Background In recent years, self-regulated learning (SRL) has become a hot topic in medical education. However, the factors that affect the SRL ability of medical-related majors, such as clinical medicine, traditional Chinese medicine (TCM) and nursing specialty in TCM colleges and universities are unclear. Whether the teaching of learning strategies can help improve students’ SRL also needs to be further examined. Method A cross-sectional survey was distributed, and 878 medical-related students which are from a TCM university were recruited for this study. An independent t‑test and analysis of variance were used to analyse the factors associated with SRL. The relationship between self‑regulated learning and learning strategies was analysed with multi-linear regression analysis. Results The average scores of SRL on learning motivation, learning setting and self-regulation. Total scores were 34.76 ± 4.62, 41.14 ± 4.30, 39.26 ± 4.74 and 115.16 ± 12.42, respectively. The mean scores were all above the midpoint. The metacognitive, affective, cognitive, resource management and the total scores of learning strategies were 58.54 ± 12.02, 43.24 ± 8.42, 35.49 ± 7.34, 22.89 ± 4.20, 160.16 ± 29.45. Some factors can predict 32% of the variation of SRL, including whether they liked their specialty, educational system, and specialty, achievement ranking scholarship, whether they were taught by a tutor in middle school, gender, monthly family income, father’s educational background, metacognitive strategy, resource management strategy and cognitive strategy. Conclusions The SRL of medical-related students was better. Learning strategies and methods as well as personal or social factors can affect SRL. Educators should improve attention on the cultivation of learning strategies, exercising learning skills and monitoring, adjustment and guidance of learning time. Educators should also strengthen the professional identity and confidence of medical-related students and strive to improve their SRL ability.

Objectives Nurses' job stress perception and psychological capital affect their job engagement. This paper explores the effects of demographic characteristics, mental workload, and AQ on the job engagement of nurses in 12 hospitals in East China. Methods A cross-sectional study was conducted with a convenience sample. Data collection was performed from July 2020 to March 2021. Mean Rank and Median were used for descriptive statistical analysis. Mann–Whitney U Test and Kruskal–Wallis H Test compared the difference of different groups. Spearman correlation analysis was conduct to detect the correlation between mental workload, AQ, and job engagement. Binary logistic regression analysis explored the predictors and abilities of job engagement. Results labor and personnel relations, department, annual salary, marital satisfaction, social support, whether there is financial pressure or not, significant stresssignificant stress in the last six months, attitudes towards the nursing profession, attitude towards the current career position, the organization provides opportunities for further study, religious belief, study to get a degree or certificate in spare time were all influencing factors of job engagement. Job engagement has a remarkable positive correlation with AQ (r = 0.623, p＜0.001) and a negative correlation with mental workload (r = − 0.422, p＜0.001). Mental workload has an apparent negative correlation with AQ (r = − 0.250, p＜0.001). Department, study to get a degree or certificate in spare time, self-assessment, and endurance predicted nurses' job engagement. Conclusions This study is based on the JD-R model, and the results are helpful in understanding the effects of demographic characteristics, mental workload (job requirements), and AQ (job resources) on the job engagement of nurses. It is necessary to take a variety of measures according to the social-demographic characteristics, improve the AQ of hospital nurses, and evaluate the mental workload correctly, to improve the job engagement of nurses.

Background: Empty-nest youth are among the most vulnerable populations in society and they are prone to social isolation. Social isolation has a significant impact on empty-nest youth themselves and the society. Through an in-depth understanding of the main factors of social isolation among empty-nest youth in East China, this study aim to address their social isolation, ensure their psychological health and quality of life, and promote social integration. Method: This study used descriptive qualitative research to conduct semi-structured interviews with 15 empty-nest youth between August and October 2021. We used directed content analysis on the resulting data. Result: By combining, generalizing, and refining, we finally derive two main themes and eleven sub-themes. According to attribution theory, we divide results into internal factors (personality traits, internet dependence, upwards comparison mindset, social cognitive bias, lack of social adaptation ability, inadequate social skill) and external factors (social network relationship disconnection, insufficient urban concern, indifferent interpersonal relationship, heavy economic burden, work factor). Conclusion: Factors contributing to social isolation among empty-nest youth are multifaceted. The study recommends that managers take appropriate measures to reduce the level of social isolation among empty-nest youth, safeguard their psychological health and quality of life, and promote social integration.

For roller fault diagnosis problems in textile spinning-frame, a method which combines an improved wavelet algorithm and support vector machine is presented. This method extracts feature information of the failure and classifies the failure. Based on the analysis of the frequency overlapping phenomenon encountered in traditional wavelet algorithm, the improved wavelet algorithm combines wavelet transform and FFT analysis to reduce the frequency overlapping phenomenon. The SVM classifiers are adopted because the sample available in the problem of roller fault diagnosis is small. Simulation results demonstrate the improved wavelet algorithm is superior to the common wavelet algorithm for the problem of the spinning-frame roller fault diagnosis, and SVM is very effective for fault identification. Keywords-wavelet analysis; mallat algorithm; vibration signal; fault diagnosis; support vector machine I.

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