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The experimental measurements have been predicted using a numerical model that considers various void fraction and pressure drop correlations from the literature for estimation of measured parameters.

The damping properties of unidirectional, laminated, and woven composites have been predicted using a multiscale implementation of the High-Fidelity Generalized Method of Cells micromechanics theory.

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Deflection of these GFRP rebar reinforced beams has been predicted using an earlier model available in the literature.

The transmembrane sequences were predicted using a transmembrane prediction algorithm (see Table 1) and docked together using the bovine rhodopsin structure as a scaffold.

Instead of a cross-validation scheme, these new samples were predicted using a class prediction scheme.

Analysis of long signal peptides was performed in two steps: First, potential domains were predicted using a novel machine-learning technique for turn prediction [33].

Genes were predicted using a combined strategy [ 79] based on the CDS prediction programs Glimmer [ 80] and Critica [ 81].

Physicochemical properties were predicted using a bioinformatics tool (ProtParam in Expasy ProtParam) (Chaparro and Da Silva Junior 2016).

As it was predicted, using a mixture of balls with different diameters causes better millings results.

Temporal changes are predicted using a probabilistic Markovian process and spatial interaction through cellular automation.

Compression failure in the laminates was predicted using a stress-based criterion.

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