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In this study, artificial neural network (ANN) with genetic algorithm (GA) as a meta-modeling tool was employed to model the change in PSD during fluidization.
A secant-modulus approach was employed to model the pre-peak nonlinear damage evolution, while the post-peak softening was modeled using the smeared crack approach (SCA).
The suggested dual-objective model was employed to model steel green supply chain.
The hidden layer of 90 wavelet neurons (wavelons) was employed to model the desired VTEC value.
The Chemical Engineering Module was employed to model the transport and diffusion phenomena.
A voxel-based approach was employed to model the inhomogeneous oral tissues.
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The SAP2000 20100) software was employed to modeling the reinforced concrete frame braced with ODBS system.
Point processes were employed to model the spike trains.
Thereafter, CFD is employed to model and simulate the refrigerator.
Multi-step kinetics is employed to model n-heptane oxidation.
And the piecewise linear isotropic plasticity model is employed to model the steel reinforcement.
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