Digital Commons Network^™

Design And Production Of A Carbon Fiber Longboard, John R. Beebe, Jacob Williams, Julia Stradinger, Vince Fitchtel, Jordan Cochran

Honors Theses

This thesis contains an overview of the design, prototyping, and manufacturing of a carbon fiber long board. The purpose of this capstone was to create a viable product for the real-world market and then determine how to manufacture it at scale with sustainable financial practices. In order to accomplish this task, engineering, accounting, and manufacturing challenges had to be overcome throughout all stages. The production process involved the carbon fiber layup, baking, water jet cutting, and assembly of the carbon fiber longboard. In order to optimize this process, continuous improvement and lean manufacturing problem solving techniques were applied. After a …

What Types Of Uncertainty Emerge In Instructional Aviation Incidents?, Abigail Henson

Mechanical Engineering Undergraduate Honors Theses

This study utilized narrative reports from the National Aeronautics and Space Administration (NASA) Aviation Safety Reporting System (ASRS) database to examine the types of uncertainty present in near-miss instructional aviation incidents occurring within the United States between 2015 and 2025. The final dataset consisted of 86 reports, with each report containing a set of narrative accounts from both the student pilot and the flight instructor operating under Part 91 regulations. Each instructional event was classified using a combined framework incorporating uncertainty categorizations and the Human Factors Analysis and Classification System (HFACS). Results indicate that epistemological uncertainty was the most common …

Port And Vessel Communication Traffic Intrusion Detection: A Variational Autoencoder‑Enhanced Multilayer Perceptron Approach, Chien-Lin Chiang, Hsien-Cheng Chou, Ming-Yuan Peng, Yi-Yuan Chiang, Yu-Shun Liu

Journal of Marine Science and Technology–Taiwan

Port and vessel networks increasingly operate on IP/Ethernet backbones with high‑noise, high‑dimensional traffic. We present a lightweight hybrid intrusion‑detection model that couples a variational autoencoder (VAE) with a multilayer perceptron (MLP) and augments training with a boundary‑oriented latent‑space mixup strategy. The VAE models the distribution of normal traffic and identifies anomalies through reconstruction errors. Subsequently, it generates robust latent vectors, enabling the MLP to perform highly accurate supervised classification. On the UNSW‑NB15 dataset, the proposed pipeline attains ≥97% accuracy and an outstanding recall of 99.56% in binary intrusion detection, and visualization of the latent space (PCA) together with reconstruction‑error analyses …

Long Range Battery-Free Wireless Power Transfer Testbed For Underground Mines Iot And Lpwan Devices, Anabi Hilary Kelechi, Samuel Frimpong, Sanjay Madria

Mining Engineering Faculty Research & Creative Works

Underground mines are susceptible to occasional roof falls and cave-ins, temporarily destroying the existing wireless communications and telemetry infrastructure. During this temporary outage, intermittent provision of electrical energy wirelessly to the already deployed low-power wireless area networks (LPWAN) and Internet of Things (IoT) devices assumes a fundamental requirement. In this article, we propose and design a long-range far-field radio frequency (RF) wireless power transfer (WPT) testbed to power LPWAN and IoT devices at 35 m in an underground mines facility. Class AB external power amplifier (PA) was introduced to achieve a long-distance RF WPT, in the 880 MHz band. Thus, …

A Data-Driven Koopman Framework For Station-Keeping On Near Rectilinear Halo Orbit, Anusha Sharma Malladi

Theses and Dissertations

Cislunar missions have gained significant attention in recent decades, motivating the need for efficient modeling and reliable control. In this work a Koopman operator based framework is developed for approximating the error dynamics around a reference Near Rectilinear Halo Orbit (NRHO) in the Earth-Moon Circular Restricted Three-Body Problem (CR3BP). A decoder free neural network is used to learn a lifted linear representation of the nonlinear CR3BP dynamics and a residual based approach is used to identify the corresponding control input matrix. The model is then implemented in a receding-horizon target point controller and compared with uncontrolled propagation and a State …

Developing Imitation Learning Policies By Exploring Diffusion Framework For Quadcopters, Likith Swamireddy

Theses and Dissertations

Imitation learning offers a promising approach in developing near-optimal onboard policies for nonlinear systems, by learning from computationally expensive trajectory planners during offline training. Existing State-of-the-art methods commonly learn deterministic mappings from observations to actions by training fast neural networks to mimic the expert demonstrations, which can limit their ability to capture the action distribution, when the policy is trained on multiple possible expert commands to reach the same target, and this may lead to unsafe scenarios in the obstacle environments. To address these challenges, this study explores Diffusion Models (DMs) as a feedback controller for quadcopters that learns the …

Cascaded Pid Control Of A Passive Bio-Inspired Rotating Empennage Aircraft, Zachary T. Jenkins

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis presents an autopilot for a Bio-Inspired Rotating Empennage (BIRE) aircraft. Most airplanes control yaw using a vertical stabilizer and a moving rudder, a design that has stayed essentially unchanged for nearly a century but adds weight, drag, and surface area to the aircraft. The BIRE removes the vertical tail entirely and instead allows the horizontal tail to rotate about the body x-axis of the airplane; tilting the horizontal tail produces a yaw force, doing the job of a rudder. This thesis first develops a controller for the BIRE where the empennage angle is commanded directly, then extends …

Dataset For Integrity Attacks On Time Synchronized Synchrophasor Data, Taylah Griffiths, Mohiuddin Ahmed, Chadni Islam

Research outputs 2022 to 2026

Phasor measurement units, also known as synchrophasors, are a vital component within smart grids to determine the stability of the grid. These devices send synchrophasor data to phasor data concentrators that collate and analyse the data. Recently, synchrophasor communication data has become beneficial for the research community. However, datasets covering cyberattacks on synchrophasor data are not public. Having access to this data would aid in investigating mitigations against cyberattacks. This paper describes a public specialized dataset, known as ECU-PMU-FDI/TSA. The dataset contains synchrophasor communication data for cybersecurity mitigation testing. Three hours of communication data was captured, from a simulated testbed. …

Efficient Charging Scheduling Through Coordination Of Electric Vehicle Platoons And Charging Stations, Liwan Qi, Bochun Wu, Shoubo Li, Yi Gong, Wei Ni

Research outputs 2022 to 2026

This paper focuses on charging allocation in a vehicle-to-infrastructure (V2I) communications-enabled electric vehicle (EV) network with heterogeneous traffic flows, where manned EVs and EV platoons coexist, and each EV platoon may have a different size and travel speed. In such a network, hybrid traffic flows pose significant challenges since platoons with multiple EVs can easily cause severe station overloading and increase the total time cost for charging service, particularly when large platoons occur. To tackle this issue, a centralized approach is proposed to plan charging allocation and optimize the velocities of manned EVs and EV platoons with the assistance of …

Where Do We Go Now: An Examination Of The Role Of 3d Modeling In Cancer Research, Ella Claire Gregory, Hannah Noelle Cochran

ABE 4523/6523 Biomedical Materials

The development of three-dimensional tumor models has become an important area of cancer research. By incorporating biomaterials that can mimic the physical, chemical, and biological characteristics of the tumor environment. 3D models can provide a more physiologically relevant environment for studying tumor growth, cell interaction, metastasis, and drug response.

Mathematical Modelling Of Nitrification And Denitrification Processes Based On Neuro-Fuzzy Bioreactor Models, Mirkhalil Agzamovich Ismailov, Boburbek Zokirjon Ugli Mannobjonov

Technical science and innovation

This paper presents the development and investigation of hybrid neural network and fuzzy models for the mathematical modelling of nitrification and denitrification processes in a biological wastewater treatment bioreactor. A comprehensive approach is proposed, integrating a mechanistic model of the ASM1/ASM2d type with neural networks (LSTM and Gaussian Process Regression), as well as a fuzzy control system based on an extended set of expert rules. A digital twin of the bioreactor was developed to allow for the prediction of the dynamic behavior of key parameters such as NH₄⁺, NO₃⁻, dissolved oxygen, etc. within a prediction range of 1 to 12 …

Artificial Neural Network-Based Modelling And Prediction Of Key Performance Indicators In Road Construction Projects, Hussein Mativila, John M. Kafuku, Beatus A. T. Kundi

Tanzania Journal of Engineering and Technology (TJET)

Road construction projects in developing countries including Tanzania experience several challenges including cost overruns, schedule delays and poor quality. The causes of these challenges include complex interdependencies project’s uncertainty factors. This makes traditional methods for predicting Key Performance Indicators (KPIs) less effective. This study has developed a robust Artificial Neural Network (ANN) model for accurate modelling and predicting KPIs for road construction projects in Tanzania. The analysis was based on data obtained from 281 projects implemented by TANROADS in 11 regions from 2015 to 2025. Fourteen uncertainty factors were measured on a five-point Likert scale and screened using Principal Component …

Voltage-Related Power Quality Issues And Impacts On Distribution Networks With Sensitive Loads - A Review, Godwin Elinazi Mnkeni, Jackson Justo, Aviti Thadei Mushi, Bakari M. M. Mwinyiwiwa

Tanzania Journal of Engineering and Technology (TJET)

Voltage disturbances are the most important power quality (PQ) complications that customers and power utilities face in this smart era. The growing adoption of sophisticated electronic equipment and integration of renewable energy sources (RES) into power grids has increased the susceptibility of power distribution networks (PDNs) to voltage sags, swells, interruptions, flicker, and voltage imbalance. These disturbances, mainly caused by upstream faults, switching operations, and RES integration, compromise voltage PQ and system reliability. Consequently, they accelerate equipment degradation, increase electronic waste (e-waste), raise reactive power demand and maintenance costs, increase power losses, and impose substantial economic losses on customers and …

Willingness To Pilot Under Varying Weather And Tower System Conditions: Mediating Effects Of Risk, Nathan Royal Schultz

Doctoral Dissertations and Master's Theses

The implementation of Remote Tower Systems (RTS) within the National Airspace System (NAS) offers potential improvements in operational efficiency, cost-effectiveness, and scalability; however, its success depends on pilots' willingness to operate in such environments. This study examined the effects of tower system type (RTS vs. Traditional Tower Systems [TTS]) and weather conditions (visual meteorological conditions [VMC], marginal visual meteorological conditions [MVMC], and instrument meteorological conditions [IMC]) on pilots’ willingness to pilot (WTP), while also evaluating the mediating role of perceived risk (PR). Grounded in Paul Slovic’s Risk Perception Theory (RPT), this research employed a quantitative experimental design using a factorial …

Factors Influencing Enterprise Adoption Of Ai-Enabled Computers: An Expert Judgment Quantification Approach Across Multiple Sectors, Yu Shan Su, Tugrul Daim, Chia-Hao Hung, Leong Chan, Dana Bakry

Engineering and Technology Management Faculty Publications and Presentations

This study examines the key factors influencing the adoption of Artificial Intelligence Personal Computers (AIPCs) by enterprises, exploring both the benefits and challenges of their business applications. As enterprises increasingly require real-time computing, autonomous decision-making, and improved cybersecurity, AIPC—combining artificial intelligence and edge computing—has become a strategic technology for boosting competitiveness. Particularly in scenarios with less reliance on cloud services, businesses are more likely to adopt devices with local processing and standalone AI capabilities to meet the dual needs of operational efficiency and data privacy. Through an extensive review of the literature, this study identifies four main dimensions and sixteen …

Disturbance-Learning Inertia Estimation Using Artificial Neural Networks For Power System Stability, Sospeter Igaanja Gabriel, Francis Mwasilu, Peter Makolo Dr.

Tanzania Journal of Engineering and Technology (TJET)

ABSTRACT

Power systems are progressively shifting towards low inertia as a result of incorporating significant amounts of intermittent and converter-based renewable energy sources, such as wind and solar power, into the current power grid network. This integration poses considerable problems to inertia and frequency control within the network due to a reduction in the proportion of synchronous generators. Furthermore, rapid frequency deviations occur due to the disparity between supply and demand during contingencies, complicating the maintenance of frequency stability within the power system. The disturbance-learning inertia estimation method for power system stability is presented. The simulation analysis is performed using …

Neutrokoopman: Channel-Preserving Koopman Spectral Analysis Of Nonlinear Dynamics With Truth, Indeterminacy, And Falsity Evidence, Ahmed Samy, Mohamed M. Abdelhafeez, K Venkatachalam, Mohamed Abouhawwash

Neutrosophic Systems with Applications

Modern dynamical systems increasingly operate with evidence that is not merely noisy but incomplete, contradictory, or only partially trustworthy. Conventional Koopman methods represent nonlinear dynamics through linear evolution of observables, while robust and adaptive variants address parameter and model uncertainty. They do not, however, preserve the semantic distinction between support, indeterminacy, and counter-support when those conditions are compressed into a single uncertainty variable. This paper develops NeutroKoopman, a channel-preserving Koopman framework in which the physical state is augmented by a single-valued neutrosophic evidence state ν_t = ( T_t,I_t,F_t ). Deterministic and Markovian formulations are …

The Algorithmic Narcissus: Ai Validation And The Atrophy Of The 'Athletic' Social Self, Ilham Phalosa Reswara, Anggi Mayangsari

Jurnal Psikologi Sosial

Artificial intelligence systems designed around continuous affirmation and minimal friction are becoming increasingly prominent as social and relational partners in everyday life. This article examines how sustained interaction with such frictionless AI systems may reshape the developmental conditions under which social selfhood forms and is maintained. Drawing on Cooley's (1902) looking glass self, Kohut's (1971) concept of optimal frustration, and the developmental literature on social competence, the article argues that frictionless AI may distort the social mirror through which identity forms, remove the manageable frustration that appears necessary for psychological growth, weaken empathic capacity, and erode the social stakes that …

Biosensor Development In East Africa: A Systematic Review Of Technologies, Applications, Challenges, And Future Directions, Ally Mahadhy

Tanzania Journal of Science

Biosensors integrate biological recognition elements with signal transduction systems to generate quantitative or semi-quantitative analytical information and have applications across healthcare, food safety, agriculture, environmental monitoring, and wildlife health. Despite substantial global advances, biosensor development in East Africa remains poorly characterized. This systematic review, conducted according to PRISMA 2020 guidelines, evaluated published evidence from 1995 to 2025 within a predefined East African Community geographical scope. Eligibility was based on IUPAC-aligned operational criteria requiring an integrated biological recognition element, transduction mechanism, and quantitative or semi-quantitative signal output. Three locally developed biosensors met the criteria: an electrochemical nano-biosensor for schistosomiasis diagnosis in …

Fast Sparse Image Reconstruction Models In Through-The-Wall Radars: A Review, Aude Kileo, Hashimu U. Iddi, Abdi Abdalla

Tanzania Journal of Science

Through-the-Wall Radar Imaging (TWRI) is a modern technology that uses electromagnetic waves to detect objects behind walls, with key applications in surveillance, rescue operations, and reconnaissance. Achieving high resolution in both down-range and cross-range requires ultra-wideband signals and long apertures, resulting in large data volumes, increased acquisition time, and high memory demands. TWRI employs Compressive Sensing (CS) to reduce computational time, which has proved its significance in many recent TWRI applications. However, in CS, image reconstruction approaches shift the computational burden from the sensing stage to the recovery stage, which prolongs the reconstruction times, making it unsuitable in time-sensitive applications. …

Survey Data For Decision Support Tool Development With A Focus On Emerging Design Spaces, Tyson Humphrey, Chris Mattson, Michael L. Anderson, Colin C. Engebretsen

ScholarsArchive Data

This dataset contains the survey responses and PASE matrix used to support the derivation of nine development principles for decision support tools in emerging design spaces. The survey captures observations from a multidisciplinary DST development team, while the PASE matrix documents relationships among desired DST outcomes, architecture strategies, effects, and supporting literature.

Increasing The Density Value Of Composite Propellant Ap/Htpb/Al For Rocket Performance Using The Sphere Packing Model, Muhammad Iqbal Alfikri, Bayu Prianto, Ariadne Lakshmidevi Juwono

Makara Journal of Science

This research employs the sphere packing volume (SPV) model to optimize the density of composite propellants thereby enhancing rocket performance. We systematically varied the diameters of spherical ammonium perchlorate (AP) particles (50 and 400 µm) and aluminum powder (Al) particles (10 and 30 µm). These particles were incorporated at an ideal AP:Al ratio of 4:1 into a matrix of hydroxyl-terminated polybutadiene and toluene diisocyanate. By adjusting the AP ratio for each Al particle size and considering theoretical and tap densities, we precisely calculated the SPV value for each composition. The composition with the highest SPV value yielded remarkable results. This …

Geotechnical Enhancement Of Noncohesive Coal Mine Overburden Using Microbially Induced Calcite Precipitation And Fly Ash Stabilization For Sustainable Pavement Applications, Atul Gautam, Laxmikant Yadu

Journal of Sustainable Mining

The majority of the waste generated from opencast mining, termed as overburden (OB), is dumped adjacent to the mine boundary and poses long-term stability issues. Various studies have been carried out to assess the risk of dump slope failure, and treatment strategies have been accordingly recommended to strengthen this OB dump. However, scant works are reported to sustainably utilize this OB for construction works. This study aims to quantify the performance of stabilized noncohesive coal mine OB collected from Gondegaon opencast mine (OCM) in India. Stabilization was achieved using two techniques, namely microbially induced calcite precipitation (MICP) through Sporosarcina pasteurii …

Exploring Conceptions Of Engineering Design And Sustainability In Informal K–12 Engineering Education, Mobina Beheshti, Eunice Y. Kang, Avneet Hira

Journal of Pre-College Engineering Education Research (J-PEER)

In this study we investigate K–12 students’ conceptualizations of sustainability, their perspectives on the role of engineers in sustainable practices, and how augmented reality (AR) can facilitate sustainability-oriented engineering design activities in informal settings. The AR technology used in this study includes the Merge Cube (a physical cube that serves as a digital canvas for AR) and CoSpaces Edu (an online platform that allows students to create 3D models and animate them using code). We employ a design-based research methodology, using qualitative narrative and artifact analysis to address the research questions: (1) What are students’ conceptions of sustainability and the …

Reducing Drag Of Vehicles With Half-Stepped Cylinder Surfaces Part I: Numerical Optimization, Ryan Moffit, Hamid Rahai, Komal Gada

Mineta Transportation Institute

Aerodynamic drag is a major source of energy loss for vehicles, increasing fuel consumption, reducing the driving range of electric vehicles, and contributing to greenhouse gas emissions. For a vehicle, the major components of its drag are pressure and viscous drag. Engineers have developed a variety of strategies to reduce drag, including streamlined vehicle designs, surface modifications, and devices that alter airflow around the vehicle. This research focuses on reducing the viscous drag. Specifically, this study investigates whether microscopic surface features called half-stepped cylinder arrays can reduce skin friction by altering the turbulent boundary layer and promoting smoother airflow near …

Modeling Urban Heat Island Mitigation Strategies In Egyptian Cities Using Envi-Met, Islam M. Gaber, Abbas M. Hassan, Tarek Elkashef, Ola A. Gad, Ahmed M. Abdo, Abdelrazek Elkomy

HBRC Journal

Mitigation techniques for urban heat islands (UHI) have emerged as a critical concern for global urban environmental sustainability. Passive cooling employing cool materials and/or urban greening has shown to be the most effective strategy for mitigating the impacts of UHI. This latter method, however, is relatively new in the literature of urban environmental studies, and research on it is still limited, particularly for cities in hot-dry climatic zones. This study aims to investigate the possibility of improving albedo rates and adopting urban greening measures, such as greening building roofs, roadways, and walkways, in several Egyptian cities located in various climatic …

Safety And Resilience In Disaster Debris Removal And Clean-Up Operations: A Field And Training Guide For Resilience Workers In Disaster Debris Removal And Cleanup, Mohsen Shahandashti, Ph.D, P.E., Abhijit Roy, Ph.D, Zhe Yin, Ph.D, P.E., Pmp, Leed Ap, Antonio Balderrama, Ph.D, P.E., Sandesh Adhikari, Santosh Acharya

Mavs Open Press Open Educational Resources

A practical safety guide for the people who clear debris after disasters, and for the trainers who prepare them. Built for crews of every experience level--from seasoned operators to first-time volunteers--it teaches how to read a damaged site, recognize hidden and shifting hazards, work safely around heavy equipment and hazardous materials, and make sound decisions under pressure. Every chapter pairs clear guidance with a one-page field job aid, facilitator notes for trainers, and a real-world scenario, all grounded in OSHA, FEMA, EPA, and NIOSH guidance.

Safety and Resilience in Disaster Debris Removal and Clean-Up Operations Copyright © 2026 by Mohsen …

Cognitive Resilience At The Edge: Hyperdimensional Computing Versus Deep Learning For Hardware-Degraded Rf Classification, Adrian B. Cisneros, Jeong Yang

Military Cyber Affairs

Autonomous Collaborative Combat Aircraft (CCA) operating in contested electromagnetic environments must classify Radio Frequency (RF) signals on edge silicon that degrades over the mission lifetime due to thermal stress, radiation, and manufacturing variation. Deep neural networks dominate RF classification on pristine hardware, but their weights are precise and interdependent, causing catastrophic accuracy collapse as the underlying chip ages. We investigate whether Hyperdimensional Computing (HDC), a brain-inspired paradigm that distributes information across thousands of dimensions, can provide a reliability floor where Deep Learning fails. Using the RadioML 2016.10A dataset filtered to five digital modulations relevant to drone command-and-control links, we trained …

Hands-On Ransomware: An Experiential Wannacry Case Study For Undergraduate Cybersecurity Education, Eli Creek Richmond, Thomas R. Devine

Military Cyber Affairs

Ransomware represents one of the most disruptive threats in the cyber landscape, yet hands-on malware analysis remains rare in undergraduate cybersecurity curricula. This paper presents the design, implementation, and evaluation of an experiential learning module centered on the WannaCry ransomware case study, deployed in a senior-level course at West Virginia University. Students performed static and dynamic analysis using industry-standard tools. Pre- and post-module assessments demonstrated measurable gains in self-reported competency across seven technical dimensions. The module's competencies align directly with DoD Cyber Workforce Framework Work Role 212, Cyber Defense Forensics Analyst, supporting education-to-workforce pipeline development.

Breaking The Build: Detecting Software Supply Chain Vulnerabilities In Ci/Cd Pipelines, Mercedes R. Wahl, Dr. Benjamin Yankson

Military Cyber Affairs

This study examines whether integrating structured DevSec- Ops security controls into CI/CD pipelines can reduce software supply chain risk by preventing vulnerable components from progressing through the software development lifecycle. Software supply chain attacks frequently originate from weaknesses or compromises within dependencies, build environments, and trusted development stages, making early detection essential. A controlled sandbox experiment compared two pipeline configurations: a baseline CI/CD pipeline with no automated security enforcement and a secure DevSecOps pipeline integrating automated vulnerability scanning, SBOM generation, and artifact integrity verification. A known vulnerable dependency, the Python requests package (version 2.19.0) associated with CVE-2018-18074, was intentionally introduced …