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We showed that the integration of transcriptomics and proteomics data at the network level could provide enhanced predictions of components important for virulence.
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A numerical method is proposed for the design and the lifetime prediction of components under thermal fatigue load.
Cumulative fatigue damage analysis plays a key role in life prediction of components and structures subjected to field load histories.
A physics-based life prediction model considering the interactions of deformation and environmental damage is reviewed in terms of its applicability to life prediction of components.
However, as the 9%Cr material is relatively new, there is relatively limited data available and understanding with respect to quantifying the effect of variables on life prediction of components fabricated from P91 is more difficult.
Prediction of components that were missing from RAST subsystems were identified with the Hidden Markov Model (HMM -based self-training system, GeneMarkS [11] for protein-coding genes and RNAmmer [34] for ribosomal RNA (rRNA) genes.
A convincing prediction of components concentrations in the stirred tank bioreactor was achieved.
In turbomachinery, the prediction of component life is crucial.
The ultimate goal of the thermal design is not the prediction of component temperatures, but rather the reduction of risk, which is thermally associated to the product [1].
In contrast to the mean-line based filters, morphological filters are function oriented and more suitable for the functional prediction of component performance.
Combining both amorphous components into a single component variable for regression resulted in lower error statistics and equally good predictions of crystalline components.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com