Digital Commons Network^™

Development Of An Access Charge Framework For High-Speed Rail Incorporating Rail Replacement Costs And Dynamic Train Characteristics, Nitesh Kumar Yadav

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis develops a framework to determine track access charges for shared high-speed rail corridors, with a focus on rail replacement cost driven by train-induced loads. The method explicitly accounts for both static loads, resulting from axle weights, and dynamic loads that arise from train speed and track geometry, particularly curvature. Train characteristics such as axle load, operating speed, frequency of service, and number of wheels are integrated with track parameters, including curve radius, to calculate the total vertical load. These loads are used to estimate the cumulative tonnage threshold for rail replacement and the resulting service life of the …

Sleep Disorder Diagnosis Using Eeg Signals And Lstm Deep Learning Method, Mohammad Reza Yousefi, Reza Rahimi

NJF Intelligent Engineering Journal

Diagnosing brain disorders, particularly sleep disorders, has emerged as a critical area of research in both basic sciences and engineering. Sleep disorders encompass a range of conditions, including excessive or insufficient sleep, frequent awakenings, difficulty falling asleep, and challenges in reaching deep sleep stages. A key application of sleep disorder diagnosis lies in supporting the medical community through the development of advanced algorithms and models capable of accurately identifying these disorders based on individual brain signals, such as those captured by electroencephalography (EEG). These sophisticated approaches facilitate the creation of personalized treatment programs, paving the way for more targeted and …

A Hybrid Deep Learning Approach For Lung Diseases Classification Using Vision Transformer And Densenet, Noor Abd Alrazak Shnain, Mohammed Abdulameer Aljanabi

NJF Intelligent Engineering Journal

Lung disorders, such as pneumonia, tuberculosis, and COVID-19, remain significant global health issues. Accurate and early detection is of paramount significance to ensure effective treatment and control. Conventional CNN models like DenseNet have shown superior performance in the classification of medical images, whereas Vision Transformers (ViTs) have recently gained momentum as formidable models to capture global context information. Each of these models, however, has limitations when operated standalone. This manuscript suggests an innovative hybrid deep learning framework that synergizes the benefits of both ViT and DenseNet to advance lung disease diagnosis using chest X-ray images. The hybrid model uses both …

Improving Particle-Phase Nitrate Measurement In Pm2.5 Filter Sampling: Evaluation Of A Denuder–Nylon Filter Modification In The Spartan Network, Wenyu Liu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Accurate measurement of PM_2.5 composition is of global importance. Evaporation-induced mass loss introduces potential bias in filter-based PM_2.5 measurements, with ammonium nitrate volatilization from Teflon filters being a major contributor to negative mass artifacts. Previous studies have demonstrated that using an acid gas denuder in combination with nylon filters can help recover the nitrate mass. This study evaluates design modifications to the AirPhoton sampling station setup within the globally distributed Surface Particulate Matter Network (SPARTAN) incorporating an additional acid gas denuder upstream and a nylon filter downstream of the existing Teflon filter.

Two co-located AirPhoton Sampling stations were …

Safe Real-Time Obstacle Detection And Navigation Using Cbf–Clf And Cbf–Pid Control, Nicolas M. Hernandez

McKelvey School of Engineering Graduate Student Theses & Dissertations

Traditional robotic navigation pipelines typically follow a three stage architecture: obstacle detection, path planning, and low-level control for trajectory tracking. While effective in static environments, these methods often introduce latency and lack formal guarantees of safety in dynamic or unplanned for scenarios. Our work addresses these limitations by developing a real-time controller grounded in Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs), unified through a Quadratic Program (QP). We first investigate a hybrid CBF-PID-QP controller on a 1/10 scale car, where the CBF serves as a real-time safety filter, modifying the PID output to prevent constraint violations. While this …

Csa-Xai: Channel–Spatial Attention And Explainable Ai In A Modular Multi-Backbone Framework For Lung Cancer Classification, Omar Ibrahim Obaid, Abdulbasit Alazzawi

Iraqi Journal for Computer Science and Mathematics

Computed tomography (CT) scans require precise and early lung cancer detection to produce better clinical results. High accuracy in deep learning approaches (DL) poses an existing challenge to interpret their functionality effectively. This research presents an innovative modular multi-backbone structure that combines channel-spatial attention together with explainable AI (XAI) methods for three-class lung cancer diagnosis (Normal, Benign, and Malignant). Research was carried out to evaluate six pre-trained CNN backbones (ResNet-50, VGG19, Inception-V3, EfficientNet-B0, MobileNet-V2, DenseNet-121) which received hybrid attention enhancement on the IQ-OTH/NCCD dataset. The experimental data showed four pre-trained models reaching perfect accuracy at 100 percent whereas the others …

Class Weighting In Imbalanced Data For Leukocyte Classification Using Fine-Tuning Inception-V3, Wahyudi Setiawan, Meidya Koeshardianto, Eka Mala Sari Rochman, Aeri Rachmad, Tutut Herawan

Iraqi Journal for Computer Science and Mathematics

This study investigates the classification of leukocyte images in an imbalanced dataset using deep learning techniques. The dataset consists of 14,514 images, categorized into five leukocyte types: basophils (301), neutrophils (8,891), lymphocytes (3,461), monocytes (795), and eosinophils (1,066). To address class imbalance, we applied class weighting alongside transfer learning and fine-tuning using the Inception-v3 architecture. The dataset was split into 80% for training and 20% for testing, and 5-fold cross-validation was conducted to evaluate model robustness. Hyperparameters were set with a learning rate of 0.0001, batch size of 32, and 30 training epochs, optimized using the Adam optimizer. Fine-tuning was …

Classify Cancers Types Using Mirnas Profiles And Machine Learning, Wafaa Khazaal Shams

AUIQ Technical Engineering Science

Cancer classification is important for early diagnosis and effective treatment. This study proposes a hybrid framework that combines Fisher Discriminant analysis (FD) with Linear Support Vector Machine (LSVM) and Long Short-Term Memory (LSTM) networks. MicroRNAs (miRNAs) expression profiles for three types of cancer are used to evaluate the model. These are: lung- adenocarcinoma, ovary and pancreas cancers. The classification process is done using 5-fold cross valuation. Results show significant performance for LSTM compared to LSVM with and without features reduction. However, using FD analysis results in high accuracy reached 0.97and 0.96 for LSTM and LSVM respectively, using 50 miRNAs. The …

Comparative Analysis Of Codes Of Ethical Conduct And Professionalism In Engineering And Project Management Associations, Valarie Denney, James W. Marion

Publications

This research details the similarities and differences in moral values and perspective from 15 engineering and project management association codes of conduct and professionalism. The methodology uses text mining of selected codes counting and summarizing keywords, evaluating document similarities through cosine similarity, and assessing the influence of various ethical schools of thought. Key findings reveal differences in the emphasis of core ethical values like fairness, honesty, and responsibility across different associations. This research is important for engineering and project management professionals to purposefully develop and evolve codes of ethics and professional development to enhance integrity, accountability, and ethical decision-making.

Orbital Maneuvers And Interplanetary Trajectory Design Via Reinforcement Learning, Roberto Cuéllar Rangel

Doctoral Dissertations and Master's Theses

This dissertation investigates the application of reinforcement learning (RL) to the design and optimization of low-thrust spacecraft trajectories, with an emphasis on autonomy, adaptability, and robustness in the presence of system uncertainties and unmodeled perturbations. Classical approaches to low-thrust trajectory design are predominantly grounded in optimal control theory, which relies on the availability of precise dynamical models and often requires problem-specific reformulation and solver tuning. While optimal control methods offer high accuracy under deterministic conditions, their sensitivity to stochastic disturbances and computational limitations in highly nonlinear or uncertain environments pose significant challenges for future autonomous space missions.

To address these …

Characterizing And Modeling The Nonlinear Behavior Of Additively Manufactured Trapped-Powder Dampers, Jonathan Knight Black

Theses and Dissertations

Laser Powder Bed Fusion (LPBF) can be used to create metal parts with pockets of retained, unfused powder that serve as mechanical dampers. These trapped-powder dampers show promise for reducing the amplitude of vibrations at resonance by orders of magnitude. However, little is currently understood about the nonlinearity and repeatability of trapped-powder dampers, meaning that the design process currently consists of expensive trial-and-error prototyping and iteration. The present work contributes a corpus of experimental measurements for rectangular beams with trapped-powder dampers of various dimensions and locations. Preliminary testing revealed the presence of a memory effect which we hypothesize to be …

Investigating Radiation Induced Rowhammer And Retention Failures In Dram, Tyler Ricks

Theses and Dissertations

It is desirable for DRAM devices to be used in harsh radiation environments. Exposure to radiation causes DRAM cells to lose their retention time as their ability to store charge effectively declines. Exposure also worsens the effect of disturbance that causes neighboring cells to flip when a row is opened multiple times: an effect known as the Row Hammer Effect. This thesis evaluates the impact of radiation-induced memory cell degradation by comparing memory cell's retention time and their resilience to the row hammer vulnerability. Three generations of DRAM were placed under a neutron radiation beam. Sections of the irradiated devices …

Understanding And Evaluating Genomic Language Models, Aadit Kapoor

Master's Theses

Large Language Models (LLMs) have shown remarkable capabilities in interpreting complex patterns across various domains, yet their application to genomic data remains limited. We see great potential in leveraging LLMs for vital biological tasks, such as predicting transcription factor binding sites and identifying antibiotic-resistant genes. This emergent behavior positions LLMs as powerful tools for enhancing our understanding of intricate biological language. LLMs trained specifically on genomic data, such as DNA sequences, operate distinctly compared to those trained on natural language. This difference is evident not only in the architectural landscape of the models but also in the methodologies employed by …

Application Of Multi-Criteria Decision Analysis For Quantifying Responsibility In Ml-Based Intrusion Detection System, Mona Mohamed, Zekra Sakr

Neutrosophic Systems with Applications

With the swift growth in Internet of Things (IoT), certifying secure and trustworthy networks has turned to be a critical challenge, particularly as IoT devices are increasingly vulnerable to sophisticated cyberattacks. As a remedy, intelligent intrusion detection systems (IDS) evolved as promising solutions in recent years, but deciding on the appropriate model remains difficult because of competing performance and trustworthiness criteria. To this end, this paper explores a novel application of an ML-augmented decision-making framework to enhance security-related decision-making in IoT environments. The framework systematically evaluates and ranks ML-based IDS systems according to different evaluation criteria with distinct trade-offs, including …

Land8fire: A Complete Study On Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, And Extensive Benchmarking, Anh Tran, Minh Tran, Esteban Marti, Jackson Cothren, Chase Rainwater, Sandra Eksioglu, Ngan Le

Electrical Engineering and Computer Science Faculty Publications and Presentations

Early and accurate wildfire detection is critical for minimizing environmental damage and ensuring a timely response. However, existing satellite-based wildfire datasets suffer from limitations such as coarse ground truth, poor spectral coverage, and class imbalance, which hinder progress in developing robust segmentation models. In this paper, we introduce Land8Fire, a new large-scale wildfire segmentation dataset composed of over 20,000 multispectral image patches derived from Landsat 8 and manually annotated for high-quality fire masks. Building on the ActiveFire dataset, Land8Fire improves ground truth reliability and offers predefined splits for consistent benchmarking. We evaluate a range of state-of-the-art convolutional and transformer-based models, …

Compact Heterogeneous Architectures And Algorithmic Methods For Enhanced Real-Time Digital True Time Delay Beamforming And Radar Applications, Nathan Lynn Burnett

Theses and Dissertations

Modern radar and signal processing systems require efficient computational architectures to handle increasing data rates while still being able to maintain dynamic configurability and complex tasks. This thesis presents and explores hardware and algorithmic approaches addressing these demands across several different applications. First, a low-cost tracking platform and architecture for unmanned aerial vehicles (UAVs) is developed, which provides detection and tracking capabilities, compliant with new FAA remote ID regulations. Second, a GPU-FPGA based digital true time delay beamformer is implemented to achieve real-time wideband radio frequency interference (RFI) mitigation. Specifically this beamformer explores weight calculation and calibration through particle swarm …

Versatile Waveform Generation And Clocking Techniques For Rfsoc-Based Digital Designs, Michael David Jones

Theses and Dissertations

Within an ever-evolving radio-frequency (RF) environment, designers are required to adapt systems to increasingly constricted parameters. The available tools continually grow to match this need, but so does the complexity in using them. The radio-frequency system-on-chip (RFSoC) is an innovation that combines a field-programmable gate array (FPGA) with an integrated RF chain. Designing with an RFSoC allows for high-precision data transmission, sampling, and processing, but it can be difficult and time-consuming to develop specialized circuits. The open-source CASPER project aims to provide a library of abstracted block-design components to simplify the design process. An arbitrary waveform generator (AWG) is created …

A New Lightweight Encryption Method Based On The Dna-Rc4 Substitution For Resource-Constrained Iot Devices, Athraa J. H. Witwit, Ahmed Fanfakh, Ali Kadhum Idrees

Iraqi Journal for Computer Science and Mathematics

Technology's impact on daily life necessitates increased data protection. Cryptographic systems improve security, ensuring confidentiality and legitimacy of Internet of Things systems. However, resource-constrained devices face challenges like memory, battery life, processing power, and small size. In light of this, lightweight cryptography (LWC) provides techniques specifically tailored to the constraints of resource-constrained Internet of Things devices. However, the presence of a fixed S-Box in some LWC algorithms, such as Advanced Encryption Standard, or the absence of one in others, such as Speck and Tiny Encryption Algorithm, renders them more susceptible to attacks. In this paper, we suggest a new lightweight …

Demystifying The Discharge Coefficient For Flow Over Thin Weirs And Sills, Joseph E. Pugh, Subhas Karan Venayagamoorthy, Timothy K. Gates

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

A revised derivation of the discharge coefficient for flows over thin weirs and sills in the limits of wall overflow to a free overfall is given. Using dimensional analysis, we show that the discharge coefficient, (Formula presented), in the classical weir-discharge equation is best understood as a weir Froude number, (Formula presented), which accounts for the combined effects of inertia, contraction and viscous energy losses within the flow field. A comprehensive set of experimental data from historical studies is complimented by new data from the authors, featuring both laboratory flume experiments and three-dimensional numerical simulations of weir flows. Synthesis of …

Development Of Numerical Tools And Performance Metrics For Analysis Of Optical Particle Counters, Doug Webb

Theses and Dissertations

Monitoring air quality is essential for understanding and mitigating the health impacts of airborne particulate matter (PM), which is linked to respiratory and cardiovascular disease. Optical particle counters (OPCs) are widely used for air quality monitoring, yet their precision and accuracy remain uncertain due to inconsistent performance evaluations. In this thesis, performance metrics are developed that can be used to assess the accuracy of OPCs in measuring PM concentrations and size distributions. Computational fluid dynamics (CFD) simulations are employed to analyze the behavior of particle-laden air streams through representative OPC models. The impact of design parameters is quantified using newly …

Design Of A Fan-Based Test Stand For Evaluating Multicopter Handling In Turbulent Flow, Salim Roig

Doctoral Dissertations and Master's Theses

With the innovation of eVTOL rotorcraft the need to analyze how turbulence-induced disturbances from orthogonal flow affect various rotorcraft configurations outside of simulations have grown significantly. To investigate the behavior of this type of flow and how it interacts with various rotorcraft configurations, a turbulent inducing test stand was designed and constructed. The test stand will have two walls of computer fans arranged in an L-shape frame, creating wind gusts that simulate the non-linear turbulent wind experienced by eVTOL. This also aids the Eagle Flight Research Center (EFRC) in their efforts to better understand how disturbances affect the handling qualities …

Genetic Algorithm Optimization To Maximize Sensitivity Of Triboelectric Mems Accelerometers, Yu Tian, Benyamin Davaji, Shahrzad Towfighian

Mechanical Engineering Faculty Scholarship

A genetic algorithm is used to optimize the sensitivity of a triboelectric MEMS accelerometer fabricated using CMOS-compatible processes. The optimization focuses on the structural parameters of an awl-shaped serpentine microspring (ASSM) connected to the proof mass. Constraints are defined by microfabrication standards and the available chip area. Three different constraint problems are considered, and the algo- rithm yields designs with maximized sensitivity in each case. Simulations under various sinusoidal vibration scenarios show that the optimized designs significantly outperform the previous version, achieving a 2.72× improvement at 1000 Hz and up to 20× at 500 Hz.

Calibration And Validation Of The Thermal Finite Element Model For Er120s-G Wire Arc Directed Energy Deposition Using Infrared Cameras And Thermocouples, Joshua Mykl Bassett

Theses and Dissertations

Wire Arc Directed Energy Deposition (WA-DED) is a promising additive manufacturing method for producing large metallic components with high deposition rates and low costs. However, its rapid heat input can lead to residual stress, microstructural changes, and dimensional inaccuracies. Accurate thermal modeling is essential to mitigate these effects. This study presents a thermal calibration of a finite element model (FEM) for ER120S-G using thermocouples and infrared (IR) imaging. Thermal data were gathered from three experiments to complete the calibration of the FEM . Calibration focused on tuning the Goldak heat source parameters, power efficiency, and heat transfer coefficients to minimize …

Towards Applying Artificial Intelligence To Solve Np-Complete Problems Using Quantum Computing, Andrew Robert Haverly

Theses and Dissertations

This dissertation explores the methodology for more thoroughly entangling artificial intelligence and quantum computing. This is explored through a background search of problems solved using Grover’s quantum algorithm, a new quantum protein folding and drug discovery algorithm, using Grover’s algorithm to train quantum artificial neural networks, using quantum artificial neural networks for reinforcement learning, mapping classical assembly instructions to quantum circuits to make quantum programming easier, and using prompt engineering to get a classical artificial intelligence agent to solve an NP-Complete problem using a Grover’s algorithm. This method not only simplifies the creation of algorithms but also opens new avenues …

Spray Characterization And Fuel Property Analysis Of Tier 3 Gasoline In A Gdi System Using Machine Learning, Kyungwon Lee

Theses and Dissertations

This study investigates the spray behavior of Tier 3 gasoline in gasoline direct injection engines using experimental methods. Tier 3 gasoline, formulated with lower sulfur content, enhances aftertreatment system efficiency and supports cleaner combustion. A comparative fuel property analysis between Tier 3 gasoline and PACE-20, surrogate fuel, was conducted to understand compositional effects on spray. A constant volume spray chamber and optical diagnostics, including diffuse back-illumination extinction, Schlieren, microscopic, and 3D CT reconstruction imaging, were used to examine spray morphology under varying injection conditions. Key spray parameters such as liquid volume fraction, projected liquid volume, droplet size, and cone angle …

Thermo-Mechanical Advancements For Very High Strength And Ultra High Performance Concretes, Lana Grayson Brown Sisung

Theses and Dissertations

Interest in the use of high-strength concrete (HSC) has increased in recent years with demand for improved concrete performance. HSCs are widely associated with high heat of hydration and elusive behavior when exposed to high temperatures commonly occurring in mass structures. The optimization of HSC mixture proportioning is difficult due to a wide variety of constituents that influence thermal and mechanical properties. Additionally, determining in-place properties of mass concrete placements is elusive, and there are currently minimal to no test methods available that are both predictive and a direct measurement of mechanical properties. The objective of this dissertation is to …

Transforming Movement Assessment In Physical Therapy Through Subaquatic Data Collection, Kaitlyn Mcdonald

Theses and Dissertations

This study explored the interest and perceived barriers to integrating subaquatic diagnostic technologies (SDTs) into hydrotherapy among licensed physical therapists. Seventeen semi-structured interviews were conducted using a mixed-methods design, with 15 interviews included in the final analysis. Quantitative data were analyzed using chi-square tests, while qualitative responses were coded thematically. Results indicated no statistically significant relationships between SDT interest and career stage or hydrotherapy access, though qualitative data highlighted concerns about cost, limited access, and usability. Despite mixed interest in adoption, participants identified several potential benefits of SDTs, including improved treatment tailoring, increased patient buy-in, and enhanced outcome monitoring. Functional …

Reactive Molecular Dynamics And Method Development For Applications In Renewable Energy And Sorption Equilibria, Woodrow Wilson

Theses and Dissertations

It is crucial to harness energy from sustainable sources to combat the dwindling nonrenewable feedstocks used to produce everyday commodities and fuels. Liquid-phase heterogeneous catalysis has demonstrated remarkable product selectivity and yields for reactions relevant to biomass valorization under mild operating conditions. However, active site characterization is challenging due to the complex solvated environment and interface. Ground-state first-principles (FP) molecular simulations have provided insights into reactive systems in catalysis. These calculations, however, scale poorly with the number of electrons. Observing atomic motion with molecular dynamics (MD) or sampling static properties with Monte Carlo (MC) simulations at statistically relevant lengths- and …

Interatomic Potential Of Multiphase Materials Using Modified Embedded Atom Method (Meam) And Artificial Neural Network (Rann), Henan Zhou

Theses and Dissertations

There is a significant need for computer simulations to explore multiphase materials because of their significance in both industry and research. These simulations are both more efficient and less expensive than many experimental approaches, making them an ideal choice for this purpose. It is possible to use molecular dynamics simulations that make use of the MEAM and RANN interatomic potentials in order to comprehend and characterize the properties, performance, and behavior of materials at the atomic scale of different material phases. In the realms of elements, alloys, and impurities, MEAM can be utilized in a wide variety of applications. Through …

Kernel Principal Component Analysis And Convolutional Neural Network-Based Approach For Obstructive Sleep Apnoea Detection Using Electrocardiogram, Aida Noor Indrawati, Nuryani Nuryani, Wiharto Wiharto, Diah Kurnia Mirawati, Trio Pambudi Utomo, Nanang Wiyono

Iraqi Journal for Computer Science and Mathematics

Obstructive Sleep Apnoea (OSA) is a prevalent sleep disorder characterised by repeated episodes of partial or complete upper airway obstruction during sleep, primarily due to the relaxation and collapse of soft tissues in the throat. These interruptions lead to disrupted sleep patterns and reduced oxygen saturation, increasing the risk of cardiovascular complications. Although Polysomnography (PSG) is considered the gold standard for diagnosing OSA, it is often uncomfortable for patients due to the extensive use of sensors and prolonged monitoring duration. As a result, there is a growing need for alternative diagnostic methods that are more efficient, comfortable, and cost-effective. This …