Engineering Commons^™

Digital Replica For Trustworthy Cooperative Autonomous Vehicles, Hady Farahat

Theses and Dissertations

Trust in an automated system can be defined as confidence in a vehicle's reliability, safety, and predictability, which is essential for the acceptance and widespread adoption of fully autonomous vehicles (FAVs); without it, users might disengage from using autonomous vehicles or reject the technology altogether. Most of the previous research has focused on trust from an ego vehicle perspective.

However, next-generation vehicles are becoming more autonomous and connected, relying on vehicle-to-vehicle technology and vehicle-to-infrastructure technology with no human intervention. Hence, trust becomes more complex and fragile as multiple agents interact with each other, and it might become harder to establish …

Harnessing Ml And Iiot For Traceability In Continuous Production Systems: A Conceptual Framework, Kholoud M. Abdelaal

Theses and Dissertations

In the era of rapid technological advancement, the manufacturing sector faces increasing pressure to leverage emerging technologies to enhance operational efficiency and minimize waste. In this context, traceability plays a pivotal role, as it provides complete visibility of processes and products throughout manufacturing systems, enabling them to identify areas for improvement and take corrective actions accordingly. Additionally, traceability ensures compliance, supports product recalls, provides a clear understanding of the system’s performance, and enables fact-driven decision-making in multiple aspects of the manufacturing system. Although the broad spectrum of traceability applications in batch production-based plants, traceability remains challenging to achieve in continuous …

A Bim-Based Flexible Flow Shop Framework For Scheduling Linear Infrastructure Projects, Mahmoud Mohamed Elbassuony

Theses and Dissertations

Repetitive, multi-stage linear projects, such as pipelines or highways, are primarily impacted by spatial and resource constraints during their execution. Cost overruns, delays, and inefficient resource allocation make construction management for such projects complex. Traditional scheduling techniques, such as Critical Path Method (CPM) and Line of Balance (LOB), are less efficient for scheduling dynamic, complex, and multi-objective projects. There are more advanced scheduling methods that can be applied as a decision-making process to determine the optimal timing of activities and achieve other objectives. One of those techniques is the Flexible Flow Shop model (FFS), which can be effectively applied to …

Mechanical Characterization And Modeling Of Rat Myocardia Under The Influence Of Epirubicin, Abdallah Mahmoud Alkhaiyat

Theses and Dissertations

Cancer remains a predominant health challenge that is responsible for a significant portion of global morbidity and mortality. Epirubicin (EPI), a chemotherapeutic anti-cancer drug, has shown remarkable efficacy in combating various malignancies. However, it is known to have undesirable side effects on the heart, collectively referred to as cardiotoxicity. This research investigates the adverse effects of chemotherapy on cardiac contractility through an in vitro examination of the mechanics of healthy and infarcted animal heart tissues. Electrically stimulated slices of rat ventricular tissue were tested using an isometric force measurement tissue bath. The tissue slices were subjected to uniaxial stretching, allowing …

Irrigation Efficiency In Green Wall Systems Utilizing Iot: Developing And Testing A Method To Optimize Water Use For Green Walls, Yomna Zaghloul

Theses and Dissertations

This study investigates the application of an Internet of Things (IoT)-based automated irrigation system for green walls in Cairo, Egypt, assessing its impact on water conservation, energy consumption, and plant health across a three-month experimental period. The research compared a sensor-controlled drip irrigation system utilizing the ESP8266 microcontroller and real-time environmental monitoring against conventional timer-based irrigation methods. Water usage data revealed that the IoT system consistently achieved substantial savings—averaging nearly 60% less water consumption than the control group—without compromising overall plant vitality. Energy efficiency was also improved, with automated cycles reducing pump operating time by more than 50%. Plant growth …

Ai-Driven Stratified Modeling For Early Liver Disease Detection: A Comparative Study Of Ensemble And Conventional Machine Learning Classifiers, Ghadeer Murtadha Ali, Ali Aqeel Hadi, Mustafa Abdulkareem Abbas, Abdullah Alkarar Mohammad, Ahmed Fadhil Abdulhussein

AUIQ Technical Engineering Science

Background: Early prediction of liver disease remains challenging in routine clinical diagnostics due to the multifactorial nature of hepatic dysfunction and the limited discriminative capacity of conventional laboratory-only assessments.

Objective: This study aims to develop and rigorously evaluate a robust machine learning framework for binary liver disease classification, emphasizing predictive stability, diagnostic balance (sensitivity–specificity), and statistical reproducibility across repeated experiments.

Methodology: A structured dataset of 1,700 records with 11 features representing demographic, behavioral, genetic, and clinical determinants was used to train and compare five supervised models: CatBoost, AdaBoost, Random Forest, Support Vector Machine (SVM), and Decision Tree. Performance was assessed …

Human-Machine Communication: Complete Volume. Volume 12

Human-Machine Communication

This is the complete volume of HMC Volume 12.

Whole Earth Machines: Human-Machine Communication For A Green Transition, Klaus Bruhn Jensen

Human-Machine Communication

The climate crisis of the 21st century represents an existential risk to humanity and biodiversity, posing essential questions of how communication may serve to coordinate mitigation of and adaptation to climate change. One recent response has been massive investments by governments and corporations in systems providing feedback on the state of Earth— Whole Earth Machines (WEMs). For human-machine communication (HMC) studies, WEMs invite sustained engagement with communication infrastructures as a key constituent of research agendas, beyond the interface encounters at the center of many HMC studies to date. The article presents a conceptualization and operationalization of WEMs as critical infrastructures …

The Effect Of The Socioeconomic Variety In Greater Cairo On Transport Mode Choices, Rafik Yanni

Theses and Dissertations

In the last decade, Egypt has experienced a noticeable upgrade in its infrastructure investment, implementing a lot of transport projects, especially in the Greater Cairo region. In a city with over 20 million residents, variations lie in socioeconomic elements like income, age, gender, and neighborhood, which shape people’s choice of mobility modes based on their culture and lifestyle. The questions then become: whom are these new projects targeting? What are the barriers to using these services? And is there any willingness to change commuting habits if conditions changed? This thesis investigates how the socioeconomic variety in Greater Cairo metropolitan region …

A Modular Llm Approach To Argument Extraction In Philosophical Texts, Nate Miller

Masters Theses

Engaging with philosophical works is a rewarding but demanding task that challenges both human readers and computational systems designed to extract arguments from dense philosophical reasoning, and although large language models (LLMs) have made substantial progress in argument extraction, the most advanced models are often costly to run. As a result, there is growing interest in determining if multi-agent pipelines that divide a task into smaller stages can reduce cost while maintaining or improving performance.

This study investigates a modular multi-agent approach for extracting arguments from philosophical texts using LLMs, and compares its performance, cost, and runtime to both single-agent …

Conserved Chromatin Programs Orchestrate Cardiac Cell States In Health And Disease And Drive Cardiomyocyte Proliferation After Injury, Xiaoxiao Geng

ETDs from 2020-2029

Chromatin structure is a central regulator of gene expression and cellular identity, yet the chromatin structural factors driving cardiac disease and regeneration remain incompletely defined. This dissertation investigates how modulation of chromatin architecture shapes pathological and regenerative cardiac phenotypes through integrated single-cell transcriptomic, epigenomic, and functional analyses. To determine whether chromatin structural regulators distinguish healthy from diseased cardiac states, we curated a comprehensive list of chromatin structural genes and applied it to single-nuclei RNA sequencing datasets from human hearts with and without dilated cardiomyopathy (DCM). Chromatin structural gene expression effectively stratified cardiomyocyte and fibroblast populations by disease status. Diseased cardiomyocytes …

The Risc-V Fpga (Rvfpga) Teaching Package, Daniel Chaver, Sarah Harris, Luis Pinuel, Olof Kindgren, Zubair Kakakhel, Chris Owen, Roy Kravitz, Jose I. Gomez-Perez, Fernando Castro, Katzalin Olcoz, Multiple Additional Authors

Electrical and Computer Engineering Faculty Publications and Presentations

RISC-V is a free and open-standard ISA based on RISC principles, allowing anyone to design, manufacture, and sell RISC-V chips and software. Its flexibility and growing ecosystem have made it popular in research, education, and industry, increasing the need for educational materials. This paper provides an in-depth description of the RVfpga course, which offers a solid introduction to computer architecture using the RISC-V instruction set and FPGA technology. It focuses on providing hands-on experience with real-world RISC-V cores, the VeeR EH1 and EL2 cores, developed by Western Digital and hosted by ChipsAlliance. The course targets students and educators in computing-related …

Retrofitting Higher Education Buildings In Egypt: A Pathway Towards A Zero-Energy Campus, Maged G. Nassim Mikhael, Hagar Samy Saad

HBRC Journal

Egypt’s higher education sector expanded rapidly over the last decade, resulting in increased energy consumption, negative environmental impacts, and economic challenges. Existing academic buildings present promising opportunities for energy savings through retrofitting; however, a validated pathway for achieving Zero-Energy Buildings ZEBs in Egypt remains underdeveloped. This study aims to develop a practical scenario and a replicable retrofitting pathway based on a multi-tiered approach that includes passive envelope enhancements, active system optimization, and the integration of renewable energy RE systems. Using the Canadian International College CIC campus as a case study, the research adapted the building energy simulation BES methodology to …

Enhancing Sustainable Cooling Solutions: Investigating Spot Radiant Cooling Systems Performance And Influence On Indoor Comfort In Hot Climates, Norhane Eldeeb, Ahmed Ahmed Fikry, Abbas Mohamed Elzafarany, Ehab El Shazly, Hinar Abo El-Maged

HBRC Journal

In hot climates, demand for effective sustainable cooling strategies to maintain comfort is high. Nevertheless, conventional radiant cooling systems are often hindered by condensation risks and complex control requirements in partially occupied spaces. To address these challenges, at top inlet conditions of approximately 26°C and 50% Relative Humidity) RH (, three-dimensional CFD simulations validated against the 1.405 kW reference case of Shin et al. were performed to quantify the effect of installation height and panel geometry on spot radiant cooling performance. Lowering the flat CRCP from 2.6 m to 2.2 m reduced volume averaged air temperature (T_a) …

Leveraging Blockchain Technology In Mining Supply Chain Management: Vietnam Coal Mining Case Study, Thu Hang Nguyen, Nguyen Trung Tuan, Hong Anh Le

Journal of Sustainable Mining

The global economy heavily relies on the mining industry for essential resources such as coal, oil, gas, and metal ores. However, the intricate nature of mining operations poses significant challenges in supply chain management (SCM). This research investigates how blockchain technology can address these challenges within mining supply chain management (MSCM). Through a systematic review of existing research and projects, a conceptual blockchain model is proposed to improve mining supply chains’ transparency, traceability, efficiency, and sustainability, specifically focusing on coal supply chain management in Vietnam. The model integrates distributed ledgers, smart contracts, IoT devices, identity management, and consensus mechanisms to …

An Improved United-Atom Potential For Molecular Dynamics Simulation Of Saturated Properties Of N-Alkanes, Wazih Tausif, Jordan Hartfield, Alex George, Zhi Liang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Multiple united-atom (UA) potential models have been developed in the literature to reproduce experimental saturated properties of n-alkanes using Monte Carlo simulations. When these UA potentials are employed in molecular dynamics (MD) simulations, MD simulations often give relatively poor predictions of saturated properties of n-alkanes, particularly the saturated vapor densities, due to the challenges in accurate calculation of long-range intermolecular forces beyond the cutoff distance in an inhomogeneous system. In this work, a new set of UA Lennard-Jones (LJ) interaction parameters for n-alkanes is proposed to reproduce the saturated properties, including saturated liquid and vapor densities (ρ_f and ρ …

The Risc-V Fpga (Rvfpga) Teaching Package, Daniel Chaver, Sarah Harris, Luis Pinuel, Olof Kindgren, Zubair Kakakhel, Chris Owen, Jose I. Gomez-Perez, Fernando Castro, Katzalin Olcoz, Julio Villalba-Moreno, Alexander Grinshpun, Freddy Gabbay, Luke Seed, Rui Duarte, Manuel Lopez, Oscar Alonso, Robert Owen

Electrical & Computer Engineering Faculty Research

RISC-V is a free and open-standard ISA based on RISC principles, allowing anyone to design, manufacture, and sell RISC-V chips and software. Its flexibility and growing ecosystem have made it popular in research, education, and industry, increasing the need for educational materials. This paper provides an in-depth description of the RVfpga course, which offers a solid introduction to computer architecture using the RISC-V instruction set and FPGA technology. It focuses on providing hands-on experience with real-world RISC-V cores, the VeeR EH1 and EL2 cores, developed by Western Digital and hosted by ChipsAlliance. The course targets students and educators in computing-related …

A Closer Look At Shielding Gas In Laser Powder Bed Fusion: A Review, Tasrif Ul Anwar, Nadia Kouraytem

Mechanical and Aerospace Engineering Student Publications and Presentations

The ongoing advancement of additive manufacturing (AM) techniques, particularly in laser powder bed fusion (LPBF), is effectively bridging the knowledge gap between AM and conventional manufacturing methods. At present, most of the research is focused on optimizing key process parameters such as laser power, laser speed, hatch spacing, layer thickness, scan pattern, powder size distribution, and so on. These studies help improve the part density and refine the microstructure of LPBF components, thereby enhancing the mechanical properties. Despite these advancements, an area in LPBF that is receiving comparatively less attention is the shielding gas: its flow during the printing process, …

Recent Advances Toward Damage-Tolerant 3d-Printed Titanium Alloys: Alloy Design Perspective, Saeid Alipour, Arezoo Emdadi, Ju Li

Materials Science and Engineering Faculty Research & Creative Works

Twenty-year uninterrupted endeavor of titanium alloys printing has opened up a new paradigm in metal additive manufacturing (AM) to fabricate engineering components with required strength–density–corrosion combinations. Despite the remarkable advances in titanium AM, controlling the grain structure to print the parts with engineered microstructures, tailored mechanical properties, and minimum anomalies remains challenging. Numerous approaches have been implemented to address this challenge, such as printing parameter control, post-AM heat treatments, and thermomechanical processing. In addition to the aforementioned conventional approaches, novel techniques have been proposed that require employing hybrid manufacturing or developing the printer itself. One of the novel pathways in …

Performance, Economic And Environmental Evaluation Of A Solar-Assisted Vacuum Deaeration Heating System., Liew Shan Kun

Student Works (2020-2029)

This research evaluates the performance of a solar-assisted heating (SAH) system for makeup water in a vacuum deaeration process. The system integrates solar energy into the heating process, with parametric analysis conducted across varying configuration of total solar radiation and water flow rates. The total solar radiation at the experimental site in Kuala Kangsar, Perak, Malaysia, characterized by tropical weather, fell within the range between 414W/m2 to 568W/m2. Performance analysis showed that a flow rate of 0.4 LPM achieved the target temperature of 61°C required for the deaeration process. Thermal efficiency peaked at 62.60% under a solar radiation of 900 …

Iron-Involved Orr Electrocatalysts Under The Lens Of In-Situ/Operando Mössbauer Spectroscopy, Sumbal Farid, Jun-Hu Wang

Journal of Electrochemistry

Exploring cost-effective and efficient catalysts for oxygen reduction reaction (ORR) poses a significant challenge, especially in the pursuit of alternatives to precious metals like platinum. Significant advancements have driven electrochemists to develop efficient ORR catalysts using abundant materials, particularly iron (Fe)-based, known for their exceptional performance in ORR. While the crucial function of Fe in boosting ORR catalytic activity is recognized, the connection between material attributes and catalytic performance remains enigmatic. Understanding the dynamic processes involved in oxygen electrocatalysis is paramount for designing precious-metals-free ORR electrocatalysts. Mössbauer spectroscopy stands out as a powerful technique for deciphering the structural characteristics of …

The Ntp Anode For Aqueous Sodium Ion Batteries: Recent Advances And Future Perspectives, Ming-Li Wang, Xue-Ying Su, Zheng-Xiang Shan, Shu-Zhe Yang, Heng-Rui Guo, Hao Luo, Dong-Liang Chao

Journal of Electrochemistry

Aqueous sodium-ion batteries (ASIBs) have attracted great attention in aqueous batteries due to their merit of high safety. However, the constrained work potential and insufficient chemical stability of anode materials in aqueous electrolytes hinder the large-scale application of ASIBs. Sodium titanium phosphate, NaTi₂(PO₄)₃ (NTP), is considered one of the most promising anode materials for ASIBs due to its excellent electrochemical performance and tunable structure. Recently, great achievements have been made in the development of NTP, however, a comprehensive review of existing studies is still lacking. This article firstly introduces the basic properties of NTP and …

Development Status And Existing Problems Of Ion-Solvation Membranes For Electrolysis Of Water, Zheng-Yuan Zhou, Yu-Tao Sun, Zheng-Bang Liu, Chuan-Zheng Wang, Yong-Nan Zhou, Xi Luo, Tian-Chi Zhou, Jin-Li Qiao

Journal of Electrochemistry

Ion-solvaing membranes (ISMs) have received extensive attention in recent years as a key component in electrochemical energy conversion and storage devices. This article provides an overview of structural composition, performance advantages, research progress, ion conduction mechanism and existing issues of ISMs, primarily classifying them according to the matrix structure. A detailed analysis of performance enhancement methods, key performance indicators of ISMs and performance influencing factors is also presented. The article contributes to further optimizing the design and application of ion-solvation membranes, providing theoretical support for the development of fields such as hydrogen production through electrolysis of water and electrochemical energy …

Carbon Supported Octahedral Ptni Nanoparticles (Oct-Ptni/C) As A Cathode Catalyst For Proton Exchange Membrane Fuel Cells (Pemfcs) With Improved Activity And Durability, Zi-Wei Feng, Hai-Zhong Chen, Xiao Duan, Ling Tang, Yun-Kun Zhao, Long Huang

Journal of Electrochemistry

Proton exchange membrane fuel cells (PEMFCs) are considered as a promising renewable power source. However, the massive commercial application of PEMFCs has been greatly hindered by their high expense and less-satisfied performance mainly due to the sluggish oxygen reduction reaction (ORR) kinetics even on state-of-the-art Pt catalyst. Octahedral PtNi nanoparticles (oct-PtNi NPs) with excellent ORR activity in a half-cell have been widely studied, while their performance in membrane electrode assembly (MEA) has much less reported. Herein, we investigated the MEA performance using the carbon supported oct-PtNi NPs (oct-PtNi/C) as the cathode catalyst. Under the mild acid washing condition, the surface …

A Point-Cloud Data Analysis Framework For Early Deformation Detection, Avinash Pandey

Theses and Dissertations

Frost heave and thaw-related deformation threaten the performance of roads, runways, embankments, and buried utilities in cold regions, yet traditional inspections remain reactive and often miss early uplift. This study evaluates the capability of LiDAR (Light Detection and Ranging) to detect small-scale ground deformation and introduces a practical point-cloud framework designed for early-stage characterization. Controlled jack-lift experiments quantified handheld LiDAR accuracy at uplift increments of 6.35, 12.7, 19.05, and 25.4 mm, with corresponding detection accuracies of 88%, 86%, 93.33%, and 94%. Registration quality had a significant impact. The targetless scans showed slight misalignment, whereas spherical targets provided precise, repeatable outputs. …

Iterative Data Augmentation For Enhancing Deep Learning Performance With Limited Training Data, Avinash Singh

ETDs from 2020-2029

Deep learning models have demonstrated impressive performance across different domains; however, their effectiveness heavily depends on large, well annotated datasets. In practice, data are often limited in size, leading to overfitting, poor generalization, and degraded model robustness and performance. Moreover, conventional augmentation techniques are typically static in nature, lack adaptability during training, and can produce geometrically inconsistent or unrealistic mixed images. This dissertation addresses three major challenges in data augmentation and model generalization: (1) the scarcity of labeled data and limited dataset size, (2) the absence of adaptive mechanisms for dynamically adjusting learning parameters during training, and (3) the creation …

In-Situ Eval: A Modular Framework For Custom And Real-Time Rag Benchmarking, Ritvik Garimella, Kaushik Roy, Chathurangi Shyalika, Amit Sheth

Publications

Retrieval-Augmented Generation (RAG) has become the standard approach for integrating domain knowledge into Large Language Models (LLMs). However, fair comparison of RAG pipelines remains difficult: data preparation is often ad hoc, subsampling methods are opaque, parameters vary across implementations, and evaluation is fragmented. We present In-Situ Eval, a unified and reproducible framework that operationalizes the full RAG pipeline with configurable subsampling strategies and both RAG-specific and generic evaluation metrics. The platform supports two execution modes: an offline Dataset mode for evaluating precomputed outputs, and a live Retrieval mode for benchmarking RAG variants with state-of-the-art LLMs. Users can flexibly select datasets, …

Amino Acid Pet Tracers For Cancer Imaging And Response Assessment, Ugur Akca

ETDs from 2020-2029

Triple-negative breast cancer (TNBC) and high-grade glioma (HGG) pose significant diagnostic and therapeutic challenges due to their aggressive biology and the limitations of [¹⁸F]FDG PET, which suffers from nonspecific uptake in inflammatory tissues, complicating differentiation of viable tumor from treatment-related effects. Computed Tomography, while strong for structural changes, has limited soft tissue contrast for distinguishing tumor from inflammation, and Magnetic Resonance Imaging, despite superior soft tissue resolution, struggles with differentiating active tumor from post-treatment changes like edema or necrosis due to nonspecific enhancement. These challenges highlight the necessity for complementary and advanced imaging strategies for improved diagnosis and monitoring. This …

Bidirectional Effects Of Transcutaneous Auricular Vagus Nerve Stimulation On Insomnia And Brain Health In Breast Cancer, Melissa Van Trang Do

ETDs from 2020-2029

Breast cancer remains the most frequently diagnosed cancer among women worldwide, with over 2.3 million new cases annually. While advances in treatment have improved survival, many women continue to suffer from persistent insomnia, fatigue, anxiety, depression, and cognitive dysfunction that collectively impair quality of life and recovery. These interrelated symptoms share common biological underpinnings, including autonomic dysregulation, vagal withdrawal, and chronic inflammation. Transcutaneous auricular vagus nerve stimulation (taVNS) is a non-invasive, bioelectronic therapy that modulates afferent vagal pathways via auricular branches of the vagus and trigeminal nerves. By engaging central autonomic and neuroimmune circuits, including the nucleus tractus solitarius and …

Clinical Decision Support Using Medical Imaging Artificial Intelligence, Yun-Chi Lin

ETDs from 2020-2029

Medical imaging plays a central role in modern clinical decision-making, yet its effective integration with artificial intelligence (AI) remains challenged by real-world clinical constraints. This dissertation investigates how medical imaging-based AI frameworks can be strategically designed to support clinical decision-making for two distinct but clinically critical scenarios: acute pandemic care and chronic neurodegenerative disease management. In acute pandemic settings, such as the COVID-19 crisis, healthcare systems face an urgent need for rapid risk stratification under conditions of data scarcity and operational stress including surges in patient volume and limited medical resources. This work explores strategies for developing deep learning models …