[1] S. C. Maroo, J. N. Chung, Nano-droplet impact on a homogenous surface using molecular dynamics. ASME Energy Nanotech. Int. Conf. I(2008), 113-121.
[2] N. Sedighi, S. Murad, S. Aggarwal, K, Molecular dynamics simulations of spontaneous spreading of a nanodroplet on solid surfaces. Fluid Dyn. Res. 43(2011), 1-23.
[3] N. Sedighi, S. Murad, S. K. Aggarwal, Molecular dynamics simulations of nanodroplet spreading on solid surfaces, effect of droplet size. Fluid Dyn. Res. 42(2010), 32-45.
[4] H. Hai-Bao, C. Li-Bin, B. Lu-Yao, H. Su-He, Molecular dynamics simulations of the nano-droplet impact process on hydrophobic surfaces. Chinese Phys. B. 23(2014), 1-6.
[5] S. Asadi, Simulation of nanodroplet impact on a solid surface. Inter. J. Nano Dim. 3(2012), 19-26.
[6] س. اسدی، شبیه سازی برخورد نانو قطره به سطح مورب در فرآیند ایجاد پوشش های نانویی توسط دینامیک مولکولی، نشریه علوم و مهندسی سطح، 13(1396)، 41-50.
[7] H. Panahi, S. Asadi, Statistical modeling for oblique collision of nano and micro droplets in plasma spray processes. Int. J. Nanosci. Nanotech. 14(2018), 71-83.
[8] M. H. Esfe, M. Afrand, W.M. Yan, M. Akbari, Applicability of artificial neural network and nonlinear regression to predict thermal conductivity modeling of al2o3–water nanofluids using experimental data. Int. J. Heat Mass Transf. 66(2015), 246-249.
[9] D. Jarušková, A. Kučerová, Estimation of thermophysical parameters revisited from the point of view of nonlinear regression with random parameters. Int. J. Heat Mass Transf. 106(2017), 135-141.
[10] S. Kim, J. Lee, S. Kim, K. S. Cho, Applications of monte carlo method to nonlinear regression of rheological data. Korea. Aust. Rheol. J. 30(2018), 21-28.
[11] Y. Kemari, A. Mekhaldi, M. Teguar, G. Teyssèdre, Nonlinear regression modeling to predict thermal endurance of xlpe material under thermal aging, Int. Conf. Dielectr. (ICD), IEEE, 2018, 1-4.
[12] M. R. Malik, B. J. Isaac, A. Coussement, P. J. Smith, A. Parente, Principal component analysis coupled with nonlinear regression for chemistry reduction. Combust. Flame. 187(2018), 30-41.
[13] C. Li, R. Zhang, J. Li, P. Stoica, Bayesian information criterion for signed measurements with application to sinusoidal signals. IEEE Signal Proc. Let. 25(2018), 1251-1255.
[14] A. Charkhi, G. Claeskens, Asymptotic post-selection inference for the akaike information criterion. Biometrika 105 (2018), 645-664.
[15] H. Panahi, Estimation for the parameters of the burr type xii distribution under doubly censored sample with application to microfluidics data. Int. J. Syst. Assur. Eng. Manage., 1-9.