Resumption of school after lockdown in COVID-19 pandemic: Three case reports.

BACKGROUND: Students in the 9th grade of junior high school in Changsha were under a 75 d lockdown due to the coronavirus disease 2019 (COVID-19) pandemic. After the resumption of school post-lockdown, the 9th grade students in Changsha faced the entrance physical examination test for senior high school. CASE SUMMARY: We report on 3 cases of occult fracture on the same site in adolescents of the same grade since resumption of school after the lockdown from the COVID-19 pandemic. Three students in the 9th grade of junior high school who were facing the physical examination in 2 wk were diagnosed with an occult fracture of the distal femur. CONCLUSION: It is recommended that the students, parents, education providers and policy makers should all pay attention to the physical exercise of students when the resumption of school after lockdown occurs and they should be aware of occult fractures when the adolescents have pain after physical exercise.

[Drug Discrimination Method Based on Near Infrared Reflectance Spectrum and Balance Cascading Sparse Representation Based Classification].

The combination of near infrared spectrum and pattern recognition methods has a wide application prospect in rapid and nondestructive supervision and management of drugs. The traditional identification methods regard the smallest error rate as the goal while the imbalance of classes is ignored. This makes the positive class is overwhelming covered by the negative class and reduces its effect for the classifier, so that the classification results tend to recognize the negative class correctly, which severely affects the identification accuracy. In this paper, we mainly studied the class imbalance problems of true or false drugs via infrared spectral data of its, and then propose a balance cascading and sparse representation based classification method (BC-SRC) by combining the Balance Cascading with SRC. We sampling majority samples from the majority class for several times, which has the same size as minority samples and the majority samples we sampled can contain all the majority class samples entirely (sampling times is ceiling the result of majority samples number divide minority samples number). We can get sets of results, and then obtain the final predict labels form those results. Experiments of three databases achieved on Matlab2012a shows that the method is effective. From the experimental results, it can be seen that the method is superior to the commonly used Partial Least Squares (PLS), Extreme Learning Machine (ELM) and BP. Particularly, for the imbalanced databases, when the imbalance factor is greater than 10, the proposed method has more stable performance with higher classification accuracy than the existing ones mentioned above.