PurposeAs the core technology of blockchain, various consensus mechanisms have emerged to satisfy the demands of different application scenarios. Since determining the security, scalability and other related performance of the blockchain, how to reach consensus efficiently of consensus mechanism is a critical issue in the blockchain.Design/methodology/approachThe paper opted for a research overview on the blockchain consensus mechanism, including the consensus mechanisms' consensus progress, classification and comparison, which are complemented by documentary analysis.FindingsThis survey analyzes solutions for the improvement of consensus mechanisms in blockchain that have been proposed during the last few years and suggests future research directions around consensus mechanisms. First, the authors outline the consensus processes, the advantages and disadvantages of the mainstream consensus mechanisms. Additionally, the consensus mechanisms are subdivided into four types according to their characteristics. Then, the consensus mechanisms are compared and analyzed based on four evaluation criteria. Finally, the authors summarize the representative progress of consensus mechanisms and provide some suggestions on the design of consensus mechanisms to make further advances in this field.Originality/valueThis paper summarizes the future research development of the consensus mechanisms.

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This study proposes a primary node election method based on probabilistic linguistic term set (PLTS) for the practical Byzantine fault tolerance (PBFT) consensus mechanism to effectively enhance the efficiency of reaching consensus. Specifically, a novel concept of the probabilistic linguistic term set with a confidence interval (PLTS-CI) is presented to express the uncertain complex voting information of nodes during primary node election. Then, a novel score function based on the exponential semantic value and confidence approximation value for the PLTS-CI, called Score-ESCA, is used to solve the problems of comparing different nodes with various voting attitudes. This method helps select the node with the highest score by utilizing complex decision attitudes, making it an accurate primary node election solution. Furthermore, the feasibility of our proposed method is proved by both theoretical analysis and experimental evaluations.

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Teaching for creativity (TfC) has received increasing attention as an important way to cultivate students’ creative thinking and behaviors. The purpose of this study is to examine the mediating role of teachers’ work engagement (WE) on the relationship between their emotional intelligence (EI) and teaching for creativity. The study is a cross-sectional design. The sample of the study is 3,307 secondary school English teachers working in Jilin Province, China. The findings show that the teachers’ perceptions of emotional intelligence, work engagement and teaching for creativity are relatively high. The findings confirm the hypotheses. The results of structural equation modeling and bootstrapping show that teachers’ emotional intelligence is positively correlated with work engagement and teaching for creativity, and teachers’ work engagement mediates the relationship between emotional intelligence and teaching for creativity.