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Objective:To improve the defect detection efficiency of the enclosure structure of food storage devices. Methods: A robotic arm inspection trolley based on host computer control was proposed. The acquisition path was planned according to the area of the enclosure structure. The improved Ostu image segmentation method was used to analyze the acquired thermal infrared images and output the "defect" parts. Size and position coordinates, and finally verify the effectiveness of the detection through the defect detection rate and false detection rate. Results: The detection mechanism can completely scan and collect thermal infrared images of the enclosure structure of food storage and transportation devices. The image segmentation method can clearly describe the size of the heat source. The detection rate and false detection rate of "defects" were 92% and 6.6%, respectively. Conclusion: The detection mechanism can completely scan and collect the thermal infrared image of the food storage room envelope, and the image segmentation method can clearly describe the size of the heat source, which meets the practical application requirements of mechanical vision detection.

The effects hypothalamic paraventricular nucleus (PVN)-induced baroreflex inhibition on neuronal firing in the nucleus of the solitary tract (NTS) were investigated. Pressor responses and baroreflex activation in anesthetized rabbits were induced using intravenous phenylephrine. Results revealed that baroreflexes are inhibited by PVN activation during the defense reaction by changing the firing of NTS neurons.

The freshness of egg is an important index to reflect the internal quality. In order to achieve non-destructive detection of freshness, micro fiber spectrometer was used to sample 550～950 nm transmittance spectra of eggs which performed quantitative analysis with haugh unit of eggs. Different pretreatment was combined with partial least squares regression(PLS) and support vector regression(SVR) respectively to find that first derivative combined with SVR predicted better than others through comparison, and it was better to model by SVR than by PLS. In order to improve efficiency and decrease adverse effects of useless information for modeling, the linear dimensionality reduction with principal component analysis (PCA) and the nonlinear dimensionality reduction with locally linear embedding(LLE) were used for the data of first derivative respectively. It indicated that LLE was better than PCA after comparison, and the correlation coefficient of calibration and prediction were 92.2%, 91.1%, and the root mean square error were 7.21, 8.80. The root mean square error of cross validation decreased 0.79.The experimental result illustrated that the nonlinear model of LLE combined with SVR improved predictive performance of egg freshness. It is feasible for the detection of visible/near-infrared spectrum of egg freshness to apply this method.