Transition of stoichiometricSr2VO3FeAs to a superconducting state at 37.2 K

The superconductor Sr4V2O6Fe2As2 with transition temperature at 37.2 K has been fabricated. It has a layered structure with the space group of p4/nmm, and with the lattice constants a = 3.9296Aand c = 15.6732A. The observed large diamagnetization signal and zero-resistance demonstrated the bulk superconductivity. The broadening of resistive transition was measured under different magnetic fields leading to the discovery of a rather high upper critical field. The results also suggest a large vortex liquid region which reflects high anisotropy of the system. The Hall effect measurements revealed dominantly electron-like charge carriers in this material. The superconductivity in the present system may be induced by oxygen deficiency or the multiple valence states of vanadium.Comment: 5 pages, 4 figure

Superconductivity at 15.6 K in Calcium-doped Tb_{1-x}Ca_xFeAsO: the structure requirement for achieving superconductivity in the hole-doped 1111 phase

Superconductivity at about 15.6 K was achieved in Tb_{1-x}Ca_xFeAsO by partially substituting Tb^{3+} with Ca^{2+} in the nominal doping region x = 0.40 \sim 0.50. A detailed investigation was carried out in a typical sample with doping level of x = 0.44. The upper critical field of this sample was estimated to be 77 Tesla from the magnetic field dependent resistivity data. The domination of hole-like charge carriers in the low-temperature region was confirmed by Hall effect measurements. The comparison between the calcium-doped sample Pr_{1-x}Ca_xFeAsO (non-superconductive) and the Strontium-doped sample Pr_{1-x}Sr_xFeAsO (superconductive) suggests that a lager ion radius of the doped alkaline-earth element compared with that of the rare-earth element may be a necessary requirement for achieving superconductivity in the hole-doped 1111 phase.Comment: 7 pages, 7 figure

Neuroprotective effects of traditional Chinese medicine in treating glaucoma:A Meta-analysis

AIM:To assess the neuroprotective effects of traditional Chinese medicine in the treatment of glaucoma. METHODS:The electronic bibliographic databases were searched,including Medline(1966-01/2011-03), EMbase(1996-2010), Cochrane library, Wanfang database,VIP(1999-2011), CNKI, the radomized controlled trials of TCM treatment compares with Western medicine treatment of the glaucoma were assembled Data were extracted and evaluated by two reciewers independently with a designed extraction formation by Meta-analysis based on the Cochrane net suggestion. RESULTS:A total of 8 theses written in Chinese were retrieved,including 719 patients.The results of Meta-analysis showed the combination therapy of TCM and western therapy significant improves the effect of neuroprotection(P<0.01). In order to boost and qualify the curative effects of the aucupuncture, more precise samples should be designed and a multi-research central need to be bulit. however, according to the existing cases, the evidences of the effectiveness are weak due to the limited numbers of samples and the methodological defect. CONCLUSION: The existing evidence supports the combination therapy of TCM and western medicine stronger than the only used of western medicine(P<0.01). But owing to the limited studies and few number of TCM treatment for glaucoma's neuroprotection, the large sample andmulticenter random ized controlled trial is still needed to verify the superiority of TCM for neuroproctive effect of glaucoma's teatment

Robust Image Analysis by L1-Norm Semi-supervised Learning

This paper presents a novel L1-norm semi-supervised learning algorithm for robust image analysis by giving new L1-norm formulation of Laplacian regularization which is the key step of graph-based semi-supervised learning. Since our L1-norm Laplacian regularization is defined directly over the eigenvectors of the normalized Laplacian matrix, we successfully formulate semi-supervised learning as an L1-norm linear reconstruction problem which can be effectively solved with sparse coding. By working with only a small subset of eigenvectors, we further develop a fast sparse coding algorithm for our L1-norm semi-supervised learning. Due to the sparsity induced by sparse coding, the proposed algorithm can deal with the noise in the data to some extent and thus has important applications to robust image analysis, such as noise-robust image classification and noise reduction for visual and textual bag-of-words (BOW) models. In particular, this paper is the first attempt to obtain robust image representation by sparse co-refinement of visual and textual BOW models. The experimental results have shown the promising performance of the proposed algorithm.Comment: This is an extension of our long paper in ACM MM 201