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A model with theoretical base to predict the gas kick rising speed is highly desired.
The molecular bases to predict the cytoprotective or the pro-death outcome of autophagy are still a topic of intense investigation as the execution of the former or the latter drives cells toward opposite fate.
It uses machine-learning methods to select the optimal set of features (sequence-and structure-based) to predict the possible impact of an amino acid substitution on the structure and function of human proteins.
Habib et al. [ 14] presented a new SVM based approach to predict the subcellular locations based on amino acid and amino acid pair composition.
Recently, Kundu (2017) suggested an entropy based approach to predict the dip-phenomena over entire cross section of open channels.
This article examines the usefulness of the GP based approach to predict the relative scour depth downstream of a common type of ski-jump bucket spillway.
Targeting both constituents helps circumventing the technical limits of cell culture approaches and the inability of PCR based methods to predict the infectious status.
We propose a least squares based algorithm to predict the LTMD at the head from a history of upstream and downstream LTMD measurements.
This paper develops an application of a wavelet neural network (WNN) based model to predict the performance of a shower cooling tower (SCT).
A physically based model to predict the increment of hardness and grain refinement of pure metals due to severe plastic deformation by high-pressure torsion (HPT) is proposed.
This paper presents an extension to the Wave Based Method to predict the absorption, reflection and transmission coefficients of a porous material with an embedded periodic set of inclusions.
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