Objective: This study aims to explore the factors influencing Chinese parents’ attitudes toward death education. Given the current lack of such education in China, this research is particularly significant. Death education is vital for shaping the values of young people and alleviating mental health issues, such as depression and suicidal tendencies. By identifying these influencing factors, this study seeks to provide guidance for policymakers and educators in promoting the development and widespread adoption of death education.Methods: To do so, a national cross-sectional quota sample of 12,435 Chinese parents was used. Borrowing from social-ecological theory, the researchers carried out multiple stepwise regression analyses to examine the individual, family, and social-level factors that shape the supportive attitudes of Chinese parents toward death education.Results: The findings revealed that at the individual level, parent (β = 0.04, p < 0.001), education level (β = 0.07, p < 0.001), and religious belief (β = −0.02, p < 0.05) were significant predictors of Chinese parents’ support for death education. Meanwhile, at the family and social level, average monthly household income (β = 0.07, p < 0.001), family health (β = 0.03, p < 0.05), family communication (β = 0.02, p < 0.05), social support (β = 0.15, p < 0.001), neighborhood relations (β = 0.11, p < 0.001), and social network size (β = 0.05, p < 0.001) were significant predictors of Chinese parents’ supportive attitudes toward death education.Conclusion: Based on these findings, it is suggested that the relevant development, planning, publicity, and public welfare groups and government departments should promote death education, provide more social support, and encourage neighborhood harmony. As higher education and average monthly household income were found to significantly impact the support, the government should improve access to higher education and actively work to increase residents’ income to facilitate the development of death education.

Background: Educational institutions worldwide have experienced the suspension of offline teaching activities in favor of online teaching due to the outbreak of the COVID-19 pandemic. However, few studies have focused on the degree of support for online learning among college students in mainland China. Therefore, the aim of this study was to investigate the degree of support for online learning among Chinese college students during the epidemic and whether depression, loneliness, family communication, and social support were associated factors. Methods: A questionnaire was used to collect cross-sectional data from 9319 college students in mainland China, and a structural equation model was analyzed. Results: The results of the study showed high degrees of support for online learning among Chinese college students during the COVID-19 pandemic, with more than half expressing support. The SEM (Structural Equation Modeling) results showed that depression had a negative and significant effect on college students’ support for online learning (β = −0.07; p < 0.001); family communication had a positive and significant effect on college students’ support for online learning (β = 0.09; p < 0.001); social support had a positive and significant effect on college students’ support for online learning (β = 0.11; p < 0.001). Conclusions: Social support and family communication can alleviate the negative psychological status of college students, and depression plays a mediating role in the effect of social support and family communication on college students’ degree of support for online learning. In addition, a significant chain-mediating effect was found of family communication, loneliness, and depression between social support and college students’ degree of support for online learning. Government and education institutions must focus on college students’ mental health issues and consider family interventions and general support that college students require.

In the industrial Internet of Things, water and electricity is the most important hidden project. Its requirements are very high, especially the intelligent control of water and electricity. Therefore, a design and application of system with dual-control of water and electricity is proposed and convolutional neural networks–based video recognition technology is used to identify the security issues that occur in the field. Some sensors are used to control the use of water and electricity, while others are used to collect scalar data which include user’s data and video multimedia data in the wireless sensor network. The scalar data are used to update the user’s database, and the video multimedia data are used to monitor and prevent anomalies from occurring in the field of dual-control of water and electricity. In order to solve the security problem of the user in wireless sensor network, this article proposes a radio frequency identification mutual security authentication protocol based on shared secret hash function. Finally, experiments show that the proposed secure authentication protocol can guarantee the secure transmission of data between the sensor node and the server, and the video recognition technology can recognize some abnormalities well.