Exploring the digital psychology of environmental sustainability: the mediating influence of technological innovation in advanced physical education development in China".

The study aims to investigate the precise processes by which the advancement of physical education and technological progress leads to ecological conservation efforts within China's distinctive socio-cultural and economic framework. Acknowledging the pivotal role that economic advancement plays in a nation's environmental sustainability, this research utilizes cross-sectional quantitative data gathered using a five-point Likert scale survey. The sample size included 503 undergraduate students from Zhengzhou, China, and structural equation modeling was utilized to analyze the data. The study investigates how technology progress influences the relationship between compatibility, environmental sustainability, and the relative benefits of physical education. It fills the gap in the literature by illuminating how technical innovation and advanced physical education development contribute to China's pursuit of a sustainable environment. The findings emphasize the critical significance of higher physical education in fostering environmental sustainability. Furthermore, the research indicates that students participating in more rigorous physical education programs tend to possess a more well-rounded and mature mindset. This mindset is essential for healthy and long-lasting mental development, motivating individuals to critically consider environmental sustainability. The study provides valuable theoretical and practical insights that can be applied to enhance environmental sustainability in the country.

Named entity recognition of Chinese electronic medical records based on a hybrid neural network and medical MC-BERT.

BACKGROUND: Named entity recognition (NER) of electronic medical records is an important task in clinical medical research. Although deep learning combined with pretraining models performs well in recognizing entities in clinical texts, because Chinese electronic medical records have a special text structure and vocabulary distribution, general pretraining models cannot effectively incorporate entities and medical domain knowledge into representation learning; separate deep network models lack the ability to fully extract rich features in complex texts, which negatively affects the named entity recognition of electronic medical records. METHODS: To better represent electronic medical record text, we extract the text's local features and multilevel sequence interaction information to improve the effectiveness of electronic medical record named entity recognition. This paper proposes a hybrid neural network model based on medical MC-BERT, namely, the MC-BERT + BiLSTM + CNN + MHA + CRF model. First, MC-BERT is used as the word embedding model of the text to obtain the word vector, and then BiLSTM and CNN obtain the feature information of the forward and backward directions of the word vector and the local context to obtain the corresponding feature vector. After merging the two feature vectors, they are sent to multihead self-attention (MHA) to obtain multilevel semantic features, and finally, CRF is used to decode the features and predict the label sequence. RESULTS: The experiments show that the F1 values of our proposed hybrid neural network model based on MC-BERT reach 94.22%, 86.47%, and 92.28% on the CCKS-2017, CCKS-2019 and cEHRNER datasets, respectively. Compared with the general-domain BERT-based BiLSTM + CRF, our F1 values increased by 0.89%, 1.65% and 2.63%. Finally, we analyzed the effect of an unbalanced number of entities in the electronic medical records on the results of the NER experiment.

Innovation in physical education: The role of cognitive factors and self-efficacy.

Among the beliefs related to teaching work, self-efficacy stands out and encourage innovation across the global education systems. Specifically, the lack of interest among instructors in introducing innovative techniques in physical education is a concern across China. Therefore, this study intends to investigate the role of cognitive indicators (mental workload, decision-making process, innovation in physical education, and self-efficacy) of innovation in physical education across China. This study opted for quantitative techniques, including using a structured questionnaire to collect data from targeted respondents through the survey techniques. Moreover, 800 questionnaires were circulated, and as a result, 420 usable responses were attained, making the overall response rate stand at 40%. The results indicate that the above-stated cognitive factors, along with self-efficacy, have a positive role in causing innovation across the physical education exchequer of China. Likewise, self-efficacy played the mediating role between cognitive indicators and innovation in physical education in China. The study has notable theoretical and practical implications for the policymakers in terms of introducing policies that could help increase the cognitive state of educationists, which in turn possibly will help make them pursue innovation within the education system of China.