Digital Commons Network^™

Enhancing The Acceptability Of Decision-Making System Using Xai Case: Crime Profiling System, Mohamed Saeed Al Shamsi

Theses

In the current world, we need to place more emphasis on how easily interpretable, accurate, and acceptable data analysis results are, given that essential operations in law enforcement, among other sectors, are backed up by the use of complex computing systems. Crime profiling systems that use crime data for profiling encounter major problems because they depend on algorithm-based methods. These methods can be ambiguous and inaccurate, leading to low public acceptability. The study investigates major problems with Complex Crime profiling systems (CPS) because their unexplained algorithms result in system performance issues and public scepticism. XAI provides a solution to handle …

Cobalt Layered Double Hydroxide As Advanced Electrode Materials For Supercapacitors And Electrocatalysis, Wafa Sultan Alaryani

Theses

Due to the high demand for sustainable energy storage options and efficient energy conversion devices, extensive research has been conducted on various electrode materials. This thesis is concerned with the fabrication of electrode material mainly using cobalt-based material, particularly cobalt layered double hydroxides (Co-LDH) with the combination of nitrogen-doped carbon nanotubes (N-CNTs) to be used in supercapacitors and oxygen evolution reactions (OERs) applications. The main objective is to enhance the electrochemical performance of supercapacitors and to develop a novel electrode material for a more stable and efficient OER in alkaline medium.

The methodology involved a one-step hydrothermal procedure to synthesize …

Groundwater Quality And Health Risk Assessment In The Fujairah Region Using Hydrogeochemical, Statistical, And Spatial Approaches, Mohammed Khaled Alawlaqi

Theses

Groundwater is a valuable resource in the arid climate of the United Arab Emirates, where growing population, agricultural, and industrial activities are putting increasing pressure on the limited groundwater reserves. This work investigates the hydrogeochemical characteristics of groundwater in the Fujairah region to assess water quality and potential health risks. A total of 64 groundwater samples were collected from the Eastern Shallow Aquifer (ESA) and the Ophiolite aquifer in the study area and were analyzed for physicochemical parameters, major ions, and trace metals. Hydrogeochemical methods such as Piper, Gibbs, Chadha, and Schoeller diagrams were used to identify water types and …

باستخدام مجموعة بيانات متعددة من الطائرات بدون طيار Yolo البحث والإنقاذ البحري القائم على في ظروف الطقس الصعبة, Aysha Ali Alshebli

Theses

Object detection models, powered by deep learning and computer vision, are revolutionizing marine search and rescue (SAR). By analyzing aerial imagery and live drone footage, these systems automatically identify critical targets like survivors, life rafts, and debris across vast and treacherous ocean areas. This capability enhances operational efficiency by reducing human workload and accelerating response times, even in challenging conditions such as poor light, high seas, or cluttered backgrounds. The result is continuous monitoring, faster decision-making, and a significantly improved probability of successful rescue.

Departing from prior methodologies, YOLO introduced a paradigm shift through its single-shot architecture, which concurrently predicts …

Extremism Governance In The United Arab Emirates: A Case Study Using Qualitative Policy Framework, Haya Saleh Almansoori

Theses

This thesis investigates how the United Arab Emirates governs extremism using various models of governance and Gareth Morgan’s metaphorical organizational models. This research goes beyond a focus on security and looks at extremism as a governance and policy problem related to social cohesion, institutional trust, and legitimacy. The research utilizes a qualitative case study design through analyzing federal laws and national strategies, as well as institutional frameworks to understand how authority, coordination and meaning are organized in the UAE’s governance of extremism.

The results show that the UAE’s governance of extremism is characterized as a hybrid governance model with hierarchical …

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 …

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 …

From Waste To Wall Finish: Developing And Characterizing Sustainable Concrete Finishes With High-Volume Fly Ash And Biochar For Interior Applications, Prajwal Dudhane

Theses

This study explores the creation of environmentally friendly interior concrete finishes by recycling agricultural and industrial waste. In a 1:3 cementitious-to-aggregate mortar mix, the study investigates high-volume (30%) cement replacement using fly ash (FA30), biochar (BC30), and a hybrid combination (FAB30). It illustrates the viability of converting waste streams into workable architectural finishes through meticulous material investigation, mechanical testing, and aesthetic evaluation. Different performance characteristics were found by the technical examination. FA30 outperformed the control mix with a 14-day compressive strength of 8.00 MPa thanks to pozzolanic activity and decreased water demand. On the other hand, BC30 (7.33 MPa) significantly …

Examining Children’S Intuitive Understanding Of Mechanical Systems With A Gears Assembly Task, Nicole Taboada

Theses

Children’s causal reasoning about machines and their components is crucial for acquiring knowledge about science and math. Mechanistic understanding involves knowing how parts can be effectively connected to work together as a functional mechanical system. Little is known about how children’s understanding of mechanical systems develops across the lifespan. Legare and Lombrozo (2014) developed a gears assembly task to assess children’s understanding and learning of mechanics but did not track this skill across ages. The present study adapted the gears assembly task to measure mechanistic understanding in children between the ages of 4 to 11 years and adults. The goal …

Advanced Nanophotonics For Gas Spectroscopy, Ajmal Thottoli

Theses

This thesis introduces advanced nanophotonic integrated devices aimed at improving miniaturized, cost-effective multi-gas detection and on-chip spectroscopic systems. Traditional spectroscopic techniques often require bulky optical components and multiple detectors, limiting their scalability for multi-gas sensing. The proposed integrated duplexers and triplexers enable switching between lasers to detect multiple gases using a single system. The work focuses on the design and optimization of broadband angled multimode interference duplexers, directional coupler-based duplexers, and cascaded directional coupler-based triplexers for combining spectroscopically relevant wavelengths in the near-infrared region. The target gases include ammonia, methane, and carbon dioxide. Through comprehensive simulations and experimental investigations, the …

Generation And Detection Of Photo-Thermal And Photo-Acoustic Waves In Solids For Advanced Near-Field Ir Imaging, Yide Zhang

Theses

Atomic force microscopy-infrared (AFM-IR) spectroscopy has become an essential tool for nanoscale chemical imaging, offering spatial resolution beyond the diffraction limit of conventional infrared spectroscopy. A typical AFM-IR setup directs a pulsed IR laser onto the sample, precisely at the tip of an AFM cantilever, detecting local absorption indirectly as photothermal expansion via the tip. This combination of AFM’s high spatial resolution and infrared spectroscopy’s chemical specificity enables detailed studies of a wide range of materials, from polymers and biological tissues to nanomaterials. However, the factors influencing AFM-IR signal generation and spatial resolution-especially in complex and inhomogeneous samples-remain challenging to …

Evaluation Of Existing Grazing Infrastructure And Development Of A Cost- Optimal Infrastructure Upgrade Methodology For Commercial Dairy Farms, Paul Maher

Theses

Grazing infrastructure is a critical component of pasture-based dairy systems, yet its performance on commercial Irish farms has not been fully quantified. This thesis aimed to: (1) assess current pasture allocation practices, (2) evaluate roadway network efficiency, (3) identify factors influencing herd movement and roadway suitability, and (4) explore cost-effective strategies to reduce labour input related to herd movement. Chapter 3 assessed pasture allocation frequency and the configuration of roadway networks across 135 commercial Irish dairy farms over the grazing season of 2020. Data were gathered from PastureBase Ireland and detailed maps of each farm. The distance between the milking …

Uncovering The Efficacy Of Unlabelled Data In Anomaly Detection, Andrew Shields

Theses

Anomaly detection is an important area of research within data mining and has applications in many domains, including cybersecurity, finance, and healthcare. Conventional methods depend on labelled training data and assume an even class distribution, restricting their 'real-world' application, where rare anomalies and unlabelled data are the norm. This research addresses these limitations by investigating how unlabelled data can support anomaly detection in imbalanced, data-scarce, complex environments. It addresses three research questions: how to integrate unlabelled data into anomaly detection models, how to improve performance when labelled anomalies are scarce, and how graph-based methods capture complex relational anomalies. Three empirical …

Functional Foods For Older Adults: Comparing The Bioactive Properties Of Cheese To Identify Products That Promote Healthy Ageing, Aimee Plante

Theses

Functional foods for the elderly: comparing the bioactive properties of cheese to identify products that promote healthy ageing.

People are living longer, and a healthy ageing population is a key goal of our National Positive Ageing Strategy. One approach to support this initiative is to promote and develop functional foods that can support healthy ageing by reducing age-related conditions, including cardiovascular disease, diabetes, obesity and cancer. Cheese particularly aged and fermented varieties like cheddar show great potential as a functional food with inherent and established health properties. It naturally contains bioactive peptides, vitamins, minerals, protein and possible cultures that confer …

Addressing Training Challenges Of Generative Adversarial Networks For Biomedical Image Synthesis, Muhammad Muneeb Saad

Theses

Artificial intelligence (AI) plays a pivotal role in computer-aided diagnosis. In image-based diagnosis, AI systems rely on biomedical images containing sensitive disease-oriented information. AI systems utilize machine learning models to automate this diagnosis. Machine learning models such as deep neural networks require large training datasets to enhance the diagnostic precision of these models. Models can underperform when they are trained on imbalanced datasets. Imbalance in datasets refers to the skewed numbers of images per image class. There is a need to enhance the training of these models via data augmentation. In the context of biomedical imagery, where acquiring large, balanced …

Bioinformatic Analysis Of Pogz Variants In Relation To White Sutton Syndrome, Hannah Rollins

Theses

White-Sutton syndrome (WHSUS) is a rare neurodevelopmental disorder caused by mutations in the Pogo Transposable Element with ZNF Domain (POGZ) gene, which encodes pogo-transposable element with ZNF domain, a chromatin regulator essential for proper mitotic progression and DNA repair. This study uses a bioinformatic framework to evaluate the structural and functional impact of missense mutations in the conserved amino acid region (positions 500–800) of the POGZ protein. Protein modeling, variant effect prediction, conservation analysis, and molecular dynamics simulations were employed to gain an understanding of the effects of POGZ missense mutations on protein structure and movement with specific emphasis on …

Investigating The Effects Of Cbd And Caffeine Interaction On Embryonic Development In Xenopus Laevis, Jeanesia M. Pierre

Theses

This study examined the interactive effects of cannabidiol (CBD) and caffeine on Xenopus laevis embryos over a 96-hour exposure period. Developmental toxicity was assessed using toxic unit isobole analysis, with LC₅₀, EC₅₀, and teratogenic index (TI) values calculated for CBD, caffeine, and mixtures at 3:1, 1:1, and 1:3 ratios.

Results showed that mixtures produced concentration-dependent interactions. When caffeine levels were higher, the mixtures displayed synergistic toxicity; as the proportion of CBD increased, the interaction shifted toward a concentration-addition model. Mortality was elevated in mixtures relative to single-compound exposures, while malformation rates decreased, indicating reduced teratogenicity. Overall, findings suggest that CBD …

Assessing The Feasibility Of Large-Scale, Selective Aqueous Quadrupole Ion Trap Separation, Daniel Brunner

Theses

The global challenges presented by water and resource scarcity and the drawbacks of the methods designed to resolve them escalate the need for innovative selective ion separation solutions. Environmental concerns, material constraints, high costs, and limited scalability are unavoidable disadvantages that hinder many selective separation technologies. Through its reliance solely on electric field manipulation and its avoidance of hazardous solvents or selective membranes, the quadrupole ion trap in an aqueous environment presents itself as a worthy alternative to contemporary separation methods. This research effort aims to gauge the feasibility of using a large-scale aqueous quadrupole ion trap (i.e. traps with …

Solar Power Forecasting Using Deep Learning Techniques, Fatma Abdulrahman

Theses

The growing utilization of solar energy and other renewable energy sources has heightened the demand for a proper forecasting model that enables efficient control of the energy supply as well as grid reliability. In this work, we concentrate about the use of novel techniques in ML models, namely Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), to try to forecast power output in a solar-powered plant using past weather and power output records. Production of solar energy is naturally volatile because it is dependent on the weather conditions and is problematic in nature when integrated into power grids. Conventional …

Enhancing Llm Code Generation: A Systematic Evaluation Of Multi-Agent Collaboration And Runtime Debugging For Improving Accuracy, Reliability, And Latency, Nazmus Ashrafi

Theses

The use of large language models (LLMs) for automated code generation has emerged as a significant focus within AI research. As these pretrained models continue to evolve, their ability to understand and generate complex code structures has opened up new possibilities for automating intricate programming tasks with greater accuracy. Although contemporary foundational models demonstrate promising results, researchers continue to explore optimal post-training strategies to enhance code quality. These include supervised fine-tuning, retrieval-augmented generation (RAG), debugging, and many others. In this thesis, I combine two such widely used post training approaches—namely (1) multi agent collaboration and (2) runtime execution of information-based …

Adaptive Security Metric For Optimizing Post-Quantum Cryptography In Constrained Iot Devices, Aisha Nasser Ahmed

Theses

Quantum Computing poses real threat to Classical Public-Key Cryptography requiring the use of Post-Quantum Cryptography for all Internet of Things Devices. However, there are greater computational, memory and communication overheads in PQC algorithms that create additional burdens on resource constrained IoT devices. At this time, there are no standard measures for systems developers to determine optimal PQC settings for the various IoT Device Classes. This Thesis develops a new framework of metrics for determining the most suitable PQC settings based on Security Strength, Performance Indicators (Latency, Memory, Energy), Communication Overhead and Reliability for each IoT device class. The Research introduces …

Physicochemical And Structural Optimization Of Date (Phoenix Dactylifera L.) Syrup: Impact Of Extraction Temperatures And Pectin Addition, Afna Thaivalappil Abdul Asiz

Theses

This thesis focuses on the optimization of date (Phoenix dactylifera L.) syrup, an emerging natural sweetener rich in glucose, fructose, and bioactive compounds, by investigating how extraction temperature and pectin incorporation influence its physicochemical and structural properties. Therefore, the main objective of this study was to examine how extraction temperatures (25°C, 50°C, 75°C, and 90°C), and the incorporation of lowmethoxyl pectin (LMP, DE 31%) and high-methoxyl pectin (HMP, DE 85%) prior to concentration, affect the quality, stability, and functionality of date syrup. The results showed that increasing extraction temperatures increased the total soluble solids (16 °Brix at 25°C to …

Illustrations For A Scientific Review Article Publication : Human Stem Cell-Derived Organoids, Sunghwan Bae

Theses

This thesis explores the role of scientific illustration in improving the comprehension of complex scientific concepts. It traces the historical development of illustrations in scientific literature and examines their functions in both research and review articles. The central contribution of this work is the development of twelve vector-based illustrations that elucidate the applications of human organoids within a review article context. These visuals transform intricate ideas into clear, engaging representations, supported by extensive research and verification to maintain scientific accuracy. The thesis concludes by addressing the future of scientific illustration, advocating for reader-oriented design approaches and the use of innovative …

Intelligent Monitoring And Alerting System Using Some Deep Learning Models, Omar Alshamsi

Theses

Road traffic crashes cause over 1.19 million fatalities per year worldwide and represent a major challenge for urban safety (World Health Organization, 2023; World Bank Group & WRI India, 2020). Smart city initiatives aim to use technology to reduce such losses through rapid accident detection and response (Arefin et al., 2025). This proposal outlines a vision for a Road Accident Detection and Response System that employs computer vision and deep learning to identify accidents from video feeds and immediately notify emergency services. The core of the system will be a custom-trained YOLOv8 object detection model, leveraging a dataset of annotated …

Democratizing Community Discourse Analysis In Computational Social Science, Md Towhidul Absar Chowdhury

Theses

Community resource inequities and undetected infrastructure vulnerabilities cost municipalities billions annually, with disproportionate impacts on marginalized communities. Current computational approaches to community needs assessment suffer from two critical limitations: they rely on aggregate-level analysis that obscures granular community expressions, and they implement sophisticated computational tools that remain inaccessible to stakeholders without technical expertise. This creates what we term the Community-Computational Gap—a persistent divide where domain experts with vital contextual knowledge cannot access the analytical tools they need, while computational experts develop models without sufficient community context. This dissertation addresses these challenges through a novel methodological framework for fine-grained utterance-level classification …

Impact Of Temperature On Larval Mortality And Performance Of The Acorn Barnacle Chthamalus Fissus In San Diego, Ca, Alberto Rivera

Theses

Rising ocean temperatures and increasingly frequent marine heatwaves threaten the physiological performance and survival of marine larvae. This study quantifies the thermal tolerance, respiration, and swimming behavior of Chthamalus fissus cyprid larvae to establish a baseline for understanding their vulnerability under future climate scenarios. Larvae were collected from the Scripps Pier in La Jolla, CA and exposed to a range of temperatures (12–50°C, dependent on the experiment) to assess oxygen consumption, mortality, and swimming speeds. Oxygen consumption rates increased significantly with temperature, peaking at 30°C, with cyprids exposed to temperatures above 26°C showing significantly higher respiration rates than those below …

Enhance Telecom-Related International Indicators Using Machine Learning, Omar Alnemer

Theses

This thesis investigates the enhancement of the Network Readiness Index (NRI) through the application of machine learning methodologies to refine indicator weighting, clustering, and dimensionality. The study builds on research promoting data-driven methods to address the limitations of equal-weighted indices in capturing indicator importance and interdependence. The research aims to evaluate how unsupervised and supervised learning methods can optimize the interpretation of NRI data. The study focuses on three primary research questions: (1) How can PCA be used to reduce the dimensionality of the NRI without significant loss of information? (2) Can k-means clustering reveal meaningful groups of countries with …

Advancing Institutional Sustainability Through Green Roofs: Performance-Based Policy For Rit, Theoren Heilman

Theses

Utilization of green roof systems continues growth as a sustainable architectural strategy, offering enhancements to key building performance metrics while contributing to overall community resilience. The ability of green roofs to deliver a wide range of performance benefits, even in environments with harsh climatic conditions, positions them as an asset for advancing sustainable development. Within this context, university campuses represent an underutilized, yet important, setting for green roof adoption. Functioning much like small cities, campuses contain diverse building types and extensive infrastructure networks, which draw significant energy demands, positioning them to lead in sustainable building practices. Yet, most lack rigorous, …

Spectroscopy Pre-Trained Transformer (Specpt): A Universal Spectroscopic Analysis And Redshift Measurement Framework, Rohan Pattnaik

Theses

Spectroscopic surveys are essential for measuring galaxy redshifts and probing the physical processes driving galaxy evolution. As datasets grow in scale and complexity, traditional analysis methods face increasing limitations, motivating the development of scalable, data-driven alternatives. This thesis presents SpecPT, a transformer-based deep learning framework for general-purpose spectroscopic analysis, with a focus on redshift estimation. The model is first trained on DESI Early Data Release spectra from the Bright Galaxy and Emission Line Galaxy samples, jointly performing spectral reconstruction and redshift regression while learning a latent representation that captures the intrinsic properties of galaxies. SpecPT is then extended to a …

Degradation Of Anthropogenic Debris In Stormwater Infrastructure Of The Lake Ontario Watershed, Evan Batte

Theses

The accumulation of anthropogenic debris (AD) in the Great Lakes is a growing issue with largely unknown consequences for ecological and human health. To better constrain estimates of debris loading into Lake Ontario and elucidate the fate of AD accumulating upstream, an incubation experiment of the most commonly identified littered products was conducted in different stormwater infrastructure within the Lake Ontario Watershed. Chip bags, cigarette filters, and shopping bags were placed into storm drains, stormwater retention ponds, and along riparian zones of tributaries in December of 2022 and July of 2023 to test the spatial (type of stormwater infrastructure [SWI]) …