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.

The emergence of the concept of "complex products and systems" began from the literature of military systems, the complexities of the system, the school of large technical system, project management and industrial organization studies. complex products which are technically manufactured by advanced companies, are technologically advanced capital goods; and investment for capacity building in production sector is one of the important landmarks of national economic power. In the area of complex products, industry structure is mainly composed of very small number of very large firms, suppliers, manufactures, and customers. These firms involve in the process of design, research and development, systems integration, further improvements of the product and maintenance. Now, economic developments require commercialization and development of complex technologies in which the innovation happens only through a complex network. So, one of the distinguishing features of these products compared with the simple ones in mass production is the difference in the related mechanism and innovation management.

Social networks are one of the types of complex networks. Identifying communities in social networks is an effective way to use their information, for which several algorithms have been presented so far. In this paper, novel algorithms are designed, in which a learning automaton is attached to each node; The number of actions of learning automata is fixed and equal to the estimate of the number of network communities. At each step, each of the learning automata chooses an action from its set of actions. Choosing any of these actions means assigning the label of that community to the node. The action chosen by each automaton is evaluated based on the chosen actions of its neighbors ((local attention) and/or communities detected by the entire method (global screening). The result of the evaluation leads to generate rewards or punish signal for the automata. By receiving a reward, the probability of re-choosing the chosen action by the automaton, or the community label, increases, and otherwise, by receiving a fine, the probability of this action decreases. By repeating the algorithm, the optimal action is determined as long as no change occurs in the selected label of the corresponding automata of each node with more iterations, and as a result, the optimal communities are determined as the output of the algorithm. The comparison of the results of the experiments shows the effectiveness of the proposed methods in comparison with the previous methods.

In recent years, the Internet has become part of the requirements of human life. With the widespread use of the Internet, the Web, and online social networks, the number of vulnerabilities and security threats has increased significantly. Various types of malwares (worms and viruses) have become a major threat to the security of systems and networks. In this regard, researchers are looking for ways to identify malware and fight against them. One of the methods used in this field is to model the malware propagation in order to identify and combat malware by modeling its behavior. In this article, a malware propagation model based on the propagation of epidemic diseases in a heterogeneous network structure, considering the devices connected to the network and the Internet, is introduced. Modeling is done based on the Susceptible-Exposed-Infected-Recovered epidemic model for devices and Internet networks. The results show that the speed of malware propagation in the proposed HD-SEIRS model is significantly reduced compared to the SEIR model. Also, in this article, the basic reproduction ratio (R_0 )  is calculated for the proposed model and the effect of parameter changes on the proposed model is investigated.

complex networks contain important structures to represent individuals' connections. In this research, the goal is to analyze the patterns of complex contagion considering current factors in network science and standard networks in this area. In this regard, essential factors in a network, such as nodes' degree, the structure of networks, and hubs in scale-free networks, are being analyzed. The result of this research demonstrates that the behavior of each network, considering the factors above, can be different from each others. Random networks have more arrangements within the changes of the number of infected nodes in each timeframe for nodes that get infected. However, scale-free networks have this property with the number of infected neighbors of each node independent of the time of contagion for the nodes of their time of getting infected.