Digital Commons Network^™

Core-Scale Study Of Miscible Co2 Foam–Oil Interactions, Kuvonchbek Abdirakhmonov

Theses

Foam is currently the most effective means for gas mobility control in a variety of geo-energy applications, including enhanced oil recovery (EOR), carbon capture, utilization and storage (CCUS), and aquifer/soil remediation. This study investigates the mobility control of miscible CO₂ foam in the presence of oil.

The primary objective is to quantify the impact of oil on CO₂ foam behavior under miscible conditions, specifically examining foam stability, strength, and flow regimes as influenced by oil composition and reservoir permeability. While most oils destabilize foam, few studies explore the coarsening mechanisms of CO₂ foam in the presence of miscible oils, which …

Enhanced Privacy Preserving Healthcare Data Management With Federated Learning Using Homomorphic Encryption, Omar Abdulla Ali

Theses

Federated Learning (FL) is a decentralized approach of machine learning on multiple clients jointly training models without sharing their raw data, which drastically improves privacy and enhance protection against security breach. This is particularly critical in the healthcare sector, where hospitals and medical institutions are often unable to exchange patient records due to strict privacy regulations and data-management policies. However, there is still a risk of privacy breach when clients send their model updates to the central server, because if a model update is intercepted or analyzed by a malicious entity, it could be used to recover sensitive data using …

Detecting Data Poisoning Attacks In Medical Imaging: A Study On Chest X-Ray Classification Tasks, Akhila Abdulla Asgar

Theses

This thesis examines the vulnerability of AI medical imaging models to adversarial threats, with a specific focus on data poisoning attacks in chest X-ray classification. The study begins with a Systematic Literature Review (SLR) to assess the existing adversarial attacks and defenses in medical imaging, revealing a significant research gap in studies exploring data poisoning attacks in the medical domain. Based on our literature search, an efficient and lightweight defense, namely friendly noise defense, against data poisoning has not been investigated in medical imaging classification tasks. Hence, in this work, we investigated its effectiveness on the chest X-ray dataset, and …

Determination Of Variations In Orbital Parameters Of Sample Of Satellites Through Spacecraft Tracking, Asma Ali Alasmari

Theses

The continues increasing density of Resident Space Objects (RSOs) in Earth’s orbit highlights the crucial need for Space Situational Awareness (SSA). This thesis studies the orbital changes in two regions, active low Earth orbit (LEO) satellites and inactive geostationary Earth orbit (GEO) satellites. For LEO satellites we used publicly available data (two line element TLE from Space-Track) and ASTRIAGraph. We verified the orbital elements series and time ordered them, applied a Hampel (median+MAD) mask to semi-major axis, eccentricity vector and inclination to find outliers and exclude them from event detection. The events are then detected using time normalized slopes and …

Startup Success Forecasting Through Machine Learning: A Comprehensive Analysis Of It Startups, Khaled Abdulla Alhassani

Theses

Lately, startups attracted significant attention from investors throughout the previous years. This raised several questions concerning startups and what they possibly define as them. It could refer to collective individuals who focus on innovative ideas with a reproducible and scalable business model; others refer to it as a newly established business. Nevertheless, all these definitions lead to a predictive question. Will these startups face success?

This study explores startup success prediction methods, focusing on forecasting information technology startup (SIT) insights using Machine Learning (ML) models such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), …

Understanding Behavioral Patterns: A Case Study Of Emirati Ex-Offenders In United Arab Emirates, Salim Ali Al Naqbi

Theses

Research on desistance has helped criminologists to better understand how people change their criminal identity and exercise personal choice. Yet in the UAE, these ideas has not often been applied to prisoners serving one or multiple offenses, even though incarceration and reoffending remain important social concerns. Because of this, desistance theory has not been fully used to study how rehabilitation is managed in UAE prisons, which means that some useful lessons may be missed. This study begins to close that gap by looking at the personal stories of 15 Emirati participants that were formerly incarcerated by adapting a qualitative and …

Enhancing It Security Management With An Advanced Intrusion Detection System Based On Machine Learning And Explainable Ai, Hanan Aslam Al Zubaidi

Theses

The fast changing landscape of cyber threats continues to challenge the development of strong and reliable security frameworks for IT management systems. Traditional defense tools, such as Intrusion Detection Systems (IDS), often struggle to keep up with today's advanced and constantly evolving attack methods. This thesis explores these ongoing challenges and looks into how machine learning (ML) and explainable artificial intelligence (XAI) can be used to boost IDS performance.

The research outlines a smart, adaptive system that combines supervised learning for real-time threat detection, unsupervised models for anomaly analysis, and proactive defense strategies. The goal is to improve detection accuracy, …

Microstrip Antenna Design Based On Ai And Machine Learning, Waleed Mohamed Sha Moulavi

Theses

Microstrip patch antennas (MPAs) rely on precise impedance matching for efficient power transfer between the antenna and feed line. This is often achieved using a number of different techniques, one of which is the quarter-wavelength transformer (QWT). While commercial electromagnetic (EM) solvers offer robust optimization capabilities, they often operate as "black boxes" without providing physical insights into parameter interdependencies. Furthermore, this thesis focuses on the specific scenario where the antenna input impedance is purely real. To address the lack of explicit design relationships for these specific conditions, this thesis develops and comparatively evaluates artificial intelligence (AI) models for QWT width …

Trustworthy Federated Learning Framework For Secure, Efficient, And Quality-Aware Distributed Ai, Asadullah Tariq

Dissertations

Federated Learning (FL) emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL and its application in various areas increased, addressing trustworthiness issues in its various aspects became crucial. In the FL process, clients contribute updates computed on their local datasets, which the server aggregates to iteratively refine the global model. However, not all client data may be relevant to the learning objective, and incorporating updates from irrelevant data can harm the model's performance. The selection of training samples significantly impacts model …

Influence Of Organic Matter And Mineral Composition On Carbonate Source Rock Wettability: Implications For Co2 Geostorage, Muhammad Usman, Nurudeen Yekeen, Muhammad Ali, Mujahid Ali, Alireza Keshavarz, Stefan Iglauer, Volker Vahrenkamp

Research outputs 2022 to 2026

Wettability is critical in determining carbon dioxide (CO2) behavior during geological sequestration in unconventional reservoirs. Unconventional reservoirs are compositionally heterogeneous, affecting CO2 plume migration, containment security, and storage capacity during geological sequestration. Previous studies on CO2 storage in unconventional reservoirs have primarily attributed to changes in organic matter content; however, this study examines how variations in the mineralogical and organic matter content combined affect wettability in CO2/brine systems under subsurface conditions. Three samples with varied mineralogy and TOC content were selected from a well drilled in Jordan source rocks—an immature analog of marine-derived, carbonate-dominated, Type IIS source rocks. Samples were …

A Century Of Sediment Metal Contamination Of Mar Menor, Europe's Largest Saltwater Lagoon, Irene Alorda-Montiel, Valentí Rodellas, Ariane Arias-Ortiz, Albert Palanques, Andrea G. Bravo, Júlia Rodriguez-Puig, Aaron Alorda-Kleinglass, Carlos Green-Ruiz, Marc Diego-Feliu, Pere Masqué, Javier Gilabert, Jordi Garcia-Orellana

Research outputs 2022 to 2026

Coastal enclosed ecosystems, such as lagoons, are vulnerable to anthropogenic impacts because they favor the accumulation of contaminants from the surrounding watersheds, particularly in their sediments. Europe's largest saltwater lagoon, the Mar Menor (SE, Iberian Peninsula), is a highly impacted ecosystem and the first in the continent to be granted personhood rights. Based on a high-resolution spatial and temporal dataset, we present the historical reconstruction of metal contamination in this ecosystem during the last century. Our results highlight that sediment metal contamination has been mainly driven by the development of the mining industry in the nearby Sierra Minera de Cartagena-La …

Adaptive Guard Band And Power Control For Resource Allocation In Mobile And Fixed Mission-Critical Iout Networks, Walid K. Hasan, Iftekhar Ahmad, Quoc Viet Phung, Daryoush Habibi

Research outputs 2022 to 2026

The Internet of Underwater Things (IoUT) is transforming underwater communication by enabling essential mission-critical applications such as precise navigation, emergency response coordination, diver safety, robust security and surveillance systems, and real-time environmental monitoring. However, Underwater Acoustic Communication (UAC), which serves as the primary communication medium for IoUT, experiences substantial challenges, including limited bandwidth availability, severe signal attenuation and Doppler-induced frequency shifts, especially pronounced in mobile underwater environments. These challenges degrade throughput and increase latency, making it difficult to meet the strict delay and reliability demands of mission-critical IoUT applications. Without adaptive solutions, real-time underwater communication remains unreliable and inefficient. This …

Multi-Criteria Decision-Making And Uncertainty Analyses Of Off-Grid Hybrid Renewable Energy Systems For An Island Community, Dibyendu Roy, Hadi Taghavifar, Kumar Vijayalakshmi Shivaprasad, Yaodong Wang, Barun K. Das, Anthony Paul Roskilly

Research outputs 2022 to 2026

This study examines advanced hybrid renewable energy system configurations designed for a remote island community. The configurations integrate multiple technologies, including wind turbines, photovoltaic panels, fuel cells, diesel generators, batteries, converters, electrolyzers, and hydrogen storage tanks, enabling a synergetic approach to energy generation, storage, and management. Excess electricity produced from the systems is utilized for battery charging and green hydrogen production, enhancing system flexibility and resilience. A detailed techno-economic and environmental assessment of the configurations was performed. Furthermore, the Technique for Order of Preference by Similarity to Ideal Solution based multicriteria decision-making framework was employed to identify the optimal configuration. …

The Role Of Fungal Proteases In Bioactive Peptide Production And Analytical Approaches For Tracking Their Activity, Tanvir Ahmed, Angéla Juhász, Utpal Bose, Netsane Shiferaw Terefe, Michelle L. Colgrave

Research outputs 2022 to 2026

Fungal proteases catalyse site-specific peptide bond cleavage in complex protein matrices, enabling bioactive peptide (BP) release. Accurate monitoring is vital for optimising hydrolysis efficiency, minimising nonspecific proteolysis, ensuring consistent yields, and preserving peptide biofunctionality. While analytical and computational tools are well-established for non-fungal systems, their adaptation to fungal proteases remains exploratory, with no comprehensive evaluation addressing the unique challenges in this field. This review synthesises current knowledge, identifies methodological gaps, and outlines future directions for developing fungi-specific analytical pipelines and computational frameworks for BP discovery. It explores fungal protease families, their roles in intracellular peptide processing, and fungal survival strategies. …

Assessing Teachers’ Practices In Supporting Gifted/Talented And Twice-Exceptional Students In Schools And Centers In Abu Dhabi, Zayed Jaber

Theses

Gifted/talented and twice-exceptional (2e) students, especially those whose needs are often overlooked in mainstream classrooms, are more likely to benefit from consistently implemented Differentiated Instructional Practices (DIPs). This study aimed to examine implelemntation of DIPs for gifted/talented and 2e students and to compare the practices implemented between of Special Educational Needs (SEN) teachers and General Education teachers in schools and centers within Abu Dhabi. This research used a quantitative, cross-sectional survey design to collect data from eighty-six teachers from Abu Dhabi. A questionnaire was designed to rate teachers’ self-reported implementation of DIPs across the four domains of content, process, product, …

The Simulation Of Hyperspectral Observations By The Upcoming Spacecraft "Arab Satellite 813", With The Radiative Transfer Model Sciatran, Sara Maher Alhasan

Theses

The Arab Satellite 813, which is scheduled to be launched in 2025/2026 by the UAE Space Agency, is set to increase earth observation and monitor the climate across the middle East and North Africa (MENA) region. This study uses the SCIATRAN radiative transfer model which was originally developed for the Envisat mission to simulate the hyperspectral observation specifically tailored for the satellite's payload. Using calibrations that were used by the previous research in their SCIATRAN V4.6 study and adjusting parameters like the Leaf Area Index (LAI), a simulation which agreed robustly with the benchmarks set by the previous research was …

Sketch-Sparsenet: Sparse Convolution Framework For Sketch Recognition, Jingru Yang, Jin Wang, Yang Zhou, Guodong Lu, Yu Sun, Huan Yu, Heming Fang, Zhihui Li, Shengfeng He

Research Collection School Of Computing and Information Systems

In free-hand sketch recognition, state-of-the-art methods often struggle to extract spatial features from sketches with sparse distributions, which are characterized by significant blank regions devoid of informative content. To address this challenge, we introduce a novel framework for sketch recognition, termed Sketch-SparseNet. This framework incorporates an advanced convolutional component: the Sketch-Driven Dilated Deformable Block (SD3B). This component excels at extracting spatial features and accurately recognizing free-hand sketches with sparse distributions. The SD3B component innovatively bridges gaps in the blank areas of sketches by establishing spatial relationships among disconnected stroke points through adaptive reshaping of convolution kernels. These kernels are deformable, …

Pricing Strategies In Global Channels: Considering The Effects Of Parallel Trade, Yuan-Mao Kao, Yang Yang, Shih-Fen Cheng, Cheng-Hung Wu

Research Collection School Of Computing and Information Systems

Pricing a global product differently across multiple regions is a common but controversial practice. Although price differentiation helps capture unique market characteristics, it also encourages parallel trade, which may affect the overall corporate performance of a global company. We study this problem with a single global business unit (GBU) and multiple local business units (LBUs). The GBU manufactures a product and sets a transfer price for supplying the product to all LBUs, and LBUs decide retail prices for their respective regional markets. Customers can purchase products in any region by comparing LBUs’ prices and other parallel-imported factors, and we construct …

When Deep Learning Meets Information Retrieval-Based Bug Localization: A Survey, Feifei Niu, Chuanyi Li, Kui Liu, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. Information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting the bug resolution process for developers. Recent years have witnessed significant achievements in IRBL, propelled by the widespread adoption of deep learning (DL). To provide a comprehensive overview of the current state of the art and delve into key issues, we conduct a survey encompassing 61 IRBL studies leveraging DL. We summarize best practices in each phase of the IRBL workflow, undertake a meta-analysis of prior studies, and suggest future research directions. …

Seeing Culture: A Benchmark For Visual Reasoning And Grounding, Burak Satar, Zhixin Ma, Patrick Amadeus Irrawan, Wilfried Ariel Mulyawan, Jing Jiang, Ee-Peng Lim, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Multimodal vision-language models (VLMs) have made substantial progress in various tasks that require a combined understanding of visual and textual content, particularly in cultural understanding tasks, with the emergence of new cultural datasets. However, these datasets frequently fall short of providing cultural reasoning while underrepresenting many cultures.In this paper, we introduce the Seeing Culture Benchmark (SCB), focusing on cultural reasoning with a novel approach that requires VLMs to reason on culturally rich images in two stages: i) selecting the correct visual option with multiple-choice visual question answering (VQA), and ii) segmenting the relevant cultural artifact as evidence of reasoning. Visual …

Metacan: Improving Generalizability Of Few‑Shot Anomaly Detection With Meta‑Learning, Zhisheng Lv, Jianfeng Zhang, Songlei Jian, Chenlin Huang, Hongguang Zhang, Guansong Pang, Zhong Liu

Research Collection School Of Computing and Information Systems

Few-shot Anomaly Detection (AD) for images aims to detect anomalies with few-shot normal samples from the target dataset. It is a crucial task when only few samples can be obtained, and it is challenging since it needs to be generalized to different domains. Existing methods try to enhance the generalizability of AD by incorporating large vision-language models (LVLMs).However, how to transform category semantic information in LVLMs into anomaly information to improve the generalizability of AD remains a challenge facing existing methods.To address the challenge, we propose a few-shot AD method called MetaCAN, a novel category-to-anomaly network trained with AD meta-learning …

Disc: Decentralized Identity System With Self-Sovereign Credential Aggregation, Yang Yang, Wai Keung Ching, Minming Huang, Supachate Innet, Guomin Yang, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

The evolution of decentralized identity (DID) and self-sovereign identity (SSI) frameworks, as endorsed by W3C Verifiable Credentials (VC) and eIDAS 2.0, underscores the need for secure, efficient, and privacy-preserving credential management. However, existing credential systems often depend on centralized issuers, lack efficient aggregation mechanisms, or fail to ensure unlinkability across authentication sessions. To address these challenges, we propose DISC (Decentralized Identity System with Self-Sovereign Credential Aggregation), a novel credential system that enables multi-authority credential issuance, user-controlled credential aggregation, and unlinkable authentication. DISC allows users to aggregate credentials from multiple issuers while maintaining constant-size authentication tokens and supporting batch verification for …

Investigation Of Mechanical Performance And Metallurgical Characteristics Of Titanium Grade 2 Tube-To-Tubesheet Joints, Ahmad Hussein Al Tamimi

Theses

Tube-to-tubesheet joints, an integral component of shell and tube heat exchangers, are known for their vulnerability to leakage problems at the joint region due to mechanical and chemical factors coupled with fabrication techniques involved in creating these joints. An ideal condition of manufacturing process parameters and geometrical variables required to produce tube-to-tubesheet joints is highly desired. One of the most promising candidate materials to fabricate tube-to-tubesheet joints is titanium or titanium alloys due to their excellent strength and resistance to corrosion. The present study investigates the structural integrity of three categories of Titanium Grade 2 based tube-to-tubesheet joints comprising of …

Enhanced Air Hockey Robot Performance Through Adaptive Control Algorithim Using Ai-Based Hierarechal Decicion Archeticture, Ayham Majed Salim

Theses

This thesis presents the development of an intelligent air hockey robot that combines precise mechanical design, computer vision, and adaptive control within an AI-based hierarchical decision architecture. The system integrates synchronized stepper motors, high-speed image processing, and a real-time decision framework to achieve competitive gameplay performance. The robot detects the puck using adaptive HSV color segmentation, supported by dynamic calibration that maintains accuracy under different lighting conditions. A two-stage trajectory prediction model, based on exponential decay velocity estimation, enables anticipation of puck motion and improves response time during fast gameplay.

At the decision level, a fuzzy-logic supervisor governs the robot’s …

Metabolic Flexibility As A Candidate Mechanism For The Development Of Postoperative Morbidity, Pietro Arina, John Whittle, Maciej R Kaczorek, Davide Ferrari, Nicholas Tetlow, Amy Dewar, Robert Stephens, Daniel Martin, S Ramani Moonesinghe, Evangelos B Mazomenos, Mervyn Singer

Peninsula Medical School

BACKGROUND: This study investigates the role of metabolic flexibility in determining perioperative outcomes. Metabolic flexibility, a key feature of metabolic health, is the ability to efficiently switch between different fuel sources (predominantly carbohydrates and fats) depending on energy demands and availability. Given the rapidly changing physiological conditions in the perioperative period, we hypothesized that good metabolic adaptability could mitigate postoperative complications. METHODS: We conducted a retrospective observational study utilizing a prospectively collected, single-center preoperative cardiopulmonary exercise testing (CPET) database of patients undergoing a range of major surgeries between 2012 and 2022. On day 3, patients were categorized into 3 groups …

How Do Community Assets Support Health In A Rural Community? An Ethnographic Case Study, Jennie Hayes, Liz Edwards-Smith, Richard Byng, Kerryn Husk, Katrina Wyatt

Peninsula Medical School

Community assets (including voluntary, community, social enterprise and grassroots initiatives) have the potential to help address health inequalities. There is a growing understanding of the importance of putting communities at the heart of public health to improve population health. Understanding the mechanisms by which grassroots activities support health and wellbeing is important for making commissioning decisions, for community confidence and for the purposes of evaluation. This study takes an ethnographic case study approach to explore one rural community in the south-west of England. We observed activities and interactions, had informal conversations with residents and community leaders and conducted focus groups. …

Exploring The Impact Of Faculty–Student Interactions Outside The Classroom On The Sense Of Belonging, Engagement, Self-Confidence, And Retention Among Undergraduates In Uae Federal Universities, Mona Malalla Alhammadi

Dissertations

The purpose of this study was to examine the ability of Faculty-Student Interaction (FSI) in influencing students' social and academic outcomes and success. In particular, this study sought to investigate the impact of the informal interactions between faculty members and their students outside the classroom on four student outcomes: Sense of Belonging (SB), Engagement (EG), Self-confidence (SC), and Retention (RT). The participants were 570 undergraduate students who were at their first- and second-year of study from three different UAE federal higher education institutions. The study employed quantitative research design while utilizing an online survey as the main data collection instrument. …

Domestic Inter-Sectorial Network Topological Evolution And Industrial Policy: A Comprehensive Network Analysis, Mahanad Ali Al Sabahi

Dissertations

While controversial, industrial policy propose a compelling argument for the government role in stimulating economic growth. The “new industrial policy” approach proposes a shift from a “Why” to “How” should industrial policy be implemented. Building upon this, in line with the increased utilization of network theory in economic research, this research examines network analysis as an econometric diagnostic tool asking, “How network analysis could be utilized in understanding the domestic economy and designing industrial policy?”. The study utilizes country input-output tables to calculate centrality measures corresponding to different connectivity concepts, and explore the dependency between domestic industries. The research demonstrated …

Pharmacogenomics Of Statin Therapy In The Uae: Genetic Predictors Of Adverse Effects, Treatment Discontinuation, And Feasibility For Clinical Implementation In A Multiethnic Population, Mais Noman Al Qasrawi

Dissertations

Statins are among the most widely prescribed medications for the prevention and treatment of cardiovascular disease (CVD), yet their use is frequently complicated by adverse drug reactions (ADRs) such as statin-associated muscle symptoms (SAMS) and liver enzyme elevations. These ADRs often lead to premature discontinuation and non-adherence, ultimately limiting the full therapeutic potential of lipid-lowering therapies. Pharmacogenomic (PGx) testing has emerged as a promising strategy to personalize statin treatment, particularly by identifying genetic variants that influence statin metabolism, transport, and toxicity. Although key PGx biomarkers such as SLCO1B1, ABCG2, and CYP2C9 have been studied extensively in global populations, their relevance …

Prevalent Vertebral Fracture Is Associated With Incident Cardiovascular Disease Events In Older Individuals Referred For Bone Densitometry, John T. Schousboe, Barret A. Monchka, J. Michael Davidson, Douglas Kimelman, Syed Zulqarnain Gilani, Zaid Ilyas, Siobhan Reid, Joshua R. Lewis, William D. Leslie

Research outputs 2022 to 2026

Background: It is unknown if prevalent vertebral fracture (PVFx) captured on bone density vertebral fracture assessment (VFA) images predicts incident CVD events. Methods: 11,760 individuals (mean [SD] age 75.7 [6.8] years, 94 % female) had VFA contemporaneously with bone densitometry in Manitoba, Canada, between February 2010 and December 2017, of whom 1919 (16.3 %) had ≥1 PVFx. This cohort was followed over a mean (SD) 3.8 (2.3) years for Major Adverse Cardiovascular Events (MACE, composite of hospitalization for myocardial infarction, non-hemorrhagic stroke, or all-cause mortality) and other CVD events (hospitalization for coronary artery disease, congestive heart failure, peripheral vascular disease, …