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Three-dimensional oriented chitosan (CS)/hydroxyapatite (HA) scaffolds were prepared via in situ precipitation method in this research. Scanning electron microscopy (SEM) images indicated that the scaffolds with acicular nano-HA had the spoke-like, multilayer and porous structure. The SEM of osteoblasts which were polygonal or spindle-shaped on the composite scaffolds after seven-day cell culture showed that the cells grew, adhered, and spread well. The results of X-ray powder diffractometer and Fourier transform infrared spectrometer showed that the mineral particles deposited in the scaffold had phase structure similar to natural bone and confirmed that particles were exactly HA. In vitro biocompatibility evaluation indicated the composite scaffolds showed a higher degree of proliferation of MC3T3-E1 cell compared with the pure CS scaffolds and the CS/HA10 scaffold was the highest one. The CS/HA scaffold also had a higher ratio of adhesion and alkaline phosphate activity value of osteoblasts compared with the pure CS scaffold, and the ratio increased with the increase of HA content. The ALP activity value of composite scaffolds was at least six times of the pure CS scaffolds. The results suggested that the composite scaffolds possessed good biocompatibility. The compressive strength of CS/HA15 increased by 33.07% compared with the pure CS scaffold. This novel porous scaffold with three-dimensional oriented structure might have a potential application in bone tissue engineering.

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Abstract. In order to realize the automatic recognition of cherry in the natural environment, this paper designed a robot vision system recognition method. The first step of this method is to pre-process the cherry image by median filtering. The second step is to identify the colour of the cherry through the 0.9R-G colour difference formula, and then use the Otsu algorithm for threshold segmentation. The third step is to remove noise by using the area threshold. The fourth step is to remove the holes in the cherry image by morphological closed and open operation. The fifth step is to obtain the centroid and contour of cherry by using the smallest external rectangular and the Hough transform. Through this recognition process, we can successfully identify 96% of the cherry without blocking and adhesion.