Engineering Commons^™

Criticality Safety Evaluation To Address Potential Over Conservative Restrictions In 49 Cfr Fissile Materials – Exceptions, Avian Blumhorst

UNLV Theses, Dissertations, Professional Papers, and Capstones

The U. S. Department of Transportation’s (DOT) 49 CFR 173.453 Fissile Exceptions provide a regulatory classification that helps eliminate burdensome regulatory requirements. The current fissile exceptions are based on past technical basis reviews. These reviews may be overly conservative, and the final ruling potentially applied significant administrative over-conservatism. This thesis reviewed and presents several recommendations to the fissile exception criteria, as well as 49 CFR 173.453 section’s additions.

Using Monte Carlo N-Particle (MCNP) code version 6.3.0, several infinite sea mixtures and array systems were modeled for fissile isotopes 233U and 235U. The research demonstrated that for fissile exception (a) there …

An Optimization Method For Near-Linear Phase Analog Frequency Sampling Filter Design, Leonardo Ledesma

UNLV Theses, Dissertations, Professional Papers, and Capstones

Analog frequency sampling filters (FSFs) realize a desired frequency response by interpolating a frequency response through a set of harmonically related frequency samples from the filter’s frequency response and are magnitude and phase coefficients used in the filters transfer function. FSFs can be designed to have exact linear phase which makes the FSF attractive for many applications. A FSF’s system transfer function (STF) shows that the filter can be implemented by a series connection of a comb filter and a parallel array of resonators. However, the FSF requires that the zeros created by the comb filter cancel the imaginary axis …

Visual Interpretability Of Multimodal Tissue Perfusion Classification Using Grad-Cam And Saliency Maps, Metehan Zorluoglu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate identification of the tissue perfusion phase from hand images can aid doctors in decision-making with non-invasive techniques. The present study proposes a multimodal deep learning model for classifying the tissue perfusion phase using infrared, thermal, and visible spectrum images of the human hand. The proposed model consists of various preprocessing techniques such as manipulation, homography alignments, and masking. The significant contribution of this thesis is the interpretability analysis of deep learning models, achieved through the analysis of saliency maps and the Gradient-weighted Class Activation Mapping (Grad-CAM) methods. The purpose of this method is to find out how the convolutional …

Vegetated Canopy Heterogeneity Footprints In The Roughness Sublayer, Giulia Salmaso, Raul Bayoan Cal, Marc Calaf

Mechanical and Materials Engineering Faculty Publications and Presentations

Turbulent flows over horizontally homogeneous rough surfaces are categorized as rough‐wall boundary layer flows, while flows over homogeneous vegetated canopies are better described through a mixing‐layer analogy. At present, numerous studies have investigated canopy density as a transition mechanism between rough‐wall and mixing‐layer‐type flows. Yet, most considered canopies have been spatially homogeneous, with few exceptions investigating agricultural arrangements. However, most vegetated canopies are not homogeneously distributed, but instead contain gaps and spatial heterogeneities of different scales. In these cases, it remains unclear which are the dominant flow traits, and how spatial heterogeneity affects them. To help overcome these knowledge gaps, …

A Descriptive Analysis Of Plant Leaf Disease Detection Using Machine Learning And Deep Learning Models: A Systematic Review, Arzoo Chamoli, Anuj Kumar

Turkish Journal of Electrical Engineering and Computer Sciences

Plant leaf disease detection (PLDD) is a growing active research area with burgeoning practical applications across various sectors such as agricultural monitoring, food security, and environmental conservation. Accurate segmentation and classification of plant leaf diseases remains a key challenge in the field of plant leaf disease prediction. The challenge demands automated methods for the plant disease identification because it needs to develop better crop management systems, which will boost agricultural production. In this article, we provide a systematic review of various machine learning (ML) and deep learning (DL) methods extensively used for PLDD. The review strategy follows a formal protocol, …

Complex-Valued Convolutional Neural Network With Time-Frequency Representation For Electrocardiogram-Based Arrhythmia Detection, Kajeeth Kumar Gurusamy, Muthurajkumar Sannasy

Turkish Journal of Electrical Engineering and Computer Sciences

This research proposes an end-to-end procedure for arrhythmia detection based on electrocardiogram (ECG) signals using complex-valued convolutional neural network (CVCNN) incorporated with time-frequency representation. The proposed model leverages complex numbers to capture amplitude and phase information that enhances the ability of the model for detecting time-frequency variation in cardiac signals. First, signal preprocessing techniques---including normalization, wavelet denoising, and R-peak detection---are applied. Subsequently, the model extracts complex features from raw ECG data by employing the Hilbert transform to derive the analytic signal and the short-time Fourier transform (STFT) to generate a time–frequency representation. The proposed CVCNN framework effectively learns spatial-temporal features …

Multiobjective Optimization Framework For Renewable Energy Iot Networks Balancing Security, Energy Efficiency, And Communication Reliability, Muhammad Hjouj Btoush, Ashraf S. Mashaleh, Amjad Gawanmeh

Turkish Journal of Electrical Engineering and Computer Sciences

The deployment of Internet of things (IoT) networks powered by renewable energy sources presents unique challenges in balancing security requirements, energy efficiency, and communication reliability. This paper presents a comprehensive multiobjective optimization framework for secure renewable energy IoT nodes that addresses fundamental trade-offs between these competing objectives. We develop a mathematical model incorporating energy harvesting dynamics, security protocols, and communication performance metrics across various environmental scenarios. The proposed framework employs a modified NSGA-II algorithm to identify Pareto-optimal configurations for different deployment contexts. Through extensive simulation analysis, we demonstrate that hybrid energy sources (solar-wind combinations) with lightweight security protocols achieve optimal …

Predictive Current Control Approach For Grid-Integrated Multifunctional Converter Under Source And Load Disturbances, Ravi Kumar Majji, Tirumalasetty Chiranjeevi, Chilukoti Varaha Narasimha Raja, Nagulapati Kiran

Turkish Journal of Electrical Engineering and Computer Sciences

This paper discusses and presents a model predictive control (MPC)-based predictive current control technique for a solar photovoltaic (PV)-integrated grid system during dynamic operation. This control technique employs extension pq (EPQ) theory to estimate reference currents and utilizes an MPC framework for tracking reference currents. Various MATLAB/Simulink simulations were conducted for solar PV generation (source disturbances) and dynamic loading. The results of the OPAL-RT OP4510 real-time simulation are also presented. A multifunctional grid-integrated converter (MFGC) integrates solar active power into the utility grid while achieving unity power factor, reactive power compensation, current balancing, and harmonic suppression. EPQ optimizes mathematical calculations, …

Cortical Bone Density And Thickness Assessment Of Intraradicular Sites In Adolescent Patients Using Cbct Imaging, Sara Endo

UNLV Theses, Dissertations, Professional Papers, and Capstones

Background: The indications for the use of cone beam computed tomography (CBCT) in orthodontics has grown since it was first introduced and dentists are discovering new ways that 3D images enhance diagnosis and treatment planning. Evaluating bone quality and quantity can be measured as bone density and thickness in CBCT imaging and is helpful for temporary anchorage device (TAD) placement in orthodontic treatment. TADs rely on primary stability and are commonly used in orthodontic treatment to increase anchorage and expand the limit that nonsurgical orthodontics can provide.

Objectives: This study aims to assess the bone thickness and density at different …

Machine Learning Assisted Development Of Al0.2cufemnni High Entropy Alloy Through Selective Laser Melting, Hareharen K Mr

Theses and Dissertations

High Entropy Alloys (HEAs) are an emerging class of advanced materials that have gained significant attention due to their exceptional mechanical strength, thermal stability, and structural performance. Unlike conventional alloys based on a single principal element, HEAs are composed of multiple elements in a near-equiatomic ratio. Despite these advantages, designing HEAs with tailored properties is difficult because of the enormous number of possible combinations and the limitations of traditional trial-and-error methods. To overcome these challenges, this study presents a machine learning (ML) based approach to accelerate the design and development of an HEA.

In this work, a newly designed composition, …

Machine Learning Approaches For Predicting Biochemical Oxygen Demand And Ammonium Nitrogen: A Decade-Long Weekly Field Study At A Full-Scale Water Resource Recovery Facility, Hoda Khoshvaght, Ratish Ramyad Permala, Amir Razmjou, Mehdi Khiadani

Research outputs 2022 to 2026

Unlike previous studies that rely on high-frequency (15-min or hourly) datasets, this study is among the first to use low-frequency (weekly) data to evaluate the performance of linear and nonlinear machine learning (ML) algorithms for predicting biochemical oxygen demand (BOD) and ammonium nitrogen (NH4⁺-N) in the primary and secondary treatment effluents from the Subiaco Water Resource Recovery Facility (WRRF) in Western Australia. Various feature selection methods, including filters, wrappers, and embedded methods, were employed to identify the most effective approach that achieves the highest model performance while enhancing computational efficiency. The results demonstrate that a reduced set of …

Model Based Control And Hil Verification Of An Integrated Battery Management System, Catalin Sabou

UNLV Theses, Dissertations, Professional Papers, and Capstones

The rapid advancement of electric vehicle technologies necessitates highly reliable Battery Management Systems (BMS); however, validating embedded supervisory logic presents a notable challenge. While physical pack testing is accurate, it is costly and hazardous for early stage software evaluation. This thesis presents the design, implementation, and rigorous validation of an integrated BMS developed for the Battery Workforce Challenge, bridging the gap between model based design and safe hardware execution. The core of this work is a model based supervisory controller, developed in MATLAB/Simulink and executed on an STM32G4 embedded target. To facilitate embedded validation while preserving a representative battery environment, …

Design And Uncertainty Methodology In High Efficiency Filtration Media Testing, Jeremy Andrei Adriano

Theses and Dissertations

Electrospun nanofiber media are being developed as potential alternatives to conventional HEPA filtration materials due to their small fiber diameters and tunable microstructures. To support future testing of these materials, the Small-Scale Test Stand (SSTS) at the Institute for Clean Energy Technology (ICET) requires modifications to ensure accurate and repeatable aerosol filtration measurements. Electrospun filters introduce challenges including fragile media structures and uncertain filtration efficiencies that may allow particle penetration to downstream instrumentation. This work evaluates adaptations to the SSTS design to support testing of electrospun filtration media. In addition, an uncertainty framework for aerosol measurements obtained using a Scanning …

Machine Learning-Based Decision Support Models With Applications In Postsecondary Education, Marco Paolo Anglesio

Theses and Dissertations

This dissertation investigates the deployment of machine learning methodologies in an industrial engineering framework for the development of advanced decision support systems in the context of enrollment management. Drawing on techniques from educational data mining, the research addresses three key phases in the lifecycle of traditional and non-traditional students. First, it analyzes student retention using predictive classification models designed to identify individuals at elevated risk of attrition. Second, it employs temporal convolutional networks for time series forecasting, estimating aggregate enrollment levels over highly variable, finite planning horizons on the basis of partially observed data and using an asymmetric loss function. …

Developing And Evaluating U.S. Army Bridging For Ship Simulation, Jacob C. Hodges

Theses and Dissertations

This research investigates the intricacies of ship simulation and developmental processes for integrating wet gap crossing capabilities into a simulated environment. The US Army Mark III Bridge Erection Boat (BEB) and Improved Ribbon Bridge (IRB) were modeled in commercial off the shelf (COTS) and Government Co-Owned (GCO) navigation software suites to determine their performance capabilities and limitations. Each model was constructed using naval architecture concepts to accurately capture their hydrostatic and hydrodynamic effects before being validated against available performance data and expert elicitation from expert US Army watercraft operators. Models were configured and tested in simulated river flows with current …

The Effects Of Experiential Learning On Engineering Self-Efficacy: A Study Of The Impact Of Inperson And Virtual Internships, Co-Ops, And Research Experiences, Sunny Bharat Patel

Theses and Dissertations

This dissertation examined the relationship between participation in Engineering Learning Experiences (ELEs) and engineering self-efficacy, with particular attention to differences among ELE types (internships, cooperative education, and undergraduate research) and delivery modalities (in-person versus hybrid/virtual). Grounded in social cognitive theory, this study sought to better understand how work-related experiential formats influence students’ confidence in their ability to succeed in engineering tasks and professional contexts. As engineering programs and industry increasingly adopt flexible and technology-mediated environments, examining how these experiences shape beliefs about engineering capability is both timely and necessary. Using a mixed-methods approach, quantitative analyses compared self-efficacy outcomes across students …

Improving And Supporting Flight Instructor’S Decisions For First Solo, Isabella Piasecki

Theses and Dissertations

Flight instructors have the burden of determining when a student is ready for their first solo flight, and many have expressed uncertainty over their own decision-making skills during this phase of a student’s training. Prior studies have examined flight instructors’ pre-solo decisions in other countries, but no such study has been conducted with American flight instructors. For this study, current flight instructors with multiple prior endorsements for a student pilot’s first solo were interviewed to identify the more abstract concepts they use to guide their decision. Qualitative themes were identified from their experiences. Using this information, a checklist was developed …

A Cradle To Grave Life Cycle Assessment Of Captured Landfill Methane Abatement Strategies, Joshua Caleb Richard

Theses and Dissertations

Landfill methane mitigation strategies are commonly evaluated using percentage-based leakage assumptions and linear scaling models. These approaches may misrepresent system performance at varying throughput levels and real-world operating scenarios. This thesis develops a system-level life cycle assessment framework that incorporates both fixed infrastructure losses and employs sensitivity analysis to more accurately characterize emissions from landfill gas management pathways. Three scenarios are evaluated: methane capture and flare, methane capture and combustion for electricity generation, and methane upgrading with pipeline injection as renewable natural gas. Results demonstrate that emissions models over- dependance on static factors may inadvertently lead to under-reporting and provide …

Development Of Carbon-Fiber-Reinforced Composite Rotor Sleeves For High-Speed, Multi-Layer Ferrite-Based Interior Permanent-Magnet Motors, Andrew Walters

Theses and Dissertations

This research investigated whether a non-wound carbon fiber reinforced composite (CFRC) sleeve could serve as an effective retention mechanism for a high-speed, multi-layer ferrite-based interior permanent magnet motor. Material characterization testing was conducted on IM7/CYCOM-5320 and IM7/8552, with IM7/8552 selected due to its superior commercial availability. Experimentally obtained properties for both materials fell within acceptable tolerances of published values. Sleeves were manufactured to rotor design specifications (50-mm width, 1.2-mm thickness, 88.6-mm diameter) and tested per ASTM D2290 Procedure A to determine apparent hoop-failure stress. These experimental results validated a finite element model of the D2290 test, confirming realistic strain patterns …

Feasibility-Aware Deep Reinforcement Learning For Sustainable Timber Procurement Under Hurricane Demand Uncertainty, Jarod Wright

Theses and Dissertations

The timber supply chain connects landowners and mills to provide wood products but faces challenges from stochastic demand, seasonal variations, and disruptions such as hurricanes. Fur- thermore, sustainability concerns like transportation emissions create trade-offs in procurement. This study proposes a feasibility-aware Deep Reinforcement Learning framework for sustainable timber procurement and inventory control under joint demand–hurricane uncertainty. We develop a stochastic mathematical model capturing mill-landowner interactions, seasonal demand, hurricane- driven pricing, and carbon emissions. The problem is formulated as a constrained Markov decision process and solved using Proximal Policy Optimization with a feasibility-enforcing layer. A Mississippi-based case study with 2,100 landowners …

Experimental Investigation Of The Effectiveness Of Various Stitching Methods On Arresting Mode-Ii Interlaminar Damage Propagation In Geometrically-Scaled Stitched Resin-Infused Composites, Allison Jean White

Theses and Dissertations

This thesis presents an experimental and analytical investigation of mode-II interlaminar fracture in geometrically-scaled quasi-isotropic resin-infused composites, with emphasis on through-thickness stitching as reinforcement. A global-local framework integrating load-displacement response, size effect fracture energy analysis, and digital image correlation-based separation measurements enabled direct comparison between unstitched and stitched configurations. Unstitched laminates exhibited pseudo-ductile behavior but significant post-peak load drops (16.7–26.8%) and a small fracture process zone (FPZ) of 0.73 mm, limiting stable crack growth. Stitching preserved pre-peak stiffness while reducing post-peak load drops by 43–95%, increasing effective fracture energy by 39.7% (to 2.413 N/mm), and expanding the FPZ to 2.85 …

Modeling Psychological And Demographic Predictors Of Analog Astronaut Mission Participation, Christian Yeara Herrero, Frányerson R. López Ochoa, Mackenzie Thomas, Phoebe Fleshman

Student Research Symposium (SRS)

As plans accelerate to send humans into orbit and to other celestial bodies, whether to lunar outposts, Mars bases, or commercial space stations, it becomes increasingly important to understand how to maintain healthy, cohesive, and productive crews in confined, isolated environments. A practical way to study human adaptation to these conditions is through analog astronaut missions on Earth. Although imperfect, these facilities provide the closest Earth-based simulation of space mission conditions. Currently, over ten analog research centers are operating worldwide, including NASA’s Human Exploration Research Analog (HERA) and the Crew Health and Performance Exploration Analog (CHAPEA) habitats. Selecting and recruiting …

A Novel Machine-Learning Based Method For Resolving Secondary Structure Topology In Medium-Resolution Cryo-Em Density Maps, Bahareh Behkamal, Mohammad Parsa Etemadheravi, Ali Mahmoodjanloo, Amin Mansoori, Mahmoud Naghibzadeh, Kamal Al Nasr, Mohammad Reza Saberi

Computer Science Faculty Research

Medium-resolution cryo-electron microscopy (cryo-EM) density maps preserve substantial information about protein secondary-structure organization; however, accurately recovering the topology and connectivity of α-helices and β-strands remains challenging due to noise, structural heterogeneity, and the intrinsic resolution limitations that obscure residue-level detail. Topology determination is a key intermediate step toward building atomic protein models from medium-resolution cryo-EM density maps. It requires identifying the correct correspondence and orientation between secondary-structure elements (SSEs), i.e., α-helices and β-strands, predicted from the amino-acid sequence and those detected in the three dimensional (3D) density map. Despite significant advances in cryo-EM reconstruction and molecular modelling, this correspondence problem …

Investigating Affordable Indoor Mobile Lidar Sensing For Bim-Based Facility Management: A Usability And Condition Sensitivity Study, Amr Mousa, Ahmed Elyamany, Mohamed Nabawy

Civil Engineering

This paper analyzes how capture parameters affect indoor mobile LiDAR scan fitness for-purpose for BIM-based facilities management. Despite extensive research on geometric accuracy and automated modelling performance, limited work has systematically examined how acquisition conditions influence the practical usability of raw scans for BIM/FM applications. A controlled experimental investigation used an Apple iPhone 16 Pro's integrated LiDAR sensor to capture 36 indoor scans under twelve factorial combinations of scan speed, illumination level, and motion pattern. Each condition was repeated three times. Usability was assessed using several components,Fco a gate-based pass/fail criterion, enclosure geometry, openings, ceiling services, and geometric clarity using …

Simulation Of Post-Tensioned Clt Rocking Wall And Platform Structure Response Under Earthquake Lateral Loads With Simplified Equivalent Model, Yunxiang Ma, Qingli Dai, Da Huang, Miaomiao Li, Xiang Zhao

Michigan Tech Publications

The post-tensioned cross-laminated timber (CLT) rocking wall is a recently developed resilient CLT lateral force-resisting system with a self-centering feature. The structural responses of the systems with different designs need to be determined and evaluated efficiently to promote the development and standardization of industrial applications. This study developed a computationally efficient, component-assembled numerical model for post-tensioned cross-laminated timber (PT CLT) rocking walls that captures decompression, post-tension self-centering, and energy dissipation within a framework. The single wall model was assembled using nonlinear zero-length springs for the compression at the CLT bottom, truss bar element for the PT tendon, and elastic shell …

Descriptive Analysis Of Career Planning And Industry Insight (Cognitive Component) Among Final-Year Mechanical Engineering Education Students, Fadliyanti Firdausia, Purnomo Purnomo, Eddy Sutadji, Tommy Tanu Wijaya, Muhammad Idris Effendi

Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan

This study aims to analyze the level of understanding of final-year Mechanical

Engineering Education students regarding career planning and industry insight as

cognitive components in preparing for the workforce. The study employed a quantitative

approach with a descriptive method involving 58 eighth-semester students who had

completed industrial internships. Data were collected using a Likert-scale questionnaire

consisting of 20 items on career planning and 18 items on industry insight, and were

analyzed using descriptive statistics, including mean scores and percentages. The results

indicate that students’ understanding of career planning falls into the high category, with

a mean score of 3.78, while …

Trussworthy - A Lunar Module Truss System, Anthony Wehrli, Sebnem Kalayci, Tyler Williams, Sofia Lopez-Linares Perez, Connor Mcdonagh, Presley Robb

Undergraduate Research and Scholarship Symposium

TrussWorthy is a modular truss-based construction platform designed to support the gradual development of sustainable infrastructure on the lunar surface. Aligned with NASA’s Moon to Mars objectives, the system emphasizes in-situ resource utilization (ISRU) by transforming lunar regolith into structural components through sintering and additive manufacturing. By reducing dependence on Earth-supplied materials, TrussWorthy aims to lower launch costs while enabling scalable, long-term construction capabilities. The design integrates autonomous excavation, 3D printing, and robotic assembly, building on technologies expected from upcoming Artemis and Commercial Lunar Payload Services missions. Engineered to withstand extreme thermal cycling, vacuum conditions, and reduced lunar gravity, the …

Agentic Scientific Machine Learning For Autonomous Model Discovery In Systems Pharmacology, Nazanin Ahmadi, George Karniadakis

Biology and Medicine Through Mathematics Conference

No abstract provided.

2025, Significant Natural Disasters And Global Carbon Capture, Utilization, And Storage—Annual Progress Report, Shangli Shi, Yun Hang Hu

Michigan Tech Publications

This annual review summarizes the progress of carbon capture, utilization, and storage (CCUS) technologies in 2025. During this year, the growth rate of atmospheric CO2 concentrations slowed, yet anthropogenic CO2 emissions continued to rise. At the same time, the increasing frequency of severe natural disasters underscored the urgency of climate action and further intensified global efforts to advance CCUS deployment. Scientific interest in CCUS expanded markedly, with the annual number of related publications increasing by 18.5% compared with 2024, reaching 63935. Meanwhile, the total number of operational commercial CCUS facilities worldwide grew by 54%, accompanied by an approximate 25% increase …

Performance Analysis Of Video Coding For Machines With Vision Transformers, Vaishnavi Dhulipudi

Electronic Theses and Dissertations

This thesis investigates the performance of Video Coding for Machines (VCM) with Vision Transformer based object detection models. While existing VCM studies and tool designs have largely been developed under CNN-based assumptions, recent advances in computer vision have shown the growing importance of transformer based models. Motivated by this shift, this work studies whether VCM compressed data remains suitable for Vision Transformer based inference in addition to conventional CNN-based task networks.

To address this problem, three representative transformer based object detection models were selected: DETR, SWIN, and YOLOS. These models were chosen to represent different architectural styles, namely a CNN …