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For the calculation of the metamorphic QD band profiles, sizes from AFM data were considered and strain effects were included, following an approach already validated in Refs. [42, 51].
Many combinations of layers, training algorithms, number of neurons, activation functions and format of data were considered and the results were validated using an independent validation data set with five goodness-of-fit tests.
Overall, 240 performance data were considered and an agreement within ±5% was found for more than the 96% of the calculated refrigerant mass flow rates and more than 97% of the calculated compressor electrical powers.
Statistical analyses of experimental data were considered and showed increases of pH and particle size had negative effects on sulfur reduction, whereas increases of pulp density and bioleaching time raised microbial desulfurization rate.
Using regression analysis, velocity data were considered and equations were developed for calculating the kinetic energy and momentum correction coefficients as a function of the flow relative depth and vegetation density.
During analysis, diverse data were considered and were presented in the report as a typical pattern.
Similar(52)
Limitations of existing data are considered and potential sources of additional data described.
Non-Gaussian functional data are considered and modeling through functional principal components analysis (FPCA) is discussed.
For Klein, et al., data is considered and managed as individual streams.
In this paper, several applications that can benefit from such data are considered, and indicative results are presented.
The effect of noisy data is considered and numerical simulations demonstrating the applicability of the method to single- and multi-degree-of-freedom dynamical systems are presented.
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