The alternate dielectric component is introduced into a nanowall skeleton for the first time. As a photoelectrode, this novel model can optimize the process of photon absorption, charge separation/migration and surface reaction, resulting in superior photoelectrochemical performance.
The static topology properties of financial networks have been widely investigated since the work done by Mantegna, yet their dynamic evolution with time is little considered. In this paper, we comprehensively study the dynamic evolution of financial network by a sliding window technique. The vertices and edges of financial network are represented by the stocks from S&P500 components and correlations between pairs of daily returns of price fluctuation, respectively. Furthermore, the duration of stock price fluctuation, spanning from January 4, 1985 to September 14, 2009, makes us to carefully observe the relation between the dynamic topological properties and big financial crashes. The empirical results suggest that the financial network has the robust small-world property when the time evolves, and the topological structure drastically changes when the big financial crashes occur. This correspondence between the dynamic evolution of financial network and big financial crashes may provide a novel view to understand the origin of economic crisis.
Covering a network with minimum number of boxes is critical for using the renormalization technique to explore the network configuration space in a multiscale fashion. Here, we propose a versatile methodology composed of flexible representation and sampling of boxes, which have so far received scant attention, and the strategy of selecting boxes to cover the network. It is exemplified via random box sampling strategies and greedy methods to select boxes. We show that the key to substantially reduce the number of boxes is to give the selection priority to those boxes containing nodes that are not included in boxes bigger than themselves. Our algorithm achieves the improvement of diminishing the number of boxes amounting to nearly 25% compared with these well known algorithms.
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