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Cascading failures of loads in isolated networks under random failures or intentional attacks have been studied in the past decade. The corresponding results for interconnected networks remain missing. In this paper we extend the cascading failure model used in isolated networks to the case of interconnected networks, and study cascades of failures in a data-packet transport scenario. We find that for sparse coupling, enhancing the coupling probability can make interconnected networks more robust against intentional attacks, but keeping increasing the coupling probability has the opposite effect for dense coupling. Additionally, the optimal coupling probability is largely affected by the coupling preference. Finally, assortative coupling is more helpful to resist the cascades compared to disassortative or random coupling. These results can be useful for the design and optimization of interconnected networks such as communication networks, power grids and transportation systems.

Organic electrochemical transistors (OECTs) are successfully used for the detection of bacteria (E. coli O157:H7) in KCl electrolytes. The transfer characteristic of the OECT shifts to higher gate voltage after bacteria are captured on the active layer of the device, which can be attributed to the electrostatic interaction between the bacteria and the transistor. The OECT with a Pt gate electrode shows a voltage shift of up to 55 mV after the capture of bacteria. The influence of the ion concentration of the electrolyte on the device performance is also studied. It is expected that the organic transistors will find promising applications as disposable bacteria sensors.

Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.

Traffic congestion in isolated complex networks has been investigated extensively over the last decade. Coupled network models have recently been developed to facilitate further understanding of real complex systems. Analysis of traffic congestion in coupled complex networks, however, is still relatively unexplored. In this paper, we try to explore the effect of interconnections on traffic congestion in interconnected BA scale-free networks. We find that assortative coupling can alleviate traffic congestion more readily than disassortative and random coupling when the node processing capacity is allocated based on node usage probability. Furthermore, the optimal coupling probability can be found for assortative coupling. However, three types of coupling preferences achieve similar traffic performance if all nodes share the same processing capacity. We analyze interconnected Internet AS-level graphs of South Korea and Japan and obtain similar results. Some practical suggestions are presented to optimize such real-world interconnected networks accordingly. DOI:PACS number(s): 89.75. Hc, 89.75.Fb, 89.20.Hh,

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