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The model utilize a set of empirically derived nonlinear equations to predict the homeostatic concentrations of fasting insulin and glucose, which reflect the varying degrees of pancreatic β-cell function and insulin resistance [ 7].
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The model utilizes a layover approach between ground penetrating radar (GPR) and infrared thermography (IR).
On the other hand, Projection Augmented Model utilized a physical model, which is projected with computer images.
The 3D model utilizes a finite differencing (FD) heat transfer algorithm, complemented with experimental boundary conditions.
The critical deposition model utilizes a more robust generalized two-parameter rheology model to account for any given slurry rheology.
The model utilizes a work energy criterion that calculates work required to induce the transformation by overcoming an energy barrier.
The model utilizes a vorticity formulation of the transport equations, which essentially eliminates the pressure field calculation and speeds convergence.
The through-the-thickness analysis is based on the model utilizing a beam on an elastic foundation.
The uncertain natures of demands and replenishment lead times are incorporated into the model utilizing a queuing approach.
The model utilized a declining xylan hydrolysis rate constant with increasing conversion in combination with direct xylooligomer degradation.
The model utilized a top-down approach, including data for production, consumption, loss, and trade flows of aluminum.
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