Optimization of environmental engineering practical teaching system based on cellular mechanics principles and construction of multi-dimensional evaluation model

作     者：Meng, Na 

作者机构：Jiangsu Key Laboratory of Industrial Pollution Control and Resource Reuse School of Environment Engineering Xuzhou University of Technology Xuzhou221018 China 

出 版 物：《MCB Molecular and Cellular Biomechanics》 (MCB Mol. Cell. Biomech.)

年 卷 期：2025年第22卷第1期

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 071010[理学-生物化学与分子生物学] 081704[工学-应用化学] 07[理学] 08[工学] 0817[工学-化学工程与技术] 0836[工学-生物工程] 

基　　金：The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (NO. 23KJA610006). The Higher education science research project of Xuzhou University of Technology (NO.YGJ2309) 

主　　题：Cell signaling 

摘      要：In the realm of emerging engineering education, the practical teaching of environmental engineering majors cries out for reform and optimization, which can be analogized to the regulatory mechanisms within cellular molecular biomechanics. Cells maintain their functionality and adaptability through a complex network of molecular interactions and signaling pathways. Similarly, an effective practical teaching system must have a well-structured and optimized framework. This study aims to explore the reform of the practical teaching system for environmental engineering majors in the context of emerging engineering education. A multi-dimensional evaluation model was constructed based on the Analytic Hierarchy Process (AHP), and the heuristic algorithm was integrated for weight optimization. The results show that the Improved Genetic Particle Swarm Optimization (IG-PSO) exhibits significant advantages in optimizing the weights of various indicators. After optimization, its Consistency Ratio (CR) decreased to 0.07, representing a 53% and 46% improvement over Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively. Additionally, the fitness value of IG-PSO after 800 iterations reached 0.046, significantly outperforming other comparative algorithms. Furthermore, the assessment of teaching effects in dimensions like experimental performance and innovation ability parallels the overall functionality and responsiveness of a cell. The IG-PSO-optimized evaluation system achieved an excellent score of over 90 in the assessment of actual teaching effectiveness across dimensions such as experimental performance and innovation ability. It shows that the teaching system is a healthy, well-regulated cell that can effectively perform its functions and adapt to different educational needs. Through the analogy with cellular molecular biomechanics, we can gain a deeper understanding of the improvement and optimization of the practical teaching system of environmental engi