Digital Commons Network^™

Detection And Quantitation Of A Drug And Pesticides In Food Products Of The Uae (Dates And Camel Milk) By Liquid Chromatographic Techniques, Afnan Wael Alanqar

Theses

In this thesis the detection and quantification of the traces of multi-residue pesticides in 26 date fruits samples as well as the presence of the anthelmintic drug thiabendazole in pasteurized commercial camel milk consumed in the United Arab Emirates is examined. The main objective of this thesis is two folds; first is to determine whether a selected 12 pesticides of 8 different chemical groups- in the case of date fruits- and the drug thiabendazole-in case of camel milk- exists in the randomly collected samples of corresponding matrices. Second, is to quantify these compounds and compare their amount to the established …

Color Constancy In Virtual Environments With Head-Mounted And Flat-Panel Displays Across Different Illuminants And Lightness Levels, Andrea Avendano Martinez

Theses

Virtual reality (VR) is commonly used as a tool for enhancing immersion within virtual environments, with one of its primary goals being to approximate real-world perception as closely as possible. In everyday life, variations in lighting conditions significantly influence how colors are perceived across different times and settings. However, the human visual system compensates for these changes, maintaining relatively stable color perception; a phenomenon known as color constancy. This process is driven by chromatic adaptation, the visual system’s adjustment to changes in illumination. Despite the growing use of VR, little research has explored how color constancy operates in virtual environments …

Energy-Efficient Distributed Algorithms: A Survey, Visualizations, And New Results In Radio Networks, Dominick Banasik

Theses

Distributed algorithms have been studied for decades, with a particular focus on round and message complexities. Recent works have presented energy-efficient distributed algorithms, where nodes are allowed to sleep, sacrificing the ability to communicate, and the goal is to minimize the amount of time nodes spend in an awake state. This thesis further explores the area and can broadly be divided into three parts. In the first part of this thesis, we provide a literature survey of existing works. Several fundamental graph problems have been studied in this context. Our survey compares the known results from different models, highlights the …

Enhancing In-Car Interaction: Analyzing Deaf And Hard-Of-Hearing Drivers’ Usage, And Interaction With Voice-Controlled In-Car Infotainment System, Sanskriti Kumar

Theses

Voice Control Interface is a technology that allows users to use spoken language to interact with a system. The system is capable of speech recognition where the user can input a command, the system understands and executes the commands, and gives an output through text and/or speech. Voice Control Interfaces (VCIs) have become an ubiquitous technology, from public information kiosks to personal computer devices and intelligent virtual assistant devices. The technology is constantly evolving and improving with the intentions to make life easier, however, research has revealed unique challenges VCIs pose for Deaf and hard-of-hearing (DHH) users due to reliance …

Detecting Compromised Hardware Integrity With Machine Learning In Multicore Processors, Alexander Beekman

Theses

Due to the globalization of the Integrated Circuits (IC) manufacturing process, the trust surrounding hardware reliability has become compromised through methods such as injection of malicious hardware (including hardware trojans), hardware backdoors, IP theft, and counterfeit hardware. This thesis will focus on the issue of counterfeit hardware. As the complexity of modern day hardware has increased, detecting counterfeits has become increasingly difficult. Current methods of physical and electrical inspection are limited in both depth and size limitations. Physical inspections are only useful for identifying counterfeits that manifest physically, while electrical testing requires expensive and extensive setups. Computational evaluation of all …

Investigating Teachers' Perceptions On The Effectiveness Of School Inspection In Public Schools In The United Arab Emirates, Ruqaia Adel Mohammed

Theses

School inspection plays a significant role in ensuring quality education in UAE public schools. This paper investigates public school teachers’ perceptions of the effectiveness of school inspections in the United Arab Emirates (UAE) and examines the impact of teachers’ demographic factors on these perceptions. A total of 218 teachers across the UAE participated in the study. The research focused on four key domains within the School Inspection Perception Framework (SIPF): Perceived Effectiveness of School Inspections (PESI), Perceived Effectiveness of School Inspection Training (PESIT), Perceived Credibility of School Inspections (PCSI), and Perceived Usefulness of School Inspection Feedback (PUSIF). Data were collected …

Implementation Of Machine Learning To Predict Cable Failures In Electrical Networks, Sultan Bader Albahri

Theses

Electrical networks are critical infrastructures that power industries, businesses, and households. Among their key components, electrical cables play a vital role in ensuring uninterrupted power distribution. However, cable failures due to aging, environmental factors, mechanical stress, and load imbalances pose significant challenges, leading to outages, financial losses, and reputational damage. Traditional maintenance approaches, which rely on periodic inspections and reactive repairs, have proven inadequate in preventing unexpected failures. In response to this challenge, predictive maintenance using Machine Learning (ML) has emerged as an effective solution. This research focuses on developing an ML-based predictive model to forecast cable failures in Dubai …

Enhancing Cyber Threat Detection In Tweets Using Diffusion Models And Convolutional Neural Networks, Amer Abdulfattah Mohamad Abdulrahman

Theses

This study looks at how two advanced methods, DDPM-v4 and uViT, work with CNN models to find cyber threats from X data. Two datasets were used—one dataset taken from GitHub and another dataset created using the X Developer API. The dataset created had two sizes: small with 500 rows and larger with 2500 rows. The study compares models with diffusion techniques (DDPM-v4 and uViT) and without diffusion techniques. The results show that DDPM-v4 performed the best overall, with the highest accuracy and F1 score. For example, on the X API dataset with 2500 rows, DDPM achieved 98.20% accuracy. The uViT …

Network-On-Chip Packet Obfuscation And Encoding For Hardware Trojan Mitigation, Sydale John Ayi

Theses

Networks-on-Chip (NoCs) represent a transformative advancement in System-on- Chip (SoC) communication architectures, addressing the scalability and performance limitations of traditional bus-based systems. The increasing reliance on NoCs in modern SoCs has raised security concerns, particularly in untrusted fabrication environments. This thesis investigates the vulnerabilities posed by malicious hardware Trojans and untrusted IP cores, such as reverse engineering, misdirection attacks, and brute-force decryption. To counter these threats, the study introduces a robust security framework leveraging encoding and obfuscation techniques. Hamming codes are employed to detect and correct errors, ensuring data integrity and correcting misdirection attacks. Discrepancies between codewords and data words …

Implementation And Dissemination Of The 'About Eating' Program: Evaluating Participant Engagement, Usability, And Module Interest, Justin Konrad Walker

Theses

Abstract Background: Online nutrition education can improve food choices among underserved populations. The About Eating New York (AENY) program is a web-based, self-paced series of modules designed to enhance nutritional literacy, resource management, and confidence in healthy eating. Leveraging dissemination and implementation (D&I) science, AE partnered with community organizations across New York’s ten fiscal regions to extend its reach. Methods: This study analyzed secondary data from the AENY dissemination effort. Participants were recruited via social media and community organizations. Pre- and post-program surveys and end-of-lesson surveys assessed demographics, engagement, and perceptions of usability. Quantitative analyses included descriptive statistics, t-tests, ANOVAs, …

Beyond The Food: A Scoping Review Of Avoidant/Restrictive Food Intake Disorder (Arfid)'S Comorbidities, Nutrition, And Growth Outcomes, Emily Szewczyk

Theses

Objective A scoping review design was chosen for this study to appropriately assess and understand the knowledge on ARFID and to identify psychological or developmental comorbidities, growth outcomes, and nutrient deficiencies and/or excesses in children and adolescents. Introduction Avoidant/Restrictive Food Intake Disorder (ARFID) is a relatively new eating disorder, characterized by the DSM-5 in 2013.1 Individuals with ARFID exhibit selective eating due to lack of interest in eating food, sensory sensitivity, or a fear of possible vomiting or choking.2 The malnutrition seen in individuals with ARFID can result in physical health complications including weight loss, malnutrition, growth delay, decreased bone …

On Studying Transformer Networks For Volume-To-Surface Registration Of Inhomogeneous Soft Bodies For Liver Laparoscopy, Michael Adam Young

Theses

An important practical consideration in laparoscopic liver surgery is the limited visual information relative to open surgery. In laparoscopic interventions, the surgeon’s view of the liver surface is generally limited to the scene provided by a single scope with a narrow field-of-view. This limits the ability to navigate towards internal lesions previously identified through pre-procedural imaging. Surgical navigation during laparoscopy could be enhanced by registration of full preoperative liver models derived from pre-procedural imaging scans onto the partial laparoscopic view of the liver. This entails both a rigid registration to match the liver surface view to the preoperative volume, as …

Bridging Human Expertise And Ai In Qualitative Research: Understanding Ownership, Trust And Bias In Ai-Assisted Coding, Anoushka Jagadeesh Puranik

Theses

Qualitative research is a multifaceted process involving data collection, transcription, organization, coding, and thematic analysis. As qualitative data analysis (QDA) tools gain popularity, artificial intelligence (AI) is increasingly integrated to automate aspects of this workflow. This study investigates researchers’ QDA practices, their experiences with existing tools, and their perspectives on AI involvement in qualitative research. We introduce ChromaScribe, a prototype QDA tool, and gather feedback to assess whether it addresses the limitations of current tools. Through interviews with 16 qualitative researchers, we examine their preferences for traditional coding, AI-initiated coding, and human-initiated coding, exploring factors such as ownership, trust and …

The Impact Of Rubric-Based Feedback On Efl Writing Proficiency: Exploring Revision Skills And Learning Transfer, Ahmed Abdelraheem Elyan

Theses

This study explores how rubric-based feedback influences the writing development of English as a Foreign Language (EFL) learners, with a particular focus on their revision practices, learning transfer, and the challenges they face with cognitive overload. The research was carried out in a high school context in the United Arab Emirates (UAE), aiming to understand the real impact of structured feedback on students’ writing performance.

Twenty Arabic-speaking students participated in the study, completing four essay writing tasks over eight weeks. These included a pre-test, two feedback-supported drafts, and a post-test. Their essays were assessed using an analytic rubric designed to …

Construction Of Stock Portfolios By Machine Learning Methods, Alfan Gehad Abulehia

Theses

We study the theory and application of two machine learning (ML) algorithms: Long-Short Term Memory Network (LSTM) and Random Forest. The study begins by providing an overview of mathematical foundation, highlighting the significance of mathematics in machine learning. We study and explain the mathematical details involved in these two algorithms. We also study closely related subjects which include but not limited to gradient descent, automatic differentiation, and recurrent neural network. The main ideas behind these topics form the foundation of any ML algorithms.

As an application of the ML algorithms, we focus on the U.S. stock market. We build stock …

Collaborative Network Traffic Management Strategies Using Distributed Reinforcement Learning And Large Language Models, Saeed Rashed Alkuwaiti

Theses

The focus of this research is to explore collaborative network traffic management strategies using the Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs) approaches. It emphasizes exploring a new tool for addressing network traffic by utilizing Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs). This is achieved by utilizing self-organizing and self-directing techniques to optimize the network performance. Using the NF-TON-IOT dataset, various classifiers such as Random Forest, AdaBoost, C4. 5, Multi-Layer Perceptron (MLP), and SVM with an RBF kernel were tested for traffic classification and intrusion detection. Research recommends that DRL optimizes the complexity of the network …

Monitoring Sand Migration In Al Ain City Utilizing Remote Sensing Techniques, Sanad Salem Mohammad Fareaa

Theses

Sand migration significantly impacts urban development, infrastructure, and ecosystems in arid regions such as Al Ain city, United Arab Emirates (UAE). This study employs advanced remote sensing techniques to monitor and predict sand dune migration across the Sieh Al Hama dune field, a critical area west of Al Ain. The research objectives include quantifying dune migration rates over three years (2018–2020) using monthly Sentinel-2 satellite imagery, identifying distinct dune fields, and analyzing the textural and mineralogical properties of dune sediments to infer their provenance. Field sampling from four dunes (Large Sabra Dune, Dune 1, Dune 2, and Dune 3) was …

A Microgravity Investigation Of The Subsurface At The United Arab Emirates University Campus, Fidha Farzana Ismail

Theses

This thesis investigates subsurface structures within the United Arab Emirates University (UAEU) campus using microgravity survey. Gravity methods are widely used in geophysical exploration to detect density variations that may indicate subsurface anomalies. The primary objective of this research is to generate the Bouguer anomaly map of the UAEU campus to map subsurface structures and to assess potential geological hazards such as cavities. A total of 76 gravity stations were measured across the UAEU campus using the Scintrex CG-6 gravimeter. The acquired gravity data underwent standard processing to obtain the Bouguer anomaly, including tidal, instrumental drift, latitude, free air, Bouguer, …

Study Of The Extension Of Cavities In Jebel Hafeet By Implementing Geophysical Methods, Mai Rashed Alkaabi

Theses

Subsurface cavities pose challenges to the buildings and their stability, leading to ground instability and subsidence. In Al Ain City, UAE, particularly in Jebel Hafeet, cavities within carbonate formations and sedimentary rocks present risks to infrastructure and urban development. This study employs Electrical Resistivity Tomography (ERT) and Gravity Surveys to detect, locate, and determine the depth of these cavities, enhancing geological understanding and geohazard assessment. The ERT survey, using a pole-dipole array, was conducted along eight lines to identify subsurface anomalies. It revealed high-resistivity anomalies (> 45 Ω·m), indicative of air-filled cavities, and low-resistivity zones (< 6 Ω·m), corresponding to water-saturated or clay-rich deposits. The ERT survey detected resistivity variations, highlighting anomalies ranging from 45 to 120 Ω·m at depths between 1 m and 17 m. The gravity survey, conducted at 28 stations, detected low-density anomalies, which aligned with high-resistivity ERT zones, confirming subsurface cavities at depths between 10 m and 220 m (below ground level). Five 3D gravity inversion models visualized density variations, revealing cavities at different depths for the models from 0 m to 195 m (a.s.l). The integration of ERT and gravity methods significantly improved cavity detection accuracy, demonstrating a strong correlation between low-density gravity anomalies and high-resistivity features. This study recommends increasing gravity station density, and avoiding heavy infrastructure in cavity-prone areas. The findings contribute to the geological understanding of Jebel Hafeet, providing insights for sustainable urban planning, risk mitigation, and infrastructure safety in Al Ain City.

Improving Reciprocating Compressor Valve Condition Monitoring Via An Accelerated Lifetime Tester And Deep Learning, Jacob Chesnes

Theses

The goal of this research is to improve the current condition monitoring method for diagnosing reciprocating compressor valves. Leakage, seat wear, and spring degradation are three common valve faults that are seeded into all the valves of a Dresser-Rand ESH-1 dual-acting reciprocating compressor. The method converts raw vibration data into the time-frequency domain and determines if the Continuous Wavelet Transformation or the Smoothed Pseudo Wigner-Ville Spectrum can achieve a higher classification accuracy. The Continuous Wavelet Transformation can vary its resolution through two parameters while the Smoothed Pseudo Wigner-Ville Spectrum can vary its resolution through smoothing window sizes. Both time-frequency transforms …

Bioinformatics Pipeline For Gene Regulatory Network Discovery: A Case Study With Exercise Response, Nadia Moore

Theses

Gene regulatory networks are at the core of many biological processes, dictating how a wide range of genes, and their products, interact with one another. Gaining further understanding on how these networks are structured and how they function within the body can aid in deriving meaningful insights on the body’s inner-workings when exposed to any number of conditions. Unfortunately, the information gathered within some studies may be lacking in, for example, sample size or measurements taken at multiple time-points. This work addresses these issues and outlines a pipeline which navigates these smaller collections of data in order to still extract …

Enhancing The Immunogenicity Of Extracellular Vesicle-Based Vaccines Against Pathogenic Nthi Bacteria, Christina Ciko

Theses

Acute otitis media (AOM), or ear infections, are highly prevalent in children, with nearly 80% experiencing at least one AOM by the age of three. As such, there is an urgent need for a vaccine against Nontypeable Haemophilus influenzae (NTHi), a main cause of AOM in the US. Most vaccines contain multiple antigens from the pathogen, inducing an immune response and antibodies to those antigens. Here, we describe a novel approach to creating a vaccine against NTHi, incorporating a unique recombinant trivalent protein (Tri) featuring three NTHi antigens (OMP26, P6, and protein F) in a single peptide chain with an …

Enhanced 3d Sub-Canopy Mapping Via Airborne Full-Waveform Lidar, Robert J. I. Wible

Theses

Airborne light detection and ranging (LiDAR) systems have been used to gather information about forests, their canopies and what lies beneath them for many decades. Recent advances in LiDAR sensor technology have enabled higher sampling rates, leading to increased point densities for discrete point clouds. However, vast portions of the forest sub-canopy still remain either unsampled or occluded. We contend, that waveform LiDAR, as a specific type of structural modality that digitizes the intensity of the laser backscatter as a function of time (range), contains additional information that can be extracted using modern artificial intelligence (AI) and machine learning (ML) …

Fragility And Strength In Times Of War: A Series Of Embroidered Watercolors, Alisha B. Whitman

Theses

This final project report follows the production of a series of embroidered watercolors that explore how war disrupts and can destroy the values which people manage to somehow both hold dear and take for granted. These values, such as peace and freedom, are fragile, and yet they can provide great strength in dark times. This dichotomy is explored through this body of work using the materials of painted paper and thread as symbols of both fragility and strength. Choosing to work with materials and methods historically seen as feminine work add additional meaning to that symbolism. Additionally, Jewish iconography and …

Machine Learning For Complex Networks: Generalization, Fairness, And Model Selection In Graph-Based Systems Across Social And Biological Domains, Angelina Brilliantova

Theses

Networks serve as a fundamental framework for modeling complex systems across diverse domains, from biological interactions and social structures to economic markets and information networks. Machine learning has significantly enhanced the ability to extract meaningful patterns from graph-based systems, improving predictive accuracy in applications such as market design, recommendation systems, and biomedical discovery. Despite the successes of machine learning in analyzing complex networks, several fundamental challenges remain. A major issue is the reliance on simplistic models for theoretical analysis, simulation, and benchmarking, which often fail to capture the complexities of real-world networked systems. Many theoretical studies focus on oversimplified generative …

Soundhapticsvr: Head-Based Spatial Haptic Feedback For Accessible Sounds In Virtual Reality For Deaf And Hard Of Hearing Users, Pratheep Kumar Chelladurai

Theses

Virtual Reality (VR) systems use immersive spatial audio that provide more localized sound awareness to convey critical information. However, these audio cues are often inaccessible to individuals who are Deaf or Hard-of-Hearing (DHH). To address this, we developed, a head-based haptic system designed to enable localizing and convert- ing audio signals directly into haptic feedback using multi-channel acoustic haptic actuators. We evaluated through three studies. The preliminary study determined the maximum tactile frequency threshold perceivable on different regions of the head of DHH users when a VR headset is attached. This was followed by a study to deter- mine the …

Deepfake Image Detection Using Explainable Ai And Deep Learning, Abdallah Abdulfattah Mohammed Abdulrahman

Theses

Deepfake technology has grown exponentially, and it has changed our perspective of managing digital content. The technology has many applications, but its ease of access has brought many risks. These risks include identity theft, misinformation spread, development of non-consensual content, and a decrease in trust in media. To address these challenges, we propose a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for deepfake image detection. We developed a CNN model and four hybrid CNN-LSTM models in our study. Our approach integrates the strengths of CNNs for spatial feature extraction and LSTMs for …

Machinery Imbalance Failure Prediction Based On Pump – Motor Vibration Readings, Abdulrahman Albuflaseh

Theses

Predictive maintenance has emerged as a transformative solution in industrial operations, reducing unexpected machine failures and minimizing downtime. This study explores the application of machine learning in vibration analysis to detect imbalances in pump-motor units, a critical issue that accelerates equipment wear and increases maintenance costs. Traditional maintenance methods, such as scheduled inspections, often fail to identify faults early, leading to significant operational disruptions, particularly in utility, oil and gas sectors. By leveraging real-time vibration data and advanced machine learning techniques, this research aims to develop an efficient imbalance detection model that enhances maintenance efficiency and reliability. The study employs …

Femtosecond Laser Figuring And Polishing For Freeform Optics Fabrication, Gong Chen

Theses

Freeform optics revolutionizes imaging systems by enabling compact designs and high-performance capabilities, which are crucial for applications such as augmented and virtual reality, medical diagnostics, biosensing, space exploration, remote sensing, and EUV lithography. However, fabricating freeform optical components presents significant challenges: conventional chemical and mechanical methods generate chemical waste and introduce mid-spatial-frequency errors that degrade imaging contrast. This research seeks to overcome these limitations by establishing ultrafast-laser-based figuring and finishing methods. These methods offer a fast, non-contact, flexible, and precise green solution, paving the way for sustainable, high-quality freeform optics fabrication. We expanded a laser pulse propagation model to simulate …

Advances In Machine Learning For Pain Recognition: A Review Of Algorithms, Modalities, And Outcomes, Abdulrahman Alsharif

Theses

The emergence of machine learning (ML) in healthcare has unlocked the transformative potential of pain detection and assessment. By providing new ways to address the challenges associated with subjective and inconsistent pain assessment methods. This review synthesizes recent advancements in ML-based approaches for pain detection, focusing on algorithms, modalities, and datasets. Techniques such as neural networks, transformer models, and multimodal fusion frameworks have demonstrated significant promise in identifying pain from diverse sources, including facial expressions, physiological signals, and synthetic datasets. Key contributions include novel transformer-based architectres like PainAttnNet for analyzing electrodermal activity [1], multimodal datasets such as BioVid for advancing …