Engineering Commons^™

Graduate School Of Engineering And Management Academic Catalog 2025-2026, Graduate School Of Engineering And Management, Air Force Institute Of Technology, Walter F. Jones

AFIT Documents

No abstract provided.

An Inference Approach For Assessing Place-Based Vulnerability To Heat Mortality, Junkang Xu, Chao Fan, Xing Xu, Haoying Han

Publications

A global increase in the frequency, severity, and scale of extreme heat raises concerns about human vulnerability to climate change and associated mortality. Heat vulnerability and mortality have largely been studied separately, lacking an understanding of their causation. Here, we create a non-parametric generalized inference approach that links socioeconomic, environmental, and infrastructure factors articulated in vulnerability theory and heat mortality between 2010 and 2020 for counties across the United States. We find that the lack of vegetation coverages drives mortality in the Southern U.S. and among Hispanic people. Limited air conditioning is a key factor in heat-related mortality among White …

Report On The First Year Of Operation Of Kentucky’S First Utility Wind Turbine, Lawrence E. Holloway, Sophia A. Hahn, Aron Patrick

Electrical and Computer Engineering Faculty Publications

This report examines the first year’s operational data from Kentucky’s first utility wind turbine, the 37 m hub-height 90-kilowatt NPS100C-27, operated by the PPL Corporation Research and Development at their Renewable Integration Research Facility in Mercer County, Kentucky. During that year, the turbine was available 95% of the time, spinning 85% of the time, and generating power 78% of the time, and had a net capacity factor of 11%. This report analyzes the turbine performance and uses the collected wind data to project the performance of an example turbine more typical of larger commercial turbines recently installed elsewhere in the …

Mae Enewsbrief Apr- June 2025, Department Of Mechanical And Aerospace Engineering, Michigan Technological University

Department of Mechanical and Aerospace Engineering eNewsBrief

No abstract provided.

Predicting Miner Localization In Underground Mine Emergencies Using A Hybrid Cnn-Lstm Model With Data From Delay-Tolerant Network Databases, Patrick Nonguin, Samuel Frimpong, Sanjay Madria

Mining Engineering Faculty Research & Creative Works

Underground mining environments are highly hazardous, often prone to gas explosions, cave-ins, and fires that may trap miners during emergencies. The accurate, real-time localization of miners is vital for effective self-escape and rescue operations. Although the Mine Improvement and New Emergency Response (MINER) Act of 2006 mandates communication and tracking systems, most current solutions rely on low-power devices and line-of-sight methods that are ineffective in GPS-denied, dynamic subsurface conditions. Delay-Tolerant Networking (DTN) has emerged as a promising alternative by supporting message relay through intermittent links. In this work, we propose a deep learning framework that combines Convolutional Neural Networks (CNNs) …

Localized Multivariate Statistical Assessment Of United States Construction Labor Shortages, Ahmed Shiha, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Craft labor shortage is a persistent challenge facing the construction industry across various markets. The historically low geographic mobility of construction labor exacerbates and localizes labor shortages. Several research efforts relied on subjective evaluations to quantify the existence of labor shortages on a national level. As such, there is a lack of empirical quantification of labor shortages at different geospatial levels. This paper fills this knowledge gap. To this end, the authors adopted a methodology that encompassed: (1) conducting a literature review to identify and collect national-level and subnational indicators that explain the changes in labor supply and demand; (2) …

Coupling Effect Of Abrasion And Erosion On Ultrahigh-Performance Seawater-Sea Sand Concrete, Dawei Ding, Wei Zhang, Lanqin Wang, Hongyan Ma, Biqin Dong, Song Han, Hongjian Xu, Dongshuai Hou

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Preparing ultrahigh-performance seawater-sea sand concrete (UHPSSC) to mitigate the scarcity of freshwater and river sand resources has generated considerable recent research interest. However, the deterioration mechanisms of UHPSSC under the effect of abrasion and erosion remain unstudied. In this paper, the underwater steel ball method was conducted to evaluate the impact of seawater, sea sand, steel fiber, and seawater abrasion on the abrasion resistance of UHPSSC. Then the stress erosion models of calcium-silicate-hydrate (C-S-H) were constructed to investigate the degradation mechanisms of UHPSSC under the effect of external stress and seawater erosion. The results show that the application of seawater …

Ai-Driven Uncertainty Quantification & Multi-Physics Approach To Evaluate Cladding Materials In A Microreactor, Alexander Foutch, Kazuma Kobayashi, Ayodeji Babatunde Alajo, Dinesh Kumar, Syed Bahauddin Alam

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

The pursuit of enhanced nuclear safety has spurred the development of accident-tolerant cladding (ATC) materials for light water reactors (LWRs). This study investigates the potential of repurposing these ATCs in advanced reactor designs, aiming to expedite material development and reduce costs. The research employs a multi-physics approach, encompassing neutronics, heat transfer, thermodynamics, and structural mechanics, to evaluate four candidate materials (Haynes 230, Zircaloy-4, FeCrAl, and SiC–SiC) within the context of a high-temperature, sodium-cooled microreactor, exemplified by the Kilopower design. While neutronic simulations revealed negligible power profile variations among the materials, finite element analyses highlighted the superior thermal stability of SiC–SiC …

Challenges And Artificial Intelligence Solutions For Clinically Optimal Hepatic Venous Vessel Segmentation, Håvard Bjørke Jenssen, Varatharajan Nainamalai, Egidijus Pelanis, Rahul P. Kumar, Andreas Abildgaard, Finn Kristian Kolrud, Bjørn Edwin, Jingfeng Jiang, Joseph Vettukattil, Ole Jakob Elle, Smund Avdem Fretland

Michigan Tech Publications

Background:: Liver vessel identification is crucial for clinical disease assessment and treatment planning, especially concerning local treatment of liver tumors. As artificial intelligence (AI) develops in radiology, opportunities arise to craft models adept at hepatic venous vessel segmentation, opening possibilities for creating patient-specific models of the liver anatomy quickly, despite the diverse features of CT images encountered in clinical settings. Objective: This research evaluates the performance of AI models combined with various pre-processing filters for liver vessel segmentation, emphasizing clinically relevant results. A novel evaluation method was introduced to offer more anatomically accurate assessments, moving beyond traditional metrics like the …

Machine Learning And Clinical Eeg Data For Multiple Sclerosis: A Systematic Review, Badr Mouazen, Ahmed Bendaouia, El Hassan Abdelwahed, Giovanni De Marco

Manufacturing & Industrial Engineering Faculty Publications

Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body’s immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms and causing disruption of axonal signal transmission. Accurate prediction, diagnosis, monitoring and treatment (PDMT) of MS are essential to improve patient outcomes. Recent advances in neuroimaging technologies, particularly electroencephalography (EEG), combined with machine learning (ML) techniques — including Deep Learning (DL) models — offer promising avenues for enhancing MS management. This systematic review synthesizes existing research on the application of ML and …

Reinforcement Learning And Virtual Human Animation: A Novel Approach To Data-Driven Animation, Portraying Dynamic, Flexible Human-Like Behaviours, Vihanga Gamage

Dissertations

Virtual characters require animation capable of portraying dynamic, context-sensitive human-like behaviours. Several approaches to generating such animation have been developed, but each carries limitations. Motion capture can produce high-fidelity animation but is expensive and ill-suited to systems that must respond in real time. Physics-based reinforcement learning (RL) enables flexible, dynamic behaviour portrayal, yet relies on simulation feedback signals that are unavailable for social gestures. Supervised approaches can learn social behaviours from motion capture data but yield agents with limited flexibility and generalisation.

This thesis presents RLAnimate, a model-based, data-driven RL framework for character animation that enables a single agent to …

Bike/Ped Data Collection And Model Calibration, Eirini Stavropoulou, Dionysios Tzamakos, Nikiforos Stamatiadis, Reginald Souleyrette, Teng Wang

Kentucky Transportation Center Research Report

Motivated by an uptick in pedestrian and bicycle traffic, as well as the inclusion of pedestrian and bicyclist weights in the Strategic Highway Investment Formula For Tomorrow, the Kentucky Transportation Cabinet (KYTC) has made significant investments in transportation facilities for nonmotorized users. Despite an intensified focus on the needs of nonmotorized users, KYTC has not yet established a formal program to track the number of people walking, biking, skateboarding, and using micromobility devices statewide. Based on a review of methods and technologies for counting nonmotorized users; analysis of counting programs established by local, state, and regional transportation agencies; and a …

Congestion And Travel Time Reliability Performance Measures For Shift 2026, Mei Chen, Eugene Antwi, Xu Zhang

Kentucky Transportation Center Research Report

Researchers obtained and processed probe speed data from HERE Technologies and integrated them with the Kentucky Transportation Cabinet’s highway inventory to develop congestion and travel time reliability performance metrics. This report summarizes analyses conducted using HERE speed data for 2022 – 2023. HERE data were processed using an established methodology, after which they were aligned with Kentucky’s highway inventory network. Analysis found that 2022 – 2023 HERE data offer significant improvements in coverage and quality over previous years. For locations that lacked sufficient data, researchers used HERE’s speed model to estimate hourly speeds. Data were used to calculate congestion and …

Traffic Prediction For Research And Education Networks: Anomaly-Aware Deep Learning And Benchmarking, Mohammad Arafath Uddin Shariff

School of Computing: Dissertations, Theses, and Student Research

Research and Education Networks (RENs) and High-Performance Computing (HPC) environments are critical infrastructures for modern scientific discovery, demanding sustained high-throughput and low-latency data transfers. Unlike commercial networks, RENs exhibit unique traffic characteristics, including predominant “elephant flows,” inherent burstiness, and complex temporal-spatial dynamics often decoupled from human-driven cycles. Traditional traffic forecasting methods, tailored for commercial Wide Area Networks (WANs), consistently fail to capture these distinct REN dynamics, leading to inefficient resource management and potential impediments to scientific progress.

This thesis addresses this critical gap by developing and validating a robust, scalable, and anomaly-aware traffic forecasting framework specifically tailored for REN/HPC networks. …

Hardware And Software Design For A Portable Surgical Training Simulator, Victoria Nelson

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

More than 51 million surgeries are performed on patients annually in the United States. That number grows to 310 million surgeries performed globally each year. Most surgeons go through a minimum of five years of residency – where they train and hone their surgical skills. It is important during this time, and throughout their careers, that they get plenty of practice. Surgical training is time intensive, expensive, and frequently under-resourced. There are finite amounts of surgical training modules for students to share at their facilities. The problem of getting adequate surgical training time continues to exist after surgeons begin their …

A Machine Learning Approach To Detect Pores In Laser Powder Bed Fusion Additive Manufacturing, Jose Galarza, Jose Barron Jr., Luis Jimenez, Tamer Oraby, Jianzhi Li, Farid Ahmed

Manufacturing & Industrial Engineering Faculty Publications

Real-time detection of pores in the Laser Powder Bed Fusion (LPBF) metal Additive Manufacturing (AM) process is proposed in this study and can be utilized for in-situ process monitoring and quality control. The average light emission data from the process captured by an optical tomography camera can be integrated into a defect detection module to characterize defects after the deposition of a layer. The light emission contains information on the process zone which could be extracted with the appropriate data techniques. In this paper, we proposed a machine-learning approach that utilizes the mean light intensity data from the melt-pool monitoring …

Navigating Transportation Barriers: Older Adults’ Familiarity With New Mobility Options And Perceptions Toward Autonomous Vehicles In Arkansas, Arna Nishita Nithila, Suman Kumar Mitra, Michelle Gray, Alishia Juanelle Ferguson, Jennifer D. Webb

Civil Engineering Faculty Publications and Presentations

The objective of this study is to analyze older adults’ familiarity with new transportation options (ride-hailing services, bike-share services, and shared e-scooter services) and their perception towards autonomous vehicles (fully autonomous cars), as well as how transportation barriers influence their familiarity and perceptions, in Arkansas, a predominantly rural state. Data from 775 older adults aged 60 years or older were collected between October 2021 and October 2022. To fulfill the study objective, the study used Latent Class Cluster Analysis to segment older adults into classes based on their familiarity with new transportation options and their perceptions of autonomous vehicles. The …

An Injectable, Dual-Curing Hydrogel For Controlled Bioactive Release In Regenerative Endodontics, Meisam Omidi, Daniela S. Masson-Meyers, Jeffrey M. Toth

Biomedical Engineering Faculty Research and Publications

Regenerative endodontics seeks to restore the vascularized pulp–dentin complex following conventional root canal therapy, yet reliable neovascularization within the constrained root canal remains a key challenge. This study investigates the development of an injectable, dual-curing hydrogel based on methacrylated decellularized amniotic membrane (dAM-MA) and compares its performance to a conventional gelatin methacryloyl (GelMA). The dAM-MA platform was designed for biphasic release, incorporating both free vascular endothelial growth factor (VEGF) for an initial burst and matrix-metalloproteinase-cleavable VEGF conjugates for sustained delivery. The dAM-MA hydrogel achieved shape-fidelity via thermal gelation at 37 °C and possessed tunable stiffness (0.5–7.8 kPa) after visible-light irradiation. …

Modification Of Tini Alloy By Fast Pulse Laser Heat Treatment, Yitao Chen, Mohammad Masud Parvez, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

In this study, the effects of local heat treatment with fast pulse laser on thin TiNi shape memory alloy strip materials were investigated. Various materials characterization methods including optical microscope, scanning electron microscope, atomic force microscope, X-ray diffraction, differential scanning calorimetry, and Vickers hardness were used to identify the differences of microstructure, mechanical properties, and functional properties between regions inside and outside the laser-scanned region. The tensile test was also conducted for both the non-laser-scanned specimen and the laser-scanned specimen. The area covered by laser scanning shows great differences by possessing a more homogeneous austenite phase without shear bands and …

Digital Twins, Ai, And Cybersecurity In Additive Manufacturing: A Comprehensive Review Of Current Trends And Challenges, Md Sazol Ahmmed, Laraib Khan, Muhammad Arif Mahmood, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The development of Industry 4.0 has accelerated the adoption of sophisticated technologies, including Digital Twins (DTs), Artificial Intelligence (AI), and cybersecurity, within Additive Manufacturing (AM). Enabling real-time monitoring, process optimization, predictive maintenance, and secure data management can redefine conventional manufacturing paradigms. Although their individual importance is increasing, a consistent understanding of how these technologies interact and collectively improve AM procedures is lacking. Focusing on the integration of digital twins (DTs), modular AI, and cybersecurity in AM, this review presents a comprehensive analysis of over 137 research publications from Scopus, Web of Science, Google Scholar, and ResearchGate. The publications are categorized …

Study Of Ai Applications In Biomedical Data Acquisition, Communication, And Analysis: Cest Mri Acceleration And Ecg Transmissions, Adarsha Bhattarai

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation investigates the application of artificial intelligence in biomedical data acquisition, communication, and analysis to advance neurological research and to enable the early detection of cardiovascular conditions. Despite significant advances in imaging and physiological modalities, challenges persist. Imaging modalities, such as the chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) technique are challenged by a prolonged data acquisition time and high operational costs. In addition, physiological modalities such as electrocardiogram (ECG) sensors face constraints in providing uninterrupted signal monitoring which is crucial for the timely detection of premature cardiac abnormalities. The primary goal of this work is to …

Evaluation Of Performance Characteristics And Groundwater Contamination Risks Associated With On-Farm Swine Carcass Disposal Via Composting And Shallow Burial With Carbon, Gustavo Castro Garcia

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation evaluates the performance of three on-farm swine carcass disposal methods: whole carcass composting (WCC), ground carcass composting (GCC), and shallow burial with carbon (SBC), focusing on biosecurity factors and groundwater contamination risks. Foreign animal diseases (FADs), such as African swine fever and classical swine fever, pose severe economic and animal well-being risks if introduced to the United States. With confirmation of an FAD, swine movement will be halted, creating an urgent need for practical, biosecure, and environmentally responsible on-farm disposal strategies for swine carcasses. Nebraska, as a leading swine-producing state, exemplifies the vulnerability of high-density livestock regions to …

Mass Effects On Energy Transfer Paths In Nonlinear Vibrating Systems, Manal Mustafa

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation examines the role of mass in nonlinear systems, uncovering its role in enabling passive energy redistribution and robust vibration control in both idealized and real-world structures. Focusing on a strongly nonlinear two-degree-of-freedom system, it investigates how changes in mass ratio influence the dynamics of energy transfer, nonlinear normal modes (NNMs), and dissipation behavior.

A number of significant contributions are introduced in this work beginning with the introduction of the frequency-energy-peaks (FE-pks) plot, a novel tool that visualizes how energy flows through the system, revealing transient resonance orbits, internal resonance effects, and effectively capturing the different nonlinear phenomena with …

Resilience Of Interdependent Transportation And Healthcare Systems: A Simulation-Driven Framework Incorporating Social Equity And Facility Optimization, S. Yasaman Ahmadi

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The overarching goal of this dissertation is to develop an integrated, simulation-driven framework to assess and enhance the resilience of interdependent transportation and healthcare systems before, during, and after natural hazards. By examining how spatial disruptions, social vulnerability, operational constraints, and infrastructure interdependencies affect access to and functionality of hospitals, this research offers a multifaceted evaluation of system performance under stress. This dissertation examines the impact of natural hazards, particularly floods, on the interdependent transportation and healthcare systems. The proposed approach combines and advances GIS-based network analysis, graph-theoretical modeling, and discrete-event simulation to evaluate system performance under different scenarios. The …

The Advancement Of Automation In Beef Packaging Pack-Off Systems, Matthew Newman

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Marble Technologies has developed a new system for beef, pork, and lamb producers that improves quality and worker experience in the packing of products. This work explores the augmentation of this system with the introduction of robotic packing to further reduce labor, increasing worker availability for value-added tasks.

Experimentation conducted to date has explored handling delicate meat products in the standard Marble system and the proposed robotic systems. These mechanical subsystems are critical intermediaries in safely and reliably delivering products to robots and from robots to boxes. Two case studies are presented to walk through the process and challenges of …

Intelligent Multi-Layer Optical Network Design And Network Softwarization, Boyang Hu

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The growing demand for high-capacity, low-latency services has placed significant pressure on the design and operation of optical transport networks. Multi-layer optical network design—which coordinates the physical layer with higher-layer protocols—has emerged as a critical strategy to enhance resource efficiency, service flexibility, and fault resilience. Enabled by advancements in software-defined networking (SDN) and network softwarization, intelligent multi-layer architectures allow for adaptive, cross-layer control of routing, grooming, and protection mechanisms, ultimately reducing both capital and operational expenditures.

This dissertation investigates the intelligent design and simulation of multi-layer optical networks through the integration of SDN, machine learning, and high-fidelity physical-layer modeling. We …

Visceral, Neural, And Immunotoxicity Of Per- And Polyfluoroalkyl Substances: A Mini Review, Pietro Martano, Samira Mahdi, Tong Zhou, Yasmin Barazandegan, Rebecca Iha, Hannah Do, Joel Burken, Paul Ki-Souk Nam, Qingbo Yang, Ruipu Mu

Chemistry Faculty Research & Creative Works

Per- and polyfluoroalkyl substances (PFASs) have gained significant attention due to their widespread distribution in the environment and potential adverse health effects. While ingestion, especially through contaminated drinking water, is considered the primary route of human exposure, recent research suggests that other pathways, such as inhalation and dermal absorption, also play a significant role. This review provides a concise overview of the toxicological impacts of both legacy and emerging PFASs, such as GenX and perfluoro butane sulfonic acid (PFBS), with a particular focus on their effects on the liver, kidneys, and immune and nervous systems, based on findings from recent …

Genwriter: Reducing Gender Cues In Biographies Through Text Rewriting, Shweta Soundararajan, Sarah Jane Delany

Conference papers

Gendered language is the use of words that indicate an individual’s gender. Though useful in certain context, it can reinforce gender stereotypes and introduce bias, particularly in machine learning models used for tasks like occupation classification. When textual content such as biographies contains gender cues, it can influence model predictions, leading to unfair outcomes such as reduced hiring opportunities for women. To address this issue, we propose GenWriter, an approach that integrates Case-Based Reasoning (CBR) with Large Language Models (LLMs) to rewrite biographies in a way that obfuscates gender while preserving semantic content. We evaluate GenWriter by measuring gender bias …

How Developers Use Type-System Related Programming Language Features, Samuel W. Flint

School of Computing: Dissertations, Theses, and Student Research

Optional type annotations are a popular feature of programming languages that allow developers to omit explicit type information in code while, in some cases, retaining many of the benefits of static typing, such as in-code documentation, improved detection of type errors, or enforcement of code properties. However, how developers use and understand optional type annotations is not clear. The focus of this dissertation is to understand the use and comprehension of optional type annotations.

Optional type annotations are examined through four lenses: first, by examining the evolution of usage in a statically typed programming language (Kotlin, the default language for …

Impact Of Geometrical Dimensions On The Shear Behaviour Of Uhpc Deep Beams Reinforced With Steel And Synthetic Fibres, Hossein Mirzaaghabeik, Nuha S. Mashaan, Sanjay Kumar Shukla

Research outputs 2022 to 2026

Ultra-high-performance concrete (UHPC) is recognized for its exceptional strength, durability, and versatility in engineering applications, making it a reliable composite in the construction industry. The incorporation of fibres into UHPC enhances its mechanical properties. Non-metallic fibres offer a promising alternative to steel fibres due to their resistance to corrosion. While much research has focused on the shear behaviour of UHPC reinforced with steel fibres, there is a notable gap in understanding the impact of non-metallic fibres. This study aims to address this gap by comparing the influence of synthetic fibres to steel fibres on the shear behaviour of UHPC through …