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Numerical predictions have also been carried out using commercial Computational Fluid Dynamic (CFD) code.
The predictions have also been used to demonstrate the importance of considering buoyancy in burner design, by comparing with a burner designed ignoring buoyancy.
Besides, predictions have also been made for the three binary systems in two ternaries and with two binaries and one datum point of six ternaries.
However, whilst a variety of positive results have been published, some strong criticisms of socially generated predictions have also been raised, particularly in the field of electoral prediction (which, perhaps because of the widespread availability of validation data or because of its importance for conventional opinion polling has been the most frequent application of this type of prediction).
The HMM predictions have also proven to be a sensitive computational tool for defining structural domains.
The COMPACT predictions have also been compared with published Ames mutagenicity data and with our own Hazardexpert predictions for carcinogenicity.
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Graph-based approaches using multi-label learning framework for prediction have also been studied [ 24- 26].
Stimulating advances in risk prediction have also been made in the field of cardiology: by adding 9p21 and another seven genetic risk factors to traditional CAD risk factor models, the deCODE MI™ (deCODE Genetics, Iceland) test demonstrated increased sensitivity to predict myocardial infarction.
Standardized capacity prediction has also been completed, based on elastic critical loads by using the direct strength method, and the results have been compared to that of shell finite element analyses.
Viscosity prediction has also benefitted from the machine learning (ML) modelling capability.
Link prediction has also been approached in the multidimensional setting [12] and in multi-relational networks [13], however, these works build on features that are endogenous to the system that hosts the network of users.
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