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A mathematical model to predict the depth of grinding marks for any chuck shape will be first developed.
Based on the results, the laboratory column provides an effective way to estimate the H2S adsorption coefficient, which can then be incorporated into the developed model to predict the depth of cover soil required to reduce emitted H2S concentrations below a desired level.
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Subsequently, a classification and regression model was applied to predict the depth of the pre-AD 79 surface combining present-day topographic indices with other physiographic data.
The quadratic regression models were developed to predict the depth of penetration and Ra by experiments using Response Surface Methodology.
Thus the developed model can be effectively used to predict the depth of penetration and surface roughness in the machining of alumina ceramics.
Two ANNs model, feed forward back propagation, FFBP, and radial basis function, RBF, were utilized to predict the depth of the scour hole.
Based on theoretical analysis and experiment results, a model to predict the carbonation depth of RAC under axial stress was established.
Specifically, we build a theoretical model to predict the equilibrium width-to-depth ratio of rivers as a function of bank materials based on an empirical flow stress-partitioning model, a model for bank cohesion as a function of grain size, and the assumption that flow stresses along riverbanks are near the threshold for bank erosion.
Here, we explore whether LOT data can be used to predict the depth and thickness of pigmented lesions using a range of simulations and phantom models.
The proposed model can effectively predict the depth of plastic deformation at different tool flank wear stages.
In this study, an artificial neural network (ANN) model has been developed, which can predict the depth of a vertical GHE according to the given design parameters.
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CEO of Professional Science Editing for Scientists @ prosciediting.com