Entrepreneurship education has a lot of research on influencing factors of entrepreneurial intention but rarely studies the influence mechanism of emotional competences on entrepreneurial intention from the perspective of social entrepreneurship. This article takes college students’ social entrepreneurs as research objects, drawing on Krueger’s model, theory of planned behavior, social cognitive theory, and triadic reciprocal determinism theory. This paper constructs a conceptual model with emotional ability, entrepreneurial self-efficacy, and entrepreneurial intention, to further study their relationship. The 312 students from China College Students’ Social Entrepreneurship Project engaged in early entrepreneurship practice, conducted a questionnaire survey and used the empirical test of the structural equation model to analyze the relationship between college students’ emotional competences, entrepreneurial self-efficacy, and entrepreneurial intention. The result show: First, social–emotional competence had a positive effect on entrepreneurial intention, and the positive effect of personal affective competence on entrepreneurial intention was not supported or only partially supported. Second, all the dimensions of entrepreneurial self-efficacy were significantly and positively correlated with entrepreneurial intention. Third, emotional competence has a significant positive impact on entrepreneurial self-efficacy. Fourth, entrepreneurial self-efficacy mediated the relationship between emotional competence and entrepreneurial intention.

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Infrared and visible image fusion can compensate for the incompleteness of single-modality imaging and provide a more comprehensive scene description based on cross-modal complementarity. Most works focus on learning the overall cross-modal features by high-and low-frequency constraints at the image level alone, ignoring the fact that cross-modal instance-level features often contain more valuable information. To fill this gap, we model cross-modal instance-level features by embedding instance information into a set of Mixture-of-Experts (MoEs) for the first time, prompting image fusion networks to specifically learn instance-level information. We propose a novel framework with instance embedded Mixture-of-Experts for infrared and visible image fusion, termed MoE-Fusion, which contains an instance embedded MoE group (IE-MoE), an MoE-Decoder, two encoders, and two auxiliary detection networks. By embedding the instance-level information learned in the auxiliary network, IE-MoE achieves specialized learning of crossmodal foreground and background features. MoE-Decoder can adaptively select suitable experts for cross-modal feature decoding and obtain fusion results dynamically. Extensive experiments show that our MoE-Fusion outperforms state-of-the-art methods in preserving contrast and texture details by learning instance-level information in cross-modal images. Our code will be available: https://github.com/SunYM2020/MoE-Fusion.

Background: Evidence of the effectiveness of intervention against extreme heat remains unclear, especially among children, one of the vulnerable populations. This study aimed to evaluate the effectiveness of a primary school-based intervention program against heatwave and climate change in China to provide evidence for development of policies for adaptation to climate change. Methods: Two primary schools in Dongtai City, Jiangsu Province, China, were randomly selected as intervention and control schools (CTR registration number: ChiCTR2200056005). Health education was conducted at the intervention school to raise students’ awareness and capability to respond to extreme heat during May to September in 2017. Knowledge, attitude, and practice (KAP) of students and their parents at both schools were investigated by questionnaire surveys before and after intervention. The changes in KAP scores after intervention were evaluated using multivariable difference-in-difference (DID) analysis, controlling for age, sex, etc. Results: The scores of knowledge, attitude, and practice of students and their parents increased by 19.9% (95%CI: 16.3%, 23.6%) and 22.5% (95%CI: 17.8%, 27.1%); 9.60% (95%CI: 5.35%, 13.9%) and 7.22% (95%CI: 0.96%, 13.5%); and 9.94% (95%CI: 8.26%, 18.3%) and 5.22% (95%CI: 0.73%, 9.71%), respectively, after intervention. The KAP score changes of boys were slightly higher than those of girls. Older students had higher score changes than younger students. For parents, the higher the education level, the greater the score change, and change in scores was greater in females than in males. All the health education activities in the program were significantly correlated with the changes in KAP scores of primary school students after intervention, especially those curricula with interesting activities and experiential learning approaches. Conclusions: Heat and health education program in primary school was an effective approach to improve cognition and behavior for both students and their parents to better adapt to heatwaves and climate change. The successful experience can be generalized to respond to the increasing extreme weather/climate events in the context of climate change, such as heatwaves, and other emergent occasions or public health education, such as the control and prevention of COVID-19.

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