The Universal Property of the Entropy Sum of Black Holes in All Dimensions

It is proposed by Cvetic et al [Phys. Rev. Lett. 106 (2011) 121301] that the product of all horizon areas for general rotating multi-change black holes has universal expressions independent of the mass. When we consider the product of all horizon entropies, however, the mass will be present in some cases, while another new universal property [JHEP 1401 (2014) 031] is preserved, which is more general and says that the sum of all horizon entropies depends only on the coupling constants of the theory and the topology of the black hole. The property has been studied in limited dimensions and the generalization in arbitrary dimensions is not straight-forward. In this Letter, we prove a useful formula, which makes it possible to investigate this conjectured universality in arbitrary dimensions for the maximally symmetric black holes in general Lovelock gravity and $f(R)$ gravity. We also propose an approach to compute the entropy sum of general Kerr-(anti-)de-Sitter black holes in arbitrary dimensions. In all these cases, we prove that the entropy sum only depends on the coupling constants and the topology of the black hole.Comment: 16 pages,no figures;v2: 17 pages, references added, minor corrections/modifications; v3: 16 pages, added references, correct some expressons, added equation (16) to make the context more clear, to appear in PL

KBNN Based on Coarse Mesh to Optimize the EBG Structures

The microwave devices are usually optimized by combining the precise model with global optimization algorithm. However, this method is time-consuming. In order to optimize the microwave devices rapidly, the knowledge-based neural network (KBNN) is used in this paper. Usually, the a priori knowledge of KBNN is obtained by the empirical formulas. Unfortunately, it is difficult to derive the corresponding formulas for the most electromagnetic problems, especially for complex electromagnetic problems; the formula derivation is almost impossible. We use precise mesh model of EM analysis as teaching signal and coarse mesh model as a priori knowledge to train the neural network (NN) by particle swarm optimization (PSO). The NN constructed by this method is simpler than traditional NN in structure which can replace precise model in optimization and reduce the computing time. The results of electromagnetic band-gap (EBG) structures optimally designed by this kind of KBNN achieve increase in the bandwidth and attenuation of the stopband and small passband ripple level which shows the advantages of the proposed KBNN method