Anum Shafiq

1. A. Shafiq, A. B. Çolak, T. N. Sindhu, Q. M. Al-Mdallal, and T. Abdeljawad. Estimation of unsteady hydromagnetic Williamson fluid flow in a radiative surface through numerical and artificial neural network modeling. Scientific Reports, 11 (1) (2021): 1-21.
2. A. Shafiq, S. A. Lone, Tabassum Naz Sindhu, Q. M. Al-Mdallal, and G. Rasool. Statistical modeling for bioconvective tangent hyperbolic nanofluid towards stretching surface with zero mass flux condition. Scientific Reports, 11 (1) (2021): 1-11.
3. A. Shafiq, G. Rasool, H. Alotaibi, H. M. Aljohani, A. Wakif, I. Khan, and S. Akram. Thermally Enhanced Darcy-Forchheimer Casson-Water/Glycerine Rotating Nanofluid Flow with Uniform Magnetic Field. Micromachines, 12 (6) (2021): 605.
4. A. Shafiq, T. N. Sindhu, and Q. M. Al-Mdallal. A sensitivity study on carbon nanotubes significance in Darcy–Forchheimer flow towards a rotating disk by response surface methodology. Scientific Reports 11 (1) (2021): 1-26.
5. A. Shafiq, F. Mebarek-Oudina, T. N. Sindhu, and A. Abidi. A study of dual stratification on stagnation point Walters' B nanofluid flow via radiative Riga plate: a statistical approach. The European Physical Journal Plus, 136 (4) (2021): 1-24.
6. I. Zari, A. Shafiq, T. S. Khan and S. Haq. Marangoni convective flow of GO-kerosene-and GO-water-based Casson Nanoliquid toward a Penetrable Riga Surface. Brazilian Journal of Physics, (2021): 1-16.
7. F. Mabood, A. Shafiq, W. A. Khan and I. A. Badruddin, (2021), MHD and nonlinear thermal radiation effects on hybrid nanofluid past a wedge with heat source and entropy generation, International Journal of Numerical Methods for Heat & Fluid Flow, Vol. ahead-of-print No. ahead-of-print.
8. G. Rasool, A. Shafiq, M. S. Alqarni, A. Wakif, I. Khan, and M. Shoaib Bhutta. Numerical Scrutinization of Darcy-Forchheimer Relation in Convective Magnetohydrodynamic Nanofluid Flow Bounded by Nonlinear Stretching Surface in the Perspective of Heat and Mass Transfer. Micromachines 12 (4) (2021): 374.
9. T. N. Sindhu, A. Shafiq, Q. M. Al-Mdallal. On the Analysis of Number of Deaths due to Covid-19 Outbreak data Using A New Class of Distributions. Results in Physics, (2021) 103747.
10. T. N. Sindhu, A. Shafiq, Q. M. Al-Mdallal. Exponentiated transformation of Gumbel Type-II distribution for modeling COVID-19 data. Alexandria Engineering Journal 60 (1) (2021), 671-689.