Meta-analysis was used to investigate the impact of entrepreneurship education on college students’ entrepreneurial intention. Based on the rules of meta-analysis, 389 empirical studies were selected from more than 1000 entrepreneurship education-related documents. The current study processed and analyzed data from 36 records (including 24 journal articles, 11 master’s theses, and 1 doctoral thesis) with a total sample of 29,736 students. The results suggested that: (1) entrepreneurship education is positively associated with entrepreneurial intention; (2) national context differences among students have a significant and moderating effect on the relationship between entrepreneurship education and entrepreneurial intention. As compared to other countries, Chinese college students’ entrepreneurial intention is more strongly related with entrepreneurship education.

This research aims to explore how the COVID-19 pandemic has affected college students’ entrepreneurial intention (EI), as well as whether the well-studied link between entrepreneurial alertness (EA) and EI is involved. Data were collected from 612 respondents, and using the stepwise regression method we examined the moderating role of college students’ perceived risk of COVID-19 on the connection between EA and EI. The results show that students’ perceived risk of COVID-19 reduces their EI. Furthermore, the perceived risk of COVID-19 attenuates the relationship between EA and EI. Specifically, those who perceived a greater risk tended to show lower EI. This article contributes to a better understanding of how the relationship between EA and EI has changed during the pandemic.

Heavy metal contamination in urban land has become a serious environmental problem in large cities. Visible and near-infrared spectroscopy (vis-NIR) has emerged as a promising method for monitoring copper (Cu), which is one of the heavy metals. When using vis-NIR spectroscopy, it is crucial to consider sample similarity. However, there is limited research on studying sample similarities and determining their relative importance. In this study, we compared three types of similarities: spectral, compositional, and spatial similarities. We collected 250 topsoil samples (0–20 cm) from Shenzhen City in southwest China and analyzed their vis-NIR spectroscopy data (350–2500 nm). For each type of similarity, we divided the samples into five groups and constructed Cu measurement models. The results showed that compositional similarity exhibited the best performance ($R_p^2$ = 0.92, RPD = 3.57) and significantly outperformed the other two types of similarity. Spatial similarity ($R_p^2$ = 0.73, RPD = 1.88) performed slightly better than spectral similarity ($R_p^2$ = 0.71, RPD = 1.85). Therefore, we concluded that the ranking of the Cu measurement model’s performance was as follows: compositional similarity > spatial similarity > spectral similarity. Furthermore, it is challenging to maintain high levels of similarity across all three aspects simultaneously.

With the rapid expansion of innovation and entrepreneurship education (IEE), understanding the competencies of university teachers in this field has become increasingly important. This study employs the latent profile analysis (LPA) to identify and examine distinct competency subgroups among university IEE teachers. The study surveyed 396 university teachers specializing in IEE in China. The findings indicated that IEE competencies encompassed eight dimensions: entrepreneurial mindset, professional mindset, ambition, optimism, self-efficacy, professional knowledge, enterprising skills, and teaching ability. Latent profile analysis identified three distinct subgroups of IEE teachers: the high IEE-competent group, the average IEE-competent group, and the moderate IEE-competent group. Moreover, multinomial logistic regression analysis demonstrated that university IEE teachers with prior experience in entrepreneurship management or innovation-entrepreneurship research possessed significantly stronger instructional competencies in IEE than those without such knowledge. These results provide empirical evidence linking teachers' personal and professional backgrounds to their IEE instructional practices, and highlight the critical role of experiential learning in developing pedagogical competence.

Monitoring soil heavy metal contamination in urban land to protect human health requires rapid and low-cost methods. Visible and infrared (vis-NIR) spectroscopy shows strong promise for monitoring metals such as copper (Cu). However, an important question is how “spectrally nearby” samples influence Cu estimation models. This study investigates that issue in depth. We collected 250 soil samples from Shenzhen City, China (the world’s tenth-largest city). During building the model, we selected spectrally nearby samples for each validation sample, varying the number of neighbors from 20 to 200 by adding one sample at a time. Results show that, compared with the traditional method, incorporating nearby samples substantially improved Cu prediction: the coefficient of determination in prediction (${\mathrm{R}}_{\mathrm{p}}^2$) increased from 0.75 to 0.92, and the root mean square error of prediction (RMSEP) decreased from 8.56 to 4.50 mg·kg⁻¹. The optimal number of nearby samples was 125, representing 62.25% of the dataset. And the performance followed an L-shape curve as the number of neighbors increased—rapid improvement at first, then stabilization. We conclude that using spectrally nearby samples is an effective way to improve vis-NIR Cu estimation models. The optimal number of neighbors should balance model accuracy, robustness, and complexity.

[This corrects the article DOI: 10.3389/fpsyg.2026.1703298.].