Digital Commons Network^™

Design With, Not For, Local Community: Utilizing E- Participation Tools In The Design Of Socially Sustainable Vertical Emirati Public Housing, Omar Sherzad Shareef

Theses

Few years ago, local and federal governments in the UAE started to design and develop vertical public housing for Emirati residents as a new public housing typology that is believed to be more environmentally sustainable. Dibba Al-Hisn Housing Scheme in Sharjah and Al-Ghurfa Project in Al-Fujairah, are two recently developed vertical Emirati public housing projects developed by Sharjah Housing Directorate and Sheikh Zayed Housing Program (SZHP) respectively. Both projects, have been professionally designed, however, the residents’ opinions were not considered in the design process. Therefore, the main objective of this research is to explore the potentials of utilizing the advanced …

The Differences Between Metacognitive Reading Strategies Used By Native Arabic Speakers In The Uae When Reading Digital Versus Printed Materials, Razan Majed Shehadeh

Theses

This study explores the differences between the metacognitive reading strategies that native Arabic speakers college students use in the UAE when reading digital vs. printed texts. A convergent Mixed-Methods Design was employed to achieve the purpose of this study. The first data comprised a background questionnaire and two metacognitive reading surveys reflecting reading from digital materials that were conducted with college students (n=100), and the second data set comprised semi-structured interviews with a subset of students sampled from the original pool (n= 8). This study's participants were college students selected randomly from some of the UAE universities. Generally, the results …

Studies On The Corrosion Of Bronze Artifacts From Sarouq Al-Hadid And Al-Qusais Archeological Sites In Dubai, Uae, Zainab Ali Salmin

Theses

This thesis compares the status of bronze artefacts from two archaeological sites in Dubai (UAE): the Sarouq Al-Hadid site (Stone Age to Early Islamic Period) and the Al- Qusais site (Late Bronze Age to Early Iron Age). The main objective of this research is to understand the two sites' environment and their influence on the corrosion of archaeological bronze artifacts found at the sites. This study uses mCed methods to comprehensively understand the corrosion behavior of bronze objects, depending on their burial environment. The two different sites presented different soil types and rates of corrosivity on the buried bronze artifacts, …

Visualizing Privately Protected Data: Exploring The Privacy-Utility Trade-Offs, Sarah Hayi Alkaabi

Theses

In a data-driven era, achieving a balance between privacy and utility is crucial. Organizations often utilize data for research, analysis, and enhancement of services, which emphasizes the significance of effective privacy-preserving techniques to protect individuals' privacy and comply with regulations. This equilibrium is vital in data visualization to derive insightful decisions from data representations. The goal is to evaluate the trade-off between privacy preservation and data utility, understanding how differentially private parameters impact effective visualizations. Valuable insights will guide strategies for achieving optimal privacy-preserving visualization techniques. The study aims to investigate the effects on privacy and data utility in different …

Neural Operator For Accelerating Coronal Magnetic Field Computations In Bifrost Mhd Model, Yutao Du

Theses

The application of the Tensorized Fourier Neural Operator (TFNO) to significantly enhance the computational efficiency of coronal magnetic field calculations within the Bifrost Magnetohydrodynamics (MHD) model is introduced in this study. Leveraging simulated data from the European Sunrise Science Data Center, the TFNO? an extension of the Fourier Neural Operator (FNO) that incorporates tensor decomposition for improved handling of high-dimensional data?is employed to solve time-varying partial differential equations (PDEs) over a 3D domain. The performance of the TFNO is compared with traditional machine learning methods, including Vision Transformer and CNN-RNN (encoder-decoder) architectures, to demonstrate its accuracy, computational efficiency, and scalability. …

Marking Estimation In Petri Nets Using Dynamic Mode Decomposition, Aditya Kale

Theses

Petri Nets (PNs) are a well-established framework for modeling and analyzing complex systems with interacting, concurrent processes. This thesis extends traditional Petri Net methodologies by integrating Dynamic Mode Decomposition (DMD), a technique originally developed for fluid dynamics, to analyze Continuous Petri Nets (CPNs). By applying DMD to CPN marking evolution, this research constructs a reduced-order model that captures its dynamics through dynamic modes and eigenvalues, enabling prediction of future markings without detailed knowledge of transition firings or underlying deterministic models. The principal contribution of this research is extending the kit of tools available for analysis of CPN dynamics, providing insights …

Effects Of Exoskeletal-Assisted Walking Rehabilitation On Gait Of Patients Post Stroke, Archisha Kanchan

Theses

Stroke can have detrimental effects on one's body. Its symptoms can range from lightheadedness to blurred vision to body paralysis. One of the more common effects of stroke is difficulty walking where many of the patients who have suffered from stroke are unable to ambulate fully. To mitigate gait difficulties, patients typically follow through with various kinds of physical therapy to help with their gait movements. Recently, robotic exoskeleton (RE) walking therapy has been used as a more innovative approach to gait rehabilitation, in conjunction with other physical therapy session techniques. However, studies involving RE therapy within the stroke population …

The Next Strike: Pioneering Forward-Thinking Attack Techniques With Rowhammer In Dram Technologies, Nakul Kochar

Theses

In the realm of DRAM technologies this study investigates RowHammer vulnerabilities in DDR4 DRAM memory across various manufacturers, employing advanced multi-sided fault injection techniques to impose attack strategies directly on physical memory rows. Our novel approach, diverging from traditional victim-focused methods, involves strategically allocating virtual memory rows to their physical counterparts for more potent attacks. These attacks, exploiting the inherent weaknesses in DRAM design, are capable of inducing bit flips in a controlled manner to undermine system integrity. We employed a strategy that compromised system integrity through a nuanced approach of targeting rows situated at a distance of two rows …

Targeted Drug Delivery: Investigating Protein Corona Behavior, Atharva Markale

Theses

Due to high specificity and less toxicity, nanoparticles have become promising for targeted drug delivery against cancer, although they face challenges when they enter the bloodstream, making their behavior unpredictable. The deposition of proteins on nanoparticles, the protein corona, changes their biosignature, affecting their circulation, drug release potency, targeting ability, and immunogenicity. The continuous exchange of protein layers on a nanoscale is complex to analyze.

Nanoparticle tracking analysis (NTA) is a method to characterize nanoparticles in blood plasma by combining principles of light scattering microscopy and Brownian motion. In this work, time-dependent experiments with plasma were conducted to analyze the …

Strawberry Nectar Preservation Through Innovative Techniques And Energy Evaluation For High-Pressure Processing, Caterina Scaioli

Theses

Strawberries are universally adored by people of all ages, owing to their richness in vitamins, nutrients, and antioxidants, offering myriad health benefits, including combating free radicals. However, despite their popularity, strawberry juices or nectars are not commonly found in markets due to their delicate structure, which makes them prone to rapid deterioration of their prized qualities and vibrant red color. In the food industry, preventing browning and nutrient deterioration are often the primary challenges faced when processing red juices. Traditional preservation methods, such as pasteurization, are inadequate in maintaining their freshness. The focus of this study is to explore alternative …

Methodology For Developing Biodegradable Polymer Blends And Composites For Agriculture, Fangzhong Xing

Theses

The use of polyethylene (PE) films to improve soil conditions and to increase crop yield has produced microplastics that are harmful to our ecosystem. The overall objective of this thesis is to develop an effective biodegradable polymer/composite to replace PE, and hence, to reduce microplastic pollution. The focus of this research is to develop a systematic protocol for assessing the suitability of the biodegradable films based on industry standards in agriculture. For this purpose, a range of biodegradable polymer blends are prepared using polylactic acid (PLA) and polybutylene adipate-co-terephthalate (PBAT) with varying PLA to PBAT ratios. Solvent casting combined with …

Tribological Performance Of Ceramic On Textured Polyethylene Contact For Artificial Hip Implants, Sandhya Vaidyanathan

Theses

Revision total hip arthroplasty (rTHA) is an expensive and surgically risky procedure that is necessary in the case of a failed primary total hip arthroplasty (pTHA). The cause of failure in artificial hip joints is often rooted in the tribological performance of the bearing surfaces such as increased wear and reduced lubrication. Surface texturing is a surface modification technique which has been studied for aerospace and automotive applications and has shown promise in the biomedical field of artificial human joints in the last two decades. Several studies have been done to assess the tribological performance of introducing surface micro-textures as …

Optimizing Social Determinant Of Health Concept Extraction, Niharika Singh

Theses

Generative Artificial Intelligence has recently garnered enormous attention for a varied number of reasons, one of them being its capacity to automate complex tasks. As the potential for integrating Generative Artificial Intelligence across technology, media, and healthcare grows, many tasks previously reliant on manual or algorithm-intensive methods can be simplified significantly. One such challenge is concept extraction, which is a labor-intensive process, especially when performed manually for building robust ontologies. It involves analyzing text to identify and extract relevant concepts, key phrases, entities, and relationships between these entities. This thesis explores various challenges of concept extraction from the ABCD (Adolescent …

Electrochemical Nitrate Reduction To Ammonia On Cu Based Nanoparticles Using Quantum Mechanical Simulations, Kathan Bhavin Shukla

Theses

Nitrate reduction to ammonia (NRA) is critical for environmental remediation and energy conservation, as it can remove harmful nitrate (NO₃^-) from water sources while producing usable ammonia (NH₃). Cu represents one of the most promising non-noble-metal NRA electrocatalysts. However, the intrinsic catalytic activity of Cu facets and their influence under different pH conditions remain unclear, particularly in the presence of ions commonly found in wastewater such as Cl^-, Na⁺, Mg²⁺, and Ca²⁺. Using density functional theory (DFT) calculations, we evaluated the nitrate reduction to ammonia (NRA) pathways …

Complementing Vehicle Trajectories Using Two Camera Viewpoints, Katsuaki Nakano

Theses

Traffic volume surveying is a crucial activity to get traffic statistics for road management and traffic congestion control. In recent years, the target environment of traffic volume surveying has become more complex, such as the fully automated surveillance of many-way intersections. Further compounding this complexity, some local governments may not be able to install a camera at a high enough elevation to capture the entire intersection due to environmental, legal, safety, or cost restrictions. Therefore, bigger objects such as buses and trucks often occlude other vehicles in the captured image. This occlusion degrades the accuracy of counting and is one …

A Programmable Look-Up Table-Based Processing In Memory Architecture For Massively Parallel, Energy-Efficient Processing Of Data-Intensive Applications Within Dram, Purab Ranjan Sutradhar

Theses

As modern computing workloads become increasingly data-intensive, the limitations of traditional digital computing hardware, especially in terms of communication bandwidth between memory and processors, are becoming more apparent. Surprisingly, the performance and energy efficiency of the state-of-the-art are not primarily constrained by the computing power of advanced processing architectures but rather by the low communication bandwidth between the memory and the processor. This challenge necessitates a clever re-architecting of digital computing hardware to make the communication between the processor and the memory more energy-efficient, faster, and capable of supporting significantly higher bandwidth. Memory-centric computing emerges as a novel, alternate computing …

Human-Machine Interactions For Behavioral Health Care, Zhiwei Yu

Theses

About one in four American adults (26%) and 20.3% of children aged 8 to 16 years reported behavioral health concerns in 2023. However, a substantial gap remains between demand for behavioral healthcare services and access to such services. In response, research efforts have been made to advance effective and accessible approaches to provide assessments and treatments for populations with behavioral health needs. Yet, two primary challenges exist in current practice and research: 1) self-reports and manual observation-based assessments are subjective and labor-intensive, as evaluations are limited to the availability of clinical professionals; 2) they only target the general experiences but …

Unravelling Micro-Mysteries: Separating Microscopic Entities Using Electrokinetic Microsystems, Nuzhet Nihaar Nasir Ahamed

Theses

There is an increasing demand for the development of rapid and reliable methods for microparticle and cell separations in applications that range from clinical analysis to food and environmental safety assessments. Microfluidic systems, being portable and offering a response within minutes, are particularly attractive. Electrokinetic (EK) methods are a growing area in microfluidics, due to their robustness, rapid response time, low cost, and label-free analysis of micron-sized particles, including intact microorganisms. EK methods require the application of an electric stimulation, such as direct-current (DC) or alternating-current (AC) potentials, to induce different EK phenomena, such as electrophoresis, electroosmotic flow and/or dielectrophoresis, …

Spatiotemporal Alignment Of Event Stream With Images And Active Illumination, Abhijan Wasti

Theses

Unlike conventional frame-based cameras that form images by sampling all pixels within the duration of the global/rolling shutter, a pixel in an event camera can be triggered independently when the log intensity change in scene luminance at the pixel exceeds a threshold. This unique feature provides several advantages over conventional sensors, including high dynamic range (HDR) (≈120dB), high temporal rate (≈10,000Hz), low latency (< 1ms), and low power requirements (≈10mW). These properties make them excellent candidates for applications such as high-speed photography, HDR image reconstruction, object tracking, depth estimation, simultaneous localization and mapping, and surveillance and monitoring. Despite their potential, the asynchronous and spatially sparse nature of events poses challenges to event processing and interpretation. This is because most advanced image processing and computer vision algorithms are designed to work with conventional image formats, and not with temporally dense streams of asynchronous pixel events (i.e., the event stream). Although emerging techniques in supervised machine learning demonstrate promise, continued and rapid progress relies on the availability of labeled event datasets, which are scarce, and difficult to produce. Moreover, generating reliable events for training models is challenging due to the scene-dependent nature of event generation, which is further complicated by varying illumination and relative motion. In this thesis, we attempt to address these limitations with a novel imaging paradigm involving the capture of frames from a conventional frame-based camera that has been spa tially aligned and temporally synchronized with an event sensor. Our active illumination source allows us to generate events more consistently even under challenging illumination and motion in the scene. We demonstrate the feasibility of such a setup for a mobile eye tracking system and acquire subpixel and microsecond accurate spatiotemporal alignment. Our method facilitates the use of pre-trained models that operate on conventional images to detect features that can then be used to train models that can be applied directly to the event stream. This serves as the next step in event-based imaging and unlocks new possibilities in computer vision and imaging technology.

Machine Learning Models For Enhanced Stock Trading Strategies, Alyaa Al Ali

Theses

The purpose of this research is to develop sophisticated machine-learning models that can predict stock price movements, taking into account the multifaceted influences that create volatility in financial markets. To navigate the complexities of market dynamics effectively, investors, financial institutions, and academia rely on precision stock price predictions. This research generates actionable buy and sell signals by using historical stock data, technical market indicators, Linear Support Vector Machines (LSVM), Neural Networks, and Logistic Regression. A distinctive feature of this model is it uses market indicators such as PPO, MACD, RSI Signal, Bollinger Bands, ROC Signal, and DX Signal instead of …

Deep Learning Approach For Detecting And Classifying Chaotic Driving Activity Using Video Surveillance, Fatma Abdulla Desmal Alhaddad

Theses

The growing rate of urbanization in the city of Dubai has contributed to the increase in vehicular activity and the prevalence of risky driving behavior on the road. This project proposes a deep-learning-based system, VGG-16, for the identification and classification of different patterns of driving behavior. The research methodology used for the study relies on the compilation of datasets from various resources, including ImageNet, Kaggle, UAH-DriverSet, D²-City Dataset, and confidential videos obtained from the Dubai Police command center. The findings of the study will be categorized into two parts. The first part will involve the compilation of the features based …

Nano Biosensing Platform For Infectious Disease Diagnostics, Li Liu

Theses

CRISPR-Cas enabled biosensors offer great potential to be at the forefront of diagnostic medicine. This thesis focuses on the principles of CRISPR-Cas technology and its application to biosensors and virus diagnostics. It also provides a detailed discussion of the different detection methods available at this stage. The breakthrough achievements of CRISPR biosensors in nucleic acid detection are summarized. The CRISPR-Cas nano biosensors exhibit high accuracy, sensitivity, selectivity, and versatility, offering great potential for next-generation diagnostic and point-of-care devices. The prospects and future trends of CRISPR biosensors are also described. From an engineering perspective, this thesis develops and applies several methods …

Using Machine Learning Algorithm For Detecting Distributed Denial Of Service Attack, Zainab Alblooshi

Theses

DDoS attacks, which stand for Distributed Denial of Service attacks, play a significant role in impacting the reliability and availability of online services and networks. Since no system is completely immune to cybersecurity threats, which evolve daily with new techniques, studying this topic is crucial for exploring machine learning methods that can effectively detect DDoS attacks. An approach has been utilized to conduct Exploratory Data Analysis (EDA) to identify patterns suggesting the presence of DDoS attacks. Multiple machine learning models have been employed, such as Random Forest, K-Nearest Neighbors (KNN), XGBoost, and Logistic Regression. These models have undergone training and …

The Future Of Smart Transport Technologies: Examining Dubai's Technological Readiness Levels (Trls) To Adopt New Smart Transport Technologies, Hamad Omran Alshizawi

Theses

This research aims to understand what Dubai is willing to do and to provide in terms of smIntelligent Transportation Systems with emphasis on capacity optimization of traffic and understanding TRLs and absorption capacity for AI in Intelligent Transportation Systems Traffic Prediction and Control Systems. The research is important in the context of the concept of a smart city for Dubai and the argument that perfect for the object of examination – transport – can and should integrate new technologies easily into the existing context. Drawing from a population of smart city literature development, traffic and Smart technology, the present study …

Using Prediction Ml Algorithm For Predicting Early Student Attrition In Higher Education, Noora Ali Mohsen Alattar Alhashmi

Theses

Abstract This study aims to use predictive models in order to figure out students that are in likelihood of attrition as well as determine the factors that may contribute to this attrition. Student attrition is the occurrence in which students leave the institution without finishing their studies or taking the degree. It includes students either willingly or involuntarily terminating their education and failing to graduate. The findings will enable relevant parties to establish successful strategies or approaches and activities to assist in reducing the number of students who leave. With the testing and analysis approaches, it was recognized that SVM …

High Heat Flux Dissipation Techniques Using Enhancement Structures And Subcooled Boiling Chamber For High Performance Computing Applications, Maharshi Y. Shukla

Theses

With a rising need for high-performance computing in data centers, enhanced heat dissipation from microprocessors becomes critical for maintaining their surface temperature in a compact setting. While air cooling has been sufficient until now, liquid cooling proves to be superior in its heat dissipation capabilities. The present work investigates enhancing liquid cooling with boiling and enhanced boiling structures in a novel subcooled boiling chamber. The single-phase forced convection heat transfer is first studied by enhancing the heat transfer performance (CHF and HTC) by more than 50 % with the injection of air bubbles at the heater surface. A self-sustaining method …

Adaptive Parameter Tuning For Robust Event-Based Slam In Diverse Scenarios, Saber Muawiyah Sakhrieh

Theses

In the ever-changing field of contemporary robotics, Visual Simultaneous Localization and Mapping (V-SLAM) stands as a crucial technology, facilitating navigation and mapping in various environments. While traditional V-SLAM approaches excel in static environments, the need for their adaptation to handle dynamic and realistic scenes is becoming increasingly apparent. This thesis delves into the advancements within the V-SLAM field, with a focus on enhancing its adaptability and performance in dynamic and challenging conditions. Initially, a comprehensive review of V-SLAM methods in the literature is presented in this thesis, offering an in-depth examination of current cutting-edge techniques, their strengths, weaknesses, and prospects …

A Study Of Gate Valve Supply Chain And Life Cycle Impacts, Saud Bindarwish

Theses

and sustainable water supplies depend on water infrastructure. However, old infrastructure, especially valves that leak and break, can cause significant water loss, economic strain, and environmental damage. This study explores best choice of gate valves to use while replacing defected existing gate valve in the networks by examining gate valve supply chains and their life cycle consequences. Traditional gate valve supply chains are lengthy and complicated. Extracting iron ore and copper destroys habitats and pollutes air and water. Factory processing and production of these materials increases energy use and air/water pollution. Valve shipping, usually overseas, emits lots of greenhouse gases. …

Smart Technology For Enhancing Indoor Air Quality, Abdulaziz Mohammed

Theses

The COP28 emission reduction plan underscores the imperative of transitioning away from fossil fuels to combat climate change. Concurrently, the rapid adoption of Internet of Things technology has emerged as a transformative force in urban governance, offering cities enhanced resilience and operational frameworks through data analytics and real-time monitoring. In response to these trends, the present research focused on leveraging IoT technology to monitor indoor air quality within a “restaurant A” in an urban area in Dubai. Employing advanced data analytics and smart sensor technologies, particularly LoRa, the project aimed to analyze and monitor air quality parameters. Disposing the sensor …

Decentralisation Of Drone Authentication Within Iod Networks., Jarryd Tayler Brits

Theses

The applications of drones have become increasingly more versatile, with their popularity also increasing rapidly in the last decade. These applications span from entertainment within civilian markets to more military-based applications, including private security firms. However, with this rise in use, the security risks involved have also increased drastically, predominantly in a drone’s lack of ability to authenticate surrounding drones efficiently and securely without the aid of a third party. Although there have been copious amounts of research to support drone authentication, they predominantly rely on a single source of truth, such as a ground station server (GCS), which presents …