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We propose a new clustering method that uses a similarlity metric deived from electrical resistance networks. The proposed metric allows us to quantify the mutual relevancy between data objects or nodes. We show how to derive the metric from the data collection and how to apply it to various application contexts. Our theoretical analyses and experiments show the excellent potential of the method to identifying clusters of networks and to improving data clustering performance on a number of data sets.

Logistics is one of the earliest realized industries in Internet of Things (IOT), IOT technology plays a great role in promoting the development of logistics industry, changing the processes and management methods of supply chain, on reducing logistics costs and improving logistics efficiency as well. Starting from the concepts and the actual situation of the supply chain management. The article analyzed various aspects of the supply chain, and described the design of supply chain management system in intelligent logistics. At last, the trends and importance of the Internet of Things in intelligent supply chain logistics management was described.

Based on the high precision terrain datas , high resolution FVCOM(Finite-Volume Coastal Ocean Model) numerical model was established to simulation for tide current and study three-dimensional dynamic characteristics of the south of Radiation shoal waters. Result show that transverse circulation was easy to produce in channel and groove. The vorticity and helicity computation method was applied in the area for the frist time. Transverse circulation and helical flow characteristics of typical sections were preliminary studied, and result show the quantitative evidence for the existence of the Spiral flow.Deep groove is not always appear double reverse spiral flow structure, it changes with the main flow velocity. Shoal grooves appear, a main spiral flow structure, and show great adaptability to the terrain, which is the reflection of the spiral flow to the terrain maintain mechanism .

It is now well known that many large-sized complex networks obey a scale-free power-law vertex-degree distribution. Here, we show that when the vertex degrees of a large-sized network follow a scale-free power-law distribution with exponent  2, the number of degree-1 vertices, if nonzero, is of order N and the average degree is of order lower than log N, where N is the size of the network. Furthermore, we show that the number of degree-1 vertices is divisible by the least common multiple of , , . . ., , and l is less than log N, where l = < is the vertex-degree sequence of the network. The method we developed here relies only on a static condition, which can be easily verified, and we have verified it by a large number of real complex networks.