The Sun is an external object that significantly impacts the earth's atmosphere and space weather conditions. Flares and coronal mass ejections are large-scale solar atmospheric features that mainly emerge at active regions above the sunspots and have influenced the earth. The sunspots, considered signatures of solar activity, are fascinating features related to the internal dynamics and activity of the Sun. The appearance of the sunspots in the photosphere shows the complexity of the magnetic field on the Sun. The frequency and size of sunspots change over time which show periods (e.g., eleven years periodicity) that may be a sign of the complex Sun. The time series of the sunspot numbers have been recorded for several centuries, and this time series is significantly changed over time. The complex network approach is a way to investigate the inherent property of complex time series, such as the sunspots time series.In this study, the growing complex network with the visibility condition is constructed using the time series of the sunspots (time and numbers) for 1922 to 2016 collected by SILSO. We compute the complex network parameters such degree of nodes, shortest path length, and clustering coefficients. We examine the sunspot complex network's scale-free, small-world, and assortative properties.We show that the degree distribution of the complex network for the time series of the sunspots obeys a power-law distribution function. We applied a method via maximum likelihood estimation in the Bayesian framework to obtain the power indicated. Therefore, the degree exponent is obtained larger than three, so the complex network for the time series of the sunspots is small-world and scale-free. The power-low behavior is an essential characteristic of self-organized or self-organized criticality systems. Limited productivity is a crucial property for these complex systems. The small word behavior indicates that the large sunspot numbers in the time series are clustered with several small values neighbors and linked with distinct large values in the time series.The small-world network represents a small characteristic path length with a high clustering coefficient. The scale-free and small-word behavior for the network of the sunspots time series may imply that the sunspots and sunspot groups forming via complex non-linear dynamics. Changing the magnetic polarity of the sunspots during the solar cycle can be a characteristic of such complex systems. The limited predictability in sunspots' time series, e.g., the intensity of activity within a solar cycle, may also be another sign of the complex Sun.The behavior of the degree of node distribution, clustering coefficient, and shortest path length indicates that the time series of sunspots is a non-random system. We showed that the degree of correlation is a function of the network size and can be considered as an assortative, dis-assortative, or neutral network.

Introduction: Carney complex is an autosomal dominant syndrome that is defined with different tumors including myxoma in different organs, endocrine tumors and lentiginosis lesions. This is the first case report of this syndrome from Iran.Case: The patient is a 27 year old girl, referred with flank pain. Physical examination revealed hirsutism, truncal obesity, hyperpigmantasion and hypertension; Cushing's syndrome was suggested and confirmed with related classic biochemical tests. She had history of cardiac myxoma during her childhood and had been operated twice.Pituitary microadenoma and right adrenal adenoma were reported on MRI and CT-scan, respectively. Initially laparascopic right adrenalectomy was done; as expected, no remission in signs of Cushing's syndrome was observed after surgery. By laparoscopic adrenalectomy of the other site, Cushing's syndrome resolved. Pathologic report of 1st operation was adrenal adenoma with surrounding pigmented micronodular hyperplasia and of the 2nd one was just pigmented micronodular hyperplasia.Conclusion: We have reported a patient with Carney syndrome along with Cushing's syndrome due to bilateral adrenal hyperplasia and an adenoma in contralateral adrenal and microadenoma of the pituitary as an incidentaloma. This is a new presentation of Carney syndrome.

This paper is an overview of studying the solar features in a complex network approach. First, we introduce the structural features of complex networks and important network parameters. Applying the detrended fluctuation and rescaled range analysis and nodes degree power-law distributions confirmed the non-randomness of the solar features complex networks. Using the HEALPix pixelization and considering all parts of the solar surface under the same conditions, as well as applying centrality parameters (the nodes with the highest connectivity, closeness, betweenness, and Pagerank) showed that the active areas on the solar surface were correctly identified and were consistent with observations. A review of the complex structure of the solar proton flux and active regions also showed that in these networks, the average clustering coefficient and Page-rank parameters are suitable criteria to use in event prediction methods. The complex network of sunspots has also shown that sunspots and sunspot groups are formed through complex nonlinear dynamics.

In every country, airports are among the most important air transport systems in that country. When an aircraft flies from one airport to another, it creates a graph that can be completed with information about each flight, such as the number of flights per path, the number of passengers, traffic load, and so on. In the present paper, the airports of Iran and the domestic flights are considered as a network and the structure of the network is analyzed, and then the measures of complex networks such as degree distribution, shortest path length, analysis of centralities, clustering coefficient and their correlation and the way these centralities behave are examined. This analysis shows the Iranian Airport network (IAN) that has a degree distribution described by the power function. The average path length in this network is 1. 9, and the average clustering coefficient is 0. 69, which meets the characteristics of a small-world network and is also considered an example of a disassortative network. The purpose of this research is to investigate the network of airports in Iran, which is ultimately important for the expansion of airports, and also to identify the important points of airports.

The analysis of complex networks become more popular through the easily access of huge network data resources in the last years. Researchers have developed techniques and models to help understanding and predicting the behaviour of complex network systems. This advanced analysis is not possible without proper softwares and tools. A large number of tools are available with specifi c features for analysing and visual-izing network systems and we can use a software or a set of suitable tools based on these features and capabilities for the project. Understanding the features of tools and softwares help to achieve better results from network analysis. In this paper,  first we review the structure of different types of networks. Based on Wenjun paper, the complex networks are divided into four categories: information networks; social networks; Biological networks and Technological networks [17]. Then we define some functional indicators including: Basic Functionalities, Graph type Support, File For-mats Support, Indicator Supports, Visualization Layouts Support, and Community Detection Support. In the next step, by using analytic hierarchical processing (AHP) and truly de nable criteria try to evaluate main complex network analysis (CNA) softwares. Eventually, an opportunity is provided using AHP to identify, understand, and evaluate completely four main CNA softwares objectively before identifying and selecting the most efficient CNA software.

In this study, an image retrieval system is proposed based on complex network model. Assuming a prior image categorization, firstly, a multilayered complex network is constructed between the images of each category according to the color, texture, and shape features. Secondly, by defining a meta-path as the way of connecting two images in the network, a set of informative meta-paths are composed to find the similar images by exploring the network. The established complex network provides an efficient way to benefit from the image correlations to enhance the similarity search of the images. On the other hand, employing diverse meta-paths with different semantics leads to measuring the image similarities based on effective image features for each category. The primary results indicate the efficiency and validity of the proposed approach.