United Arab Emirates University
United Arab Emirates University: Scholarworks@UAEU / جامعة الاماراتNot a member yet
5830 research outputs found
Sort by
التقويم الميلادي والتقويم الهجري في الشريعة الإسلامية والتشريعات الإماراتية ومدى إمكانية توحيدهما: دراسة تحليلية
Full text linkAbstract
Islam pays great attention to time. Based on time, the individual calculates his date of birth, age, and all the dates related to his life. On the same basis, the countries calculate the date of their inception, the date of their independence, the date of the revolutions that changed their paths, the history of the wars in which they were victorious, the date of birth or death of their founders, and other important events. In view of the importance of time in determining the timings upon which many of the Shari’a rulings are based and to explain the signs of the Almighty Allah in this universe, the Almighty has sworn in by its parts in the Holy Qur’an at the beginnings of several Surahs.
Time, in this sense, is the criterion on which the calendar is based, upon which, in turn, the time of enactment and the time of enforcement of legislations in the country is determined and on its basis the periods and dates are calculated. In this research, we will discuss the issue of the Gregorian calendar and the Hijri calendar in Islamic Law and the UAE legislations and investigate the possibility of unifying them or dispensing with one of them and being satisfied with only one calendar, especially since the UAE legislations in their entirety have adopted the Gregorian calendar as a basis for calculating the periods and dates stipulated therein, including the federal constitution of the state. ملخص البحث اهتم الإسلام بالوقت وبالزمن أيما اهتمام؛ فبناءً على الزمن يحسب الفرد تاريخ ميلاده وعمره وجميع التواريخ ذات الصلة بحياته. وعلى أساسه تحسب الدول تاريخ نشأتها وتاريخ استقلالها وتاريخ الثورات التي غيرت مساراتها وتاريخ الحروب التي انتصرت فيها وتاريخ ولادة أو وفاة مؤسسها وغير ذلك من الأحداث المهمة. ونظراً لأهمية الزمن في تحديد المواقيت التي تبنى عليها الكثير من الأحكام الشرعية ولبيان آيات الله تعالى في هذا الكون، فقد أقسم سبحانه بأجزائه في كتابه العزيز في مطالع عدة سور.
والزمن بهذا المفهوم يعد المعيار الذي يبنى عليه التقويم الذي يتمّ اعتماداً عليه تحديد وقت صدور ووقت نفاذ التشريعات في الدولة، والذي تحسب على أساسه المدد والمواعيد. في هذا البحث سنناقش مسألة التقويم الميلادي والتقويم الهجري في الشريعة الإسلامية والتشريعات الإماراتية. وسنبحث مدى إمكانية توحيدهما أو الاستغناء عن أحدهما والاكتفاء بتقويم واحد فقط خاصة أنّ التشريعات الإماراتية في مجملها اعتمدت التقويم الميلادي كأساس لحساب المدد والمواعيد المنصوص عليها فيها بما في ذلك الدستور الاتحادي للدولة
The Effectiveness of Video Modelling to Teach Asking Permission for People with Intellectual Disabilities in a Primary School
No full textThe study aims to measure the effectiveness of video modelling in teaching three students with intellectual disabilities in primary school the skill of asking permission. The sample of the study included three female students with mild intellectual disabilities, their ages ranged from 8 to 12 years. A Multiple probe design across participants has been used to verify the effectiveness of video modelling in teaching three female students with intellectual disabilities to ask permission. The study applied in a private primary school in the city of Riyadh. Specifically, the baseline procedures and the intervention and maintenance procedures were applied in one of the classrooms in the school. Video modelling consisted of two short clips, the duration of each clip is 12 seconds, with a different scenario and tools presented in each clip, and it was presented through a laptop. The results indicated that all students were able to acquire the target skill and achieve the required standard, and two students were able to maintain the skill. The results of social validity collected from school teachers indicate that the intervention can be easily used by teachers in the classroom environment.
Keywords: Intellectual disability, Adaptive behavior, Special education, Social skills, School environment, Video modeling
INVESTIGATING TEACHERS\u27 PERCEPTIONS ON THE EFFECTIVENESS OF SCHOOL INSPECTION IN PUBLIC SCHOOLS IN THE UNITED ARAB EMIRATES
No full textSchool inspection plays a significant role in ensuring quality education in UAE public schools. This paper investigates public school teachers’ perceptions of the effectiveness of school inspections in the United Arab Emirates (UAE) and examines the impact of teachers’ demographic factors on these perceptions. A total of 218 teachers across the UAE participated in the study. The research focused on four key domains within the School Inspection Perception Framework (SIPF): Perceived Effectiveness of School Inspections (PESI), Perceived Effectiveness of School Inspection Training (PESIT), Perceived Credibility of School Inspections (PCSI), and Perceived Usefulness of School Inspection Feedback (PUSIF). Data were collected using a structured, five-point Likert-scale survey distributed online via email to teachers in UAE public schools by the Emirates Schools Establishment (ESE). Findings indicate that teachers perceive the school inspection process as effective, credible, and beneficial for enhancing educational quality and professional development. However, the study found that gender and location significantly influenced teachers’ perceptions of the school inspection framework. Other demographic factors—such as qualifications, teaching positions, and years of experience—did not significantly affect perceptions in the examined domains. Finally, the study identifies areas for improvement, including clarifying inspection criteria and increasing access to school inspection training for teachers
DATA-DRIVEN MACHINE LEARNING APPLICATIONS FOR PREDICTIVE MODELING OF PETROCHEMICAL AND ECOFRIENDLY SYSTEMS
No full textTraditional experimental approaches in industrial processes, such as Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy, thermogravimetric analysis (TGA), and well-drilling operations, are often constrained by time, cost, and operational limitations. This research explores the application of data-driven Machine Learning (ML)-based predictive modeling to improve efficiency and reduce dependency on resource-intensive experimentation. The study develops ML models for three distinct processes: FTIR intensity prediction of bitumen thermal cracking products, thermal degradation of Medium-Density Fibreboard (MDF) using TGA data, and Rate of Penetration (ROP) prediction in petrochemical industry. Six algorithms: Linear Regression (LinReg), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Gradient Boosting Regression (GBR), Random Forest (RF), and K-Nearest Neighbors (KNN) were evaluated across multiple scenarios. The models were assessed using metrics such as the coefficient of determination (R²) and Root Mean Squared Error (RMSE) to ensure both accuracy and generalization capabilities. All computational modeling, including data cleaning, feature engineering, ML modeling and Bayesian Optimization (BO), was performed using Python.
Results show that ensemble models, particularly GBR and RF, consistently outperformed other techniques in predictive accuracy and generalizability. In the FTIR analysis, GBR achieved 99.65% accuracy under an 80/20 data split, while RF yielded 94.37% accuracy when trained on lower temperatures and tested on unseen high temperatures. For the TGA data, RF achieved 100% test accuracies in oxidation and pyrolysis under full dataset splits, while GBR maintained strong performance in extrapolative scenarios achieving 98.91% accuracy for oxidation and 99.67% for pyrolysis when trained on lower heating rates and tested on higher ones. In ROP prediction, the GBR model reached 96.2% accuracy, outperforming empirical models such as the Bourgoyne and Young (BY) and Bingham models. The findings emphasize the importance of data distribution in training/testing splits, particularly when extrapolating to high-temperature conditions.
This study demonstrates the transformative potential of ML in enhancing predictive accuracy across various industrial systems. The integration of Python-based modeling, scenario-driven analysis, and advanced hyperparameter tuning through BO establishes a versatile framework for data-driven optimization. These outcomes support the broader adoption of ML in petrochemical and environmentally focused industries, offering pathways toward more sustainable, efficient, and intelligent process management
HYBRID MULTI-LAYER SANDWICH BEAMS USING 3D PRINTING TECHNOLOGY WITH ENERGY ABSORPTION FEATURES
No full textThis study explores the use of additive manufacturing (AM) technology to create sandwich composite structures, focusing on the manufacturing process, testing, and anticipated impact. The rising cost of manufacturing prototypes using conventional methods has led to the exploration of 3D printing as a viable alternative. The research problem addresses the challenges in manufacturing sandwich composites and the need for automation across the civil, mechanical, aerospace, and aviation industries. The study aims to investigate 3D-printed sandwich composite structures and compare experimental, finite-element analysis, and theoretical results. The proposed methodology includes preparing a CAD model with five different core infill patterns (Cross 3D, Honeycomb, Lines, Gyroid, and Grid) and three core infill percentages (100%, 40%, and 20%). For the face sheets, seven materials were selected for testing. In addition, 3D printing the specimen, inspection, conducting finite element analysis, preparing the specimen for testing, performing a 3-point bending test, data collection, and theoretical calculations. Seventy-seven samples were tested, and it was concluded that as TPU core density decreases from 100% to 20%, the structural response transitions from face-dominated peak strength to core-controlled energy absorption. Grid and Line infill achieve the highest load transfer and stable plateaus at moderate densities. PET and PA-GF faces deliver the most efficient energy absorption. Experimental results align with theoretical and FEA trends but show slight discrepancies due to interface slip and delamination. Overall, optimizing face-core bonding and selecting infill patterns based on load or energy absorption requirements can significantly enhance performance efficiency in lightweight structural designs
STARTUP SUCCESS FORECASTING THROUGH MACHINE LEARNING: A COMPREHENSIVE ANALYSIS OF IT STARTUPS
No full textLately, startups attracted significant attention from investors throughout the previous years. This raised several questions concerning startups and what they possibly define as them. It could refer to collective individuals who focus on innovative ideas with a reproducible and scalable business model; others refer to it as a newly established business. Nevertheless, all these definitions lead to a predictive question. Will these startups face success? This study explores startup success prediction methods, focusing on forecasting information technology startup (SIT) insights using Machine Learning (ML) models such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). Benefiting founders, investors, capital ventures & policymakers. These research findings will point to several startup prediction enhancement methods to increase success probabilities. Startups are crucial for innovation support, community productivity, and economic growth. However, newly established startups face one of the most controversial challenges, which is existence. The research aim is to point arrows at main success parameters to overcome this challenge. With the help of previously conducted research studies. Analysis will be implemented, presenting essential features such as funding amount, funding status, operating status, and number of founders. Additionally, suitable criteria, such as achieving a merger and acquisition or reaching Series B funding, will be considered. Effective classifiers for productively forecasting SIT success insights will also be explored. Furthermore, key solutions to previous challenges and effective characteristics contributing to startup success are identified through a literature survey
EXPERIMENTAL INVESTIGATION: PERFORMANCE OF RANQUE–HILSCH VORTEX TUBES UNDER VARIOUS SURFACE BOUNDARY CONDITIONS
No full textThe Ranque–Hilsch vortex tube (RHVT) is a passive thermofluid device that splits compressed gas into simultaneous cold and hot streams through strong swirling flow inside a slender tube, without moving parts. This study experimentally examines how external boundary conditions applied to the tube wall influence RHVT performance. Three wall conditions were imposed: (i) externally cooled (cooling), (ii) externally heated (heating), and (iii) adiabatic (insulated). Tests were conducted with air over an inlet pressure range of 2–6 bar. The primary objective was to understand the extent to which wall thermal condition modifies temperature separation and energy efficiency. Performance was quantified using the refrigeration coefficient of performance (COPc), together with measurements of cold-end and hot-end air flow stream temperatures. Compared with the baseline unconditioned case, externally cooling the wall consistently produced the lowest cold-end temperatures across all pressures and the highest refrigeration performance. At a tube wall setpoint of 25°C, the refrigeration coefficient of performance (COPc) improved by ~89% on average over 2–6 bar, with the maximum average gain at 4 bar (~109%); gains increased monotonically as the wall setpoint was reduced from 45 to 25°C. Insulation yielded a slight but robust uplift (mean +4.24% in COPc across 2–6 bar, peaking at +5.3% at 3 bar). By contrast, heating the wall reduced temperature separation and depressed COPc, with penalties that grew with the tube wall temperature setpoint. These findings highlight the sensitivity of RHVT behavior to heat transfer at the wall and suggest that controlled external cooling can be an effective lever for improving cold-stream performance in practical applications. The results provide guidance for the design and operation of vortex-tube-based spot cooling and process temperature control, and they motivate further work to resolve optimal combinations of pressure, cold mass fraction, and wall condition under various operating parameters
PHYSICOCHEMICAL AND STRUCTURAL OPTIMIZATION OF DATE (Phoenix dactylifera L.) SYRUP: IMPACT OF EXTRACTION TEMPERATURES AND PECTIN ADDITION
No full textThis 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 21 °Brix at 90°C) and phenolic content (6.45 to 34.93 μg GAE/mL) of date juice, while reducing soluble dietary fiber at higher temperatures (4.36% at 50°C to 2.98% at 90°C). Rheological analysis revealed optimum structural strength especially in 75°C extracted date juice, with storage modulus (G′) reaching 13.85 Pa, whereas juice at 90°C exhibited weakened gel networks. Pectin incorporation significantly enhanced the syrup rheological properties, where pectin added syrups showed more gel-like properties compared to non-added pectin. LMP retained up to 50% moisture, increased water activity to 0.91, and lowered the browning index, while HMP enhanced viscosity and improved thermal stability. Hydroxymethylfurfural (HMF) was not detected in any of the samples, confirming the safety of the process. These findings provide new insights into the optimization of conventional date syrup production, highlighting pectin as a promising hydrocolloid to improve stability, structural integrity, and functional quality of syrup in food systems
NUMERICAL MODELING OF CIRCULAR REINFORCED CONCRETE COLUMNS CONFINED WITH FRCM COMPOSITES
No full textFabric-reinforced cementitious matrix (FRCM) composites are considered a promising alternative for strengthening reinforced concrete (RC) columns due to the noncorrosive nature of the fiber strands and the enhanced thermal resistance of the cementitious mortar. The interaction between the confinement provided by the internal steel ties and the external FRCM composites represents a key parameter that has not yet been thoroughly investigated. The complexity of this combined FRCM–steel confinement mechanism increases under eccentric compression loading, which is commonly encountered in practical applications. This study aimed to investigate the structural behavior of short circular RC columns confined with FRCM composites under concentric and eccentric compression through numerical simulation. Three-dimensional (3D) finite element (FE) column models were developed with the adoption of constitutive laws capable of capturing the nonlinear behavior of the constituent materials. The FE models were validated against published experimental data, showing a margin of error within 10% for the peak load. A comprehensive parametric study was carried out to assess the influence of key parameters on the structural performance of FRCM-confined short circular RC columns under different loading conditions. The effects of load eccentricity and the presence of internal steel ties on the confinement mechanism were identified. A realistic stress–strain model for concrete confined with dual confinement provided by transverse steel hoops and FRCM wraps was developed. The proposed model produced predictions that differed from those of the adopted reference model by no more than ±2.5%. Interaction diagrams predicting the strength of FRCM-wrapped columns were introduced. Design recommendations were provided to guide practitioners and researchers in the design of circular RC columns confined with FRCM composites under both concentric and eccentric compression
The Mediating Role of Intrinsic Motivation in the Relationship Between Corporate Entrepreneurship and Entrepreneurial Behavior in State Owned Entities in the UAE
No full textThere has been growing interest in corporate entrepreneurship in academia and practice. A key focus is on promoting it and increasing entrepreneurial behavior in organizations. The pervasive narrative in the current literature is that employees will behave more entrepreneurially if they perceive their organization as entrepreneurial, suggesting a direct causal relationship. This study aims to examine the link between an organization’s corporate entrepreneurship and its employees’ entrepreneurial behavior, and the role of intrinsic motivation in that link. Specifically, by drawing on Self Determination Theory and Amabile’s model for creative performance as a theoretical framework, the study posits that despite the literature’s suggestions, that the relationship between the perception of an organization’s corporate entrepreneurship and the employees’ entrepreneurial behavior is mediated via intrinsic motivation. Therefore, a novel conceptual model was developed and primary quantitative data was collected from the public sector in the UAE, including state-owned entities, via questionnaires. Overall, a sample of 99 respondents was used to perform path analysis (i.e. PLS SEM). The results of the analysis found that intrinsic motivation is a significant mediator in the relationship between perception of corporate entrepreneurship and entrepreneurial behavior, although the effect was complementary partial mediation. The study makes several contributions and implications academically and practically. On the academic side, the study validates many of the theories and models developed in western environment in the context of public organizations in the UAE, a unique and intriguing context that has been largely overlooked in academia. Furthermore, the theoretical framework highlights the significant role of intrinsic motivation in driving behavior in public organizations. On the practical side, the study enhances the working model in the public sector, specifically in the UAE. Also, the results help direct organizations’ focus to effectively enhance entrepreneurial behavior among their employees by focusing on intrinsic motivation