Advances in nano research

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Application of machine learning and deep neural network for wave propagation in lung cancer cell

  • Xing, Lumin (The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital) ;
  • Liu, Wenjian (City University of Macau) ;
  • Li, Xin (Shandong University of Political Science and Law) ;
  • Wang, Han (Zhuhai Institute of Advanced Technology Chinese Academy of Sciences) ;
  • Jiang, Zhiming (The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital) ;
  • Wang, Lingling (Shandong Academy of Chinese Medicine)
  • Received : 2022.03.15
  • Accepted : 2022.05.06
  • Published : 2022.09.25
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Abstract

Coughing and breath shortness are common symptoms of nano (small) cell lung cancer. Smoking is main factor in causing such cancers. The cancer cells form on the soft tissues of lung. Deformation behavior and wave vibration of lung affected when cancer cells exist. Therefore, in the current work, phase velocity behavior of the small cell lung cancer as a main part of the body via an exact size-dependent theory is presented. Regarding this problem, displacement fields of small cell lung cancer are obtained using first-order shear deformation theory with five parameters. Besides, the size-dependent small cell lung cancer is modeled via nonlocal stress/strain gradient theory (NSGT). An analytical method is applied for solving the governing equations of the small cell lung cancer structure. The novelty of the current study is the consideration of the five-parameter of displacement for curved panel, and porosity as well as NSGT are employed and solved using the analytical method. For more verification, the outcomes of this reports are compared with the predictions of deep neural network (DNN) with adaptive optimization method. A thorough parametric investigation is conducted on the effect of NSGT parameters, porosity and geometry on the phase velocity behavior of the small cell lung cancer structure.

Keywords

Acknowledgement

This work was supported d in part by the Academic promotion programme of Shandong First Medical University under Grant 2019LJ005.

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