Engineering Commons^™

Understanding Human-Agent Teaming: A Mixed-Methods Examination Of Perceptions And Interactions, Valerie Robbins-Roth, Kendall Carmody, Bella Deloach, Meredith Carroll, Amanda Thayer, Jessica Wildman

Aeronautics Faculty Publications

While quantitative research on trust in HATs is well-established, there is a significant gap in understanding individuals’ subjective experiences during trust violations and repair. This study explores how people perceive and respond to these events, guided by three key questions: (1) How do participants describe emotional and behavioral responses to trust violations and repair? (2) How do personality traits influence reactions? (3) What qualitative themes emerge in the dissolution of trust? Using a mixed-methods approach, participants engaged in a 1-hour simulated search-and-rescue mission with four autonomous agents. During the task, one agent committed two trust violations, followed by repair attempts …

Emotion Processing In Adolescents With Epilepsy And Healthy Controls Using Multi-Modal Neuroimaging, Frances Kathryn E. King

Bioengineering Dissertations - Archive

Epilepsy is increasingly recognized as a disorder not only of seizures but also of widespread network dysfunction, impacting cognitive and emotional processing. One critical cognitive process affected in epilepsy is emotional conflict processing, which relies on the interplay of multiple brain networks. However, the spatiotemporal dynamics of emotional conflict processing in epilepsy, particularly in adolescents, have remained poorly understood. This dissertation investigated the neural mechanisms underlying emotional conflict processing in typically developing adolescents and adolescents with epilepsy using magnetoencephalography (MEG). The first study elucidated the spatiotemporal profile of emotional conflict processing in 24 typically developing adolescents using magnetoencephalography (MEG). Cluster-based …

Neurovascular Coupling Impairments In Acute Traumatic Brain Injury: An Eeg-Nirs Analysis, Zachary Armstrong

Bioengineering Theses - Archive

Traumatic brain injury (TBI) is a major cause of neurological impairment, often leading to variable recovery and uncertain prognosis in the neurocritical care setting. There is a pressing clinical need for robust, physiologically grounded biomarkers to inform prognosis and therapeutic decision-making in acute TBI. This thesis investigates neurovascular coupling (NVC), the physiological coordination between neuronal activity and cerebral blood flow, as a candidate biomarker for brain function and recovery after injury.

A prospective cohort study was performed using simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings in patients with moderate-to-severe TBI and healthy controls. Wavelet transform coherence (WTC) analysis was …

Consumer Preferences And Willingness To Pay For Vertiport Design, Amenities, And Services, David C. Ison

International Journal of Aviation, Aeronautics, and Aerospace

The rise of urban air mobility (UAM) and electric vertical takeoff and landing (eVTOL) aircraft necessitates the development of well-designed vertiports that align with consumer expectations. This study examines consumer preferences and willingness to pay (WTP) for vertiport design, amenities, and services to inform infrastructure planning and investment strategies. Using a survey-based quantitative research design, data were collected from 500 participants to assess key factors influencing WTP, including convenience, security measures, service efficiency, and premium offerings. Findings indicate that location accessibility, affordability, and streamlined security procedures are primary determinants of vertiport adoption. While aesthetic and retail enhancements contribute to passenger …

Learning From Leads: A 1d Dilated Resnet For Ecg Chagas Disease Screening, Somesh Saini, Matheus Lima Diniz Araujo

Student Scholarship

No abstract provided.

An Educational Framework For The Disruptive Technologies And Their Integration In The Un Sdgs Curriculum, Tulsi Pawan Fowdur, Aishani Radhakeesoon

Journal of Educational Technology Development and Exchange (JETDE)

Disruptive technologies such as 5G, AI, IoT, cloud computing, and blockchain are revolutionizing life on our planet and at the same time contributing immensely towards sustainable development and the achievement of the UN SDGs. Nowadays, optimizing the management of resources in various fields such as manufacturing, education, health, transportation etc. in a much more efficient way is possible. This can be achieved by capturing a wealth of real-time data via IoT sensors with the ultra-reliable and low latency connections provided by 5G. Additionally, with cloud computing, blockchain, and AI, these real-time systems can securely transmit, store, and analyze a massive …

Energy, Exergy, Economic, And Environmental Analyses (4e) Of Geothermal Power Plant With Double Flash System For Power And Heat Production, Mamdouh El Haj Assad, Yashar Aryanfar, Imen Ben Salem, Shek Atiqure Rahman, Laveet Kumar, Ahmad K. Sleiti, Mohammad Alhuyi Nazri

All Works

Geothermal energy is a reliable and sustainable renewable energy source due to its continuous availability and eliminating the need for energy storage systems. Among various types of geothermal power plants, double flash (DF) geothermal plants are among the most widely utilized. This paper presents a comprehensive thermodynamic analysis of a DF geothermal power plant, integrating energy, exergy, economic, and exergoenvironmental (4E) evaluations. The study examines the influence of key parameters, including the high-pressure separator and geothermal production well temperature, on the system performance. The results indicate that the expansion valve of the high pressure separator exhibits the highest exergy destruction …

Assessing The English Language Needs Of Student Pilots: A Comprehensive Analysis For Effective Aviation English Training, Triyani Rps Dewi, Lilis Kurnianingsih

International Journal of Aviation, Aeronautics, and Aerospace

This research aims to identify the English language material needs for pilot students at the Indonesian Civil Aviation Polytechnic in order to improve communication skills that are essential for flight safety and operational effectiveness. In data collection, the researcher distributed questionnaires to students and conducted a Focus Group Discussion (FGD) involving students, flight instructors, English lecturers, and experienced pilots. The research results show that pilot students need two main skills in English learning. First, listening skills, especially in understanding instructions from ATC. Second, speaking skills used to respond to ATC instructions and to make requests or ask for information from …

Detecting Sars-Cov-2 In Ct Scans Using Vision Transformer And Graph Neural Network, Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng

Electrical & Computer Engineering Faculty Publications

The COVID-19 pandemic has presented significant challenges to global healthcare, bringing out the urgent need for reliable diagnostic tools. Computed Tomography (CT) scans have proven instrumental in detecting COVID-19-induced lung abnormalities. This study introduces Convolutional Neural Network, Graph Neural Network, and Vision Transformer (ViTGNN), an advanced hybrid model designed to enhance SARS-CoV-2 detection by combining Graph Neural Networks (GNNs) for feature extraction with Vision Transformers (ViTs) for classification. Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. The model was …

Time-Marching Quantum Algorithm For Simulation Of Nonlinear Lorenz Dynamics, Efstratios Koukoutsis, George Vahala, Min Soe, Kyriakos Hizanidis, Linda Vahala, Abhay K. Ram

Electrical & Computer Engineering Faculty Publications

Simulating nonlinear classical dynamics on a quantum computer is an inherently challenging task due to the linear operator formulation of quantum mechanics. In this work, we provide a systematic approach to alleviate this difficulty by developing an explicit quantum algorithm that implements the time evolution of a second-order time-discretized version of the Lorenz model. The Lorenz model is a celebrated system of nonlinear ordinary differential equations that has been extensively studied in the contexts of climate science, fluid dynamics, and chaos theory. Our algorithm possesses a recursive structure and requires only a linear number of copies of the initial state …

Are We Ready For Synchronous Conceptual Modeling In Augmented Reality? A Usability Study On Causal Maps With Hololens 2, Anish Shrestha, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Participatory modeling requires combining individual views to create a shared conceptual model. While remote collaboration tools have enabled synchronous online modeling, they are limited to desktop settings. Augmented reality (AR) offers a new approach by potentially providing the sense of presence found in physical collaboration, which may better support participants in achieving the sense of presence found in physical locations, thus supporting them in negotiating meaning and building a shared model. (2) Methods: Building on prior works that developed technology, we performed a usability study with pairs of modelers to examine their ability at performing key conceptual modeling …

Leveraging Intrinsic Properties For Classification Of Coal Seams Towards Spontaneous Combustion Proclivity And Predicting Susceptibility Using Machine Learning: Smart And Sustainable Mining Approach, Siddhartha Agarwal, Pradeep K. Gautam, Yuhao Zou, Rishabh Dwivedi, Durga C. Panigrahi, Cihan H. Dagli, A. Singh

Engineering Management and Systems Engineering Faculty Research & Creative Works

Mine fires and other hazards caused by spontaneous coal combustion are a pervasive and longstanding issue in Jharia coalfields, India. This study proposes a novel approach to classify coal seams based on their propensity to spontaneous combustion using the intrinsic properties of 30 coal samples from different seams. This method eliminates the need for expensive and time-consuming experimental determinations of susceptibility indices (SI) such as crossing point temperature (CPT), critical air blast (CAB), and differential thermal analysis (DTA). All clustering models, viz. hierarchical, k-means, and multidimensional scaling, aptly classify coal seams into three categories: highly risky, medium risky, and low …

Data-Driven Layout Design For Smart Remanufacturing: A Flexible Optimization Model And A Case Study, J. A. Afari, A. Gosavi, J. Hu, R. J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

Abstract: In remanufacturing, a vital segment of the sustainable, low-carbon circular economy, existing versions of the traditional unequal-areas facility layout problem (UA-FLP) model face significant limitations in designing layouts. To be specific, in the process of minimizing the material-handling cost (MHC), these models also alter departmental dimensions, often diverging from construction specifications. This poses a difficulty, as critical equipment required for remanufacturing, e.g., sorting and cleaning machines, have unalterable dimensions, which implies that departmental dimensions cannot be changed from specifications provided. To address this, a novel Flexible Envelope UA-FLP (FE-UA-FLP) model is proposed in this work for designing layouts wherein …

Transplant Surgeon Fuzzy Associative Memory (Tsfam): Model For Capturing Surgeon Perspective, Rachel Dzieran, Cihan H. Dagli, Robert J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

AI-driven healthcare decision-making is multi-faceted, requiring complex logic to adapt to evolving policies and societal demands. Effective change implementation by healthcare providers and multidisciplinary organ transplant teams depends on adaptive decision-making. The proposed Transplant Surgeon Fuzzy Associative Memory (TSFAM) model introduces a novel approach to Human-AI Teaming, keeping human expertise central while dynamically adjusting to changing requirements. TSFAM employs fuzzy logic to manage imperfect data and human ambiguity, integrating the transplant surgeon perspective with the AI deep learning decision-making tool, creating a resilient solution in this critical domain. By embedding adaptive capabilities into the architecture, TSFAM exemplifies the adaptability of …

Electricity Theft Detection With An Adaptive Deep Learning Architecture, Mohammed Sleiman, Cihan Dagli, Rui Bo

Engineering Management and Systems Engineering Faculty Research & Creative Works

Electricity theft presents a significant challenge to the power industry. This paper demonstrates an adaptive deep framework integrating dimensionality reduction, graph modeling, attention mechanisms, and dynamic feature refinement for improving theft detection. Principal Component Analysis squeezes consumption data while an Autoencoder extracts latent representations and denoises the input. A Gated Graph Convolutional Neural Network uses k-Nearest Neighbors to model local relationships, while Transformers capture long range global dependencies. Neural Ordinary Differential Equations then refine features over continuous time, improving adaptability to complex patterns. The framework achieves 94.01% accuracy with stratified 5-fold cross validation. However, class imbalance challenges the minority class …

Cyber Forensics With Deep Learning Recurrent Neural Networks, Pfautch Ric, Dagli Cihan, Ashiku Lirim

Engineering Management and Systems Engineering Faculty Research & Creative Works

Detection of anomalies and anti-patterns is essential for adaptive systems with the ability to perform without foreknowledge. Some problems require both classification and regression along with sensitivity tuning and explainability. Some have highly dimensional datasets that are time dependent. This research offers results for Long-Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) algorithms using the BETH dataset. It unpacks metadata attributes and stages a unique approach via Abstract-Feature Analysis (AFA), hyper parameter tuning, and Principal Component Analysis (PCA) within the RNN model. By removing foreknowledge, this research offers insights into RNN anomaly detection performance when an event absent in training …

Estimating Electric Power Consumption Of Grid-Enabled Residential Heat Pump Water Heaters, Dana Paresa, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

We present two methods for estimating compressor electric power consumption of residential heat pump water heaters. Power is calculated using data from voltage and current instrumentation, which add costs and potential failure points to the unit. The two methods presented herein use thermal instrumentation or derived data that are typically included within modern residential heat pump water heaters. One method uses data from lower and upper tank temperature sensors. The other method uses an attribute that is commonly available within grid-enabled flexible load heat pump water heaters. For both methods, fit equations were derived, for which coefficients are tuned to …

Grid Services Demonstration Using Service-Oriented Derms, Midrar Adham, Sean Keene, Tylor E. Slay, Jaime T. Kolln, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

This work demonstrates four grid service use cases using a service-oriented DER Management System within an Energy Grid of Things network. Imposed by a set of rules referred to as the Energy Service Interface, the DER Management System uses IEEE 2030.5 as its primary communication protocol. To simulate the efforts of Grid Operators, Grid Service Providers, and consumers to provide grid services, a co-simulation environment that leverages the GridAPPS-D simulation platform was developed. The results show that a service-oriented DER Management System provides a means of achieving reliable and impactful grid services while protecting customers’ privacy and ensuring their agency …

Electro-Vascular Dynamics During Auditory Processing: Variation Along The Schizotypy Continuum, John P. Mclinden

Open Access Dissertations

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are complementary noninvasive neuroimaging modalities that have contributed greatly to our understanding of the mechanisms of auditory processing. As an emerging technology, fNIRS has produced promising insights into the mechanisms of auditory processing, although considerable work remains to characterize hemodynamic responses in auditory tasks. In addition, the complementary nature of EEG and fNIRS and the relationships between the signals they record have been underexplored. In particular, nonlinear interactions between these signals remain under-characterized, despite the potential benefit to future multimodal neuroimaging studies. A challenge in understanding these interactions is the influence of systemic …

Democratizing Deep-Sea Technology Development Using Rapid Prototyping Techniques, Breanna Motsenbocker

Open Access Dissertations

Deep-sea research and exploration have consistently been inaccessible to a broader oceanographic community due to the lack of affordable technology and resources that are required for this type of work. Traditionally, to investigate the deep sea, large research vessels are required to operate the expensive and massive equipment that can withstand the intense pressure and harsh environment beyond 200 meters of water. Not only does this lessen the capacity for exploring the deep sea but it also introduces an extreme inequity for who can participate and lead these efforts. With a growing demand for the global Blue Economy and a …

Beyond The Phonon Gas Model (Pgm): Unraveling The Unique Mechanistic Processes Dictating Thermal Transport In Highly Anharmonic And Disordered Materials, Sandip Thakur

Open Access Dissertations

Thermal transport in nonmetallic solids has traditionally been described by the phonon gas model (PGM), in which heat is carried by weakly interacting phonon quasiparticles in an ordered crystalline lattice. However, this model breaks down in materials characterized by strong anharmonicity, dynamic disorder, or structural complexity, features prevalent in many next-generation materials used in energy conversion, optoelectronics, and thermal management. This dissertation investigates thermal transport in such complex materials, including metal halide perovskites (MHPs), covalent organic frameworks (COFs), metal organic frameworks (MOFs), and 2D-3D heterostructures, through large-scale molecular dynamics (MD) simulations and frequency-resolved spectral analyses.

In MHPs, the work reveals …

Design And Deployment Of A Compact Rov For Deep-Sea Research And Exploration, Joshua F. Wood

Open Access Master's Theses

The scientific community has utilized unmanned underwater vehicles for ocean exploration since the mid-twentieth century, resulting in a well-cultivated and ever-changing discipline of ocean technology. The deep-sea environment, specifically, presents unique and harsh criteria due to the increased pressure found at depth and the vehicle components necessary to overcome it. The size and weight of underwater vehicles typically increase as operational depth increases, resulting in physically large systems with high operational costs to accommodate deep-sea research. This project first characterized the effect of length and diameter on carbon fiber pressure housing’s depth tolerance. It then leveraged this data to reduce …

A Compact Frequency Comb Generator For Large-Format Superconducting Microwave Kinetic Inductance Detectors, Sungyoun Seo

Open Access Master's Theses

Microwave Kinetic Inductance Detectors (MKIDs) are promising superconducting detectors that achieve superior sensitivity to silicon-based photo detectors by leveraging a small bandgap of superconductors. They are advantageous for sensitive imaging instruments, particularly space telescopes. However, a large-format array with several hundreds of thousands of detectors is required for large-scale observations in space, and it encounters multiple challenges. The biggest challenge is the absence of compact, energy-efficient, and radiation-tolerant readout solutions. Of multiple functions in the MKID readout, the new frequency comb generator is presented in this thesis. It features a low on-chip memory requirement while promising the relaxed frequency resolution …

Nanotube Spectral Fingerprinting And Machine Learning For Optimized Bioimaging/Sensing And Disease Detection Applications In Als, Rodrigo Monroy Lopez

Open Access Master's Theses

Single-walled carbon nanotubes (SWCNTs) possess unique physicochemical and optical properties that make them ideal candidates for biomedical imaging, biosensing, and disease diagnostics. This thesis explores the potential of SWCNT-based spectral fingerprinting combined with ML (Machine Learning) algorithms to optimize bioimaging, disease detection and prediction, with a specific focus on differentiating between healthy and amyotrophic lateral sclerosis (ALS) lymphoblastic patient samples. By functionalizing SWCNTs with single-stranded DNA, we enhance their stability and target specificity, enabling their application in serum patient samples.

A comprehensive spectral analysis of DNA-SWCNTs was conducted using near-infrared fluorescence spectroscopy and other characterization techniques, including UV-Vis-absorption spectroscopy. The …

The Impact Of Urbanization On Water Scarcity And Waterborne Diseases In Eastern Africa: A Case Study Of Nairobi, Lucy N. Kamau

Open Access Master's Theses

Urbanization in Eastern African cities has rapidly accelerated, placing immense strain on existing water infrastructure and sanitation systems. In Nairobi, this has resulted in spatial disparities in access to clean water and heightened vulnerability to waterborne disease risks. The study aimed to (1) assess the impact of urbanization on water scarcity and waterborne diseases in Nairobi using geospatial analysis and remote sensing (2) quantify urbanization trends 1999–2024 (3) identify spatio-temporal water scarcity hotspots for the years 2019 and 2024 (4) model waterborne disease risk maps for 2019 and 2024 (5) Overlay disease risk map with hospitals. To quantify urbanization for …

Finite Element Modeling Of Offshore Wind Monopile Impact Pile Driving In Comsol Multiphysics, Brendan J. Giordano

Open Access Master's Theses

The primary foundation type for offshore wind farms are large-diameter monopiles, which are installed with an impact hammer that produces a high-intensity, broadband impulsive signal that radiates acoustic energy in the water column and seafloor. This signal generates multiple waves such as compressional, shear and Scholte (or interface) waves. Scholte waves travel on the water-sediment interface and contain high-intensity, low-frequency energy that benthic species of fish and invertebrates could possibly detect. COMSOL Multiphysics, which is a finite element modeling software, was used to develop two transient models of impact pile driving to measure Scholte wave particle motion (displacement, velocity, acceleration) …

The Coral Carousel: A Device And Method For In-Situ Propagation Of Deep-Sea Corals, Gregory Bales

Open Access Master's Theses

The research described herein covers the development of a tool station for performing in-situ propagation of corals using a work class ROV. This includes a system for manipulating coral fragments and affixing them to a cement base plug. To validate this tool station, testing was performed both in the lab as well as at depth in the Gulf of Mexico. As restoration of shallow-water corals has grown in popularity, many techniques have been developed for propagation. However, it is difficult or inappropriate to directly apply these techniques to deep-sea corals. While some forms of diving are capable of approaching the …

Numeric Simulation Of A Realistic Hydrokinetic Turbine In Exposed Ocean Environments, Anneke Neber

Open Access Master's Theses

As renewable energies become increasingly more important, it becomes necessary to tap into new resources, such as energy from tidal or ocean currents. A recent development is the use of hydrokinetic turbines, deployed on floating plat- forms. Numerical modeling can be an important tool to determine the forces acting on the floating platform and the turbine.

In this thesis, a realistic floating hydrokinetic turbine was simulated in OpenFAST. Experimental data and hand calculations, were used to partially verify the simulation. Environmental data at a site was analyzed to determine conditions to run the simulations for different tidal current speeds and …

Little Problems: Photosynthetic Textile Treatments As A Source Of Oxygen, Morgan Campbell

Open Access Master's Theses

During human space travel, oxygen (O2) must be stored and carried aboard the shuttle for the entire journey; compressed O2 is highly flammable and combustible, thus doing so puts the crew’s lives at risk in the event of a fire (Mandel, 2019). By creating O2 onsite, the necessity of compressed O2 canisters and the associated risks can be reduced or erased entirely. Photosynthesis, the process through which plants convert atmospheric carbon dioxide (CO2) into O2, is one method of creating O2 (Lorch, 2020). The National Aeronautics and Space Administration (NASA) has experimented with photosynthetic O2 systems since its inception but …

Minimalistic Communication-Based Swarm Slam, Felix B. Koch

Open Access Master's Theses

This study presents and validates Range of Communication Swarm SLAM(RCS-SLAM), a novel SLAM approach designed for multi-robot systems using minimal hardware. Unlike traditional methods that rely on range or bearing measurements, RCS-SLAM only uses the identifiers of nearby robots within communication range. In the front-end, each robot constructs an individual pose graph based on its own odometry, while recording communication events with neighboring robots. The key novelty lies in the use of one-hop communication, where robots not only detect nearby peers but also relay received identifiers, effectively acting as transmitters themselves. This mechanism extends the communication graph and enables the …