Possible DDˉD\bar{D} and BBˉB\bar{B} Molecular states in a chiral quark model

We perform a systematic study of the bound state problem of $D\bar{D}$ and $B\bar{B}$ systems by using effective interaction in our chiral quark model. Our results show that both the interactions of $D\bar{D}$ and $B\bar{B}$ states are attractive, which consequently result in $I^G(J^{PC})=0^+(0^{++})$ $D\bar{D}$ and $B\bar{B}$ bound states.Comment: arXiv admin note: substantial text overlap with arXiv:1204.395

Orthogonal learning particle swarm optimization

Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood’s best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when searching in complex problem spaces. Hence, designing learning strategies that can utilize previous search information (experience) more efficiently has become one of the most salient and active PSO research topics. In this paper, we proposes an orthogonal learning (OL) strategy for PSO to discover more useful information that lies in the above two experiences via orthogonal experimental design. We name this PSO as orthogonal learning particle swarm optimization (OLPSO). The OL strategy can guide particles to fly in better directions by constructing a much promising and efficient exemplar. The OL strategy can be applied to PSO with any topological structure. In this paper, it is applied to both global and local versions of PSO, yielding the OLPSO-G and OLPSOL algorithms, respectively. This new learning strategy and the new algorithms are tested on a set of 16 benchmark functions, and are compared with other PSO algorithms and some state of the art evolutionary algorithms. The experimental results illustrate the effectiveness and efficiency of the proposed learning strategy and algorithms. The comparisons show that OLPSO significantly improves the performance of PSO, offering faster global convergence, higher solution quality, and stronger robustness

Tuning electronic structure of graphene via tailoring structure: theoretical study

Electronic structures of graphene sheet with different defective patterns are investigated, based on the first principles calculations. We find that defective patterns can tune the electronic structures of the graphene significantly. Triangle patterns give rise to strongly localized states near the Fermi level, and hexagonal patterns open up band gaps in the systems. In addition, rectangular patterns, which feature networks of graphene nanoribbons with either zigzag or armchair edges, exhibit semiconducting behaviors, where the band gap has an evident dependence on the width of the nanoribbons. For the networks of the graphene nanoribbons, some special channels for electronic transport are predicted.Comment: 5 figures, 6 page