A combination of FDM, EWM, and GRA to measure performance in higher vocational college: An empirical case from China.

Given the problems of unclear division of evaluation factors, inadequate utilization of objective data and unreasonable distribution of weight in college performance measurement, this study extracts relevant indicators and quantitative data from the survey report of administrational institutions, and constructs a comprehensive performance measurement model based on the integration of fuzzy Delphi method (FDM), Entropy weight method (EWM), and grey relational analysis (GRA). The study seeks to identify differences in measurement analysis by comparing the weights, the performance and the rankings of the alternatives about higher vocational colleges in Zhejiang province. Finally, the results show the following: (1) by FDM, 33 indicators about colleges' performance are selected to form the evaluation system of the college's performance, (2) the indicators' weights are obtained through EWM and GRA, and a nonparametric test shows that there is no significant difference between the two types of weights, (3) the grey relational degrees of the alternatives are obtained and ranked on the basis of comprehensive evaluation model. By nonparametric test, there is a significant difference between the two types of relational degrees. On the contrary, no significant difference is found in the ranking of relational degrees, (4) based on the analysis results, this study further compares the performance of alternatives in different forms. There are significant differences between the performance of public colleges and private colleges, while no significant differences are observed between the performance of vocational colleges in Hangzhou and non-Hangzhou. Given the reliability and validity of the model, the comprehensive measurement model provides a relatively objective reference for college governance and administrational institutions, and also becomes an effective tool of colleges' evaluation to assist and improve the management practice in educational institutions.

Anti-asthmatic miR-224-5p inhibits the FHL1/MAPK pathway to repress airway smooth muscle cell proliferation in a murine model of asthma-like airway inflammation.

BACKGROUND: The proliferation of airway smooth muscle cells (ASMCs) contributes to the contractility and inflammation in the pathophysiology of asthma. This intrigued us to clarify the effect of microRNA (miR)-224-5p on biological characteristics of ASMCs in mice with asthma-like airway inflammation and responses through the FHL1-dependent MAPK pathway. METHODS: An ovalbumin (OVA)-induced asthma mouse model was established, where ASMCs were isolated. The expression of FHL1 was determined in asthmatic mice. Artificial modulation of FHL1 expression was performed to explore its effect on airway inflammation of asthmatic mice and ASMC proliferation and apoptosis. Afterwards, we analyzed the interaction among miR-224-5p, FHL1 and the MAPK pathway, and explored their combined impacts on airway inflammation of asthmatic mice and ASMC proliferation and apoptosis. RESULTS: FHL1 was highly expressed and miR-224-5p was poorly expressed in asthmatic mice. FHL1 was verified to be a target of miR-224-5p. Loss of FHL1 function reduced airway inflammation in asthmatic mice and proliferation of ASMCs while inducing their apoptosis. Besides, miR-224-5p inhibited the MAPK pathway by binding to FHL1. Overexpression of miR-224-5p relieved airway inflammation, inhibited ASMC proliferation, and increased apoptosis, which could be reversed by overexpression of FHL1. CONCLUSION: Altogether, miR-224-5p inhibited airway inflammation in asthmatic mice and ASMC proliferation through blocking the MAPK pathway by down-regulating FHL1.

A Study of English Learning Vocabulary Detection Based on Image Semantic Segmentation Fusion Network.

College students learn words always under both teachers' and school administrators' control. Based on multi-modal discourse analysis theory, the analysis of English words under the synergy of different modalities, students improve the motivation and effectiveness of word learning, but there are still some problems, such as the lack of visual modal memory of pictures, incomplete word meanings, little interaction between users, and lack of resource expansion function. To this end, this paper proposes a stepped image semantic segmentation network structure based on multi-scale feature fusion and boundary optimization. The network aims at improving the accuracy of the network model, optimizing the spatial pooling pyramid module in Deeplab V3+ network, using a new activation function Funnel ReLU (FReLU) for vision tasks to replace the original non-linear activation function to obtain accuracy compensation, improving the overall image segmentation accuracy through accurate prediction of the boundaries of each class, reducing the intra-class error in the prediction results. The accuracy compensation is obtained by replacing the original linear activation function with FReLU. Experimental results on the Englishhnd dataset demonstrate that the improved network can achieve 96.35% accuracy for English characters with the same network parameters, training data and test data.