Digital Commons Network^™

Multi-Point And Multi-Station Orbit Propagation For Non-Functional Drifting Geo Satellites, Hritik Mitra

Theses

Currently, there are thousands of man-made space objects that are orbiting the Earth. These satellites serve a host of essential purposes (scientific research, technical applications, services) and they are all required to remain within their mission parameters. Most critical for them is to maintain the orbital parameters that are specified to achieve a particular mission’s objectives. If a satellite deviates beyond a certain limit, there is not only a risk of mission failure but there is a major hazard for possible collisions with other objects such as active satellites, space debris and even natural objects in some cases. There is …

Investigation And Prediction Of Excessive Water Production In Bottom Water-Drive Naturally Fractured Reservoirs Using Machine And Deep Learning, Sami Abderraouf Belkhir

Theses

Naturally Fractured Reservoirs (NFRs) are characterized by dual-porosity and dual-permeability systems, posing significant challenges in managing water production due to highly conductive fracture networks that facilitate rapid water migration from bottom aquifers, often bypassing oil stored in the matrix, thus resulting in early water breakthrough and water channeling phenomena. The main objective of this thesis is to develop and validate deep learning and machine learning models to predict water production, water breakthrough time (tbt), and ultimate water cut (WCult) in NFRs, thereby enabling more effective reservoir management strategies. This work also aims to evaluate the sensitivity of water behavior to …

Data-Driven Machine Learning Applications For Predictive Modeling Of Petrochemical And Ecofriendly Systems, Noora Saleh Al Mansoori

Theses

Traditional experimental approaches in industrial processes, such as Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy, thermogravimetric analysis (TGA), and well-drilling operations, are often constrained by time, cost, and operational limitations. This research explores the application of data-driven Machine Learning (ML)-based predictive modeling to improve efficiency and reduce dependency on resource-intensive experimentation. The study develops ML models for three distinct processes: FTIR intensity prediction of bitumen thermal cracking products, thermal degradation of Medium-Density Fibreboard (MDF) using TGA data, and Rate of Penetration (ROP) prediction in petrochemical industry. Six algorithms: Linear Regression (LinReg), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Gradient …

Design Of A Deployable Rolled Antenna System For Satellite Applications, Ashwaq Abdulla Alkaabi

Theses

This project presents the development of a deployable Synthetic Aperture Radar (SAR) antenna designed for a 16U CubeSat platform. The primary challenge in SAR satellite design lies in the need for large antennas to achieve high-resolution imaging, which traditionally results in increased satellite size and cost. To address this, the proposed solution employs a scalable 4 × 21 patch antenna array operating at 1.275 GHz, fabricated on a flexible Polyimide-based Printed Circuit Board (PCB). This flexible PCB allows the antenna to safely roll during deployment, avoiding damage and facilitating compact storage. However, Polyimide poses challenges due to higher losses and …

Numerical Methods For Approximating Line Integrals Over Implicitly Defined Curves, Raghd Alsaadawi

Theses

In this thesis, we develop and investigate a predictor-corrector method for the numerical tracing of implicitly defined curves. The study begins with the introduction of modified numerical integration techniques — specifically, the modified trapezoidal and modified midpoint rules — for evaluating the line integral of a vector field along an implicitly defined curve. Furthermore, we explore higher-order methods aimed at improving the accuracy of such integrals. Theoretical and numerical results, including asymptotic error expansions, are presented to support the analysis. In addition, several numerical experiments are carried out to illustrate the effectiveness and robustness of the proposed approaches.

Wavelet-Based Multi-Step Methods For Systems Of Differential Equations, Rashad Assad Hijji

Theses

Wavelets have been widely used in many areas of engineering and mathematics, including the development of multistep algorithms to solve initial value problems (IVPs) in the context of the Galerkin method using Daubechies' wavelets. The main scope of our work is to build a comprehensive framework for solving Systems of Differential equations using the compactly supported wavelets proposed by I. Daubechies. Wavelets are mathematical functions that decompose data into distinct frequency components, and each element is analyzed with a resolution that matches its scale. Compact support of Daubechies wavelets is key in allowing them to be computationally efficient for high-dimensional …

Electrospun Membranes From Bio-Renewable Poly (Ethylene Furanoate)/Poly (Ethylene Teraphthalate) (Pef/Pet) Blend For Emulsification Application, Aya Ismat Sayed

Theses

This thesis focuses on manufacturing electrospun fibrous membranes from a blend of poly (ethylene terephthalate) and poly (ethylene furanoate) and using in premix emulsification. Poly (ethylene furanoate), which is a 100% bio-renewable polymer being blended with synthetic poly (ethylene terephthalate) for membrane fabrication contributes towards sustainability in the production of food and pharmaceutical emulsions. The primary objective of this research is to optimize the fabrication conditions of poly (ethylene terephthalate)/poly (ethylene furanoate) porous membranes, characterize their properties, and evaluate their performance in emulsification. This study compares poly (ethylene terephthalate) / poly (ethylene furanoate) blend membranes with individual poly (ethylene terephthalate) …

Experimental Evaluation Of Novel Nano Polymer Hybrid Enhanced Oil Recovery In Carbonate Reservoir, Altamish Ahmed Pakeer

Theses

Innovative enhanced oil recovery (EOR) strategies are needed to unlock additional reserves in heterogeneous, oil-wet carbonate reservoirs. This study evaluates hybrid nano-polymer flooding by integrating silica nanoparticles (SiO₂) and single-walled carbon nanotubes (CNT) with novel HPAM polymers (Sav10 and Sav10 VHM) to optimize wettability alteration, rheological parameters, and recovery mechanisms.

A comprehensive experimental approach was employed, where rheological tests assessed viscosity enhancement under varying shear conditions, and wettability alteration was quantified via contact angle measurements using HPAM-SiO₂, HPAM-CNT, Sav10 VHM-SiO₂, and Sav10 VHM-CNT systems. Core floods were performed in three consecutive phases; waterflooding (baseline), standalone nanoparticle flooding, and hybrid nano-polymer …

Advancing Academic Advising With Knowledge Graphs: Integrating Machine Learning And Llms For Personalized Course Planning, Sara Alshamsi

Theses

Academic advising plays a critical role in helping students make informed decisions, improve academic performance, and successfully navigate their university journey. However, with increasing university enrollment, traditional advising methods often struggle to scale, leading to student frustration and overburdened advisors. Additionally, designing course offerings that match student demand is a complex and error-prone process involving multiple stakeholders. To address these challenges, this thesis proposes an automated, data-driven system for generating personalized academic plans for students. The primary aim of this thesis is to develop a system that reduces students’ dependency on advisors while simultaneously providing accurate estimates of course demand …

Utilizing Date Fruit Pomace As A Corrosion Green Inhibitor For Non Ferrous Alloys, Sardor Rustam Odilov

Theses

This thesis explores a novel green corrosion inhibitor derived from date fruit pomace extract (DFPE) for the protection of aluminum alloy 3003 in highly corrosive environments, specifically hydrochloric acid (HCl) and natural seawater. The central aim is to promote the sustainable reuse of agro-waste as an effective and eco-friendly alternative to conventional toxic inhibitors, aligning with environmental and industrial safety goals.

A comprehensive methodology was employed, including Soxhlet extraction (using both non-polar n-hexane and polar methanol-water solvents), rotary evaporation, and FTIR analysis to identify the active functional groups responsible for inhibition. Corrosion behavior was systematically evaluated using both mechanical (weight …

A Novel Framework For Dynamic Graph Representation Learning With Mamba, Ashish Pandey

Theses

Dynamic graph embedding is a key technique for modeling temporal dependencies in evolving networks. While transformer-based models perform well, their quadratic complexity limits scalability on long graph sequences. This thesis compares transformer approaches with the Mamba architecture-a linear-complexity state-space model—for temporal graph embedding.

Two frameworks are proposed: DG-Mamba and GDG-Mamba. DG-Mamba uses standard GCN-based spatial encoding, while GDG-Mamba incorporates domain-aware edge features using Graph Isomorphism Network with Edge Convolution (GraphGINE). Experiments on UCI, Reality Mining, Slashdot, Bitcoin-OTC, and SBM datasets show that Mamba-based models match or exceed transformer performance, especially on graphs with high temporal variability.

The thesis also applies …

Interaction Of Liquid Phase Diisopropyl Methyl Phosphonate (Dimp) With Material Surrogates For Components Of Soil And Combustion Products Of Metal Fuels, Khushi Sunil Patel

Theses

Combustion products of three different Al-based materials, Al-CaCO₃, Al-SiO₂, and Al-Fe₂O₃ , have been produced to understand their interaction with liquid diisopropyl methyl phosphonate (DIMP), a surrogate for chemical weapon agents. The purpose of this is to see which materials were more effective at DIMP adsorption and decomposition at different temperatures. Prepared materials were characterized using electron microscopy, x-ray powder diffraction, and nitrogen adsorption. Prepared powders were submerged in DIMP and heated in a thermal analyzer, tracking the mass changes as a function of temperature. Surfaces of the recovered materials were studied using …

Gamified Gait Rehabilitation Via Real-Time Biofeedback And Adaptive Hip-Exoskeleton Control, Mariya Huzaifa Tohfafarosh

Theses

Gait impairments arise from systemic diseases, age-related degeneration, musculoskeletal dysfunctions, or neurological conditions. While traditional rehabilitation can be effective, they often face challenges such as high costs, inaccessibility, and low patient engagement. To address these challenges, my work introduces a virtual reality-based rehabilitation (VRBR) system, integrating real-time motion and electromyographic (EMG) muscle activation feedback with a gamified virtual environment for enhanced adaptability and engagement. The system includes a custom-designed hip-exoskeleton that provides adaptive spring-like assistance or resistance, supporting both mobility-impaired users and strength training. Assistance levels can be tuned to match the user's progress. Additionally, a custom pressure insole was …

Predicting Patient No-Show Rate In Healthcare To Improve Operational Excellence, Saeed Ahmed Al Wari

Theses

The challenge of integrating no-show predictive models into healthcare environments is complex and multifaceted, extending far beyond the distinct technical challenge of building accurate models. This thesis will discuss more on the subject of incorporating machine learning into healthcare. While algorithmic accuracy and model validation are critical, they are a mere sub-system of a much larger operational, technological, ethical, legal and human ecosystem. Predictive models face mass deployment in healthcare systems which are complex networks of stakeholders across strict regulatory frameworks and deeply embedded workflows (AlMuhaideb et al., 2019). However, for these models to result in long term and real …

Predicting Customer Churn In E-Commerce, Hind Tawfiq Kazim

Theses

Customer retention has become a critical focus for businesses seeking to sustain growth and profitability in an increasingly competitive market. In this thesis, advanced machine learning techniques are used to develop a data-driven churn prediction model for Majid Al Futtaim's customer base. In an e-commerce environment, the research focuses on identifying the key transactional and behavioral factors that influence customer churn, based on customer relationship management theory. The study is contextualized in the context of increased digital consumer engagement and high customer acquisition costs, which make retaining existing customers more cost- effective than acquiring new ones. Our primary research questions …

Hybrid Sentiment Analysis Of Police Social Media: Managing Big Data Impact On Youth, Alunood Yousuf Alamiri

Theses

The pervasive influence of social media has reshaped public communication, particularly for institutions like law enforcement agencies that rely on these platforms to engage with the community. As police departments increasingly utilize social media to disseminate information, interact with the public, and manage public relations, the need for effective sentiment analysis becomes crucial. Understanding how police messages are received by the public, especially by the youth, who are among the most active and impressionable users of social media, is essential for maintaining public trust and ensuring effective communication. This thesis, seeks to develop a comprehensive solution for this challenge. The …

Transmit And Deceive: Addressing The Insider Threat To Beacon Security, Noah Troy

Theses

IEEE 802.11 Beacon frames exist at the forefront of modern Wi-Fi networks and facilitate a wide array of essential network functionality, from discovery to time synchronization and even transmit power control. However, the central role Beacon frames play in initial network advertisement comes at the price of incomplete security guarantees, leaving these frames historically vulnerable to forgery and granting significant leverage over proximal wireless devices to a malicious actor that can effectively spoof them. With the publication of IEEE Std 802.11-2020, beacon protection was introduced to enable the authentication of received Beacon frames, placating most concerns. However, beacon protection is …

The Role Of Digitalization Technologies In Enhancing Supply Chain Performance In The Service Industry: A Framework For Decision-Makers, Laith Hiasat

Theses

Digitalization technologies are proving to be effective in many different situations across the service industry. Technologies such as AI, blockchain, big data, IoT, IoE, 6G, digital twin, and cobots are showing positive effects when implemented in different types of companies. This research highlights the important role of digitalization technologies in enhancing supply chain performance within the service industry. Despite their adoption across sectors, limited research examines their specific impact on service supply chains. A comprehensive search of 3 academic databases identified 429 peer-reviewed studies published since 2019. PRISMA was implemented, resulting in a final dataset of 66 studies. A survey …

On The Use Of Nonlinear Electrophoresis For Separating Particles And Cells In Insulator-Based Electrokinetic Systems, Alaleh Vaghef-Koodehi

Theses

The increasing demand for efficient separation processes of micron-sized particles, including microorganisms, highlights the importance of developing low-cost, non-labor-intensive, and time-efficient techniques. Insulator-based electrokinetic (iEK) systems have proven to be a practical alternative to bench-scale methodologies such as centrifugation and membrane filtration. Several studies have reported the separation of microparticles by employing direct current (DC) voltages. However, all of them have overlooked the phenomenon of nonlinear electrophoresis (EPNL) and its role in the electrokinetic migration of particles. This study combines mathematical modeling and experiments to extend the capabilities of iEK systems. This dissertation has four aims: (1) to separate microparticles …

Wooden Spatial Analogies: Playful, Transformative Design Process Kit With Inclusion In Mind, Sarah R. Baxter

Theses

This paper concerns solutions to insufficient support in educational inclusivity through the subject of the five design process steps. It leverages the insights of researchers, such as Linda Silverman, regarding ‘visual-spatial learners’, individuals who perceive the world through images rather than words and thrive in navigating complex tasks, yet often face challenges with linear, step-by-step processes. This work critiques the conventional educational methods that frequently overlook the profound potential of spatially adept students. A final prototyped solution was assembled. The outcome was shaped, sanded, and painted wooden spatial analogies of play, designed for rearrangement, and interactively representative of the design …

On-The-Go Handbag: Sustainable & Biodegradable Handbag Design, Sydney Neff

Theses

Handbags have been an ever-present accessory in society for centuries, coming to symbolize not only fashion trends but status and femininity. They have evolved to hold extreme significance, being able to show a snapshot of who a woman is by taking a simple look inside. Purses have become a companion for consumers in many ways, accompanying them throughout important days and events, providing talking points, and even compliments for the user. However, these constant companions come at a cost. The fashion industry and the accessories produced within are an immense cause of environmental pollution. This provides an ever-present internal fight …

Beyond Barcodes, Ritul Chaudhary

Theses

Beyond Barcodes is a conceptual zine that investigates the narratives encoded within barcodes and reinterprets receipts as tools to examine the emotional layers of consumer behavior. Often dismissed as transactional byproducts, receipts in this project are reframed as carriers of memory, identity and self-worth—reflecting personal values shaped by frugality, guilt, and inherited beliefs about money. The zine unfolds through a series of illustrated vignettes, each centered around a category of spending: groceries, essentials, dining, vanity, and experiences. This arc traces a journey from survival to indulgence, ending on a bittersweet note—critiquing how consumer culture quietly shapes how we spend, what …

End-To-End Systems Limitations In Hyperspectral Target Detection Using Parametric Modeling And Subpixel Lattice Targets For Validation, Chase Canas

Theses

Hyperspectral target detection applies algorithms to high-dimensional images to identify rare targets, or objects of interest, within cluttered scenes of diverse backgrounds. Computational demands of processing hyperspectral datasets, along with their limited availability in both public and private sectors, imposes challenges in assessing system-level limitations of detection. This research presents a methodology to quantify end-to-end sensitivities and limitations through statistical modeling across thousands of target detection scenarios. The objective is to identify specific parametric thresholds where detection begins to degrade, based on “knees” in detection curves derived from model outputs of various scenarios. The model considers subpixel targets, where an …

First-Order And Second-Order Adjoint Method And Stochastic Approximation For Inverse Problems, Adrian Heldt

Theses

We present a unified framework for estimating stochastic parameters in general variational problems. This nonlinear inverse problem is formulated as a stochastic optimization problem using the output least-squares (OLS) objective, which minimizes the discrepancy between observed data and the computed solution. A key challenge in OLS-based formulations is the efficient computation of first- and second-order derivatives of the OLS functional, which depend on the corresponding derivatives of the parameter-to-solution map—often costly and difficult to evaluate, especially in stochastic settings. To address this, we develop a rigorous computational approach based on first- and second-order adjoint methods for inverse problems governed by …

Development Of A Miniaturized Extracorporeal Membrane Oxygenation (Ecmo) Device On A Microfluidic Platform, Nayeem Imtiaz

Theses

This research investigates the design, performance, and optimization of blood-contacting microfluidic medical devices, with each aim contributing to a broader understanding of how engineering choices influence hemocompatibility and oxygen transfer. Collectively, the findings from Aims 1A–2B offer integrated strategies to develop safer, more effective microfluidic systems for ECMO and dialysis. Aim 1A assessed hemolysis across device geometries using computational and experimental methods. All designs showed low device-induced hemolysis (< 2 ppm), validating their hemocompatibility. However, Power-Law models frequently overpredicted hemolysis, indicating a need for refined parameters tailored to microfluidic flow conditions. Aim 1B demonstrated the effectiveness of thrombosis assay techniques in evaluating clot formation in microfluidic device geometries. Results showed that geometry and local shear patterns strongly influence thrombogenesis. While hemolysis remained minimal even in high-shear devices, thrombus formation was more geometry-sensitive—underscoring the need to account for both factors in design. Modifying surface properties and accounting for blood composition could further reduce thrombotic risk. In Aim 2A, we compared PDMS, polypropylene (PP), and nanoporous silicon nitride (NPSiN) membranes for oxygen transport. Findings showed that in ECMO devices, blood-side resistance, not membrane permeability, often dominates oxygen transfer. Thus, membrane choice must be paired with optimized blood-side flow for full performance gains. Aim 2B directly addressed these limitations by integrating staggered herringbone mixers (SHBs), which introduced secondary flows and enhanced mixing. The Single Channel Herringbone design yielded the highest oxygen transfer, confirming the synergy between flow geometry and oxygen transport performance. Altogether, Aims 1A and 1B establish a strong framework for hemocompatibility evaluation, while Aims 2A and 2B provide complementary insights into optimizing oxygen transfer. Their interconnections highlight the value of a systems-level approach to designing microfluidic blood-contacting devices.

Enhancing Aptv Reliability: A Study Of Design And Operational Efficiencies At An Aluminium Manufacturing Company, Mohammad Tayeb Alawadhi

Theses

This paper aims to focus on enhancing the reliability of Anode Pallet Transport Vehicles (APTVs) used in Aluminium Manufacturing Company. The primary objective of the project is to establish the causes of failure modes, assess the operational problems, and implement strategies that would prevent, reduce, or minimize the frequency of failure, low equipment utilization, and high maintenance costs. APTV performance analysis involves the use of maintenance logs, operational data, and engineering specifications to obtain the best results. Applying Failure Mode and Effects Analysis (FMEA), the study highlights the most critical parts causing the largest effect on operations, which include traction …

Silicon Photonics For Quantum Information, Evan Manfreda-Schulz

Theses

Quantum information science and technology (QIST) combines principles of quantum mechanics and information theory to develop new approaches for processing, transmitting, and sensing information. Practical realization of applications in QIST requires generation and validation of a significant amount of entanglement, which is not only a property but a logical resource. Silicon photonic integrated circuits (PICs) are an attractive platform for applications in QIST and for generating the entanglement resource because they are foundry-fabricated, are compatible with existing fiber optic infrastructure, have a small spatial footprint, and afford access to several high-dimensional (HD) degrees of freedom. This dissertation covers three projects …

Beyond The Badge: Predicting Military Employee Attrition In The Police Force, Alia Abdulla Alhajji

Theses

This study explores the issue of employee attrition within the military police force, with a focus on predicting which employees are at risk of leaving. Attrition in law enforcement is costly and disruptive, especially in roles that require long training periods and operational readiness. The research specifically targets the internal organizational factors influencing turnover in military police environments and aims to leverage machine learning models to support the early identification of high-risk individuals. Guided by the CRISP-DM framework, the study followed a structured process across six stages: business understanding, data exploration, preparation, modeling, evaluation, and insight generation. A simulated dataset …

Using Generative Ai For Tutoring Data Science, Yusra Khalid

Theses

A large increase in the use of Generative AI has been observed in the last few years. Data science is also a rapidly growing field with a high demand for skilled professionals. The goal of this thesis is to explore the potential of Generative AI, specifically ChatGPT, in facilitating data science education. The focus is on how ChatGPT can be used as a tutor to help solve practical exercises. The capabilities of Generative AI were explored along with the comparison of a few different models in terms of data science. Exploratory analysis was conducted to compare Generative AI models and …

Classification Of Linear Systems Of Equations For Quantum Computing Implementation, Mark Danza

Theses

Drawing nearer to an error-corrected era of quantum computing, it is necessary to understand the suitability of certain post-NISQ algorithms for practical problems. One of the most promising, applicable, and yet difficult to implement in practical terms is the Harrow, Hassidim and Lloyd (HHL) algorithm for linear systems of equations. An enormous number of problems can be expressed as linear systems of equations, from machine learning to fluid dynamics to electrical circuit analysis. However, in most cases, HHL will not be able to provide a practical, reasonable solution to these problems. This work seeks to determine whether problems can be …