In the very same era that Paivio's ideas were becoming influential, rapid developments in psycholinguistics, the influence of Artificial Intelligence research, and the rise of the computational information processing approach to cognition were profoundly affecting psychological conceptions of mental representation.
The robustness, reliability and efficiency of modern numerical methods for obtaining solutions to flow problems have given rise to the adoption of Computational Fluid Dynamics (CFD) as a widely used analysis tool for membrane separation systems.
This paper's originality hinges upon the maximal reuse of the space time-varying local modespace time-varying-physicalocalulations and the adaptively coupling of sub-domodesbehaviors, which give rise to great improvement ofrommpriorional complexity while guaranteeing high-fidelity simulation effects.
The robustness and efficiency of the numerical methods for obtaining solutions to MD membranes and module problems (e.g. temperature and concentration polarizations and, distillate flux enhancement) have given rise to the adoption of Computational Fluid Dynamic (CFD) as a widely used analysis tool to study transport phenomena for MD processes.
The availability of information relating molecular connection tables to important properties has made possible the rise of the subfield of computational chemistry commonly known as cheminformatics [11, 12], and the usual data entry method for chemical structures is via software designed for sketching 2 D diagrams of molecules.
Current limitations of experimental procedures for identifying protein subcellular localization have given rise to ongoing development of computational methods for predicting localization data (Bhasin and Raghava, 2004; Emanuelsson et al., 2000; Nakai and Horton, 1999; Scott et al., 2004; Shatkay et al., 2007).
Researchers have compared the recent availability of large-scale behavioral datasets, such as the ones generated by mobile phones, to the invention of the microscope, giving rise to the new field of computational social science.
Entropic Lattice Boltzmann method (ELBM) parallelization, like many parallel algorithms in the field of rapid scientific and engineering computing, has given rise to much attention for applications of computational fluid dynamics.
The exponential increase and ubiquitous use of computational technology has given rise to an era of "Big Data" that pushes the limits of conventional data analysis [ 1- 3].
The article shows that this is reached at the cost of only a negligible rise in computational and memory complexities.
