BackgroundThe pandemic of Coronavirus Disease 2019 has burdened an unprecedented psychological stress on the front-line medical staff, who are at high risk of depression. While existing studies and theories suggest that factors such as gratitude, social support, and hope play a role in the risk of depression, few studies have combined these factors to explore the relationship between them.ObjectiveThis study examined the mediating roles of social support and hope in the relationship between gratitude and depression among front-line medical staff during (...) the pandemic of COVID-19.MethodsThis study used the Gratitude Questionnaire, the Perceived Social Support Scale, the State Hope Scale, and the Center for Epidemiologic Studies Depression Scale to examine the gratitude, social support, hope, and depression among 344 front-line medical workers in Wuhan, which was the hardest-hit area of COVID-19 in China.ResultsThe results showed that the prevalence of mild depressive disorder was 40.12% and the prevalence of major depressive disorder was 9.59% among front-line medical staff during the pandemic of COVID-19; gratitude has a direct and negative effect on depression. Gratitude was negative predictors of depression through the mediating variables of social support and hope [βgratitude–social support–depression = −0.096, 95%CI; βgratitude–hope–depression = −0.034, 95%CI], as well as via an indirect path from social support to hope [βgratitude–social support–hope–depression = −0.089, 95%CI ].ConclusionThe study findings indicate that gratitude as a positive emotion can reduce depression in medical staff by promoting social support and hope, respectively. Gratitude also reduced depression in health care workers through a chain mediating effect of social support and hope. Overall, gratitude can directly foster social support and hope, and protect people from stress and depression, which has implications for clinical interventions among front-line medical staff during the pandemic of COVID-19. (shrink)

The paper presents a Chinese philosophical point of view of AI, and presents a novel system of the AI machine. There are two basic relations or contradictions which drive computer developments forward. One is between software and hardware and the other is between data structure and system organization. It is suggested that a description of a future AI system should primarily start from these contradictions.

The problem of the dynamic of development is a fundamental problem over which materialist dialectics has been struggling with metaphysics for a long time. The metaphysical cant of the "gang of four," which frantically promoted the "philosophy of struggle," distorted and perverted this problem beyond recognition. In order to correct the confusion and accelerate the realization of the four modernizations, a thorough critique of the false doctrines of the "gang of four" must provide a correct response to this problem.

With the prevalence of values and the advent of the idea of rationalist education, the values characteristic of distinct subjectivity and affectiveness face many theoretical and practical problems when taught within the framework of modern education, which seeks certainty of knowledge. The challenges that values education encounters in today’s world urgently demand that we return to the origins of human spiritual life. We must be informed by the Marxist disclosure of the intrinsic value of human existence, and grasp the intrinsic (...) isomorphism between values education and human existence within the web of original and historical dialectic relations by relying on a complementary understanding of values and the essence of education: real men possess a logic of existence that endows value independence and consciousness of edification, and on the basis of the nature of the value of human existence, values education obtains a solid foundation both in the sense of fact and of value. Human existence thus displays the value content of self-selection, self-edification, and self-improvement peculiar to it due to its mutual dependence on and fulfillment of values education. (shrink)

In school education, teaching-learning interaction is deemed as a core process in the classroom. The fundamental neural basis underlying teaching-learning interaction is proposed to be essential for tuning learning outcomes. However, the neural basis of this process as well as the relationship between the neural dynamics and the learning outcomes are largely unclear. With non-invasive technologies such as fNIRS, hyperscanning techniques have been developed since the last decade and been applied to the field of educational neuroscience for simultaneous multi-brain scanning. (...) Hyperscanning studies suggest that the interpersonal neural synchronization during teaching-learning interaction might be an ideal neural biomarker for predicting learning outcomes. To systematically evaluate such a relationship, this meta-analysis ran on a random-effects model on 16 studies with 23 independent samples. Further moderator analyses were also performed to examine the potential influences of the style, mode, content, and the assessment method of learning outcomes. The random-effects modeling results confirmed a robust positive correlation between INS and learning outcomes. Subsequent analyses revealed that such relationship was mainly affected by both interaction style and mode. Therefore, the present meta-analysis provided a confirmatory neurocognitive foundation for teaching-learning interaction, as well as its relation to the learning outcomes, consolidated future learning and teaching studies in various disciplines including second language education with a firm methodological reference. (shrink)