In this study, a series of numerical calculations are carried out in ANSYS Workbench based on the unidirectional fluid–solid coupling theory. Using the DTMB 4119 propeller as the research object, a numerical simulation is set up to analyze the open water performance of the propeller, and the equivalent stress distribution of the propeller acting in the flow field and the axial strain of the blade are analyzed. The results show that FLUENT calculations can provide accurate and reliable calculations of the hydrodynamic load for the propeller structure. The maximum equivalent stress was observed in the blade near the hub, and the tip position of the blade had the largest stress. With the increase in speed, the stress and deformation showed a decreasing trend.

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Objective Little is known about the efficacy of using artificial intelligence (AI) to identify laryngeal carcinoma from images of vocal lesions taken in different hospitals with multiple laryngoscope systems. This multicentre study aimed to establish an AI system and provide a reliable auxiliary tool to screen for laryngeal carcinoma. Study design Multicentre case–control study. Setting Six tertiary care centres. Participants Laryngoscopy images were collected from 2179 patients with vocal fold lesions. Outcome measures An automatic detection system of laryngeal carcinoma was established and used to distinguish malignant and benign vocal lesions in 2179 laryngoscopy images acquired from 6 hospitals with 5 types of laryngoscopy systems. Pathological examination was the gold standard for identifying malignant and benign vocal lesions. Results Out of 89 cases in the malignant group, the classifier was able to correctly identify laryngeal carcinoma in 66 patients (74.16%, sensitivity). Out of 640 cases in the benign group, the classifier was able to accurately assess the laryngeal lesion in 503 cases (78.59%, specificity). Furthermore, the region‐based convolutional neural network (R‐CNN) classifier achieved an overall accuracy of 78.05%, with a 95.63% negative predictive value and a 32.51% positive predictive value for the testing data set. Conclusion This automatic diagnostic system has the potential to assist clinical laryngeal carcinoma diagnosis which may improve and standardise the diagnostic capacity of laryngologists using different laryngoscopes.