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The proposed modeling method takes advantages of both physical and empirical modeling approaches, can accurately predict the transient behaviors in real-time and significantly reduce the computational burden.
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Simulated aberrant segments are sampled from the same distribution, and the sojourn modeling in our method takes advantage of this property.
This manuscript presents a stochastic model updating method, taking both uncertainties in models and variability in testing into account.
Compared with other simplified models, the calculation model in API RP 11L method takes into account both static load and dynamic load, so it is more precise and reliable.
To determine parameters of the CRF model for training data, the method takes several protein-RNA pairs with their sequences, and known pairs of positions that a residue and a base interact.
Our method takes as input a solid model defined by its triangulated boundary surface.
This method takes the benefits of background modelling and the invariant feature detector to improve the accuracy in various environments.
It is not difficult to show that this interpolation method takes into account rigid rotations of the model.
The new method takes a simple approach.
This method takes around 3 weeks.
The recommended thawing method takes much longer.
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