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A novel initiator system containing KMO4, HIO4, and H2SO4 for synthesizing grafting starch copolymers is reported. In this system, KMnO4 was used to oxidize the primary hydroxyl group to aldehyde group of glucose in the starch, and the formed aldehyde group reacted with Mn(4+), Mn(3+) to afford starch free radical. At the same time HIO4 perform as the oxidant to open the C2C3 bond of glucose ring in starch to form two more aldehyde groups, and then two more free radicals are generated. As a result one glucose unit could provide ultimately three active sites for starch grafting reaction. Graft copolymers with a higher grafting ratio and grafting efficiency could be obtained by using the composite initiation system than the KMnO4/H2SO4 initiation system. The grafting of polyacrylamide onto the corn starch backbone was confirmed by viscometry, elemental analysis, infrared spectroscopy, nuclear magnetic resonance, X-ray diffraction and scanning electron microscopy.

Using deep learning for visual scene parsing will satisfy the demand of the next generation of automatic driving technology. However, current parsing algorithms are not mature enough for practical applications unless higher accuracy and efficiency are obtained. We propose a novel scene parsing algorithm framework which integrates the object detection technologies into convolution neural network to improve the overall effectiveness. The framework consists of three components: i) a scene parsing network presenting primary semantic segmentation result. ii)an object detection network calculating the location and confidence of the targets in images. iii) an integration and filter module that cascades previous two results. Extensive experiments suggest that our model is capable of practical use and achieving more favorable scene parsing performance of mIoU score as 69.4% on CamVid dataset.