Digital Commons Network^™

Application Of Polymer Nanocomposites In The Design Of Prosthetic Sockets That Feature Auxetic Meta-Structures, Sumit Suryakant Kolte, Vinayak Vijayan, Lihua Lou

Publications

This research evaluates the response of prosthetic sockets constructed using polymer nanocomposites and incorporating auxetic meta-structures. The prosthetic sockets feature lightweight meta-structures between the inner and outer walls of the socket. Three different prosthetic designs featuring chiral, reentrant hexagon, and honeycomb meta-structures are evaluated in this study. The prosthetic sockets were designed in SolidWorks and compared using finite element analysis. The polymer nanocomposites used in this study include polypropylene and ultra-high molecular weight polyethylene containing uniformly distributed inclusions of either titanium dioxide, zinc oxide, or graphene nanoplatelets. Our results show a favorable pressure profile at the interface between the prosthetic …

Geophysical Tools For Agricultural Management: Trends, Challenges, And Opportunities, Sophia Becker, Trenton Franz, Yufeng Ge, Joe Luck, Derek M. Heeren

School of Natural Resources: Faculty Publications

Agrogeophysics is a convergence of proximal soil sensing and hydrogeophysics in which geophysical methods are used to describe soil patterns and dynamics that shape agronomic management (including precision agriculture). To fully realize its potential for optimizing agricultural production, current barriers and targets for future agrogeophysics research must be identified. In this review, we compare the trajectory of agrogeophysics within peer-reviewed literature, patents, and the US university extension literature and examine the current challenges and opportunities for supporting agricultural production and sustainability with agrogeophysical tools. We conducted a repeatable search of Web of Science and Scopus for seven geophysical methods (electromagnetic …

Aeolus-Ds: Dust-Aware Ai Decision Support For Coccidioidomycosis (Valley Fever) — A Design Science Research Framework Integrating Aerosol Remote Sensing, Land Disturbance, And Clinical Sentinel Signals, Harsha Sammangi, Aditya Jagatha, Harshini Balaga

Research & Publications

This paper presents Aeolus-DS, a design science research (DSR) artifact that integrates aerosol remote sensing (MAIAC AOD; dust fraction), mesoscale meteorology and soil moisture (ERA5), land-disturbance telemetry (construction, off-road vehicle activity, nightlights) and clinical sentinel signals (syndromic ED chief complaints, pneumonia rule-out) into a dust-aware, AI-driven early warning and decision-support system for Coccidioidomycosis (Valley Fever). Methodologically, we propose a graph spatiotemporal transformer with direction-aware attention and physics-guided regularisation reflecting aeolian transport. Using county-week panels (2014-2024) for the U.S. Southwest (AZ–CA–NV), Aeolus-DS improves now-casting mean absolute error (MAE) by 18% and two-week area under precision-recall curve (AUPRC) by 21% over …

Creating Blood Analogs To Mimic Steady-State Non-Newtonian Shear-Thinning Characteristics Under Various Thermal Conditions, Hang Yi, Alexander Wang, Christopher Wang, Jared Chong, Chungyiu Ma, Luke Bramlage, Bryan Ludwig, Zifeng Yang

Mechanical and Materials Engineering Faculty Publications

Blood analogs are widely employed in in vitro experiments such as particle image velocity (PIV) to secure hemodynamics, assisting pathophysiological diagnoses of neurovascular and cardiovascular diseases, as well as pre-surgical planning and intraoperative orientation. To obtain accurate physical parameters, which are critical for diagnosis and treatment, blood analogs should exhibit realistic non-Newtonian shear-thinning features. In this study, two types of blood analogs working under room temperature (293.15 K) were created to mimic the steady-state shear-thinning features of blood over a temperature range of 295 to 312 K and a shear range of 1~250 s−1 at a hematocrit of ~40%. Type …

Differential Privacy Enabled Deep Skin Image Classification Model Development, Prasun Kumar Mandal

Master’s Dissertations

Abstract In the era of big data, the explosive growth in data volume has significantly accelerated the development of deep learning. Deep learning is the most promising area of AI, yielding significant advancements in medical image classification. However, healthcare data contains important sensitive information and so privacy and security are crucial to preventing unauthorized access. Note that there are several data protection rules from multiple regulations to penalize any kind of data security violation, for example, the data protection principles (Article 5.1-2) and the data protection by design and by default (Article 25) of the General Data Protection Regulation from …

Experimentally Driven Numerical Model Of Carbon/Polyaniline-Based Glucose Monitoring Sensors: An Evaluation Using A New Figure Of Merit, Ziad Khalifa, Kyrillos Selim, Amira Ali, Sameh Abdellatif

Chemical Engineering

This study presents an experimentally driven numerical model for evaluating carbon/polyaniline (PANI)- based glucose monitoring sensors (GMSs), focusing on innovative configurations using graphene-PANI and carbon nanotube (CNT)-PANI composites. We performed a thorough analysis of the morphological, electrophysical, and electrical properties of these materials, ultimately leading to the extraction of key electrical parameters for integration into a finite element model (FEM). This model simulates the entire sensor, enabling the estimation of critical performance metrics such as sensitivity, limit of detection (LOD), linearity, and power consumption. Our findings demonstrate that the CNT-PANI configuration significantly outperforms the laser-induced graphene (LIG)-PANI electrode, achieving a …

A Wearable-Based Approach To Spinal Posture Tracking And Assessment, Sydney Marie Sherman, Noah Jeffery

Biomedical Engineering: Graduate Reports and Projects

This paper describes the design, manufacturing, and testing of a novel spinal posture tracking wearable device with embedded sensors. Currently on the market, most of the spinal posture tracking devices only focus on one segment of the spine and therefore do not track the entirety of human posture. The objective for this project was to create a wearable device in the form of a “smart shirt” that offers insights into posture trends throughout the day by tracking the entire spine. To begin the project, multiple designs were developed for the sensor array and wearable materials. These concepts were compared against …

Decentralised Consensus Control Of Hybrid Synchronous Condenser And Grid-Forming Inverter Systems In Renewable-Dominated Low-Inertia Grids, Hamid Soleimani, Asma Aziz, S. M.Muslem Uddin, Mehrdad Ghahramani, Daryoush Habibi

Research outputs 2022 to 2026

The increasing penetration of renewable energy sources (RESs) has significantly altered the operational characteristics of modern power systems, resulting in reduced system inertia and fault current capacity. These developments introduce new challenges for maintaining frequency and voltage stability, particularly in low-inertia grids that are dominated by inverter-based resources (IBRs). This paper presents a hierarchical control framework that integrates synchronous condensers (SCs) and grid-forming (GFM) inverters through a leader–follower consensus control architecture to address these issues. In this approach, selected GFMs act as leaders to restore nominal voltage and frequency, while follower GFMs and SCs collaboratively share active and reactive power. …

Data-Driven Methods For Signal Analysis And Nonlinear System Identification, Cristian Felipe López Ruano

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

In recent years, data-driven modeling has gained considerable attention in nonlinear system identification, offering a powerful alternative to traditional physics-based methods. This has been possible by advances in machine learning, sparse regression, and differential equations in a weak form, which allow the accurate capture of complex nonlinear dynamics, even when the measurements are in strong background noise. Moreover, integrating these models with physical knowledge further enhances their reliability and interpretability, making them suitable for a wide range of science and engineering applications.

In this dissertation, we investigate algorithms for data-driven nonlinear system identification to obtain ordinary differential equations that govern …

Impactor Displacement As A Predictor Of Thoraco-Abdominal Organ Injury: Comparison Of Isolated Organ To Whole Body Tests., Jack Seifert, Jared Michael Koser, Alok Shah, Lance Frazer, Frank A. Pintar, Narayan Yoganandan, Dan Nicolella, Brian D. Stemper

Biomedical Engineering Faculty Research and Publications

Purpose

Body armor is used to protect the wearer from penetrating injuries. However, when the armor defeats the projectile, it deforms at high rates into the wearer, referred to as back face deformation (BFD). This deformation can cause a variety of superficial and internal injuries and consequently should be considered when designing body armor. However, current design standards do not adequately consider the effects of BFD, and new data are necessary to inform future design thresholds for BFD. The objective of this study was to develop blunt impact injury risk curves for in vitro thoraco-abdominal organs and determine their applicability …

Novel Respirometry Measurement And Comparison Of Monod Kinetic Constants And Methanogen Concentrations For 27 Full-Scale Anaerobic Bioreactors, Nicholas Benn, Antonio Martins, Daniel H. Zitomer

Civil and Environmental Engineering Faculty Research and Publications

Monod kinetic coefficient values (km and Ks) as well as methanogen concentrations (Xac and Xh2) for various anaerobic bioreactors are required to gain a deeper understanding of anaerobic biotechnology. However, there are no existing, practical methods to measure or estimate these parameters or variables in typical anaerobic digester biomass that contains mixed cultures with inert organic solids. Current measurement methods rely on pure cultures developed in the laboratory. In this work, a novel respirometric method was developed and used to determine methanogen Monod parameters and active biomass concentration for samples from 27 full-scale anaerobic reactors. Differences (one to three orders …

A Uas-Centered Investigation Of Vorticity Characteristics And Cold Pool Structure Across Forward And Left-Flank Boundaries In Supercells, Mark R. De Bruin

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

Supercell internal boundaries are the locus of tornadogenesis; thus, understanding the characteristics of these boundaries, particularly in terms of vorticity, is important for identifying the role they play in tornado formation. Insight into the overall characteristics of internal boundaries and their possible role in tornadogenesis have been driven by studies reliant on numerical modeling-based experiments. Observational studies often neglect above-surface conditions or, when these observations are made, lack the spatial resolution to resolve boundary characteristics. During TORUS (Targeted Observation by Radars and UAS of Supercells) 2019 and TORUS-LItE (TORUS Left-flank Intensive Experiment) 2023, uncrewed aircraft systems (UAS) and mobile mesonets …

Development And Implementation Of A Vector-Quantized Transformer Autoencoder For Monitoring Bicycle Road Conditions, Kewei Ren

Department of Construction Engineering and Management: Dissertations, Theses, and Student Research

Adviser: Chun-Hsing Ho

The long-term, sustainable assessment of bicycle road conditions is essential for ensuring cyclist safety and promoting equitable infrastructure management. While traditional detection methods based on manual thresholds have demonstrated some effectiveness, they carry limitations due to the diversity of pavement materials, cyclist behaviors, and sensor platforms. Although recent advances in deep learning offer promising alternatives, established techniques are primarily applied to highway anomaly detection and are rarely adapted for bicycle roads. Furthermore, most related studies lack clear definitions for anomalies and transparent annotation procedures, resulting in a shortage of effective datasets and detection methods specifically for bicycle …

Conservation Management Effects On Soil And Agronomic Properties In A Furrow-Irrigated Corn System In East-Central Arkansas, Jeferson P. Pimentel, Christopher G. Henry, Kris R. Brye, Benjamin R. Runkle, Amanda J. Ashworth, Edgar Mersiovsky, Kevin C. Thompson

Biological and Agricultural Engineering Faculty Publications and Presentations

Cover crops can improve soil physical properties and help to sustain agricultural crop yields. A 6-year study conducted from 2017 to 2022 examined the effects of conservation soil management practices in a furrow-irrigated continuous corn (Zea mays L.) system on a silt-loam soil (Typic Albaqualf) in the Lower Mississippi River Delta region of eastern Arkansas. This study compared the effects of conventional tillage (CT), no-tillage (NT), and no-tillage with cover crops (CC) in selected soil physical and chemical properties (i.e., soil bulk density (D b), porosity (& fnof;t), field capacity (Theta FC), Permanent wilting point (Theta PWP), Plant available water …

Aircraft Bird Strike Risk Prediction Using Machine Learning And Analytic Hierarchy Process, Jason Anthony Powell

Doctoral Dissertations and Master's Theses

To address the limitations of Next Generation Radar-based bird strike forecasting, this study modeled 12 spatiotemporal weather features from the National Oceanic and Atmospheric Administration alongside bird strike risk using Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), XGBoost regression tree, and Bayesian network algorithms. Five years of bird strike data from four geographically diverse airfields served as the target risk variable, categorized as low, moderate, or severe based on Department of the Air Force risk models. The ensemble model, which combines the LSTM-RNN and XGBoost regression algorithms, yielded the most accurate forecasts, achieving 80% to 93% accuracy across all airfields, …

Statistical Monitoring Of Hard Faults In Digital Systems, Dany Akshay Deep Isukapalli

Electrical Engineering Theses and Dissertations

Achieving a high test coverage is crucial for helping to ensure that integrated circuits are working correctly and are non-defective. Although scan-based structural tests are used throughout the industry, high-level functional tests may be needed to detect some defects— especially those that are environmentally sensitive. Unfortunately, the character of functional test makes it difficult to obtain high coverage, and it is even hard to estimate coverage because fault simulation times of large circuits are long. As a result, some method is required for predicting the ability of a functional test that has not been fault simulated to detect defects. In …

Ai Questions; "The Ai Tea", Sally Brown

Artificial Intelligence Exhibit

This section includes a series of questions related to AI and the exhibit content, as well as button designs by WVU students and Art in the Libraries committee members, a list of the exhibition sponsors, and information on the exhibition launch panel.

Artificial Intelligence In Education, Sally Brown, Jill Woods, Mohamed Hefeida, Jennifer Sano-Franchini, Megan Vendemia, Erin Brock Carlson, Megan Leight, Gangqing Hu, Nicole Fuller

Artificial Intelligence Exhibit

This section explores the intersection of AI and education, including an overview of WVU's AI statement and submissions from WVU faculty, classes, students, and staff on AI related projects.

Teaching Learning Newsletter Vol 5 Issue 1, Sastra Deemed To Be University

TLC Newsletter

No abstract provided.

Jme 4110: Automatic Umbrella Opener, Sam Nieder, Adam Freeman

Washington University / UMSL Mechanical Engineering Design Project JME 4110

Manually opening and closing a patio umbrella can be time consuming and laborious. While there are remote controlled umbrellas for sale, they are expensive and may not be a reasonable option for most people. Our task was to create a device that can be retrofitted onto any patio umbrella, that allows it to be opened and closed remotely.

Jme 4110: Remotely Actuated Rail Switch, Calysta Yalew, Chibale Anwisye, Ron Almira, Andy Sengsavang

Washington University / UMSL Mechanical Engineering Design Project JME 4110

This project focuses on developing a device that will switch the direction of rail by moving the manual rail switch itself. This design consists of two winches strategically placed on either side of the rail switch. It is then attached to a beam clamped to the manual switch and via a button on a phone, can pull the handle left and right. With this device patrons are able to keep safe and railway operations would be more efficient.

Advancing Food Nutrition Estimation Via Visual-Ingredient Feature Fusion, Huiyan Qi, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is limited due to the lack of datasets with nutritional annotations. To address this issue, we introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations. In addition, we propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF2 ) method to enhance nutrition estimation by integrating visual and ingredient features. Ingredient robustness is improved through synonym replacement and resampling strategies during training. The ingredient-aware …

Secure Query On Encrypted Data By Using Fully Homomorphic Encryption, Mrinmoy Bera

Master’s Dissertations

Cloud service providers typically store user data in an encrypted form (data at rest). However, when a user performs a query, the server first decrypts the data, processes the query on plaintext, and then sends the result back to the user (data in transit). This process exposes a critical vulnerability—if the cloud server is ever compromised, the decrypted data becomes accessible to the attacker. To address this security gap, we design a secure query protocol that eliminates the need to decrypt data on the server side. Fully Homomorphic Encryption (FHE) offers a groundbreaking solution by enabling arbitrary computations directly on …

Federated Learning Using Fully Homomorphic Encryption, Sk Golam Kuddus

Master’s Dissertations

Traditional machine learning approaches require centralizing data for training, which raises significant privacy concerns when dealing with sensitive information. Federated learning (FL) addresses this by keeping data local and enabling multiple users to collaboratively train a shared machine learning model. In spite of this, FL remains vulnerable to inference attacks, as sensitive information can still be extracted from the model’s learned parameters. While traditional privacy-enhancing techniques such as di!erential privacy introduce noise to model updates to obscure individual data points, they often present a fundamental trade-o! between privacy and utility. Furthermore, these approaches still carry risks of data leakage if …

Seconds From Impact: Anticipatory Vehicular Crash Prediction Using Video Vision Transormers, Ryan P. Geisen

Master's Theses

Vehicular collisions represent a significant public health concern, necessitating re search into advanced emergency notification systems. While deep learning has shown promise in accident detection, a research gap persists in applying state-of-the-art transformer architectures to the task of anticipatory, real-time crash prediction from video. This thesis addresses this gap by developing and evaluating a Video Vision Transformer (ViViT) for the binary classification of imminent vehicular collisions. Utilizing a curated dataset of 1,493 unique collision sequences, this study systemati cally investigates the impact of temporal context by comparing the ViViT against a single-frame Vision Transformer (ViT) baseline and conducting comprehensive exper …

Reduced Order Model-Based Framework For Microsecond Model Updating Of Two-Dimensional Structural Systems Using The Local Eigenvalue Modification Procedure, Emmanuel Abiodun Ogunniyi

Theses and Dissertations

Real-time model updating is crucial for active structures and electronic assemblies subjected to high-rate dynamic events. High-rate dynamic events refer to events that occur at high speeds and with rapid changes in the forces and energy involved. These events can include explosions, impacts, and crashes. These systems are characterized by a high dynamic response with a high rate (< 100 ms), high amplitude (> 100 gn), highly nonlinear, meaning that the response is not proportional to the applied force, and involve complex interactions between multiple objects or materials. A system exposed to high-rate dynamic environments is frequently prone to rapid plastic deformation, involving violent and …

Hardware Accelerated Simulation Of Buck Converters Using Physics-Informed Neural Networks, James Clayton Crews

Theses and Dissertations

Physics-informed neural networks (PINNs) are an emerging machine learning method for learning the behavior of physical systems described by governing differential equations. Dc-dc power-electronic converters are used in a variety of industry applications such as motor drives or power supplies where real-time simulation is critical for control and safety. This thesis investigates physics-informed machine learning as an approach to develop a real-time digital twin for dc-dc power converters. Traditional numerical integration methods are used to approximate discretized behavior, and the results are compared with a trained PINN model. Modern ML frameworks (such as PyTorch and TensorFlow/Keras) are used to quickly …

Strain And Redox Engineering For Enhanced Thermoelectric Performance In Transition Metal Oxide Thin Films, Mohammad Jamal El Loubani

Theses and Dissertations

The discovery of highly efficient and cost-effective thermoelectric (TE) materials is essential to envision high-performance TE power generators that enable the direct conversion of waste heat into electricity. Transition metal oxides (TMOs) are considered promising candidates for high-temperature TE applications due to their excellent thermal stability and low cost. However, their maximum power output (power factor, PF = σ•S2) is constrained by the intrinsic trade-off between thermopower (S) and electrical conductivity (σ), both of which are inversely related to carrier concentration. This trade-off poses a significant barrier to improving the performance of TE devices. Among various strategies to overcome this …

Characterizing The Relationships Between Cranial Geometry, Brain Morphometry, And Brain Mechanical Properties Using Mr Imaging And Elastography, Jessica Restivo

Theses and Dissertations

Traumatic brain injury (TBI) is a global health concern, with over 69 million cases annually. TBI risk depends on subject-specific neuroanatomical variation, yet research often only considers generic finite element (FE) models, based on the geometry and material composition of a 50th percentile male head. Although highly accurate, subject-specific models require expensive magnetic resonance (MR) imaging, elastography, and time-consuming computational resources. There is a need to create subject-specific TBI models that account for subject-specific variations, without neuroimaging. This study aims to create mathematical models to predict brain morphometry (dimensions, volume) [Objective 1] and mechanical properties (shear stiffness, damping ratio, octahedral …

Elevating Next Generation Wireless Devices Towards Contactless Sensing For Healthcare Applications, Aakriti Adhikari

Theses and Dissertations

There is an increasing interest in technologies that can understand and perceive at-home human activities to provide personalized healthcare monitoring, aimed at early detection of disease markers and assisting physicians in making clinical decisions. Existing approaches, such as wearables, require users to wear sensors that can be cumbersome and cause discomfort. Vision based solutions, such as optical cameras, IRs, LiDARs, etc., can be used to design contactless at-home monitoring systems. However, these systems are limited by poor lighting and occlusion, and they are privacy-invasive. Fortunately, high-frequency millimeter-wave wireless devices provide an effective alternative to the existing systems to enable fine-grained …