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Accumulating evidence has indicated an increased risk of acute pancreatitis in individuals with inflammatory bowel disease (IBD); however, the establishment of a clear and direct causal connection between IBD and acute pancreatitis remains uncertain. Utilizing genetic data from publicly accessible genome-wide association studies (GWAS), we conducted a 2-sample MR analysis to identify the associations between IBD, ulcerative colitis (UC), Crohn disease (CD), and acute pancreatitis risk. Rigorous quality control steps ensured the selection of eligible single nucleotide polymorphisms (SNPs) with strong associations to IBD. The primary estimation used the inverse-variance weighted method. We also assessed heterogeneity, potential pleiotropy, and conducted sensitivity analyses. The direction of causality was confirmed using the Steiger test. The MR analysis showed that IBD increased the risk of acute pancreatitis (IVW: OR = 1.032, 95% CI: 1.006–1.06, P= .015). Among the subgroup of IBD, CD (IVW: OR = 1.034, 95% CI: 1.008–1.06, P= .007) indicates a significant increase in the risk of acute pancreatitis compared to UC (IVW: OR = 1.02, 95% CI: 0.99–1.051, P= .189). The MR analysis assessing the association between CD and acute pancreatitis showed no evidence of heterogeneity or horizontal pleiotropy. Likewise, the leave-one-out (LOO) method indicated no significant influence of any individual SNP on the overall findings. In addition, the Steiger direction test revealed that CD was the cause for increased risk of acute pancreatitis, but not vice versa. In summary, this research pioneers in proposing a causal relationship between CD and acute pancreatitis among the European population.

In this study, we propose a development scheme of litchi robot vision system based on stereo vision. The system includes three components: image acquisition, image analysis, and image processing. The obtained image can be corrected according to the light environment conditions with the corresponding algorithm for image color, calculate the Hue color layer, analyze the external characteristics and positional data (2D) of litchi, obtain the 3D coordinate information of fruits and vegetables, and provide to the running device for route planning and vegetable and fruit picking claw tools for picking. The integration test results for the robot arm and claw tool demonstrate the following: (1) Using image acquisition, the system can acquire high-quality images in real-time via a combination of automatic exposure and white balance conversion. (2) Using image analysis techniques such as background separation, edge detection, and external feature search, it is possible to successfully calculate the location coordinates of the litchi's tip, center, and bottom. (3) Using image processing, the claw of the picking machine can determine the condition of overlapping lychees and their state of ripening based on the depth information of feature points used in the picking operation. (4) Under the working range of 18.4 cm × 33.6 cm, the success rate of picking litchi with a diameter of 28 mm, 34 mm, and 42 mm was 93.3%, 91.1 %, and 84.4%, respectively, exceeding the design expectation level.

In order to solve the problems of uneven thickness, poor hardness and wear resistance of traditional curved glass coating technology, a new three-dimensional (3D) curved glass hot-pressing technology of bionic nanomaterials was studied. According to the principle of machine vision, whether the curved glass to be coated meets the requirements of hot-pressing was checked. In addition, a biomimetic nanomaterial film was prepared through the mixed-solvent method. According to the characteristics of 3D curved glass at different temperatures, the parameters of the hot-press film machine were set, and the hot-press film machine was used to complete the research on the 3D curved glass hot-press film technology. Compared with the traditional curved glass film technology, the experimental results show that the new bionic nanomaterial 3D curved glass hot-pressing film technology can achieve uniform coating on the glass surface, and the hardness and wear resistance of the film are better than those of films produced by the traditional coating technology, showing that the new technology has great practical application value.

ABSTRACTIn the diversified development of glass industry and technology products, the demand of quality performance of the glass surface increases. The surface quality detection is becoming more and more strict, especially the evaluation of the surface quality defects. The glass surface quality detection means, and ability has certain limitations, most methods for artificial detection, improve the detection accuracy and objectivity. The authors studied the glass surface quality of machine vision detection method based on the detailed discussion of computer vision and image processing technology. This study draws the conclusion that machine vision plays an important role in the quality detection of the glass surface and its high efficiency & accuracy has broad application prospects.