United Arab Emirates University
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HYBRID TREATMENT METHOD OF BIOFILTRATION PRECEDED WITH ADVANCED OXIDATION PROCESS FOR THE REMOVAL OF EMERGING CONTAMINANTS FROM THE WASTEWATER TREATMENT PLANT EFFLUENT
Full text linkRenewable energy systems face significant challenges in ensuring stability, efficiency, and The global demand for potable quality water has been on a continuous rise. Consequently, water reuse is now considered as an alternative to significantly expand supplies of freshwater in communities facing water shortages. Most of the conventional wastewater treatment plants (WWTP) are not efficient in the removal of emerging contaminants from the water, which will cause serious issues to the environment, human and animal life. This research investigated the possibilities of treating wastewater for reuse. First, the feasibility of utilizing a biofiltration system without pre-treatment for removing emerging contaminants from the WWTP effluent was investigated. Two types of biofilter media- activated carbon (AC) and expanded glass (EG) were explored. Second, a hybrid treatment system, in which an advanced oxidation process (AOP) was provided as the pre-treatment to the biofiltration system. The adopted AOP for this study was UV/TiO2 photocatalysis. The conditions adopted for the biofilters were 15 minutes EBCT with a flow rate of 2.9 mL/min and for the photocatalysis pretreatment 1g/L TiO2 dosage, 60 minutes HRT, and 365nm UV lamps. Solid-phase extraction was performed for the contaminant extraction from the samples, which were analyzed using gas chromatography-mass spectrometry. It was found that 4 emerging contaminants were detected- dibutyl phthalate (DBP), phenothrin (PHN), malathion (MAL), and 9-aminoacridine (9-AA) at μg/L level only. Activated carbon removed 19.2% of TOC; removals increased to 60.2% when preceded by UV/TiO2 photocatalysis. AC was found to perform better than EG biofilters for TOC, DBP, PHN, and MAL removal in both individual and hybrid systems. EG biofilters showed better removal in both biofiltration-only and hybrid systems for the 9-AA than the AC biofilter and AOP-only systems. Both biofilters demonstrated similar nutrient removal and improved performance by the application of AOP pretreatment. In general, this investigation indicates that the integration of AOPs and biofiltration systems has the potential to address both emerging and conventional pollutants, as well as to facilitate water reuse
Criminal Protection of Genomic Data: A Comparative Analytical Study
Full text linkThis study aims to provide an analytical comparison of legal protection mechanisms for genomic data in the United Arab Emirates and the United States by examining the legislative frameworks governing this sensitive information. Genomic data is among the most private forms of personal information due to its significant use in medicine, insurance, and employment—necessitating robust legal protections against misuse and genetic discrimination. In the UAE, the research focuses on the 2021 Personal Data Protection Law, which outlines rules for handling genomic data and imposes penalties for violations. It also examines the UAE Human Genome Law, which regulates the collection and use of genetic information, emphasizing confidentiality and compliance with ethical and international standards. In the U.S., the study analyzes the Genetic Information Nondiscrimination Act (GINA), which prohibits the use of genetic data in employment and health insurance. The research assesses the law’s effectiveness amid rapid advancements in genetic science. The study concludes with a comparative analysis of both legal systems, identifying similarities and differences, and offers recommendations to strengthen legal frameworks and ensure comprehensive protection of individual genetic privacy and rights
Students’ Perceptions of Using Video as a Tool to Acquire Teaching Practices in Early Childhood Teachers’ Education Programs
Full text linkThis qualitative study sought to ascertain early childhood student teachers\u27 perspectives regarding the utilization of teaching videos, together with their associated advantages and disadvantages. Rich data was collected through reflective papers from 56 students and three focus groups (20 students). The participants were female student teachers enrolled in the Department of Early Childhood at King Saud University during the 2023–2024 academic year. The study concluded that the purpose of using teaching videos was limited to clarification and representations of practice. The findings also showed that the students’ role during video presentation revolved around watching, generating ideas and activities, and focusing on the video content. Furthermore, the data indicated that the lecturer\u27s responsibilities included facilitating discussions, posing questions post-viewing, translating content, and correlating videos with the course material. Several benefits of the instructional videos surfaced, such as enhancing self-confidence, clarifying courses’ theoretical content, improving practices related to interacting with and managing children\u27s behavior. The findings also revealed some disadvantages of teaching videos, such as poor image, sound, and content quality; a lack of correlation between the videos and course material; and cultural and social context discrepancies.
Keywords: Teacher education programs, early childhood, videos of practice, practice-based approac
EXPLORING THE FUNCTIONAL POTENTIAL OF BEETROOT AND GREEN APPLE POMACE: CHARACTERIZATION, POLYPHENOLIC EXTRACTION AND APPLICATION IN THE BAKERY PRODUCT
Full text linkPomace is a by-product from the juice processing industry that contains around 30-50% of the whole fruit or vegetable. The peel, pulp, seeds, and stem are still a rich source of nutrients, polyphenols, and bioactive compounds. This study was proposed to explore the potential of green apple pomace (GAP) and beetroot pomace (BP) for the extraction of polyphenolic compounds and fortification of bakery products. For a comprehensive characterization, the pomaces were processed into powders of three different particle sizes (≤250, 250-500, and ≥500 μm). The nutritional composition analysis concluded that GAP is a rich source of content (29.06 g/100 g), and BP is a good source of protein (11.45 g/100 g). The hydration properties and oil holding capacity (OHC) of BP outperformed the capacities of GAP. The color analysis showed that the lightness (L*) and yellowness (b*) values were higher for GAP, whereas the redness (b*) value was higher in BP, owing to its high betalain pigment content. Particle size distribution analysis of GAP across all particle sizes showed that it possesses a better proportion of finer particles than BP. The Fourier Transmission Infra-Red (FTIR) spectrum of GAP exhibited more evident peaks than BP. According to Differentia Scanning Calorimetry (DSC) analysis, BP formed less intense curves than GAP, indicating lower thermal activity. The Scanning Electron Microscopy (SEM) images of GAP exhibited a flaky and compact structure, while that of BP was fractured and loosely packed. The phenolic compounds were analyzed using homogenization-assisted water and acetone-based conventional extraction. Aqueous acetone extracts were more efficient in extracting polyphenols from GAP and BP, which also affected the polyphenol profiling, where many compounds could not be detected in the water extracts of both pomaces. The highest polyphenols recovered from GAP and BP were 7.16 and 1.56 mg GAE/g, respectively of particle sizes ≤250 μm. Further, the conditions for polyphenol extraction using Microwave Assisted-Extraction (MAE) and Ultrasound Assisted-Extraction (UAE)-coupled NADES were optimized. The extraction of polyphenols was carried out using lactic acid and fructose in the molar ratio of 2:1 and solid-to-solvent ratio of 1:40 at nine different time-power combinations for MAE and time-amplitude combinations for UAE. The highest Total phenolic content (TPC) was 14.48 mg GAE/ g and 19.27 mg GAE/ g in the NADES-MAE extracts of GAP and BP, respectively. While yield of UAE-NADES extraction was 30.92 mg GAE/g and 24.55 mg GAE/g TPC in GAP and BP, respectively. Ferric Reducing Antioxidant Power (FRAP) and 1,1-diphenyl-2-picrlthydrazyl (DPPH) values of MAE extracts of BP and GAP were the highest at 5 minutes and 0.8 kV, while that of UAE was at 6 minutes and 90% amplitude. The overall results suggested that MAE has better extraction efficiency for BP and GAP. Further, the pomaces and beetroot juice were utilized in the fortification of bread at varying concentrations (2.5%, 5%, 7.5%, and 10%). The nutritional composition resulted in the highest protein content of 8.39 g/100 g in the bread with only beetroot juice. The moisture content was more in GAP-enriched breads than in the control and BP-added breads. The color analysis showed a constant decline in the L* values in both pomaces added bread with an increase in the inclusion level. BP-added bread had the lowest L* values and the highest a* value was the highest in BP-added bread, but the b* values did not follow the general trend and varied across the samples. The specific volume of breads declined with an increase in pomace addition levels. Compared to control (2.49 cm3/g), bread with 10% GAP addition recorded the lowest volume (1.76 cm3/g). The texture analysis indicated that the hardness level increased with the pomace substitution, with the most hardness expressed by the highest pomace addition level of 7.5% and 10% in BP and GAP-added bread, respectively. TPC of bread significantly increased with the addition of pomaces when compared to control. GAP-added bread recorded the highest TPC of 3.33 mg GAE/g with a 10% addition. TPC of BP and juice-added breads were higher than those with only BP added and the highest value was 2.78 mg GAE/g with 7.5% addition. The FRAP and DPPH also followed the same trend as TPC, with the values increasing with the pomace addition level. In-vitro digestion of breads suggested that TPC was significantly affected by the digestion process. The values declined overall pomace levels in BP and GAP bread with much difference compared to un-digested samples, pointing out the incompatibility of polyphenols during digestive processes. This research concluded the potential of BP and GAP as a rich source of bioactive compounds when extracted using highly competent techniques. It can also be utilized as a sustainable fortification ingredient in foods. This study demonstrates the role of food-based research in reducing food waste, enhancing food product value, and contributing to circular economy practices within the food industry
DYNAMICS OF MANGROVE CARBON STOCK IN SELECTED UAE COASTLINE: A REMOTE SENSING APPROACH
Full text linkMangrove ecosystems play a vital role in carbon sequestration by capturing atmospheric CO₂, storing it in aboveground and belowground biomass, and enhancing soil organic carbon (SOC) through litterfall. Arid mangrove ecosystems, such as those in the UAE, experience extreme environmental stressors, leading to distinct variations in biomass allocation and carbon stock estimates compared to tropical regions. This study aims to evaluate biomass accumulation, litterfall production, and SOC storage in selected UAE mangroves. It analyses seasonal litterfall dynamics and assesses the influence of bulk density, soil moisture, and tidal regimes on SOC and aboveground biomass (AGB) across distinct mangrove zones. Field-based measurements, remote sensing techniques, and machine learning models were integrated to estimate mangrove tree biomass, litterfall production, and SOC across selected UAE mangrove systems. Litterfall was monitored monthly and analyzed in relation to vegetative indices, SOC was estimated across mangrove zones using ML models, and AGB was estimated through allometric equations with field data and used to find a relationship with remote sensing variables using ML models. Results revealed that remote sensed vegetation indices emerged as strong predictors of AGB. Litterfall production peaked during the summer, with predictive estimation achieved using remotely sensed vegetation indices and random forest regression models. SOC storage showed spatial variability, with inner mangrove zones exhibiting higher SOC content than water-edge mangrove zones and landward edge mangrove zones, highlighting the influence of hydrological and sedimentary processes on carbon sequestration. This study provides a region-specific approach for monitoring carbon stock dynamics in arid ecosystems, improving mangrove biomass and SOC estimations. The study enhances predictive capabilities for carbon sequestration in extreme environments by leveraging remote sensing and machine learning techniques. The findings directly impact mangrove conservation, afforestation programs, and climate mitigation strategies in arid coastal regions. The study addresses a critical gap in understanding the carbon sequestration potential of arid mangrove ecosystems, offering a replicable approach for estimating biomass and SOC under extreme climatic conditions. It highlights the necessity of tailored models for assessing carbon storage and informs sustainable management practices essential for preserving mangrove-based carbon sinks
TOWARDS SUSTAINABLE MANUFACTURING: A FRAMEWORK FOR RESOURCE CONSERVATION AND WASTE MITIGATION
Full text linkThe manufacturing industry drives economic expansion, but it also contributes significantly to environmental change and shortages of resources. Therefore, the transformation to sustainable manufacturing has become an urgent need, driven by worldwide efforts to address climate change, minimize waste, and protect valuable assets. This thesis develops a robust framework tailored to the manufacturing sector, focusing on resource conservation and waste reduction. The framework is built on the identification and categorization of Key Performance Indicators (KPIs) and assessment tools, which address five major implementation challenges: complex supply chains, regulatory barriers, economic challenges, technological limitations, and social and cultural challenges.To build the proposed framework a Modified Analytical Hierarchy Process (MAHP) is implemented to utilize expert opinions on ranking the challenges, and their corresponding KPIs and assessment tools, of implementing resources conservation and waste reduction in the manufacturing sector. The study offers actionable ideas on how to align the implementation of resource conservation and waste mitigation strategies with operational constraints. The findings emphasize the importance of incorporating sustainability standards into manufacturing practices to improve resource efficiency, reduce waste generation, and meet environmental regulations. Finally, the study offers targeted recommendations for policymakers, industry leaders, and researchers. These include fostering collaboration across supply chains, investing in advanced technologies, and enhancing awareness of sustainability practices. By addressing critical barriers to sustainable manufacturing, the study contributes to shaping a more environmentally responsible and efficient manufacturing sector
ANALYTICAL METHOD FOR SOLVING SYSTEMS OF FRACTIONAL INITIAL VALUE PROBLEMS USING THE MODIFIED ATANGANA-BALEANU DERIVATIVE
Full text linkThis thesis presents an analytical method to solve systems of fractional initial value problems using the Modified Atangana-Baleanu Fractional Derivative. The proposed approach leverages an improved operational matrix method, incorporating an iterative, direct computation technique to simplify the solution process and reduce computational costs. Foundational concepts in fractional calculus and block pulse functions are introduced, followed by the derivation of operational matrices tailored for fractional systems. Theoretical analysis establishes the existence and uniqueness of solutions, along with uniform convergence and error estimates. Practical examples and applications are presented that demonstrate the efficiency, accuracy, and versatility of the method in solving complex fractional problems. Both theoretical findings and numerical results validate the robustness of the proposed method, showcasing its potential to address a wide range of fractional systems efficiently
CONSTRUCTION OF STOCK PORTFOLIOS BY MACHINE LEARNING METHODS
Full text linkWe review the theoretical foundations and empirical workings of Long Short-Term Memory Networks (LSTM) and Random Forest for stock market prediction. To be precise, our work initiates with a deep-dive exploration in the mathematics of these algorithms which covers topics like gradient descent, automatic differentiation, and a bit on recurrent neural networks as well.An application creates stock portfolios through LSTM and Random Forest rules- based strategies, using either technical or fundamental indicators as input variables. The process involves predicting returns and ranking stocks accordingly to build portfolios with a certain level of diversification and annual rebalancing over a defined back-testing period. The paper then benchmarks the performance of such portfolios against market benchmarks like the S&P 500, with CAGR as the key metric to be calculated.The results of our study measure the ability of Machine Learning based strategies to outperform the traditional market indices and, in a broader sense, to provide more information on whether or not Machine Learning has the potential to aid in financial decision-making. Such results will, thus, have possible extensions for future research
EXPLORING FAMILY-FRIENDLY POLICIES IN THE UAE
No full textThe Family constitutes the fundamental pillar of societies, fostering stability and driving progress. However, contemporary families in the United Arab Emirates (UAE) face increasing challenges due to the growing participation of women in the labor market. While this shift reflects significant social and economic progress, it simultaneously intensifies the pressures on parents to reconcile professional and family responsibilities. Moreover, the scarcity of family-friendly policies (FFPs) exacerbates these challenges even further. This study explores the lived experiences of working parents with young children in the UAE, examining how existing workplace arrangements facilitate or hinder family engagement. A qualitative research design was employed, using in-depth interviews with parents working in public, semi-governmental, and private entities. Additionally, a review of existing UAE laws and policies was conducted to examine the policies that support work-life balance (WLB) for working parents and identify areas for reform. The analysis reveals that current policies and work arrangements significantly influence parents’ ability to maintain WLB. Findings highlight the need for flexible work structures and consistency in implementing FFPs across sectors. By identifying the policies most valued by parents and highlighting the gaps that persist, this research contributes to a growing body of knowledge on FFPs in the UAE. It emphasizes the importance of developing supportive frameworks that promote both parental well-being and family cohesion, ultimately advancing social stability and sustainable development. The study addresses a critical gap in the literature and provides evidence-based recommendations for enhancing family-friendly workplace practices across sectors
A COMPARATIVE ANALYSIS OF YOLO, SSD, AND R-CNN MODELS FOR SHIP DETECTION IN SATELLITE IMAGERY
No full textThe importance of rapid, reliable ship detection in satellite imagery is underscored by needs in maritime safety, environmental protection, and sustainable fisheries. In this study, a comparative assessment of three widely used object detectors—Faster R-CNN, YOLOv3, and SSD (300/512)—is presented to clarify how accuracy and speed are balanced for ship and dock detection. A unified pipeline (MMDetection) was employed so that model training, validation, and evaluation were standardized. ShipRSImageNet, a high-resolution dataset with COCO-style annotations, was used as the primary benchmark, while Airbus Ship Detection data were reformatted from masks to bounding boxes and standardized to COCO to ensure consistency. Performance was measured using COCO metrics (mAP@[0.50:0.95] with size-wise AP) and batch-1 latency to reflect realistic inference conditions.
In baseline experiments, superior accuracy was achieved by Faster R-CNN (mAP ≈ 55.1%), with particular gains observed on small vessels and cluttered scenes. Lower but competitive results were obtained by SSD512 (≈ 47.5%), YOLOv3-608 (≈ 45.5%), and SSD300 (≈ 42.1%). Speed advantages were exhibited by single-stage models: the highest throughput was recorded for SSD300 (≈ 73 images/s), followed by YOLOv3, SSD512, and then Faster R-CNN (≈ 32 images/s). Robustness was further examined under two constraints. When training was performed on down-scaled imagery and testing on full scale, smaller degradations were observed for Faster R-CNN and YOLO, whereas larger drops—most notably for SSD300—were recorded. Whenmodels were trained on only half of the data, greater resilience was demonstrated by YOLO,although the highest overall accuracy continued to be maintained by Faster R-CNN.From these outcomes, guidance is provided for deployment: where fine-grained detection andsensitivity to small targets are prioritized, Faster R-CNN is recommended despite higherlatency; where near-real-time operation on constrained hardware is required, YOLO and SSDare indicated, with YOLO favored for data-limited settings and SSD512 for stronger single-stage precision. The results are anticipated to support model selection and scalableintegration in maritime monitoring systems