Over the past few decades, several severity scoring systems and machine learning mortality prediction models have been developed for predicting hospital mortality.
In addition, computational methods have been developed for predicting molecular interactions, some of which reach prediction quality comparable to that of experimental high-throughput data (Ramírez et al., 2007; Shoemaker and Panchenko, 2007).
While several computational methods have been developed for predicting the localization of mature miRNAs within the precursor transcript, the prediction accuracy requires significant improvement.
A numerical modeling approach has been developed for predicting temperatures in municipal solid waste landfills.
A system has been developed for predicting snow distribution in built-up environments.
A CFD-based model has been developed for predicting performance of a domestic central heating boiler fired with solid fuels.
Simple criteria have been developed, for predicting the kinetic parameters for which a unique, unstable steady-state solution exists.
Based on the data an empirical correlation have been developed for predicting the pressure drop in the system.
A phenomenological model has been developed for predicting separation factors obtained in concentrating protein solutions using batch-foam columns.
The models based on the physics behind the seepage mechanism has been developed for predicting the SWRC.
Three different energy design scenarios have been developed for predicting the energy consumption for each economic sector.
