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The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of recent studies. The studies of automatic defect inspection are synthetically concluded from three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of traditional defect classification method based on the feature extraction, selection and classifier are summarized. Then the applications of novel models based on learning(especially deep learning) were introduced. Finally, the achievement of automation methods were discussed and the challenges of current technology are presented for future research for both weld quality management and computer science researchers.

Valtrate is a principle compound isolated from Valeriana jatamansi Jones, which is a Traditional Chinese Medicine used to treat various mood disorders. The aim of the present study was to investigate the anxiolytic effects of valtrate in rats. The animals were orally administered valtrate (5, 10, and 20 g/kg daily) for 10 days and exposed to open field test (OFT) and elevated plus-maze (EPM). Then the corticosterone levels in the rat serum were measured by enzyme-linked immunosorbent assay (ELISA). The valtrate (10 mg/kg, p.o.) exhibited the anxiolytic effect in rats by increasing the time and entry percentage into the open arms in the EPM and the number of central entries in the OFT. Valtrate (10 mg/kg, p.o.) significantly reduced the corticosterone level in the rat serum. Taken together, these results suggest that the valtrate has anxiolytic activity in behavioral models that might be mediated via the function of hypothalamus-pituitary-adrenal axis.

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