Objectives Reading performance has been considered as an effective functional endpoint for low vision. Contrary to many extensive studies for reading performance in English, there are few systematic studies for Chinese reading. Methods In the present study, the reading performance of 30 normally sighted Chinese college students was systematically investigated. All participants passed the equivalent test of Cambridge ESOL PET in China. The reading speeds for Chinese and English text at a variety of text sizes were measured with rapid serial visual presentation (RSVP). The threshold acuities for Chinese characters and English letters were measured. Maximum reading speed, critical font size, and critical acuity reserve were derived according to the individual's reading speed curve. Results The maximum reading speed for Chinese characters was 259.5 ± 38.2 characters/min, which was significantly faster than that for English letters (135.7 ± 18.5 words/min, p = 2.8 × 10‐18). The critical font size for Chinese characters was larger than that for English letters (24.2 ± 2.8 arcmin vs. 20.7 ± 1.0 arcmin, p = 1.6 × 10‐7). Interestingly, the critical acuity reserve was similar for these two languages (3.4 ± 0.4 for Chinese and 3.4 ± 0.2 for English, p = 0.4). Conclusion The present study provides the first step for establishing visual functional endpoints for Chinese reading. Our findings pose rigorous constrains on present theories in language information processing and brain plasticity.

Objective This article proposes a named entity recognition model for electronic medical records in ophthalmology that integrates professional vocabulary information. The aim is to achieve structured processing of important clinical decision-making data and to develop a clinical aided diagnosis platform based on this. The effectiveness of this platform in improving the efficiency and accuracy of ophthalmologists in clinical diagnosis decision-making was validated. Methods Based on the best entity recognition model, we constructed the aided diagnosis platform. By conducting a controlled experiment that compared the use of the platform by doctors with different levels of experience, we analyzed the effectiveness of the aided diagnosis platform in improving diagnosis decision-making efficiency and accuracy. Results The SoftLexicon-Glove-Word2vec model had the highest F1 score at 93.02%. Both junior and senior doctors showed significant improvement in diagnosis efficiency and accuracy (P < 0.05) when using the platform. Regardless of whether the aided diagnosis platform was used or not, there were significant differences in diagnosis decision-making efficiency and accuracy between junior and senior doctors (P < 0.05). Conclusion The use of artificial intelligence technology to construct the aided diagnosis platform for fundus diseases can effectively improve the clinical decision-making ability of junior doctors, and improve the diagnosis efficiency and accuracy.

Most of the existing nonlinear ship course-keeping control systems are designed with the Nomoto model, which solely considers the yawing of the ship with only one Degree of Freedom (DOF), and it does not consider the coupling between the longitudinal and the lateral velocity of the ship. In this paper, a nonlinear ship course controller design method that can be used in a nonlinear coupled model was proposed. A stable nonlinear ship course controller with anti-wind and anti-wave interference was constructed based on the Lyapunov stability principle and robust control theory, which can be used in the course control of autopilot in the case of wind and waves. In this method, the coupling among the longitudinal and lateral velocity as well as yawing of the ship was considered. The simulation results showed that the method can not only effectively control the ship’s course but also can track the dynamic course effectively. At the same time, compared with the PID control method based on backstepping, the steering angle of the rudder angle of our method is smaller and the wear and tear of steering gear will be smaller.