United Arab Emirates University
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THE DEVELOPMENT OF YEAR ONE STUDENTS’ READING ABILITY IN A CURRICULUM USING MONSTER PHONICS SCHEME IN THE UAE
No full textThis study explores the impact of the Monster Phonics program on the reading development of Year 1 students in Al Ain, UAE. Reading proficiency is a fundamental skill in early education, and phonics instruction plays a crucial role in literacy acquisition. Monster Phonics, a color-coded synthetic phonics program, has been increasingly adopted in classrooms, yet its effectiveness in multilingual settings remains underexplored. This research aims to investigate how students\u27 reading ability develops in the context of a curriculum using the Monster Phonics scheme. A mixed-methods approach was employed, combining quantitative assessments pre- and post-tests on grapheme-phoneme correspondence, word reading, and reading comprehension with qualitative data collected through teacher interviews and classroom observations. The study involved 25 Year 1 students from diverse linguistic backgrounds and four teachers familiar with Monster Phonics. Statistical analysis of test scores revealed significant improvements in students\u27 ability to recognize graphemes, blend sounds, and comprehend texts. Qualitative findings highlighted high engagement levels, with teachers noting the program’s effectiveness in reinforcing phonics rules, though additional scaffolding was recommended for struggling learners. The study provides empirical evidence supporting the effectiveness of Monster Phonics in multilingual classrooms, demonstrating its potential to enhance phonics instruction and early literacy skills. By evaluating Monster Phonics within a diverse learning environment, this research contributes to the broader discourse on phonics instruction, offering practical insights for educators and policymakers aiming to optimize early reading strategies
PROBLEMS IN EXTREMAL GRAPH THEORY AND SPECTRAL GRAPH THEORY
No full textSpectral graph theory is a subfield of algebraic graph theory that studies the matrices associated with graphs. It lives at the nexus of Linear Algebra and Combinatorics. Many intriguing results in the domains of Matrix Theory and Combinatorics have come from studying the eigenvalues of graph matrices; in fact, several open problems in both areas have been resolved. Beyond its theoretical appeal, spectral graph theory has found meaningful applications in theoretical chemistry, particularly in the mathematical classification of chemical graphs. These classifications underpin quantitative structure–property relationships (QSPRs), facilitating the prediction of physicochemical properties such as enthalpy of vaporization, molar refractivity, and boiling point. Adjacency matrix, inverse sum index (ISI) matrix, Sombor matrix, Distance matrix and other similar degree/distance-based topological matrices are among the many matrices that can be linked to graphs.
Let G be a graph of order n with vertex set {v₁, v₂, …, vₙ}, and let dᵢ denote the degree of vertex vᵢ. The inverse sum index matrix A_ISI of G is defined such that its (i, j)-entry is dᵢdⱼ/(dᵢ + dⱼ) if vᵢ is adjacent to vⱼ, and 0 otherwise. The Sombor matrix S(G) is defined similarly, with its (i, j)-entry equal to √(dᵢ² + dⱼ²) when vᵢ and vⱼ are adjacent, and 0 otherwise. The distance matrix D(G) = (d_{vᵢvⱼ}) of G is indexed by the vertices of G, where each entry d_{vᵢvⱼ} represents the shortest path length (distance) between the vertices vᵢ and vⱼ. This dissertation focuses on the spectral properties of the ISI matrix, the Sombor matrix, and the distance matrix. In particular, we address problems involving the characterization of extremal graphs with respect to their spectral radius (i.e., the largest eigenvalue). We identify graphs that attain the maximum and minimum trace norm (also referred to as graph energy, defined as the sum of the absolute values of eigenvalues) among a given family of graphs. Due to the complexity of classifying such matrices, we also explore the distribution of eigenvalues, including the characterization of graphs having exactly two or three distinct eigenvalues. Additionally, we perform a detailed analysis of the ISI index for q-broom-like graphs. We also derive bounds for the Sombor and ISI indices in terms of graph invariants such as maximum degree, minimum degree, order, and size for several standard graph operations, including the corona product, Cartesian product, strong product, composition, and join of graphs. An important direction in spectral graph theory involves the interplay between algebraic structures and graph-theoretic representations. An example of this may be found in the zero-divisor graph of a commutative ring R with unity (1 ≠ 0). We compute the distance spectra of zero-divisor graphs corresponding to the commutative rings ℤₙ [x]/⟨x⁴⟩ (n is any prime), ℤ₂ [x]/⟨x²⟩ (t ≥ 3 any prime) and Fₜ + uFₜ + u²Fₜ (t is an odd prime), with ℤₙ denoting the ring of integers modulo n, and Fₜ denoting the finite field on t elements. For these graphs, we establish sharp bounds on their distance energy. Lastly, we also discuss the statistical analysis of the ISI index and the ISI energy in relation to the physicochemical properties of chemical compounds. Specifically, we examine correlations with experimental attributes such as molar refractivity, molar volume, boiling point, flash point, and molar weight, thereby highlighting the utility of spectral indices in chemical graph theory
ANALYZING SEA LEVEL RISE SCENARIOS IMPACT ON THE MOBILITY, INFRASTRUCTURES, ENVIRONMENT OF ABU DHABI AND DEFINING SOLUTIONS BY CREATING A DIGITAL TWIN USING GIS AND GAME ENGINE
No full textThis dissertation investigates the potential impact of future sea level rise (SLR) scenarios on the city of Abu Dhabi. With climate change, many coastal towns are at risk of experiencing this type of natural hazard. Currently, no precise scenario simulations have been developed, mainly due to the use of low spatial resolution elevation data. Additionally, the difficulty in understanding the real impacts persists when analyses rely solely on traditional cartography and GIS methods. This dissertation aims to advance the field by offering a new approach, which involves creating a comprehensive dynamic 3D simulation with dynamic flowing water. This model, defined as a “digital twin”, is applied to specific areas to extract statistics related to four custom sea level rise scenarios: 1 m, 2 m, 3 m, and 4 m. These statistics focus on three aspects commonly analyzed separately: buildings, the road network, and vegetation. The Unity game engine—software initially used for video game creation—is integrated with Geographic Information Systems (GIS) to create a virtual replica of the city and perform simulations. The main results reveal a generally moderate sea level rise along the shoreline at 1 m. Unexpected initial water intrusions appear at 2 m in the eastern part of the Corniche area. The 3 m rise represents a significant global flood with some regions still accessible. However, the final scenario shows nearly complete inundation, except for a few high-elevation zones. The conclusions highlight the high vulnerability of most regions, especially at 3 m, and offer a comprehensive explanation of sea level rise impacts on each aspect studied. This simulation automatically and constantly measures the effect of each scenario on the number of buildings, road distances, and tree species. The sea level is dynamically updated in the interface, and a compass facilitates orientation. This allows the user to move freely within the area, either walking or flying. This novel and innovative 3D simulation provides one of the most comprehensive and up-to-date assessments of Abu Dhabi’s vulnerability to sea level rise. It delivers detailed, dynamic statistics, a 3D virtual city environment, and in-game freedom of movement with both first-person and third-person view modes implemented
SMART TRAFFIC INTERSECTIONS: LEVERAGING ISAC AND MILLIMETER-WAVES FOR ADVANCED VEHICLE PLATOONING
Get PDFThe rapid advancement of self-driving cars is reshaping the transportation industry and accelerating the development of smart cities. Vehicle platooning, a key capability of autonomous vehicles, has the potential to enhance traffic efficiency, reduce congestion, and improve safety at intersections. However, maintaining platoon cohesion, minimizing latency, and optimizing traffic signal interactions remain significant challenges. This study addresses these issues by leveraging Integrated Sensing and Communication (ISAC) technology with millimeter-Waves (mmWaves) signals to optimize platooning performance at traffic signal intersections.
The research identifies gaps in existing Vehicle-to-Everything (V2X) communication frameworks, particularly in managing platoon movements in urban traffic intersections. To bridge these gaps, a novel algorithm is proposed to enhance the coordination of vehicle platoons through an intelligent traffic signal system. The capabilities of ISAC-based mmWaves communication are evaluated through key metrics, including latency, throughput, and bit error rate (BER), under varying modulation schemes (M-PSK, M-QAM). Results demonstrate that ISAC technology significantly improves intersection management by reducing clearance times, strengthening platoon stability, and mitigating accident risks. These findings highlight the transformative impact of ISAC-driven communication on urban mobility, offering a scalable and efficient solution for future smart city applications
COMPARATIVE GENOTOXICOLOGICAL ANALYSIS OF OCCUPATIONAL PESTICIDE EXPOSURES: AN EXPERIMENTAL APPROACH AND SYSTEMATIC REVIEW
Get PDFThis dissertation investigates the genotoxic and cytotoxic effects of pesticide exposure through three complementary research components: a systematic review of occupational pesticide exposure in Arab countries (AraSys), a systematic review on encapsulated versus conventional pesticide formulations (Encap-Sys), and experimental toxicological assessments of various pesticide formulations in human cell lines (Pyr-Ex). This experimental component includes cytotoxicity assays using propidium iodide and DNA damage assessments via alkaline comet assays in HL60 and HepG2 cell lines exposed to pyrethroid insecticides and glyphosate-based formulations.
The AraSys systematic review revealed a critical positive association between occupational pesticide exposure and DNA damage in agricultural workers in Arab countries, consistent with findings from other global regions. However, the scarcity of research in this geographical area—with only five eligible studies from three countries— highlights a significant knowledge gap that limits comprehensive risk assessment. The included studies employed various methods to evaluate DNA damage but often lacked specificity regarding pesticide compositions, exposure parameters, and routes. Methodological limitations included the absence of female participants in all studies, inadequate control of confounding factors like smoking, and inconsistent reporting of control population characteristics, presenting challenges for interpretation and generalizability.
The Encap-Sys systematic review evaluated whether encapsulated pesticide product formulations present lower health risks than conventional alternatives by analyzing in vitro and in vivo animal model studies. While encapsulated formulations generally demonstrated reduced toxicity compared to conventional pesticides, the results varied considerably depending on encapsulation material, pesticide type, and experimental conditions. Chitosan-based and silica-based encapsulations frequently showed promising reductions in toxicity, but some polymeric formulations and lipid nanoparticles showed similar or even increased toxicity in certain assays. The significant variations in dose selection, exposure durations (from acute to chronic), encapsulation techniques, and evaluation endpoints created challenges for definitive conclusions, highlighting the need for standardized protocols, more comprehensive chronic exposure studies, and real-world environmental assessments.
The Pyr-Ex experimental component provided original data on the cytotoxicity and genotoxicity of various pesticide formulations in HL60 and HepG2 human cell lines. The study on cyphenothrin revealed consistent patterns across different concentrations where the encapsulated formulation Bombex Farumy exhibited the lowest genotoxicity in both cell lines, followed by released Bombex Farumy, while the cyphenothrin active ingredient alone consistently induced the greatest DNA damage. This suggests encapsulation technology effectively mitigates the genotoxic potential of cyphenothrin formulations, with HepG2 cells demonstrating greater resistance to both cytotoxic and genotoxic effects compared to the more susceptible HL60 cells. Evaluation of glyphosate-based herbicide formulations revealed complex, formulation-specific, and cell type-specific toxicity profiles. In HL60 cells, Roundup Mega demonstrated remarkable genotoxic potential in tail length at concentrations as low as 0.1 μM, well below cytotoxic thresholds, suggesting direct DNA-damaging mechanisms independent of cell death. In contrast, HepG2 cells showed different patterns, with Glyfos demonstrating genotoxic effects at the lowest concentration (100 μM) across multiple parameters, while Roundup Mega exhibited significant genotoxicity with the largest effect size only at higher concentrations (500 μM). Co-formulants like ROKAmin SR22 and Embigen BB showed varying cytotoxicity but generally lower genotoxicity when tested alone. This ability to induce genotoxicity at subcytotoxic levels emphasizes the need for independent assessments of genetic damage, particularly for complete formulations rather than ingredients in isolation. This dissertation contributes to our understanding of pesticide-related health risks by highlighting the genotoxic potential of occupational pesticide exposure, the complex and variable safety profiles of encapsulated pesticide formulations, and the critical role of formulation technology in modulating pesticide toxicity. The findings emphasize the need for formulation-specific risk assessment approaches, multi-cell line toxicity evaluations, and improved safety measures for agricultural workers, particularly in developing regions. These insights have significant implications for pesticide regulation, occupational health policies, and the development of safer formulation technologies
MEASUREMENT AND IMPROVEMENT OF PHOTON IDENTIFICATION EFFICIENCIES USING MACHINE LEARNING TECHNIQUES IN THE ATLAS DETECTOR AT THE LHC
Get PDFPhotons play a crucial role in numerous analyses at the Large Hadron Collider (LHC), particularly in studies like the Higgs boson decay to two photons. Precise photon identification is essential for enhancing the sensitivity and accuracy of such measurements. This thesis focuses on the development of a machine learning (ML)-based photon identification algorithm to improve the photon identification efficiency within the ATLAS detector, using a Deep Neural Network (DNN) approach. The primary goal is to boost photon identification efficiency by using advanced neural network techniques. Traditional photon identification relies on cuts applied to shower shape variables, which can limit the effectiveness of separating prompt photons from background signals. To overcome this, a new ML-based identification algorithm using a DNN is proposed, building on previous research that demonstrates improvements in photon identification through neural networks. This work investigates the optimization of photon identification efficiency by training a DNN with shower shape variables to differentiate between prompt and background photons. The performance of the ML-based algorithm is benchmarked against the traditional cut-based approach. The data comes from Monte Carlo Simulations
OVERCOMING MOTOR IMAGERY BCI ILLITERACY: ADAPTIVE DECODING AND KNOWLEDGE TRANSFER IN EEG-BASED BRAIN-COMPUTER INTERFACES
Get PDFBrain Computer Interface (BCI), Also known as brain-machine interface (BMI) is a mean of controlling machines without the need to activate peripheral nerves or muscles. It has received the attention of research for decades. Motor imagery-based BCI is a paradigm that is characterized by its user friendliness where users can generate control commands at their freewill, without waiting for a que from the BCI module. Motor imagery brain–computer interface (MI–BCI) has considerable potential in increasing the quality of the lives for people with mobility impairment and the healthy ones as well. Though, its diffusion in application still has many pitfalls due to different limitations. The decoding of the signal, typically EEG, requires frequent calibration to maintain an acceptable accuracy threshold. Those limitations stem from many factors related to the nature of the signal, availability of training examples, and user related aspects. BCI-illiteracy is one of the open challenges that have been in literature for decades. This dissertation addresses the motor imager BCI-illiteracy by investigating the effect of Riemannian adaptive decoding and transfer learning on the performance of the classification accuracy. It introduces a variety of methods including supervised, unsupervised, and rebiased decoding of EEG signals. Also, it uses statistical methods to investigate the relationship between the motor execution and motor imagery in an endeavor to reframe the BCI-illiteracy. Finally, it transfers the domain knowledge between motor execution and motor imagery reducing the need for model calibration on motor imagery examples. The transfer of knowledge used resembles a straight forward and simple transfer approach where the weights of the class prototype presented a noticeable improvement with zero calibration. The methods presented in this dissertation contribute to the literature by reframing the BCI-illiteracy from an unprecedented approach by comparing the accuracy of motor imagery and motor execution as well. This dissertation paves the road towards having more reliable user-centered BCIs and cater for better understating and overcoming of BCI-illiteracy
ASSESSMENT OF HEAVY METAL CONCENTRATION IN BLUE SWIMMING CRABS FROM DUBAI AND SHARJAH FISH MARKETS IN UAE
Get PDFGrowing urbanization and industrialization along the UAE\u27s coastline exposes human health and marine life at risk of increasing heavy metal pollution in marine ecosystems. This study evaluated the levels of 19 essential and non-essential elements in blue swimming crabs (Portunus pelagicus) collected from fish markets in Dubai and Sharjah. Among these, potentially harmful heavy metals included arsenic (As), copper (Cu), cadmium (Cd), nickel (Ni), lead (Pb) and mercury (Hg). The main goals of the study were (i) to evaluate whether the concentrations of these metals exceeded maximum permissible limits and (ii) to investigate if there were male and female specific variations in metal accumulation. Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) was used to measure the concentrations of elements in crab tissues. Mean concentrations of As and Cu in male and female crabs were higher than the international food safety standards. Furthermore, the concentrations of Cd and Ni in female crabs were much higher maximum permissible limits. However, levels of mercury (Hg) in both sexes were detected to be below maximum permissible limits. Overall, while some metal concentrations exceeded safety thresholds, the total heavy metal burden across all samples was not considered toxic based on global health standards. This study highlights potential risks to the public\u27s health for seafood consumers by providing valuable information on heavy metal contamination in blue swimming crabs from the United Arab Emirates. Additionally, research closes a significant knowledge gap on the sex-specific bioaccumulation patterns of certain potentially harmful elements in blue swimming crabs, suggesting that male and female crabs accumulate different heavy metals at different rates. This study provides insightful information that will be useful for regulating food safety, facilitate strategic environmental monitoring to detect potentially harmful elements, and help regulate industrial pollution into marine ecosystems
NUTRITION EDUCATION INTERVENTION FOR PATIENTS LIVING WITH HIV ATTENDING AN OUTPATIENT CLINIC IN DUBAI, UAE
Get PDFNutrition is crucial to HIV/AIDS management. HIV affects the immune system, making infections and illnesses harder to resist. Despite the importance of nutrition for people living with HIV (PLHIV), there have been no studies of nutritional interventions for PLHIV in the MENA region. Aim: The objective of this study is to assess the baseline status of PLHIV and the impact of a lifestyle intervention on various aspects of their health and lifestyle status, including nutritional knowledge, attitude and practices (KAP), and intake of immune-boosting food/nutrients. Methods: Sixty-three patients attending an outpatient clinic in Dubai were randomly assigned to an intervention group (n=31) or a control group (n=32). Baseline data was collected from August to November 2023. The intervention group took part in an individualized 6-session nutrition education program guided by the Health Belief Model (HBM), while the control group received usual care and an educational manual and a brochure on HIV nutrition and health. Both groups completed nutrition, physical activity, and mental health questionnaires at baseline and after the intervention period. A questionnaire adapted from the literature was used to assess nutrition-related knowledge, attitudes, and behaviors (KAP). Dietary intake was measured using a food frequency questionnaire and two non-consecutive 24-hour dietary recalls to assess intake of immune-boosting nutrients. Participants\u27 medical records provided biochemical data, weight, height, and physical activity were measured using the Exercise Vital Sign questionnaire. The Hospital Anxiety & Depression Scale (HADS) was used to screen for anxiety and depression. Results: Significant differences in the KAP score distribution between the control and intervention groups were observed for knowledge, attitude, and practices post-intervention (p-value
THE CELLULAR TRAFFICKING AND TARGETING OF ANGIOTENSIN-CONVERTING-ENZYME-2 (ACE2) AND NEUTRAL-AMINO-ACID-TRANSPORTER (B0AT1) VARIANTS: IMPLICATIONS FOR THE PATHOGENESIS OF ASSOCIATED DISEASES AND THERAPY
Get PDFAngiotensin-converting enzyme 2 (ACE2) and the amino acid transporter B0AT1 are essential for blood pressure regulation, amino acid absorption, and viral entry. Their interaction is crucial in both normal physiology and disease, including hypertension, Hartnup disease, and SARS-CoV-2 infection. However, the effects of genetic variants on their biogenesis, trafficking, and function remain poorly understood. This PhD thesis investigates how specific ACE2 and B0AT1 variants influence their subcellular localization and interactions. A multidisciplinary approach, incorporating site-directed mutagenesis, confocal microscopy, Western blotting, and in silico modelling, was used to examine 39 ACE2 variants and 18 B0AT1 mutations. Results showed that wild-type ACE2 reaches the plasma membrane within 10 hours. While most genetic variants had no major impact on intracellular trafficking or membrane targeting, disruption of the signal peptide completely blocked trafficking. Drug screening revealed that ACE2 maturation is generally rapid and robust. Of 23 tested compounds, 8 significantly reduced ACE2 maturation levels, with 3 causing an approximate 20% decrease. Screening of trafficking inhibitors demonstrated strong effects from most molecular modulators, mild effects from proposed COVID-19 drugs, and no effects from statins. Given that altering ACE2 levels can be beneficial or detrimental depending on the context, therapeutic modulation requires careful evaluation. For B0AT1, 9 of 18 Hartnup disease-associated variants led to endoplasmic reticulum retention, thereby affecting ACE2 trafficking. Notably, two mutations significantly impaired ACE2 plasma membrane targeting. This study deepens our understanding of ACE2 and B0AT1 biogenesis and their roles in disease, with potential implications for therapeutic development and biomarker discovery. In conclusion, this research confirms that while ACE2 is intolerant to loss of function, it is prone to aggregation with B0AT1. By identifying key variants and modulators, these findings contribute to potential targeted therapies. Future studies should further explore the clinical relevance of these insights and investigate additional molecular modulators to mitigate disease progression and improve patient outcomes