From transportation networks to complex infrastructures, and to social and economic networks, a large variety of systems can be described in terms of multiplex networks formed by a set of nodes interacting through different network layers. Network robustness, as one of the most successful application areas of complex networks, has attracted great interest in a myriad of research realms. In this regard, how multiplex networks respond to potential attack is still an open issue. Here we study the robustness of multiplex networks under layer node-based random or targeted attack, which means that nodes just suffer attacks in a given layer yet no additional influence to their connections beyond this layer. A theoretical analysis framework is proposed to calculate the critical threshold and the size of giant component of multiplex networks when nodes are removed randomly or intentionally. Via numerous simulations, it is unveiled that the theoretical method can accurately predict the threshold and the size of giant component, irrespective of attack strategies. Moreover, we also compare the robustness of multiplex networks under multiplex node-based attack and layer node-based attack, and find that layer node-based attack makes multiplex networks more vulnerable, regardless of average degree and underlying topology.

Composite dynamic surface control of hypersonic flight dynamics using neural networks SCIENCE CHINA Information Sciences 58, 070203 (2015); Neural control of hypersonic flight dynamics with actuator fault and constraint SCIENCE CHINA Information Sciences 58, 070206 (2015); Robust adaptive control of hypersonic flight vehicle with asymmetric AOA constraint SCIENCE CHINA Information Sciences 63, 212203 (2020); Adaptive neural control based on HGO for hypersonic flight vehicles SCIENCE CHINA Information Sciences 54, 511 (2011); An overview on flight dynamics and control approaches for hypersonic vehicles SCIENCE CHINA Information Sciences 58, 070201 (2015);. RESEARCH PAPER .

A new flight control law for unmanned aerial vehicles based on robust servo linear quadratic regulator control and Kalman filtering is proposed. This flight control law has a simple structure with high dependability in engineering. The pitch angle controller, which is designed based on the robust servo linear quadratic regulator control, is given to show the flight control law. Simulation results show that the pitch angle controller works well under noise-free conditions. Finally, Kalman filtering is applied to the pitch angle controller under noisy conditions, and the simulation results show that the proposed method reduces the influence of noise.

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Determining attribute weights is an indispensable step in multi-attribute decision-making problems, and it is also a top priority in the study of multi-attribute decision-making problems. Existing methods for determining attribute weights do not completely and effectively reflect the decision-maker's dependency preferences, which will result in unreasonable ranking results for decision-makers. To solve this problem, this article proposes a feature-weighted multi-attribute decision-making method based on Taylor expansion. The method uses the natural base and the eigenvalues of the matrix to construct the feature-weighted coefficients and weights; normalizes all the feature vectors of the matrix; and constructs a new weight vector. Combined with the example to analyze and verify, the method makes reasonable use of all decision information, which saves the decision time of decision-makers.

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