Since small scale is key for successful introduction of continuous techniques in the pharmaceutical industry to allow its use during formulation development and process optimization, it is essential to determine whether the product quality is similar when small quantities of materials are processed compared to the continuous processing of larger quantities.
While in adults the IPS seems to be mainly involved in the processing of numerical quantities, number comparison and simple quantity manipulations, including approximation, the left angular gyrus and left prefrontal regions are mainly implicated in exact, verbal memory based, language-dependent calculation [ 20, 26, 27].
The capabilities of the approaches selected are addressed in view of realistic modeling and processing of uncertain quantities in engineering.
In cloud computing, supercomputers in large data centers as a distributed system of many servers are used to deliver services in a scalable manner as well as to enable the storage and processing of vast quantities of data.
They may be preferable to computer simulations when (routine) high-speed processing of large quantities of analyses is needed.
A function for latherin in horse saliva is less obvious, but the presence of a surfactant protein there may assist mastication and processing of large quantities of relatively dry vegetable matter, a diet for which the equids as a group are specialised, unlike ruminants (cows, sheep, etc).
Thus it can be easily adopted into an automated processing routine for batch processing of large quantities of images.
Automatic detection and statistical classification of sounds will continue to be fertile topics for further research, as these will allow the efficient processing of large quantities of data (e.g. Parsons & Jones, 2000; Acevedo et al., 2009), making methods cheaper and faster, and hence more appealing.
Since a large-scale data centre has to manage a large number of machines and jobs, deploying multiple independent schedulers (termed as distributed schedulers in literature) to make scheduling decisions simultaneously has been shown as an effective way to speed up the processing of large quantity of submitted jobs and data.
The algorithms utilized for the big data learning must satisfy three conditions to improve the performance in the processing of big quantity of data: (1) they need to be compact, (2) they need to be effective, and (3) they need to be stable.
