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The derived PLS regression model for the prediction of solubility parameters was based on an in-house data set of 2473 drug-like compounds.
Identification of the thermo-mechanical model parameters was based on the experimental stabilized cyclic tension compression tests performed at 20 °C and 300 °C.
The estimation of genetic parameters was based on the phenotypic records of 12,208 animals randomly sampled at harvest time (random group).
The validation of the updated bridge material properties' parameters was based on the results of material testing, on existing bridge design data and on observations resulting from in situ visual inspections.
ANOVA showed LoC and cutting force were more significant than other input parameters for the estimation of flank wear, and the design of fuzzy sets for input parameters was based upon this analysis.
This is attributed to a solutal undercooling of the equiaxed front region in the modeling which is similar in comparison to the dendrite tip undercooling predicted by an analytical 3D calculation, on which the estimation of nucleation parameters was based.
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We considered variable mutation rates amongst loci, and the Mij and θi starting parameters were based on FST and Ne calculations, respectively.
This model is also free of empirical fitting parameters, being based on pure flow physics scaling.
The model parameters are based upon the member sizes, properties and connection designs.
The statistical parameters are based on the available literature, test data and load surveys.
The model parameters are based on local conditions in the Leipzig area.
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