This article aims to study the dynamic change of teachers’ beliefs among pre-service teachers. A longitudinal design was adopted to investigate English language teaching (ELT) pre-service teachers’ belief change after a 3-month teaching practicum by administering pretest and posttest questionnaires, semistructured interviews, and reflective journals. Repeated measures and paired sample t-test analyses showed significant differences across different aspects of beliefs in all the participants, but belief changes were significant after the practicum only within the experimental group, particularly in the aspects of student management, teaching evaluation, and student learning. In contrast, belief changes were not significant within the control group. Further inductive content analyses of semistructured interviews and reflective journals from the experimental group confirmed these changes and conclusively revealed some potential factors contributing to the changes. The results shed light on how pre-service teachers evolve in their career development and help educators adjust appropriate education policies to improve the quality of English teacher education, particularly in the Chinese context.

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Wind power generation is considered as one of the very promising new energy power generation methods. Offshore wind farms are usually located in open spaces far from the coast, where the wind is strong enough to generate electricity efficiently and reliably. The operation and maintenance of offshore wind power generation is more important, it will greatly affect the life cycle cost, although it is more difficult. Different from the methods used in other papers, this paper uses the Internet of Things (IOT) technology to collect and analyze wind power generation data to accurately and efficiently realize the operation and maintenance of offshore wind power generation. This paper also establishes an economic model for further analysis. Estimated electricity production under real weather is integrated into the model. According to the estimated model, with IOT technology can reduce maintenance costs by about 75% compared to without IOT technology, and according to our operation and maintenance data, we found that the downtime caused by blades, gearboxes, and generators accounted for more than 87% of the total unplanned downtime, and maintenance costs accounted for more than 3/4 of the total maintenance costs. These data have reference significance for the operation and maintenance of offshore wind power generation.