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Abstract

Senecavirus A (SVA) the only member of the Senecavirus genus within the Picornaviridae family, is an emerging pathogen causing swine idiopathic vesicular disease and epidemic transient neonatal losses. Here, SVA strain (CH-HNKZ-2017) was isolated from a swine farm exhibiting vesicular disease in Henan Province of Central China. A phylogenetic analysis based on complete genome sequence indicated that CH-HNKZ-2017 was closely related to US-15-40381IA, indica- ting that a new SVA isolate had emerged in China.
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Abstract

Based on the mould temperature measured by thermocouples during slab continuous casting, a difference of temperature thermograph is developed to detect slab cracks. In order to detect abnormal temperature region caused by longitudinal crack, the suspicious regions are extracted and divided by virtue of computer image processing algorithms, such as threshold segmentation, connected region judgement and boundary tracing. The abnormal regions are then determined and labeled with the eight connected component labeling algorithm. The boundary of abnormal region is also extracted to depict characteristics of longitudinal crack. Based on above researches, longitudinal crack with abnormal temperature region can be detected and is different from other abnormalities. Four samples of temperature drop are picked up to compare with longitudinal crack on the abnormal region formation, length, width, shape, et al. The results show that the abnormal region caused by longitudinal crack has a linear and vertical shape. The height of abnormal region is more than the width obviously. The ratio of height to width is usually larger than that of other temperature drop regions. This method provides a visual and easy way to detect longitudinal crack and other abnormities. Meanwhile it has a positive meaning to the intelligent and visual mould monitoring system of continuous casting.
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