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As social networks grow, they become more and more complex, and analyzing them becomes complicated. One way to reduce this complexity is to divide the network into subnets, which are also called communities. Dividing social networks into desirable communities can help analysts and experts to understand the behavior and function of the networks. Community detection in networks is a challenging topic in network science and various methods have been proposed for that. Modularity maximization is one of the state-of-the-art methods suggested for community detection. Modularity maximization is an NP-hard problem meaning that no polynomial-time algorithm exists that could solve the problem optimally unless P=NP. A group of approaches that could solve such problems is approximate algorithms. Identifying the influential nodes has many important applications in social networks. This technique could also be used in community detection. To maximize modularity, in this paper, we propose approximate algorithms based on identifying the influential nodes and their influence domain. We used the concept of scale-free networks to prove the approximate factor. Experiments on real-world networks show that the proposed algorithm can compete with the state-of-the-art methods of community detection algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a new single-stage power factor correction (PFC) converter is presented. In the input stage of this structure, a buck converter is used to correct the power factor. So, the voltage stress of bulk capacitor reduced, and one of the main problems in the single stage PFC converters is solved. Moreover, by employing a compensator circuit, the input current dead angle which is the main drawback of the existing buck type PFCs is obviated, thus the total harmonic distortion is very low. In converters with buck PFC, input current dead angle results in high THD. In the proposed converter, using an inductor coupled with the main transformer, this problem is solved. A part of input power is transmitted directly to the output that helps to improve efficiency. The proposed converter is analyzed and the validity of the proposed solution is proven in the form of theoretical analysis, simulation and practical results. [ABSTRACT FROM AUTHOR]

With the Penetration of renewable energies into power system, the influence of uncertainties in solving various problems in the field of power system operation has increased. One of the most important concepts in this field is optimal power flow, which, with the presence of uncertainties, cannot be modeled by definite methods, and should be revised based on applying the probabilistic approaches. In this paper, numerical methods including the Monte Carlo Simulation method and analytical methods including point estimation methods, internal point method and unscented transformation method are used to solve the POPF in an IEEE-118 bus system. The obtained results indicate that the methods based on point estimation are able to find the optimal points in less computational time than other techniques. This is mainly due to the limited points, which these methods need as the starting points. From another perspective, the magnitude changes in the voltage profile of the generation units are also more stable in the internal point method. Furthermore, in terms of the convergence rate, the internal point method is much faster than the Monte Carlo Simulation method. [ABSTRACT FROM AUTHOR]

In order to make good decisions and solve complex problems of society today, creativity is one of the basic aspects of education systems. The purpose of this paper was to examine the efficacy of creativity training using Lego on self-confidence and problem-solving ability of children (4-6 year old) in Yazd City. The research design was experimental with pretest and posttest with training and control groups. The population of this study was all of the children (4-6 year old) attending kindergartens of Yazd city. A total of 40 of them were selected by random multilevel sampling and assigned to the experimental and control groups (20 in each group). To assess the children's self-confidence and problem-solving ability a semi-structured clinical interview was used. Creativity training program using the Lego as an experimental factor in 9 sessions of 60 minutes in three times a week was conducted for the experimental group. The results of MANCOVA showed that the experimental group at the end of training had more self-confidence and problem-solving ability than the control group. It can be concluded that creativity training increases self-confidence and problem-solving of children. [ABSTRACT FROM AUTHOR]