Financial networks have become extremely useful in characterizing the structure of complex financial systems. Meanwhile, the time evolution property of the stock markets can be described by temporal networks. We utilize the temporal network framework to characterize the time-evolving correlation-based networks of stock markets. The market instability can be detected by the evolution of the topology structure of the financial networks. We employ the temporal centrality as a portfolio selection tool. Those portfolios, which are composed of peripheral stocks with low temporal centrality scores, have consistently better performance under different portfolio optimization schemes, suggesting that the temporal centrality measure can be used as new portfolio optimization and risk management tools. Our results reveal the importance of the temporal attributes of the stock markets, which should be taken serious consideration in real life applications.

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To address the issue that multiple missiles intercepting a highly maneuvering target simultaneously with desired angles, a novel cooperative guidance law based on the decoupled model in the line-of-sight (LOS) and normal LOS direction is proposed in LOS frame. However, there is control coupling in the two directions of the cooperative guidance model. In this article, we first decouple the guidance model and then separately design estimation decouple cooperative guidance law in two directions based on the decoupled model. The guidance law component in the LOS direction is designed with the integral sliding mode control and multi-agent consensus theory, where the combining disturbance is compensated by an adaptively estimated term. The guidance law component in the normal LOS direction is designed based on nonsingular terminal sliding mode control with fast power reaching law, and a nonlinear disturbance observer is introduced to estimate the combining disturbance. Then, the finite time convergence of time-to-go and LOS angle is proved. Finally, numerical simulation results demonstrate the effectiveness and superiority of the proposed cooperative guidance law.

In view of traditional point-line feature visual inertial simultaneous localization and mapping (SLAM) system, which has weak performance in accuracy so that it cannot be processed in real time under the condition of weak indoor texture and light and shade change, this paper proposes an inertial SLAM method based on point-line vision for indoor weak texture and illumination. Firstly, based on Bilateral Filtering, we apply the Speeded Up Robust Features (SURF) point feature extraction and Fast Nearest neighbor (FLANN) algorithms to improve the robustness of point feature extraction result. Secondly, we establish a minimum density threshold and length suppression parameter selection strategy of line feature, and take the geometric constraint line feature matching into consideration to improve the efficiency of processing line feature. And the parameters and biases of visual inertia are initialized based on maximum posterior estimation method. Finally, the simulation experiments are compared with the traditional tightly-coupled monocular visual–inertial odometry using point and line features (PL-VIO) algorithm. The simulation results demonstrate that the proposed an inertial SLAM method based on point-line vision for indoor weak texture and illumination can be effectively operated in real time, and its positioning accuracy is 22% higher on average and 40% higher in the scenario that illumination changes and blurred image.