Reliability of a k-out-of-n: G System Subjected to Marshall-Olkin Type Shocks Concerning Magnitude

In this paper the reliability of a k-out-of-n: G system under the effect of shocks having the Marshall-Olkin type shock models, is studied. The magnitudes of the shocks are considered. The system contains n components and only functions when at least k of these components function. The system is subjected to (n + 1) shocks coming from (n + 1) different sources. The shock coming from the it h source may destroy the it h component, i = 1, . . . , n, while the shock coming from the (n + 1)t h source may destroy all components simultaneously. A shock is fatal, destroys a component (components), whenever its magnitude exceeds an upper threshold. The system reliability is obtained by considering the arrival time and the magnitude of a shock as a bivariate random variable. It is assumed that the bivariate random variables representing the arrival times and the magnitudes of the shocks are independent with non-identical bivariate distributions. Since the computation of the reliability formula obtained is not easy to handle, an algorithm is introduced for calculating the reliability formula. The reliability of a k-out-of-n: G system subjected to independent and identical shocks is obtained as a special case, as well as the reliabilities of the series and the parallel systems. As an application, the bivariate exponential Gumbel distribution is considered. Also, numerical illustrations are performed to highlight the results obtained

Fine structure of the dorsal lingual epithelium in Tarentola annularis and Crocodylus niloticus

The present study examined the morphological features, histological and histochemical aspect of the tongue of two reptilian species, Tarentola annularis (family: Gekkonidae) and Crocodylus niloticus (family: Crocodylidea), with different habitats, feeding patterns and behaviours, by light and scanning electron microscope. It was observed, that the bifurcation of the tongue was more visible in Tarentola annularis. Conical and filamentous papillae were observed on the lingual body of Tarentola annularis, while in Crocodylus niloticus both mechanical filiform and gustatory papillae appeared. The lingual mucosa in Tarentola annularis is covered by stratified squamous epithelium and keratinised but in Crocodylus niloticus it is highly folded and more heavily keratinised in the folded region and have a localised thickenings structure resembling taste buds. Mucous glands appeared in Tarentola annularis and compound tubular glands in Crocodylus niloticus. At scanning electron microscopy, abundant microridges and microvilli in both species were exhibited on papillae surface facilitated feeding habits. Histochemically, the tongue of two species is strongly positive for carbohydrate stain but with variable degree with others stains. In conclusion, there is a marked correlation between the structure of the tongue of the present reptilian species, habitats and feeding mechanism of the two species.

Adaptive E-Learning Based on Learner's Styles

In this paper, a new model for adaptive e-learning based on learner's styles is presented. In the previous work, the dimensions of learner's styles given by Felder-Silverman did not consider some important issues of the learner himself. Here, new learner's parameters such as his social environment, health conditions, psychological and economical states are taken into account. Such parameters greatly affect the ability of student to learn and understand. Therefore, in order to perfectly recognize the ability of the student to be interactive in the leaning environment and accept information, new learner's styles are added to the dimensions of Felder-Silverman learning style model and our previous work [24]. The new proposed model is applied for logic gates and functions used in data encoding and computer networks. Such model presents suitable courses for each student in a dynamic and adaptive manner using existing database and workflow technologies. Furthermore, it is powerful, user friendly and easy to interpret. Moreover, it suggests a learning strategy and appropriate electronic media that match the learner’s preference

Solving The Problem of Adaptive E-Learning By Using Social Networks

This paper propose an enhanced E-Learning Social Network Exploiting Approach focused around chart model and clustering algorithm, which can consequently gathering dispersed e-learners with comparative premiums and make fitting suggestions, which can at last upgrade the collective learning among comparable e-learners. Through closeness revelation, trust weights overhaul and potential companions change, the algorithm actualized a programmed adjusted trust association with progressively upgraded fulfillments. Keywords: Relations, Adaptive E-Learning, Clustering , Social Network , E-learning ,  and Collaborative Learnin

Open Social Learning Network

This paper considers the affordances of social networking theories and tools to build new and effective e-learning practices. We argue that “connectivism” (social networking applied to learning and knowledge contexts) can lead to a reconceptualization of learning in which formal and non-formal learning can be integrated as to build a potentially lifelong learning activities to be experienced in “personal learning environments”. In order to provide a guide in the design, development and improvement both of personal learning environments and in the related learning activities we provide a knowledge flow model called Open Social Learning Network (OSLN) —a hybrid of the LMS and the personal learning environment (PLE)—is proposed as an alternative learning technology environment with the potential to leverage the affordances of the Web to improve learning dramatically and  highlighting the stages of learning and the related enabling conditions. The derived model is applied in a possible scenario of formal learning in order to show how the learning process can be designed according to the presented theory. Keywords: Open Social Learning Network OSLN, Learning Theory, Connectivism, Networked Learnin, Collaboration Technologies, Collaborative Learning and Relationship Classification.