Engineering Commons^™

Analysis Of Leak Testing Planning For Carbon Dioxide Piping Systems In The Development Of Carbon Capture, Utilization, And Storage Facilities In The Bintuni Basin, Devi Sentiani, Akhmad Herman Yuwono Professor

Journal of Materials Exploration and Findings

Carbon Capture, Utilization, and Storage (CCUS) technologies have become a key strategic response to global efforts to reduce CO₂ emissions. This study presents a leak testing strategy designed for a carbon dioxide (CO₂) piping system as part of a Carbon Capture, Utilization, and Storage (CCUS) project in the Bintuni Basin, West Papua, Indonesia. This study aims to ensure the system is properly prepared before commissioning by designing a leak test plan that is both practical and technical, based on ASME B31.3, ASME PCC-2, and API STD 520 Part 1. We conducted the process, which involves identifying the system boundaries, …

Advancing Comfort And Efficiency: Radiant Heating And Cooling Systems For Sustainable Architecture, Tarek Abedin, Md Mahfuzur Rahman, Zihad Hasan Joy, Khaled Alnamasi, Irfan Anjum Badruddin, Muhammad Nasir Bashir, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

This paper provides a thorough examination of Radiant Heating and Cooling (RHC) systems in the context of sustainable building practices. It covers the evolution, theoretical foundations, technological advancements, financial considerations, and practical uses of RHC systems. A comprehensive historical overview, tracing the evolution of RHC from ancient times to its current state, highlighting significant innovations that have greatly influenced its application in modern architecture. The scientific principles of thermal comfort, including Mean Radiant Temperature (MRT) and thermal asymmetry, which play a crucial role in the design and effectiveness of RHC systems has been explored. This article delves into recent technological …

Beyond Single Metrics: A Holistic Benchmarking Framework For Low-Power Embedded Systems, Hassan Adam

UNLV Theses, Dissertations, Professional Papers, and Capstones

Modern embedded systems encounter a notable challenge in evaluation. While devices may meet traditional benchmarks, they often underperform in real-world applications due to neglected interactions at the system level. Current benchmarking suites, such as MLPerf Tiny and EEMBC ULPMark, evaluate specific metrics including computational throughput, energy efficiency, and memory usage. However, they do not consider the complex interdependencies that affect real-world performance. This thesis presents a benchmarking framework that concurrently evaluates multiple performance dimensions under realistic workloads, revealing system behaviors that are often hidden in conventional benchmarks.Through the comprehensive evaluation of three representative algorithms: Fast Fourier Transform, quantized neural network …

Functional Biopolymers Applied To Sustainable Technologies In The Environment And Healthcare, Fengjie He

UNLV Theses, Dissertations, Professional Papers, and Capstones

Biodegradable polymeric materials (biopolymers) are naturally derived materials known for their excellent biocompatibility, biodegradability, sustainability, and versatile chemical functionality. They have attracted increasing attention in various applications as alternative to synthetic materials ranging from food packaging to tissue engineering. Meanwhile, with intrinsic advantages, biopolymers have also emerged as promising materials in addressing contemporary challenges in both biomedical and environmental fields. Motivated by the significant potential of biopolymers and the growing need for sustainable materials, my research focuses on the design and engineering of biodegradable polymers with novel modification methods and application directions. In this work, two representative biopolymers are selected: …

Integrated Uav Platform For Multi-Spectral, Thermal, And Eos Imaging In Wildfire Monitoring And Modeling, Md Shariful Islam

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis presents the design and development of a modular unmanned aerial vehicle (UAV) system based on a quadcopter platform for flexible and efficient wildfire-related multimodal image acquisition. Addressing key limitations in ecological UAV monitoring such as sensor inflexibility, time-consuming reconfiguration, and imprecise image georeferencing, the system introduces a versatile payload integration framework supporting three distinct imaging sensors: MicaSense Altum-PT, FLIR Vue Pro R, and Sony Alpha 6000.All onboard components, including the flight controller, autopilot software, GNSS module, motors and ESCs, were selected to optimize stability and payload performance. A gimbal-free, downward-facing mount simplifies field deployment, while custom integration enables …

Investigating Information Extraction And Language Models In Medical Domain Text Processing, Pouyan Nahed

UNLV Theses, Dissertations, Professional Papers, and Capstones

This dissertation demonstrates that carefully adapted language-model pipelines can transform unstructured clinical-trial and pharmacological prose into reliable, low-latency structured data. Four interconnected studies support this claim.Tri-AL platform. An open-source dashboard ingests all 440 k+ ClinicalTrials.gov records—including every historical revision—into a normalized schema and parses the 20 GB XML archive over 10x faster than a BeautifulSoup baseline, while exposing hooks for demographic analytics and supporting integration of user-defined modules. Clinical trial summarization. An encoder–decoder model is trained on 57k description–summary pairs to condense clinical trials into a few sentences. ROUGE evaluation shows a 20% improvement over the baseline, while graph-based evaluation …

Bio-Inspired Electroactive Polymer (Eap) Sensors For Surface And Canal Flow Sensing In Dynamic Environments, Nazanin Minaian

UNLV Theses, Dissertations, Professional Papers, and Capstones

Nature can often create some of the most efficient and elegant solutions to complex problems, and engineering stands to benefit greatly from these time-tested designs. One of the more sophisticated examples of this is the lateral line system in fish: a distributed network of superficial and canal neuromasts that enables aquatic species to detect fluid disturbances with remarkable precision. This dissertation leverages that biological framework to explore the potential of electroactive polymers (EAPs), aiming not just to replicate structure, but to emulate function.Two classes of EAPs form the basis of this work: electroactive plasticized polymer gels (EPPGs) and ionic polymer-metal …

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 …

Ai-Driven Solutions For Electricity Fraud Detection: A Data-Centric Approach, Fatemeh Alimoradi, Zahra Alimoradi, S. Mohammadali Zanjani, Ghazanfar Shahgholian

NJF Intelligent Engineering Journal

Electricity fraud detection presents a significant challenge for power distribution companies, as non-technical losses resulting from fraudulent activities adversely affect revenue and operational efficiency. This study presents a machine learning-driven approach for accurately identifying fraudulent electricity consumption patterns. A comprehensive analysis of electricity usage data is conducted using multiple classification models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), a Stacking Model integrating SVM, KNN, Random Forest, and Gradient Boosting, as well as a Weighted Model leveraging confidence-based prediction adjustments. Model performance is evaluated using key metrics, including accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrices. The results indicate that …

Application Of Ann Artificial Network In Slope Behavior Evaluation Using Machine Learning Technique, Marziyeh Tourani, Hadi Bahadori

NJF Intelligent Engineering Journal

In geotechnical engineering, evaluating slope stability remains a significant challenge due to inherent soil variability and environmental uncertainty. This study explores the application of Artificial Neural Networks (ANN) in predicting the factor of safety (FoS) of slopes using dimensionless input variables. An initial dataset of 349 samples was refined to 259 validated cases after removing outliers and incomplete entries. The input parameters were transformed into nondimensional forms c / γh, (tan(⌀)) / (tan (β)), and p / γh based on fluid mechanics principles to improve generalization and reduce dimensional bias. A feedforward ANN model …

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 …

Nanofluid-Enhanced Hvac&R Systems (2015–2025): Experimental, Numerical, And Ai-Driven Insights With A Strategic Roadmap, Aung Myat, Md Mashiur Rahman, Muhammad Akbar

Mechanical and Manufacturing Engineering Faculty Research

Heating, ventilation, air conditioning, and refrigeration (HVAC&R) systems account for a significant share of global energy demand, prompting intensive research into advanced thermal enhancement techniques. Among these, nanofluids—colloidal suspensions of nanoparticles in base fluids—have shown promise in boosting heat transfer performance. This review provides a structured and critical evaluation of nanofluid applications in HVAC&R systems, synthesizing research published from 2015 to 2025. A total of 200 peer-reviewed articles were selected from an initial pool of over 900 through a systematic filtering process. The selected literature was thematically categorized into experimental, numerical, hybrid, and AI/ML-based studies, with further classification by fluid …

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 …

A Meta-Analysis Of Historic Drinking Water Emergency Events Reported In News Articles, Victor Yu, Haiying Wang, Weiwei Mo

Faculty Publications

This study seeks to investigate historical drinking water contamination events that reported by news media to find the key determinants of their outcomes, including the duration and economic cost, and to quantitatively describe the characteristics of past contamination events. ProQuest Global Newsstream and Regional Business News were used to identify an initial list of events and supplemented by additional search on specific information including the response actions, economic cost, and more. These events were analyzed using descriptive statistics, correlation analysis, and regression modeling. Smaller community water systems were found to be more frequently impacted by contamination events. Use restrictions, except …

Career: Flavones As Safe, Sustainable Additives To Tune Biodegradable Plastic Stability, Kyle Moor

Funded Research Records

No abstract provided.

Rapid: Data Collection On Roadway Vehicle Abandonment In The Fast-Moving Los Angeles Wildfire Evacuations, Sarah Grajdura

Funded Research Records

No abstract provided.

Validity Of Body Composition Estimates In Women Assessed By A Multifrequency Bioelectrical Impedance Device, Mitchell Evan Zaplatosch, Juliana F. Meireles, Janeen S. Amason, Sadaf Dabeer, Brian M. Kliszczewicz, Gerald Mangine, Valene G. Barry, Barbara A. Gower, Katherine H. Ingram

Faculty Articles

Background: Multifrequency bioelectrical impedance devices such as the InBody 770 (IB770) offer faster measurements and lower costs compared with other body composition assessments. This study validated measures from IB770 against the deuterium oxide dilution technique (D2O) and DXA and compared a four-compartment (4C) model using total body water (TBW) derived from IB770 compared with D2O. Methods: A total of 55 adult females (mean ± SD, age: 21.1 ± 2.6 years) completed IB770 and DXA scans and the D2O protocol. Lin’s concordance correlation coefficients (CCCs), Bland–Altman analyses, and other equivalence tests evaluated agreement between IB770 and the criterion for measures of …

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 …

Effects Of Spent Coffee Grounds On Improvement Of Resistive Switching Characteristics In Natural Rubber-Based Memory, Muhammad Awais, Nadras Othman, Mohamad Danial Shafiq, Feng Zhao, Kuan Yew Cheong

Electrical and Computer Engineering Faculty Research & Creative Works

The recent upsurge in environmental awareness provokes the widespread usage of green materials in sustainable electronic applications. Herein, the effects of spent coffee grounds (SCGs) on natural rubber (NR)-based resistive switching (RS) memory are systematically investigated. This study presents the fabrication of a metal-insulator-metal (MIM) structure using NR incorporated with SCGs (0 to 8 wt.%) as a memristive layer and sandwiched between electrodes. A significant improvement in the ON/OFF ratio from 10⁴ for pure NR to 10⁷, read memory window increased from 2.03 to 2.45 V with improved stability even after 130 cycles of switching is achieved …

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 …

Symmetric Toeplitz Matrices For A Class Of New Subclass Functions And Bazilevič Functions, Nihad H. Shehab, Abdul Rahman S. Juma

Iraqi Journal for Computer Science and Mathematics

In this work, we describe and investigate a novel collection of analytic functions, including the new functions and the Bazilevič functions. An important component of analytic functions, Bazilevič functions have numerous uses in both pure and practical mathematics. For this class of functions, we concentrate on building Toeplitz matrices, examining their structural characteristics, and evaluating their eigenvalues and trends. We utilise these studies to draw sophisticated mathematical conclusions on the stability and convergence characteristics of Bazilevič functions, as well as possible uses in geometry and differential equations. This work aims to determine coefficient estimates for the functions in this family …

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 …

Architectural Optimization Of Façade Design Using Nano-Ceramic Films For Green Buildings In Baghdad’S Hot Climate, Lina M. Shaker, Wan Nor Roslam Wan Isahak, Ahmed Salih Mahdi

AUIQ Technical Engineering Science

This study aims to optimize the architectural integration and energy performance of nano-ceramic window films in a three-story Baghdad commercial building having an overall area of 2,000 m². In a 40% window-to-wall ratio (WWR) and climate-responsive orientation, films were selected for solar exposure; north façade overhanging high-transmittance movies, east and west vertical fins on medium-transmittance movies, and south horizontal louvers on low-transmittance movies. Architectural strategies for daylighting, comfort, and façade appearance with low solar heat gain (SHG) were considered. MATLAB 2022 simulations evaluated energy savings, daylight transmission, and solar rejection. The most effective film (either film, HD-IR05100) achieved …

Using Large Language Models For Enhanced Fraud Analysis And Detection In Blockchain Based Health Insurance Claims, Ruba Islayem, Senay Gebreab, Walaa Alkhader, Ahmad Musamih, Khaled Salah, Raja Jayaraman, Muhammad Khurram Khan

Industrial Engineering: Faculty & Staff Publications

Traditional health insurance claim processing systems are plagued by inefficiencies and vulnerabilities, often resulting in significant financial losses due to fraudulent activities. Existing fraud detection methods are largely manual, time-consuming, and inadequate for handling the complexity and scale of modern fraudulent schemes. Moreover, the trust-based relationships between insurers and healthcare providers lack mechanisms to ensure data integrity and prevent manipulation. While several blockchain-based systems have been proposed to improve transparency and tamper resistance, they typically focus on structured data and predefined fraud types, offering limited adaptability and analytical insight. This paper proposes a novel solution leveraging blockchain technology and Large …

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 …

Development Of Construction Materials With Natural Fibers And Flax Shives For Green Eco-Friendly Buildings. Mrs Advances (2025), Iman El-Mahallawi, Lamiaa Mohamed, Rania El‑Shorbagy, Mahmoud Tash, Aliaa Abdelfatah

Mechanical Engineering

Sustainability-oriented solutions are essential to address the depletion of natural resources, solid waste accumulation, and high CO2 emissions in the construction industry. This research focuses on the development of environmentally friendly green bricks by replacing high-carbon-footprint constituents with local, natural materials. Specifically, flax shives, a natural agricultural by-product, and marble powder, an industrial waste, were used as partial substitutes in the binder matrix. The natural fibers not only promote sustainability but also enhance insulation and reduce the environmental burden. The bricks were molded into rectangular specimens (60 × 100 × 200 mm) and tested for thermal conductivity, moisture absorption, and …

Integrated Algorithm And Hardware Design For Hybrid Neuromorphic Systems, James Seekings, Mahsa Ardakani, Peyton Chandarana, Arshia Eslami, Mohammadreza Mohammadi, Ramtin Zand

Faculty Publications

This paper investigates the combined potential of neuromorphic and edge computing to develop a flexible machine learning (ML) system designed for processing data from dynamic vision sensors. We build and train hybrid models that integrate spiking neural networks (SNNs) and artificial neural networks (ANNs) using the PyTorch and Lava frameworks. We explore the effects of quantization on ANN models to assess its impact on both accuracy and energy efficiency. Additionally, we address the challenges of deploying hybrid models on hardware by implementing individual components on specific edge platforms. We also propose an accumulator circuit to bridge the spiking and non-spiking …