What Is Known and Objective Propofol is widely used in painless gastroscopy. However, sedation with propofol alone might increase the risk of respiratory and circulatory complications. This randomized clinical study compares the efficacy and safety of esketamine or dezocine combined with intravenous (IV) propofol in patients undergoing gastroscopy. Methods A total of 102 patients were enrolled in this study and randomized into two groups. All patients were adults aged 18–64 years who underwent upper gastrointestinal gastroscopy. Patients were randomly assigned to two groups to receive esketamine (0.3 mg/kg) combined with propofol (group E) or dezocine (0.05 mg/kg) combined with propofol (group D). In both groups, the drugs were administered intravenously. The primary outcome was the dose of propofol which provided a satisfactory sedative effect, both to the endoscopist and the patients. Secondary outcomes included recovery time, side effects (such as hypotension, nausea and vomiting and agitation), and the number of adverse circulatory and respiratory events. Results Data of 83 patients were analysed in the present study. Dosage of propofol required in group E (1.44 mg/kg ± 0.67 mg/kg) was significantly lower compared with that in group D (2.12 mg/kg ± 0.37 mg/kg) (p < 0.0001). There was no statistically significant difference in recovery time, side effects, and the frequency of sedation‐related adverse events between the two groups. What Is New and Conclusion The study indicates that intravenous injection of propofol and esmketamine is more effective for gastroscopy. Use of esketamine reduces the total amount of propofol required in ASA I–II patients undergoing gastroscopy compared with single use of dezocine. It also provides more stable hemodynamics, without affecting the recovery time and side effects such as respiratory and circulatory adverse events. Trial Registration The study was registered at the Chinese Clinical Trial Registry (http://www.chictr.org.cn; registration number: ChiCTR2100051814) on 05/10/2021.

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To improve the quality of human life in the city, the first thing to solve is the problem of urban garbage. So far, the best way to solve this problem is garbage classification. At present, many algorithms have been put forward one after another. Previous research proposed some computer vision systems to solve the problem of urban garbage classification. In recent years, with the development of computer hardware and large-scale data sets, the algorithm based on depth learning has shown superior performance in the field of image classification. Thus, the features designed by traditional methods are gradually replaced, which far exceed these traditional image classification algorithms in classification accuracy. This study proposes an algorithm based on InceptionV3 networks and test the algorithm on a large-scale garbage classification data-set. The data set was divided into 80% training sets, 10% validation set, and 10% test set and use the transfer learning approach. The model achieved an accuracy of 93.125%, which solved image garbage classification very well. What is more, the algorithm can play an important role in the medical area and help control the mechanical arm.