Sentence examples for the accuracy needed for from inspiring English sources

Thus, depending on the aim and the accuracy needed for the mathematical models, the test environment and test set up must be chosen correctly.

Our results suggest that fine fuel moisture prediction models should be species-specific in order to achieve the accuracy needed for precise prediction of fire behaviour.

Since coarse resolution burned area products cannot meet the accuracy needed for fire management and climate modelling at local scales, the medium resolution Landsat data is a promising alternative for local scale studies.

Images can help in achieving the accuracy needed for a scientific manuscript.

The accuracy needed for TOF-PET is of course higher, as this information is used in reconstruction to center the Gaussian profile in forward and back projection.

In the book Useless Arithmetic (Columbia University Press, 2007), written with his daughter, Linda Pilkey-Jarvis, they argue that the outcome of natural processes in general cannot be predicted by mathematical models with the accuracy needed for engineering purposes.

Simulation results indicate that the multi-scale modelling approach followed, meets the accuracy needs for capturing the cracking process of the welded joint with high computational efficiency.

Solid state formulations such as mini tabs have a limitation in that they still lack the dosing accuracy needed for dosing the very young patient less than 1 year of age where liquid formulations are preferred for ease of administration as well as accuracy of dosing since dosing in this group of patients is generally weight based and often results in fractional dosing.

In particular, it assumes that any change in the field described by spherical harmonic degrees between 9 and 13 may be neglected over five years, given the uncertainties already involved in the determination of all other coefficients, and the practical accuracy needed for most IGRF applications.

The BCRAT, which employs only clinical risk factors but has long been used to identify populations that can benefit from prevention protocols [7], [8], has also been criticized for lacking the discriminatory accuracy needed for more individualized risk assessment and counseling [12], [13].

Kapelonis hopes that this technology may soon be up to the scale and accuracy needed for the building industry.