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Using this model, we predict the total logistics values for years 2010-2015.
Using the developed model, we predict the load drop during cyclic fatigue testing of the two chosen alloys.
Based on the dislocation model we predict the coseismic deformations at 847 GPS sites and compare them with GPS displacement observations.
In the regression model, we predict natural logged WTP by participation in intervention (main variable of interest) while controlling for demographic and socioeconomic characteristics.
Using a bioclimatic model we predict that it has a large potential distribution in South Africa, especially along the southern coast.
For the logit model, we predict the likelihood of having any level of health care organizations within a neighborhood, with 0 being no HCOs and 1 being having at least one HCO.
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After estimating the probit model, we predicted the propensity scores and the common support region.
Applying the CFD model, we predicted the local yielding rate of nanotubes along the axis of the reactor.
By simulating the osteotomy on a 3D model, we predicted the movements of the cut chin bone and used these data to 3D-print a surgical guiding device.
Without other adjustments to the model, we predicted the correct stress levels in the free and channel-die compression experiments as well as values of Lankford coefficients.
In summary, using in silico TLR4 model we predicted and experimentally validated the enhancement of TRAM-dependent pathways due to SFR in the removal of MyD88 and TRAF6.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com