United Arab Emirates University
United Arab Emirates University: Scholarworks@UAEU / جامعة الاماراتNot a member yet
5830 research outputs found
Sort by
EXPLORING FAMILY-FRIENDLY POLICIES IN THE UAE
No full textBackground: Family-friendly policies (FFPs) have become a critical element in global debates surrounding work–family balance (WFB), organizational performance, and gender equity. In the United Arab Emirates (UAE), rapid legal reforms, including Federal Decree–Law No. 33 of 2021, the introduction of paid parental leave, expanded maternity protections, and increased recognition of flexible and remote work, reflect a national commitment to strengthening family well-being.Yet, despite these advancements, many working parents continue to experience tensions between regulatory intentions and workplace realities.
Purpose: This study examines the lived experiences of fourteen working parents in the UAE to understand how current FFPs and workplace arrangements facilitate or hinder family engagement. The research also explores the alignment and gaps between statutory frameworks, organizational culture, and employees’ personal experiences.
Methods: An Interpretative Phenomenological Analysis (IPA) was employed to explore how participants interpret and navigate their work–family environments. Fourteen semi-structured interviews were conducted across public, semi-government, and private sectors. The analysis followed IPA’s idiographic and interpretative stages, enabling deep insight into emerging themes related to parental leave, flexibility, workplace culture, gender norms, and sector-specific constraints.
Findings: The analysis revealed seven interconnected themes that shape how working parents in the UAE experience FFPs. Parents consistently reported significant work–family strain, emotional pressure, and feelings of guilt linked to long working hours and limited organizational flexibility. Despite statutory entitlements such as maternity leave, parental leave, breastfeeding breaks, and flexible work options, participants reported inconsistent implementation across sectors, with private-sector parents facing the greatest challenges. Mothers described substantial difficulties related to breastfeeding, early return expectations, inadequate facilities, and limited postpartum support, while fathers highlighted minimal leave, cultural barriers to involvement, and restricted access to flexibility. Participants across all sectors expressed a strong desire for flexible or hybrid arrangements, part-time options, and greater autonomy in managing schedules. Long working hours and high workloads contributed to emotional exhaustion, reduced parenting quality, and reliance on external caregivers, raising concerns about family cohesion and well-being.
Conclusion: The study provides the first UAE-based qualitative examination of FFPs using an IPA lens and highlights critical gaps in policy implementation, cultural norms, and workplace practices. It contributes recommendations for enhancing equity, strengthening organizational accountability, and aligning family policies with global best practices and national strategic priorities
EXPERIMENTAL AND COMPUTATIONAL STUDY OF SLOW CRACK GROWTH OF HIGH DENSITY POLYETHYLENE
No full textThis dissertation investigates the slow crack growth (SCG) behavior of High-Density Polyethylene (HDPE) under various mechanical and environmental conditions. The study combines experimental analysis and computational modeling to enhance the understanding of SCG mechanisms in HDPE, particularly in pressurized pipes and under the exposure to hydrocarbons. A novel Crack Layer (CL) theory-based SCG model is developed and validated through experimental data, offering a predictive framework for HDPE failure assessment. The main objective of this dissertation is to quantify and model the viscoelastic-viscoplastic behavior of HDPE under monotonic and cyclic loading conditions while addressing SCG kinetics in structural applications. The study explores the effects of strain rate, temperature (23°C to 95°C), and hydrocarbon exposure on HDPE’s behavior. Additionally, a viscoplastic constitutive model is calibrated to capture thermo-viscoplastic responses, enabling accurate predictions of material deformation and crack propagation.
To achieve these objectives, SCG experiments are conducted on stiff constant-K (SCK) specimens and pressurized HDPE pipes with circumferential and butt-fusion joint cracks. A parametric study examines the influence of key factors such as stress intensity factor, transformation energy, and crack front kinetics. Computationally, Green’s functions, thermodynamic forces, and time-marching simulations are utilized to extend CL theory for SCG modeling in HDPE components. Furthermore, a diffusion-assisted SCG framework is introduced to assess the plasticization effects of hydrocarbons on fracture behavior and lifetime predictions.
The study successfully validates the CL-based SCG models with experimental results, demonstrating their accuracy in predicting failure times, crack growth rates, and discontinuous crack jumps in HDPE pipes. Findings reveal that external circumferential cracks in thin-walled pipes (SDR \u3e 20) experience a 20–40% reduction in lifetime compared to internal cracks, highlighting the need for conservative design criteria. Additionally, hydrocarbon-induced plasticization accelerates SCG up to 5 times, significantly altering viscoelastic properties, including the glass transition temperature.
This dissertation makes important contributions to SCG analysis, computational modeling, and failure prediction of HDPE materials. Future work should focus on high-pressure in-situ testing and extended SCG validation to further refine HDPE lifetime predictions and ensure enhanced reliability in critical infrastructure applications
COLLABORATIVE NETWORK TRAFFIC MANAGEMENT STRATEGIES USING DISTRIBUTED REINFORCEMENT LEARNING AND LARGE LANGUAGE MODELS
Get PDFThe focus of this research is to explore collaborative network traffic management strategies using the Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs) approaches. It emphasizes exploring a new tool for addressing network traffic by utilizing Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs). This is achieved by utilizing self-organizing and self-directing techniques to optimize the network performance. Using the NF-TON-IOT dataset, various classifiers such as Random Forest, AdaBoost, C4. 5, Multi-Layer Perceptron (MLP), and SVM with an RBF kernel were tested for traffic classification and intrusion detection. Research recommends that DRL optimizes the complexity of the network by allowing agents to make decisions independently of the other agents; LLM optimizes the interaction between the agents in the network. By using the performance analysis, it has been explored that Random Forest and AdaBoost are more effective as compared to other classifiers that have been tested such as SVM with RBF kernel. However, the RBF kernel SVM has the drawbacks. The main drawback of the SVM RBF kernel is associated with its computational expenses and the longer training time, especially in datasets of sizeable amounts such as NF-TON-IOT. This is not scalable and inefficient for real-time network traffic management scenarios since fast adaptive responses are further needed.
The scalability restrictions of SVM with RBF kernel prevent it for real-time applications, while Random Forest and AdaBoost prove to be better than other classifiers in the traffic classification tasks. Additionally, the integration of DRL-LLM is shown to exhibit good adaptive behaviour and responsiveness under dynamic traffic conditions. This research presents a new flexible and extensible network traffic management framework based on the synergy between LLM\u27s communication capabilities and DRL\u27s autonomous learning to have a future-proof strategy for managing intelligent, scalable networks
المسؤوليَّة الجِنَائِيَّة عن الاتجار بالأعضاء والأنسجة البشريَّة في ضوء المرسوم بقانون اتحادي رقم (25) لسنة 2023 – دراسة مقارنة
Get PDFCriminal Liability for Trafficking in Human Organs and Tissues in Light of Federal Decree Law No. (25) of 2023 – “A Comparative Study”
The crimes of trafficking in human organs and the legal responsibilities they raise are among the most heinous, complex, and multidimensional criminal phenomena, and are considered the most dangerous sources of human rights violations, because they have taken an approach that violates the integrity, dignity, and humanity of the human body
The trade in human organs, as one of the forms of human trafficking crimes, which is an organized cross-border crime, has become a fast trade and has professional brokers who hunt down their victims from the poor of society and even persuade them to transfer any part of their bodies at the lowest price to one of the rich people in exchange for obtaining huge sums of money from this process without any effort.
This research aims to explain the truth about this criminal phenomenon and the crimes arising from it and related to it and the extent to which the UAE legislator has responded, in particular through the issuance of the UAE Federal Law Decree No. (25) of 2023 regarding donation and transplantation of human organs and tissues.
The study relied on the descriptive, analytical and comparative approach, which is based on collecting facts from their legislative sources, making a comparison between Emirati and Egyptian legislation, and extracting the legal principles and provisions related to the subject of the research in order to determine the extent of their compatibility and disagreement with the general rules
INVESTIGATION OF INNOVATIVE HEAT PIPES / FINS SYSTEM FOR DIESEL ENGINE COOLING
Get PDFEngine cooling is considered one of the most important factors that affect their efficiency and therefore their performance, so a new cooling method is proposed using both heat pipes and fins to improve the cooling efficiency of internal combustion engines and shall be examined theoretically and experimentally. A test rig has been designed and built using a specimen material with similar properties to the real engine materials with similar dimensions as the engine cylinder head / liner thickness. The experiment employed a specially designed testing apparatus that included a gas burner for consistent heating, a modifiable support structure, and an electric motor-powered air blower equipped with an intake wheel system to produce regulated airflow. Accurate temperature and airflow readings were guaranteed by utilizing a thermocouple and flow meter. To make the effectiveness of this research, a comparison between the cooling by fins alone and cooling the engine by fins built-in heat pipes (heat pipe manufactured inside the fins) total of 10 cylindrical fins, each with an internal diameter of 8mm filled with a working fluid and acting as a heat pipes. The heat pipes are filled with a suitable working fluid, selected based on its thermal properties. As the fluid evaporates in the evaporator section, it transports heat to the condenser section, where it condenses and releases the absorbed heat into the surrounding air, thereby facilitating efficient heat dissipation from the engine. The objective is to increase the heat flux that the specimen (or the engine cylinder head) can undertake without being over heated. This will enable the engine to produce more power without material-thermal failure. Measurements included the surface and internal temperatures of specimen material, cooling air temperatures and the heat flux from the engine specimen. Numerical simulation by ANSYS software to correlate between cooling flow conditions, geometry of fins and heat flux have been estimated for the solid fins. The possibility and feasibility of using the heat pipes to cool the engine have examined and presented
INVESTIGATION AND PREDICTION OF EXCESSIVE WATER PRODUCTION IN BOTTOM WATER-DRIVE NATURALLY FRACTURED RESERVOIRS USING MACHINE AND DEEP LEARNING
Get PDFNaturally Fractured Reservoirs (NFRs) are characterized by dual-porosity and dual-permeability systems, posing significant challenges in managing water production due to highly conductive fracture networks that facilitate rapid water migration from bottom aquifers, often bypassing oil stored in the matrix, thus resulting in early water breakthrough and water channeling phenomena. The main objective of this thesis is to develop and validate deep learning and machine learning models to predict water production, water breakthrough time (tbt), and ultimate water cut (WCult) in NFRs, thereby enabling more effective reservoir management strategies. This work also aims to evaluate the sensitivity of water behavior to key reservoir parameters and optimize recovery under uncertainty. To achieve this, a combination of reservoir simulation and different machine learning methods were used. Simulation experiments were conducted on various fracture-matrix configurations using dual-porosity/dual-permeability models, and a series of statistical designs (e.g., Box-Behnken design) were employed to efficiently explore the parameter space. Ensemble tree models, neural networks, and other ML models were trained and tested using the simulation results. The study reveals that AI models offer high predictive accuracy (R² = 0.99, RMSE \u3c 0.03) in forecasting both water breakthrough time and water cut trends. Additionally, the developed logistic and ML-based models effectively capture the non-linearity caused by the variations of reservoir parameters and their effect on water production dynamics. In effect, these models offer interpretable outputs suitable for real-time decision-making. This thesis makes a significant contribution by bridging the gap between numerical simulation and intelligent prediction models, providing a robust framework for understanding and managing early water channeling in NFRs. These findings not only enhance predictive capability in data-scarce environments but also offer practical solutions to one of the most persistent challenges in petroleum reservoir engineering
Inclusion of Sustainable Development Dimensions in First Grade Language Textbooks
No full textThe study aimed to identify the level of inclusion of sustainable development dimensions in the content of the Arabic Language book for the first grade in the primary stage of the first and second semesters in the Kingdom of Saudi Arabia. The study followed the descriptive methodology based on content analysis, and the sample consisted of the content of the 2021 edition of the first grade primary book in its various cognitive representations (units): texts, images, activities, evaluation. To answer the questions, a content analysis tool was prepared according to the dimensions of sustainable development, comprising a content analysis card with (13) sub-indicators. The results of the study revealed a variation in the distribution of the sub-dimensions of sustainable development in the content of the Arabic Language book, as the social dimension ranked first with a frequency of (451) out of the total frequency of (498), accounting for (90.56%). The environmental dimension ranked second with a frequency of (47), making up (9.44%) of the total, indicating that the content of the Arabic Language book focused more on the social dimension, followed by the environmental dimension, while the economic dimension was neglected, which is considered no less important than the two of them.
Keywords: content analysis, textbook, sustainable development, social dimension, environmental dimension
Constructing a Scale for Self-Disclosure among University Students (Short Version)
No full textThe current research aimed to develop a shortened version of the self-disclosure scale. The scale was applied to several samples to ensure the availability of psychometric properties. It was also applied to a stratified random sample consisting of (408) male and female students from Umm Al-Qura University to determine their level of self-disclosure. The results showed that the scale has good psychometric properties, as the exploratory factor analysis showed that the scale consists of (11) items that saturate one factor, and the percentage of total explained variance reached (52.1%). The results of the confirmatory factor analysis also showed the availability of good matching indicators, which confirms that the scale is unidimensional. The results indicated that the level of self-disclosure was low among male and female students, and the level was also low in the three directions of disclosure: close relationships, social relationships such as acquaintances from friends, and relationships with strangers. The results also concluded that there are statistically significant differences in self-disclosure in the two directions: social relationships with friends, and relationships with strangers, attributed to gender differences; in favor of males; However, the practical significance of the differences was small. Thus, the overall results indicate the validity and reliability of the self-disclosure scale for further application and use in other studies.
Keywords: scale, self-disclosure, students, universit
Implementation of High-Leverage Practices in Special Education by Teachers of Students with Disabilities
No full textUsing a sample of special education teachers (n=339), this study investigated how current special education teachers implement high-leverage practices with students with disabilities and identify the most- and least-implemented practices. This study also used regression analysis to determine if factors such as years of experience, disability category, school level, and caseload can predict the level of implementation. The study used an instrument containing 22 high-leverage practices that were updated in 2024 by the Council for Exceptional Children and the CEEDAR. The findings show that low-to-medium levels of implementation of the 22 practices. The most frequently implemented practices were establishing an organized learning Environment and using instructional Technologies. Practices that have lower levels of implementation were teaching cognitive and metacognitive strategies and conducting functional behavioral assessments. Results also showed that years of experience, disability category, and caseload are significant predictors of level of implementation. Teachers who teach students with high-incidence disabilities or have more years of experience have higher levels of implementation compared to those who teach students with low-incidence disabilities or are less experienced. School level appeared to be a nonsignificant predictor. In general, findings indicated the importance of using high-leverage practices as the core curriculum in teacher preparation programs.
Keywords: high-leverage practices, special education teachers, students with disabilities, effective teaching, evidence-based practice
INTEGRATING MICROBIAL COMMUNITIES WITH ABOVE- AND BELOW-GROUND CARBON AND MULTISPECTRAL SATELLITE DATA IN MANGROVE FORESTS
No full textMangrove ecosystems are vital blue carbon sinks increasingly threatened by human activities and climate change, especially in arid, hypersaline regions. This study examines Avicennia marina, the dominant UAE mangrove species, recognized for its significant carbon retention capability0. Its biomass and stability are influenced by sediment chemistry, and bacterial diversity and community structure which drive nutrient cycling and organic matter decomposition—key processes sustaining soil fertility and carbon sequestration. Remote sensing enables large-scale assessment of mangrove distribution and density, but single spectral indices often miss ecological variability in the fragmented UAE mangrove habitats. Combining multiple spectral indices with field data improves detection, monitoring, and understanding of mangrove resilience in these arid habitats. Nevertheless, few studies have integrated remote sensing with field and microbial data, a connection essential for comprehending the ecological and carbon dynamics of UAE mangroves. This study aimed to (1) develop high-accuracy digital maps of UAE mangroves using multispectral vegetation, soil, and water indices validated through field observations, and (2) explore links between below-ground bacterial communities, mangrove growth, sediment chemistry, and remote sensing indices. Landsat 8 imagery was processed to derive spectral indices, which were verified with field data from 15 mangrove sites. Tree height and diameter were measured to estimate age and biomass using allometric equations. Sediment properties, including pH, salinity, nutrients, and organic matter, were analyzed using standard methods. DNA extracted from sediments underwent 16S rRNA sequencing on the MGI platform, and microbial data were processed using QIIME2, DADA2, and VSEARCH, with statistical analyses performed in R. Remote sensing maps showed that UAE mangroves were mainly distributed in sheltered intertidal zones with carbonate-rich sediments. Multispectral indices effectively distinguished vegetation density, with moderate vegetation index values overall and the lowest in sparse stands. Water and soil indices exhibited strong inverse relationships with vegetation indices, highlighting their complementary sensitivities. Mangrove growth parameters were strongly correlated with age and biomass, indicating stands dominated by middle-aged trees—ranging from just over 2 years in Ras Al Khaimah to nearly 29 years in Abu Dhabi. Consequently, carbon storage was uneven, highest in Abu Dhabi, intermediate in Umm Al Quwain, and lowest in Ras Al Khaimah. Sediment salinity ranged from 5.96–31.1 dS/m, while both phosphorus and organic matter (0.18–2.37%) were consistently low. Alpha diversity was unrelated to mangrove growth parameters but showed a moderate positive correlation with soil organic matter, while the Scaled Shadow Index exhibited a weak positive association with Shannon diversity. Bacterial community mangrove sediments was primarily affected by soil organic matter, with vegetation structure affecting it through canopy density. Salinity, pH, and tree size showed no relationships with bacterial communities. Actinomycetota and Pseudomonadota dominated across sites, while sulfate-reducing and organic-matter-degrading families were most abundant in carbon-rich sediments. Overall, organic matter was identified as the key determinant of below-ground bacterial diversity and composition in arid mangrove ecosystems. Data also suggested a link between bacterial diversity and vegetation canopy cover derived from remote sensing. The multi-index approach demonstrated in this study provided a scalable framework for satellite-based monitoring and aligned with the objectives of the UAE-led Arab Satellite 813 project, a hyperspectral mission designed to support environmental mapping, land-cover analysis, and natural-resource monitoring across the Arab region