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This study explored the relationship between teacher job stress and job burnout using a sample of 558 primary and secondary school teachers, who were administered with a teacher job stress scale, teacher job burnout scale, work–family conflict questionnaire, and general self-efficacy scale. The results showed that: (1) job stress had a significant predictive effect on work–family conflict and job burnout; (2) work–family conflict played a mediating role in the relationship between job stress and job burnout; (3) self-efficacy was found to play a moderating role in work–family conflict and job burnout. However, this indirect effect was stronger for teachers with high self-efficacy, which means that the protective effects of self-efficacy were limited. These findings add to research on the relationship between teacher job stress and job burnout, and provide ideas for teachers to balance work–family relationships and reduce job burnout.

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ObjectiveThis study examined problematic mobile phone use (PMPU) and its relationship with life satisfaction in Chinese university students during the pandemic.MethodsAn anonymous online survey was conducted in a university in China. The Mobile Phone Addiction Index (MPAI) and the Satisfaction with Life Scale (SWLS) were used to assess the severity of problematic mobile phone use and life satisfaction, respectively. Data on demographic and health-related factors were also collected.ResultsA total of 1,491 undergraduate students (73.3% were male) completed the survey. On average, students in the survey reported spending 7.4 ± 4.3 h/day on phone use. Their MPAI score was 38.1 ± 13.3 and SWLS score was 24.9 ± 6.8, respectively. After controlling for confounding factors, the MPAI score was significantly associated with lower life satisfaction. Multiple linear regression revealed that higher monthly allowances, frequent insomnia, longer phone use duration were significantly associated with PMPU.ConclusionUniversity students in China spend nearly half of their waking hours on mobile phone use, significantly longer than before the COVID-19 pandemic. PMPU is associated with insomnia, lower life satisfaction and higher allowances. If the trend continues after the pandemic, interventions may be needed. Increase in-person interactions, limiting online social and gaming time, awareness campaign may be effective in reducing the impact of PMPU and improve life satisfaction.

Rolling bearing fault diagnosis is conventionally performed by vibration-based diagnosis (VBD). However, VBD is restrained in some cases because vibration measurement usually requires the contact with the machine. Acoustical-based fault diagnosis (ABD) has the advantage of non-contact measurement over VBD. However, ABD has received little attention and rarely applied in bearing fault diagnosis. In this paper, a new non-contact ABD method for rolling bearings using acoustic imaging and convolutional neural networks (CNN) is proposed. Firstly, a microphone array is used to acquire the acoustic field radiated by rolling bearings. Then, acoustic imaging is performed with the wave superposition method (WSM). The reconstructed acoustic images can depict the spatial distribution of the acoustic field, which add a new spatial dimension in the acoustic data representation for fault diagnosis and makes it possible to localize the sound sources. Finally, CNN is applied to accomplish bearing fault diagnosis, which can overcome the problems of handcrafted feature extraction in traditional ABD methods. Experimental results verify the effectiveness of the proposed ABD method. Comparisons with peer state-of-the-art ABD methods further validate that the proposed method can mitigate the drawbacks of the existing ABD methods, and obtain more accurate and reliable diagnosis results.INDEX TERMS Bearing fault diagnosis; acoustic imaging; CNN; wave superposition method; acousticalbased fault diagnosis