Azarbaijan Shahid Madani University

About: Azarbaijan Shahid Madani University is a education organization based out in Tabriz, Iran. It is known for research contribution in the topics: Graphene & Nanocomposite. The organization has 1477 authors who have published 3186 publications receiving 30278 citations. The organization is also known as: Azarbaijan University.

The Relationship between Sleep Quality and Social Intimacy, and Academic Burn-Out in Students of Medical Sciences

Abstract: INTRODUCTION: Academic burnout leads to creation of a series of negative and scattered thoughts, loss of hope and emotional and physical exhaustion in carrying out activities. Two factors that affect academic burnout are sleep quality and social intimacy. This study was conducted in order to investigate the relationship between sleep quality and social intimacy, and academic burn-out in the students of Tabriz University of Medical SciencesMATERIALS & METHODS: This study was descriptive and correlational. The population of this study consisted of the students in Tabriz University of Medical Sciences and 196 medical students were selected. They completed Berso et al. Academic Burnout Questionnaire, Pittsburgh Sleep Quality Index (PSQI) and Miller Social Intimacy Scale (MSIS). The validity of the questionnaires confirmed by experts’ views. Their reliability were obtained as 77%, 64% and 85% for academic burnout, sleep quality and social intimacy questionnaires respectively by calculating the internal consistency (Cronbach’s alpha). For data analysis, descriptive statistics and Pearson correlation test, Regression, cluster analysis and t-test were used.RESULTS: The results showed that there was a positive and significant relationship between sleep quality and academic burnout at the level p<0.05 (r=0.38). There was a negative and significant relationship between social intimacy and academic burnout at the level p<0.05 (r= -0.40). Also, the regression results showed that sleep quality and social intimacy were able to predict 37% and 39% of academic burnout respectively. Moreover, the students were divided into two clusters of individuals with high social intimacy and individuals with low social intimacy. No significant difference was found between the two types in terms of the variable of academic burn-out.CONCLUSION: Based on the research results, it can be stated that the variables of sleep quality and social intimacy are the predictor factors of academic burn-out.

A three-stage algorithm for local community detection based on the high node importance ranking in social networks

Abstract: Community detection aims to discover and reveal community structures in complex networks. Some community detection method is called local methods that only apply local information in discovering steps. Local community detection methods are actually an attempt to increase efficiency in large-scale networks. Most of local community detection methods concentrate on finding the important nodes as initial communities. The quality of the detected communities fundamentally depends on the selected important nodes as community cores. Most of the existing works have disadvantages such as low accuracy, weak scalable, and instability in outcomes that makes the algorithm to detect different communities in each run. In order to solve these problems, this paper proposes a novel local community detection based on high importance nodes Ranking (LCDR). In the proposed algorithm, a new index for computing node importance is presented. With regards to the network locality, the proposed index can fully reflect the node importance of all nodes in the network. LCDR method initially selects important nodes to expand the initial communities based on a local similarity criterion until all nodes become members of one of the communities. Finally, it merges the discovered communities to form final community structures. Experiments on real and synthetic networks show that LCDR can significantly improve the accuracy of communities. Correspondingly, it is promising in different settings based on accuracy and modularity with near-linear time complexity.