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In this paper, we performed a series of first-principles calculations to quantify the intrinsic properties of transition metal (TM) solutes and their interactions with point defects in W, including vacancies and 〈1 1〉-crowdionsns.
We performed a series of density functional theory (DFT) calculations to quantify the binding energy of hydrogen to a number of point defects in body-centered cubic Fe, e.g. vacancies, interstitial carbon and substitutional solutes.
Figure 7 Schematic for calculations to quantify HWP storage and emissions.
Compiled values of average crop yield and crop water requirement (CWR) were used in calculations to quantify water requirements for the selected crops and countries.
The biodegradation rate constant data from each laboratory batch experiment were used in model calculations to quantify the fraction emitted (fe) and the fraction biodegraded (fbio) for each system.
Schematic showing the flow of calculations to quantify HWP products in use, products in SWDS, emissions with energy capture, and emissions without energy capture using the IPCC/EPA approach.
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Horwitt proposed a calculation to quantify the vitamin E requirement in humans.
However there is no consensus on what rate of escalation would be defined as "rapid" and to date no mathematical calculation to quantify and compare such dose escalations has been considered.
Instead of the data discretization step needed in the original framework, we propose the usage of a beta-uniform mixture distribution on the P-value profile, resulting from differential gene expression calculation, to quantify effects.
In our work, we extend the framework by Markowetz et al. in several directions in order to overcome these restrictions: instead of the data discretization step needed in the original framework, we propose the usage of a beta-uniform mixture distribution on the P-value profile, resulting from differential gene expression calculation, to quantify effects (Pounds and Morris, 2003).
However, this does not imply that g A (p) (1 - g B (p))/(g B (p) (1 - g A (p))) = OR for all p. In the Appendix we present a calculation to quantify the possible bias from using OR in a particular trial.
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