The Mamlouk of Today, the Sultan of Tomorrow "Mamlouks of Authority and their Legitimacy in Various Areas of the Islamic Caliphate"

The Mamlouk, those white aristocratic slaves, have been by some Islamic Kingdoms and states since the first century of Hijra – seventh century A.D The Mamlouk was brought as a child from various places and was sold Costly to be raised in wataks (barracks) a cording to the Islamic and chivalry laws in order to contribute to the security, protection, construction and evolution of the state. Thus, He was promoted along important and sensitive state posts, parents and educators recognized his promising future. Consequently, Mamlouk groups were named after their princes-leaders and kept some sacred links among themselves such as Ostanic place. What is more, these links could be inherited among Colleagues. He got more power and dignity which tempted him to intervene into the state's internal affairs. After being freed; he deserved a legitimized authority through his distinguished sense of leadership and achievements according to some people. Further move, power was taken by the Mamlouk Shajarat Al Door and her successors in Egypt in 648H-1250A.D when the Ayoubite family deteriorated. The authority went to the MamloukTatemish and his successors in 603H-1206A.D as the Ghrite family collapsed in India. Keywords: Mamlouk Islamic state,Authority, Caliphat

3D face recognition based on machine learning

3D facial data has a great potential for overcoming the problems of illumination and pose variation in face recognition. In this paper, we present a 3D facial system based on the machine learning. We used landmarks for feature extraction and Cascade Correlation neural network to make the final decision. Experiments are presented using 3D face images from the Face Recognition Grand Challenge database version 2.0. For CCNN using Jack-knife evaluation, an accuracy of 100% has been achieved for 7 faces with different expression, with 100% for both of specificity and sensitivity

Distance Learning in Emergencies: Social and Pedagogical Relations in the Context of the COVID-19 Pandemic

The current study aims to investigate social and pedagogical relations during the experience of distance learning in emergencies, in the context of the spread of the Covid-19 pandemic. The study identifies the impact of the transition towards this new system in rebuilding social and pedagogical links between professors and students through a case study at Ajman University, as one of the institutions of higher education in the United Arab Emirates. The study relied on the quantitative approach and the sample of the study consisted of (730) students selected in a simple random manner. The study found that most of the sample members had advanced infrastructure that would enable them to keep up with the transition to the distance learning system, and that the level of access to electronic tools and distance learning platforms and the ability to deal with them were high. The study also found that the distance learning system increases the level of interaction, discussion and communication between students and between students and teachers, which extends beyond the lesson period, as an attempt to replace direct interaction

Forecasting Spare Parts Demand Using Statistical Analysis

Abstract Spare parts are very essential in most industrial companies. They are characterized by their large number and their high impact on the companies' operations whenever needed. Therefore companies tend to analyze their spare parts demand and try to estimate their future consumption. Nevertheless, they face difficulties in figuring out an optimal forecasting method that deals with the lumpy and intermittent demand of spare parts. In this paper, we performed a comparison between five forecasting methods based on three statistical tools; Mean squared error (MSE), mean absolute deviation (MAD) and mean error (ME), where the results showed close performance for all the methods associated with their optimal parameters and the frequency of the spare part demand. Therefore, we proposed to compare all the methods based on the tracking signal with the objective of minimizing the average number of out of controls. This approach was tested in a comparative study at a local paper mill company. Our findings showed that the application of the tracking signal approach helps companies to better select the optimal forecasting method and reduce forecast errors

A systematic review of outcome and outcome-measure reporting in randomised trials evaluating surgical interventions for anterior-compartment vaginal prolapse: a call to action to develop a core outcome set

INTRODUCTION: We assessed outcome and outcome-measure reporting in randomised controlled trials evaluating surgical interventions for anterior-compartment vaginal prolapse and explored the relationships between outcome reporting quality with journal impact factor, year of publication, and methodological quality. METHODS: We searched the bibliographical databases from inception to October 2017. Two researchers independently selected studies and assessed study characteristics, methodological quality (Jadad criteria; range 1-5), and outcome reporting quality Management of Otitis Media with Effusion in Cleft Palate (MOMENT) criteria; range 1-6], and extracted relevant data. We used a multivariate linear regression to assess associations between outcome reporting quality and other variables. RESULTS: Eighty publications reporting data from 10,924 participants were included. Seventeen different surgical interventions were evaluated. One hundred different outcomes and 112 outcome measures were reported. Outcomes were inconsistently reported across trials; for example, 43 trials reported anatomical treatment success rates (12 outcome measures), 25 trials reported quality of life (15 outcome measures) and eight trials reported postoperative pain (seven outcome measures). Multivariate linear regression demonstrated a relationship between outcome reporting quality with methodological quality (β = 0.412; P = 0.018). No relationship was demonstrated between outcome reporting quality with impact factor (β = 0.078; P = 0.306), year of publication (β = 0.149; P = 0.295), study size (β = 0.008; P = 0.961) and commercial funding (β = -0.013; P = 0.918). CONCLUSIONS: Anterior-compartment vaginal prolapse trials report many different outcomes and outcome measures and often neglect to report important safety outcomes. Developing, disseminating and implementing a core outcome set will help address these issues

3D facial feature extraction and recognition : an investigation of 3D face recognition : correction and normalisation of the facial data, extraction of facial features and classification using machine learning techniques

Face recognition research using automatic or semi-automatic techniques has emerged over the last two decades. One reason for growing interest in this topic is the wide range of possible applications for face recognition systems. Another reason is the emergence of affordable hardware, supporting digital photography and video, which have made the acquisition of high-quality and high resolution 2D images much more ubiquitous. However, 2D recognition systems are sensitive to subject pose and illumination variations and 3D face recognition which is not directly affected by such environmental changes, could be used alone, or in combination with 2D recognition. Recently with the development of more affordable 3D acquisition systems and the availability of 3D face databases, 3D face recognition has been attracting interest to tackle the limitations in performance of most existing 2D systems. In this research, we introduce a robust automated 3D Face recognition system that implements 3D data of faces with different facial expressions, hair, shoulders, clothing, etc., extracts features for discrimination and uses machine learning techniques to make the final decision. A novel system for automatic processing for 3D facial data has been implemented using multi stage architecture; in a pre-processing and registration stage the data was standardized, spikes were removed, holes were filled and the face area was extracted. Then the nose region, which is relatively more rigid than other facial regions in an anatomical sense, was automatically located and analysed by computing the precise location of the symmetry plane. Then useful facial features and a set of effective 3D curves were extracted. Finally, the recognition and matching stage was implemented by using cascade correlation neural networks and support vector machine for classification, and the nearest neighbour algorithms for matching. It is worth noting that the FRGC data set is the most challenging data set available supporting research on 3D face recognition and machine learning techniques are widely recognised as appropriate and efficient classification methods.EThOS - Electronic Theses Online ServiceGBUnited Kingdo