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An integrated carbon-sulfur (CSG/PC) membrane with dual shuttle-inhibiting layers was prepared by inserting graphene "nets" and a porous carbon (PC) skin, and the membrane achieved an extraordinary cycling stability up to 1000 cycles with an average Coulombic efficiency of ∼100%.

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Developing high-performance electrode materials with high energy and long-term cycling stability is a hot topic and of great importance for sodium ion batteries (SIBs). In this work, a highly porous carbon/tin sulfide aerogel with a “skeleton/skin” morphology (SSC@SnS2) has been developed and further used as a binder-free anode for SIBs. This SSC@SnS2 electrode delivers a high specific capacity of 612 mA h g–1 at 0.1 A g–1, a good rate capability, and a long-term cycling stability up to 1000 times with an average Coulombic efficiency of ∼99.9%. Meanwhile, this SSC@SnS2 aerogel also achieves a stable cycling performance even at a high current density up to 5.0 A g–1. The fast-yet-stable sodium ion storage performance of the prepared SSC@SnS2 aerogel can be ascribed to the reasons that (i) the carbon nanofiber/graphene skeleton provides unimpeded pathways for the rapid transfer of electrons; (ii) thin SnS2 skin with nonaggregated morphology can provide a great number of active sites for sodium ion storage; (iii) the porous structure of the SSC@SnS2 aerogel ensures a rapid penetration of electrolyte and can further accommodate the volume expansion of active SnS2 nanoflakes; and (iv) the intermediate product of Na15Sn4 alloy contributes greatly to the sodium ion storage performance of the SSC@SnS2 aerogel. The excellent electrochemical performances coupling with the unique structural features of this SSC@SnS2 aerogel make it a promising anode candidate for SIBs.

As an emerging class of spatial trajectory data, mobile user trajectory data can be used to analyze individual or group behavioral characteristics, hobbies and interests. Besides, the information extracted from original trajectory data is widely used in smart cities, transportation planning, and anti-terrorism maintenance. In order to identify the important locations of the target user from his trajectory data, a novel division method for preprocessing trajectory data is proposed, the feature points of original trajectory are extracted according to the change of trajectory structural, and then important locations are extracted by clustering the feature points, using an improved density peak clustering algorithm. Finally, in order to predict next location of mobile users, a multi-order fusion Markov model based on the Adaboost algorithm is proposed, the model order k is adaptively determined, and the weight coefficients of the 1~k-order models are given by the Adaboost algorithm according to the importance of various order models, a multi-order fusion Markov model is generated to predict next important location of the user. The experimental results on the real user trajectory dataset Geo-life show that the prediction performance of Adaboost-Markov model is better than the multi-order fusion Markov model with equal coefficient, and the universality and prediction performance of Adaboost-Markov model is better than the first to third order Markov models.

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