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.

A High Sensitive TNT Sensor Based on Electrochemically Reduced Graphene Oxide‐Poly(amidoamine) Modified Electrode

Abstract: In this paper, the electrochemically reduced graphene oxide-poly(amidoamine) hybrid (ErGO-PAMAM) have been used for fabrication of TNT electrochemical sensor. The prepared modified electrode is characterized with X-ray photoelectron spectroscopy (XPS), fourier transform infrared spectroscopy (FT-IR), electrochemical impedance spectroscopy (EIS), energy-dispersive X-ray (EDX) spectroscopy, scanning electron microscopy (SEM) and atomic force microscope (AFM). Based on obtained results, it is can be seen that the ErGO-PAMAM/GCE has high response to TNT than the other graphene based modified electrodes. The resulting electrochemical sensor exhibited good response to TNT with linear range from 0.05 to 1.2 ppm with a low detection limit of 0.0015 ppm.

Two sequential fractional hybrid differential inclusions

Abstract: The main objective of this paper is to concern with a new category of the sequential hybrid inclusion boundary value problem with three-point integro-derivative boundary conditions. In this direction, we employ various novel analytical techniques based on α-ψ-contractive mappings, endpoints, and the fixed points of the product operators to obtain the main results. Finally, we provide two examples to illustrate our main results.

Identifying influential spreaders based on edge ratio and neighborhood diversity measures in complex networks

Abstract: In recent years, notable number of research studies have been conducted on the analysis of diffusion process in complex networks. One fundamental problem in this domain is to find the most influential spreader nodes. For achieving a successful spreading process, nodes having high spreading ability should be selected as spreaders. Many centrality measures have been proposed for determining and ranking the significance of nodes and detecting the best spreaders. The majority of proposed centrality measures require network global information which leads to high time complexity. Moreover, with the advent of large-scale complex networks, there is a critical need for improving accurate measures through using nodes’ local information. On the other hand, most of the formerly proposed centrality measures have attempted to select core nodes as spreaders but global bridge nodes have the highest spreading ability since they are located among several giant communities of the network. In this study, a new local and parameter-free centrality measure is proposed which is aimed at finding global bridge nodes in the network. Hence, two new local metrics, namely edge ratio and neighborhood diversity, are firstly defined which are used in the proposed method. Considering edge ratio of neighbors ensures that the selected node be not in the periphery location of the network. Furthermore, a node with high neighborhood diversity is likely a connector between some modules (dense parts) of the network. Therefore, a node with a high edge ratio and more diverse neighborhood has high spreading ability. The major merits of the proposed measure are near-linear time complexity, using local information and being parameter-free. For evaluating the proposed method, we conducted experiments on real-world networks. The results of comparing the proposed centrality measure with other measures in terms of epidemic models (SIR and SI), Kendall’s tau correlation coefficient and Rank-Frequency measures indicated that the proposed method outperforms the other compared centrality measures.