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Improved models are developed for representing the turbine power coefficient and estimating the probable power production.
Improved models are being developed and there is much interest in this problem.
These improved models are applied to investigate temperature distributions and temporal evolution of the junction temperature in SOI nMOSFETs.
Improved models are iteratively generated using a genetic algorithm feature selection technique.
The results obtained using these improved models are in excellent agreement with the experimental data, much better than the agreement with earlier models reported in the literature.
It has been applied widely, and several improved models are introduced based on MoG.
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RBDFs were not developed for these cases; however, either specific ways to improve the model prediction accuracy were recommended or improved models were provided and demonstrated.
Improved models were also achieved for estimates of the individual chemical, biological, and physical soil health scores, demonstrating reductions in RMSE of 2.8, 5.4, and 10.0%, respectively.
Improved models were those in which the −2 log likelihood ratio was greater than a critical value derived from the chi-square distribution table based on degrees of freedom at an alpha level of 0.05.
Field data obtained from the log and field reports and improved model were used to simulate injector, fracturing and permeability damage performance.
The transformed (improved) models can be utilized for a better estimation of measured rating factor.
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CEO of Professional Science Editing for Scientists @ prosciediting.com