Cancer is a complex and dangerous disease in which cells uncontrollably begin to grow. Some cells, with mutated genes, cause abnormalities in the cell. These abnormalities are transferred to other genes through specific interactions between genes, leading to disruptions in the normal function of cells. The result of these cell abnormalities will be the occurrence of cancer. In cancer, modules are considered as clusters of genes and regulatory molecules that play a role in the processes of cancer initiation and progression. These modules usually have a specific gene sequence as a central unit that is important in controlling and regulating cellular processes related to cancer.In this study, a novel network-based method called mdGRN is proposed for identifying modules effective in lung cancer occurrence in the gene regulatory network. In this method, first, using gene expression data and regulatory interactions, a lung cancer regulatory network is constructed. Then, using a greedy Modularity optimization approach, communities related to lung cancer are identified. Subsequently, the obtained communities are ranked using influence diffusion metrics in the network. Finally, the top-ranked communities are introduced as effective modules.To assess the efficacy of the proposed method, the standard Cancer Genome Atlas (TCGA) database and four classifiers including a decision tree, k-nearest neighbors, support vector machine, and random forest were utilized. The results obtained demonstrated that the proposed mdGRN method outperforms other methods in identifying cancer modules in terms of the average harmonic mean metric with the support vector machine classifier. Additionally, in terms of the AUC metric, the proposed method achieved a value of 0.997 using the random forest classifier, indicating better performance compared to other previous methods in identifying cancer modules. Furthermore, the number of genes identified by the top module is compared with other previous computational and network methods. The results show that the top-ranked module, besides containing a considerable number of driver genes, contains unique genes that have not been identified by other methods.

Community detection in dynamic social networks is one of the most important research topics that has been considered in recent years. There are various approaches to detecting communities in dynamic social networks, among which the label propagation approach has chosen due to simplicity and efficiency. This approach involves many methods that are often random. Among these methods, LabelRankT(Time) is a deterministic method. Of course, this method also has problems, one of the problems is that when a node wants to join a community, the internal structure of that community is not considered. So, for solving this problem, a greedy approach has been used to improve the label publishing approach. The proposed approach and other evaluated methods are tested in real and Synthetic datasets. The results show that the proposed method has performed relatively better than the other methods in terms of accuracy and Modularity.

Massive Modularity Hypothesis of Mind assumes that the mind is composed largely, or perhaps even entirely, of modules. Since the publication of Modularity of the Mind (Fodor, 1983) Modularity has occupied a central role in the studies of mind and cognitive science. But in philosophy there is no agreement about this role. In this paper, first I propose that many of these problems are the effects of the Fodorian base of Massive Modularity Hypothesis, and then I suggest taking Simon’s works on complexity as a proper base for Modularity. In the last section of this paper, I consider two common characteristics of mind, i.e. domain- specificity and encapsulation, and argue that in shadow of Massive Modularity Hypothesis these characteristics can be explained better.

In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

Application of the network equivalent concept for external system representation for power system transient analysis is well known. However, the challenge to utilize an equivalent network, approximated by a rational function, is to guarantee the passivity of the corresponding model. In this regard, special techniques are required to enforce the passivity of the equivalent model through a post processing approach that minimizes its impact on the original model characteristics. In this paper, the passivity is enforced by expressing the problem in terms of a convex optimization problem that guarantees the global optimal solution. The convex optimization problem is efficiently solved by recently developed numerical interior–point methods. This passivity enforcement is also global which indicates that the passivity enforcement in one region does not lead to passivity violation in other regions.

Abstract. In this paper, we have introduced and studied the notion of a fuzzy independent pair and obtain some properties of fuzzy α-modular pairs and independent pairs.

In this paper, relying on the clustering of complex networks that can determine large scale features of ‎the network, we study 48 financial markets across the world. To this end, we develop a Modularity ‎maximization method for directed and weighted networks. According to the linear correlation measure, ‎we construct the adjacency matrix, and by using the theory of random matrices, we divide the space of ‎eigenvalues of our matrix into two irrelevant and relevant fragments. By considering the temporal ‎window and its evolution over time series, our results demonstrate that in the vicinity of so-called ‎financial crisis clusters, which are often affected by geographical characteristics, are formed and from the ‎perspective of complex networks, they show more random behavior‎.‎‎

In literature, for the four common classes of uninorms, the Modularity equation has been solved except for the kind of ones having continuous underlying functions. This paper is devoted to solving the Modularity equation involving two uninorms with continuous underlying functions.We discuss this Modularity equation in detail by dividing the main section into two parts. The structure characterization of the two uninorms is almost completely obtained and it is found that they are equal in the unit square except in a subdomain.

The focus of this paper is to investigate the Modularity equation involving uninorms and Mayor's aggregation operators.  Necessary and sufficient conditions are established for this equation. And, it finds that theModularity equation of a Mayor's aggregation operator over a uninorm is reduced to the Modularityof a commutative semi-t-norm over a uninorm and the Modularity equation of a uninorm over a Mayor's aggregation operator is reduced to the Modularity equation of a uninorm over a commutative semi-t-conorm. Among them, the cases of a uninorm which is locally internal on the boundary are studied in \cite{Su_Riera_Aguilera_Torrens_2019}. In this paper, we consider whether the neutral element $e$ of the uninorm is idempotent element of the Mayor's aggregation operator in Modularity equation to get solutions in the corresponding cases.