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Design and Study of Mountaineering Wear Based on Nano Antibacterial Technology and Prediction Model

Author

Listed:
  • Chun-feng Xia

    (Zhejing Industry Polytechnic College, China)

  • Jiang Wu

    (Zhejing Industry Polytechnic College, China)

  • Wei Wang

    (Zhejing Industry Polytechnic College, China)

Abstract

In order to improve function of mountaineering wear to promote the development of outer mountaineering wear industry, nano antibacterial technology is applied to make mountaineering wear. Firstly, the antibacterial properties of nano materials are discussed. Secondly, the antibacterial performance experiment of nano Ag ion is design, and experimental results show that the antibacterial performance of nano Ag ion, and nano Ag ion has better antibacterial effect on staphylococcus aureus, Escherichia coli, and candida albicans. Thirdly, the antibacterial performance prediction model of nano materials is constructed based on wavelet neural network, and then the training algorithm is designed. Finally, the prediction simulation analysis of antibacterial performance of nano Ag ion in moutaineering wear is carried out, results show that the wavelet neural network has good prediction effect, prediction results from wavelet neural network are agreed with real values, therefore the wavelet neural network has higher prediction precision.

Suggested Citation

  • Chun-feng Xia & Jiang Wu & Wei Wang, 2022. "Design and Study of Mountaineering Wear Based on Nano Antibacterial Technology and Prediction Model," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 17(1), pages 1-16, January.
    • Handle: RePEc:igg:jhisi0:v:17:y:2022:i:1:p:1-16
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    References listed on IDEAS

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    1. Nie, Xiaobing & Liang, Jinling & Cao, Jinde, 2019. "Multistability analysis of competitive neural networks with Gaussian-wavelet-type activation functions and unbounded time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 356(C), pages 449-468.
    2. Li Zhang & Wenfang Zhang & Jinxin Liu & Tong Zhao & Liang Zou & Xinghua Wang, 2017. "A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network," Energies, MDPI, vol. 10(12), pages 1-13, December.
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