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This research aims to provide an appropriate approach to enhance asphalt mixtures creep compliance performance predictions and presents two predictive models, one with multiple regression analysis and the other with feed-forward artificial neural networks (ANN).
The paper elaborates the laboratory evaluation methodology adopted; modeling techniques used for making yield predictions and presents case studies of successful catalyst and additive selection and use in a commercial FCC unit.
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Policy implications, suggestions and predictions and presented.
In this paper, we discuss the importance of assessing and quantifying timing error in hydrologic predictions and present a new approach, which is based on the cross wavelet transform (XWT) technique.
We report here an experimental realization of this theoretical prediction and present an o-polarized conjugator.
Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance.
In this work, we consider a threshold approach of binary, ordinal, and censored Gaussian observations for Bayesian multilocus association models and Bayesian genomic best linear unbiased prediction and present a high-speed generalized expectation maximization algorithm for parameter estimation under these models.
To address these issues, this paper implements a flexible and effective on-line training strategy in RVM algorithm to enhance the prediction ability, and presents an incremental optimized RVM algorithm to the model via efficient on-line training.
On some models, a more elaborate test on the compatibility between model predictions and results presented in the associated paper is also performed.
By carefully refining the gene models for these putative immune genes we have produced revised protein predictions, and here present an analysis of both the diversity of the D. pulex immune genome and its relation to those of other arthropods.
The in vivo-in silico assessment by GastroPlus™ showed good prediction accuracy and presented best-fit model.
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CEO of Professional Science Editing for Scientists @ prosciediting.com