A key performance metric in blockchains is the latency between when a transaction is broadcast and when it is confirmed (the socalled, confirmation latency). While improvements in consensus techniques can lead to lower confirmation latency, a fundamental lower bound on confirmation latency is the propagation latency of messages through the underlying peer-to-peer (p2p) network (in Bitcoin, the propagation latency is several tens of seconds). The de facto p2p protocol used by Bitcoin and other blockchains is based on random connectivity: each node connects to a random subset of nodes. The induced p2p network topology can be highly suboptimal since it neglects geographical distance, differences in bandwidth, hash-power and computational abilities across peers. We present Perigee, a decentralized algorithm that automatically learns an efficient p2p topology tuned to the aforementioned network heterogeneities, purely based on peers' interactions with their neighbors. Motivated by the literature on the multi-armed bandit problem, Perigee optimally balances the tradeoff between retaining connections to known well-connected neighbors, and exploring new connections to previously-unseen neighbors. Experimental evaluations show that Perigee reduces the latency to broadcast by 33%. Lastly Perigee is simple, computationally lightweight, adversary-resistant, and compatible with the selfish interests of peers, making it an attractive p2p protocol for blockchains.

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According to strong proponent of energy conservation and emission reduction, the development of new energy vehicles has been upgraded to the national strategic level in "Twelfth Five-Year Plan" period, and it becomes the main theme of Chinese automobile industry. And the planning of electric vehicle battery-switch station (BBS) has a great impact on the popularisation of electric vehicle and the security of power system. For reasonably planning of the location and sizing of the BBS, an optimization planning cost model and a new demand model of battery-switch station are proposed. This demand model is based on analysis of customers' characteristics. And in the optimization cost model, the objective function includes such variables as network loss cost, building cost and running cost of a particular battery-switch station, electrovalence and so on. Its constraints include the acceptable load power of the power system, the demand of electric vehicles, and the limitation of service territory of the BBS and so on. Finally, a practical example is carried out to demonstrate the performance of the demand model and the planning method.

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