Bayesian Species Delimitation Can Be Robust to Guide-Tree Inference Errors

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Centrality Scaling of the pTp_T Distribution of Pions

From the preliminary data of PHENIX on the centrality dependence of the $\pi^0$ spectrum in $p_T$ at midrapidity in heavy-ion collisions, we show that a scaling behavior exists that is independent of the centrality. It is then shown that $$ degrades with increasing $N_{\rm part}$ exponentially with a decay constant that can be quantified. A scaling distribution in terms of an intuitive scaling variable is derived that is analogous to the KNO scaling. No theoretical models are used in any part of this phenomenological analysis.Comment: 4 pages RevTex, 5 figures include

An integrated wind risk warning model for urban rail transport in Shanghai, China

The integrated wind risk warning model for rail transport presented has four elements: Background wind data, a wind field model, a vulnerability model, and a risk model. Background wind data uses observations in this study. Using the wind field model with effective surface roughness lengths, the background wind data are interpolated to a 30-m resolution grid. In the vulnerability model, the aerodynamic characteristics of railway vehicles are analyzed with CFD (Computational Fluid Dynamics) modelling. In the risk model, the maximum value of three aerodynamic forces is used as the criteria to evaluate rail safety and to quantify the risk level under extremely windy weather. The full model is tested for the Shanghai Metro Line 16 using wind conditions during Typhoon Chan-hom. The proposed approach enables quick quantification of real- time safety risk levels during typhoon landfall, providing sophisticated warning information for rail vehicle operation safety