combine large deformation, structural stability, and integrated sensing within a single soft system. Conventional fabrication techniques often limit design flexibility and the incorporation of functional materials into soft structures. This dissertation addresses these challenges through the development of UV-curable and dual-curable silicone elastomers for the direct ink writing (DIW) of soft robotic actuators and embedded sensors.

UV-curable elastomers were first synthesized through thiol-ene click chemistry using poly(mercaptopropylmethylsiloxane) (MMPS) and vinyl-terminated polysiloxane (VPS). Their curing behavior, rheological response, printability, and mechanical properties were evaluated for DIW. These materials enabled rapid …

A Multiobjective Framework For Joint Coverage And Motion Planning In Uav Inspection Tasks, Luis Fernando Escobar Carvajal

Graduate Theses, Dissertations, and Problem Reports (ETD)

Unmanned Aerial Vehicles (UAVs) have become essential for data acquisition in complex 3D environments. However, traditional Coverage Path Planning (CPP) methodologies often rely on a sequential pipeline that isolates viewpoint generation from flight path routing. This decoupling fails to account for the interdependence between viewpoint distribution and minimum flight paths, effectively restricting the search space and preventing the identification of a global optimum. This dissertation proposes a unified multi-objective framework for joint coverage and motion planning. The primary contribution of this work is the transition from isolated, sequential steps to a simultaneous optimization of the number and position of viewpoints …

Adaptive Multi-Grade Deep Learning For Highly Oscillatory Fredholm Integral Equations Of The Second Kind, Jie Jiang, Yuesheng Xu

Mathematics & Statistics Faculty Publications

This paper studies the use of Multi-Grade Deep Learning (MGDL) for solving highly oscillatory Fredholm integral equations of the second kind. We provide rigorous error analyses of continuous and discrete MGDL models, showing that the discrete model retains the convergence and stability of its continuous counterpart under sufficiently small quadrature error. We identify the DNN training error as the primary source of approximation error, motivating a novel adaptive MGDL algorithm that selects the network grade based on training performance. Numerical experiments with highly oscillatory (including wavenumber 500) and singular solutions confirm the accuracy, effectiveness and robustness of the proposed approach.

Handwriting Recognition In Vr, Dominique Mosley

EWU Masters Thesis Collection

Virtual Reality (VR) is slowly becoming more popular for more than just entertainment. VR can be found in educational, office, and even healthcare settings to help discover more intuitive ways to teach, collaborate, and treat patients. Outside of the virtual world, these environments typically rely on writing for communicating or note-taking. Currently, VR input forces users to rely on clunky on-screen keyboards which disrupts the user’s immersion and breaks the flow of natural interaction. This thesis explores the potential of VR as a learning platform by combining it with artificial intelligence (AI). It aims to develop a VR-enhanced handwriting practicing …

Engineering Plga Nanoparticle Size To Modulate Immune Responses After Spinal Cord Injury, Daniel J. Kolpek

University of Kentucky Doctoral Dissertations

Spinal cord injury is a severe and debilitating condition that often results in lifelong complications, including paralysis. Many of these chronic outcomes are driven by a sustained inflammatory response at the injury site, which limits tissue regeneration and functional recovery. As a result, therapeutic strategies that modulate this immune response have gained significant interest. Polymer based nanoparticle therapies have emerged as a promising approach due to their ability to interact with and influence immune cell behavior. Importantly, the physicochemical properties of nanoparticles can be precisely tuned to optimize these effects. Among these properties, size has been shown to influence key …

Personality Exceptions: Enhancing Character In Narrative Planners With Internal Memory, Elinor Rubin-Mcgregor

University of Kentucky Doctoral Dissertations

Stories and narratives are powerful tools for forging connections between people, as they induce emotions shared through their audience and help build empathy and understanding through sharing unique perspectives. Interactive stories can do all this and more, as they can be used to better educate and engage their audience. But in such stories, the characters must have consistent personality and human-like emotions in order for the audience to relate to them. Current implementations of personality in interactive narratives either portrays characters with static personality that feel less realistic, or else require a great deal of additional work from the author …

A Nonlocal Lattice Particle Method For Modeling Hcp Polycrystalline Solids, Di Liu

University of Kentucky Doctoral Dissertations

Polycrystalline materials, composed of individual grains with varying grain size, shape, and crystallographic orientation are widely used in various engineering and industrial applications. Conventional applications of polycrystalline materials rely on the macroscopic performance, and the microstructural features are usually homogenized out. With the advancement of modern technologies and improvement of characterization methods, polycrystalline materials find their applications in meso- and micro-scale systems. At these length scales, the microscopic features including grain boundary characteristics significantly affect the material physical responses. As a result, the conventional continuum mechanics models become problematic, especially for material failure problems where the microstructural features play dominant …