Instability of Reissner-Nordstr\"{o}m black hole in Einstein-Maxwell-scalar theory

The scalarization of Reissner-Nordstr\"{o}m black holes was recently proposed in the Einstein-Maxwell-scalar theory. Here, we show that the appearance of the scalarized Reissner-Nordstr\"{o}m black hole is closely related to the Gregory-Laflamme instability of the Reissner-Nordstr\"{o}m black hole without scalar hair.Comment: 22 pages, 10 figures, version to appear in EPJ

The production of neutral N∗(11052)N^*(11052) resonance with hidden beauty from π−p\pi^-p scattering

We investigate the discovery potential of the predicted neutral hidden beauty $N^*(11052)$ resonance through $\pi^- p$ scattering within an effective Lagrangian approach. Two reactions $\pi^-p\rightarrow K^-\Sigma^+$ and $\pi^-p\rightarrow \eta_bn$ are studied in this work, with nucleon pole exchange as the background. It is found that the contributions of the $N^*(11052)$ resonance give clear peak structures in the magnitude of 1 $\mu b$ near the threshold of the $N^*(11052)$ in the total cross sections. The numerical results indicate that the center of mass energy $W\simeq$ 11-11.1 GeV would be a best energy window for searching the $N^*(11052)$ resonance, where the $N^*(11052)$ signal can be easily distinguished from the background. The COMPASS experiment at CERN's Super Proton Synchrotron (SPS) with pion beam of $\simeq$ 280 GeV will be an ideal platform for searching the super-heavy resonance with hidden beauty, which is hopeful to test the theoretical results

Reply to [arXiv:1105.5147] "Are GRB 090423 and Similar Bursts due to Superconducting Cosmic Strings?"

The GRB outflow driven by superconducting cosmic strings is likely to be an arc rather than a usually-considered spherical cap. In such a case, the afterglows of the cosmic string GRBs could be basically consistent with the observation of the high-redshift GRBs.Comment: 2 pages, 1 figure, to appear in Phys. Rev. Let

Forecasting bus passenger flows by using a clustering-based support vector regression approach

As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio