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The local vibrations are predicted using an isolated part of the panel, the I-beam model.

Vegetation changes through time under alternative future scenarios are predicted using an individual-based plant community model.

Global ionospheric VTEC maps are predicted using an adaptive autoregressive model, as described in "Basic methodology of ionospheric forecasting" section.

Initially, genes are predicted using an optimal set of the di-codon regression models (bacterial, archaeal or prophage models).

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

The measured critical loads are predicted using a rate dependent slow crack growth (RDEASCG) model.

Free and forced vibrations of a tyre are predicted using a wave/finite element (WFE) approach.

The ignition and oxidation of n-heptane are predicted using a reaction mechanism consisting of 34 species and 56 steps.

It associates the advantages of existing models in that different receiving states are predicted using a deterministic approach, whereas the channel behavior is simulated using adapted statistical laws.

Motif occurrences are predicted using a Bayes-optimal classifier that has been described previously by Bailey and Elkan (1994).

Traditionally, univariate biological activities are predicted using a range of methods, including classical regression, support vector machines and Random Forests.

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