Lattice QCD calculation of ππ\pi\pi scattering length

We study s-wave pion-pion ($\pi\pi$) scattering length in lattice QCD for pion masses ranging from 330 MeV to 466 MeV. In the "Asqtad" improved staggered fermion formulation, we calculate the $\pi\pi$ four-point functions for isospin I=0 and 2 channels, and use chiral perturbation theory at next-to-leading order to extrapolate our simulation results. Extrapolating to the physical pion mass gives the scattering lengths as $m_\pi a_0^{I=2} = -0.0416(2)$ and $m_\pi a_0^{I=0} = 0.186(2)$ for isospin I=2 and 0 channels, respectively. Our lattice simulation for $\pi\pi$ scattering length in the I=0 channel is an exploratory study, where we include the disconnected contribution, and our preliminary result is near to its experimental value. These simulations are performed with MILC 2+1 flavor gauge configurations at lattice spacing $a \approx 0.15$ fm.Comment: Remove some typo

Exposure to hazardous volatile organic compounds, PM 10 and CO while walking along streets in urban Guangzhou, China

Toxic air pollutants in street canyons are important issues concerning public health especially in some large Asian cities like Guangzhou. In 1998 <18% of Guangzhou citizens used public transportation modes, with a majority commuting on foot (42%) or by bicycle (22%). Of the pedestrians, 57% were either senior citizens or students. In the present study, we measured toxic air pollutants while walking along urban streets in Guangzhou to evaluate pedestrian exposure. Volatile organic compounds (VOCs) were collected with sorbent tubes, and PM 10 and CO were measured simultaneously with portable analyzers. Our results showed that pedestrian exposure to PM 10 (with an average of 303 μg m -3 for all samples) and some toxic VOCs (for example, benzene) was relatively high. Monocyclic aromatic hydrocarbons were found to be the most abundant VOCs, and 71% of the samples had benzene levels higher than 30 μg m -3. Benzene, PM 10 and CO in walk-only streets were significantly lower (p<0.05) than in traffic streets, and the differences in exposure levels between new urban streets and old urban streets were highly significant (p<0.01). Pedestrian exposure to toxic VOCs and PM 10 was higher than those reported in other public transportation modes (bus and subway). The good correlations between BTEX, PM 10 and CO in the streets indicated that automotive emission might be their major source. Our study also showed that the risk to pedestrians due to air pollution was misinterpreted by the reported air quality index based on measurement of SO 2, NO x and PM 10 in the government monitoring stations. An urban roadside monitoring station might be needed by air quality monitoring networks in large Asian cities like Guangzhou, in order to survey exposure to air toxics in urban roadside microenvironments. © 2004 Elsevier Ltd. All rights reserved

Numerical simulation of the influence of welding direction on residual stress after double welding of Q345 stacked-plates

Based on the welding numerical simulation software Visual-environment, this paper calculates and analyzes the residual stress field for the double pass welding of Q345 stacked plates. The paper mainly studies the influence of different welding directions on the residual stress after welding. The results show that different welding methods have little effect on the lateral residual stress, while the longitudinal residual stress and the initial and end of the weld have a greater influence, while the post-weld residual stress distribution of the anisotropic two-pass weld is more uniform

Part-Based Deep Hashing for Large-Scale Person Re-Identification

© 1992-2012 IEEE. Large-scale is a trend in person re-identi-fication (re-id). It is important that real-time search be performed in a large gallery. While previous methods mostly focus on discriminative learning, this paper makes the attempt in integrating deep learning and hashing into one framework to evaluate the efficiency and accuracy for large-scale person re-id. We integrate spatial information for discriminative visual representation by partitioning the pedestrian image into horizontal parts. Specifically, Part-based Deep Hashing (PDH) is proposed, in which batches of triplet samples are employed as the input of the deep hashing architecture. Each triplet sample contains two pedestrian images (or parts) with the same identity and one pedestrian image (or part) of the different identity. A triplet loss function is employed with a constraint that the Hamming distance of pedestrian images (or parts) with the same identity is smaller than ones with the different identity. In the experiment, we show that the proposed PDH method yields very competitive re-id accuracy on the large-scale Market-1501 and Market-1501+500K datasets