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During the design stage, realistic life time predictions for components help to reduce test cycles and allow for an optimization with respect to both production and repair costs.
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The cross-validation results show that the proposed framework allows for accurate and fast thermal field prediction for components with different process settings and geometric designs.
Further steps could be (i) to compare predictions with already published experimental evidence in target species, (ii) to predict signaling reactions in other species, and (iii) to use a database that explicitly allows orthology-based predictions for pathway components between organisms.
We showed that the integration of transcriptomics and proteomics data at the network level could provide enhanced predictions of components important for virulence.
Performance prediction methods for component-based software systems aim at supporting design decisions of software architects during early development stages.
Existing reliability prediction methods for component-based systems use Markov models and assume that the software architect can provide the transition probabilities between individual components.
In this paper, we present a formal performance prediction approach for component-based systems, which is parameterisable for the number of CPUs or CPU cores.
Fatigue life prediction for notched components is an essential step within the design process of machines.
Obtained results indicate that sliding leads to a significant reduction of contact fatigue life, and rough surface asperity contacts result in accelerated pitting failure that needs to be considered in life predictions for various mechanical components.
A comparison of the performances of the two methods shows that the HBM can capture the well-known jump phenomenon, but is restricted by computational limits for some strongly nonlinear systems and can fail to provide accurate predictions for some harmonic components.
However, the sum of the biomass predictions for the root components will not equal the biomass prediction for the root system, and a desired and logical feature of the root component regression equations is that the sum of biomass predictions of the root components equals the prediction for the root system.
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CEO of Professional Science Editing for Scientists @ prosciediting.com