Sufficient design conditions are presented as linear matrix inequalities (LMIs).
In this review, impedance sensors are presented as linear sensors with two entries whose signals are influenced by both pressure and volume velocity.
Model results are presented as linear regression coefficients with corresponding 95% confidence intervals (β (95% CI)).
> -wrap-foot> Results are presented as linear regression coefficients with standard error, B (SE).
Model results are presented as linear regression coefficients for standardized BMIs and as odds ratios (OR) for being overweight (yes/no), with corresponding 95% confidence intervals (CI).
Results are presented as linear-regression coefficients with standard error, B (SE).
For more information on the use of randomisation-based efficacy estimators and their core assumptions, including the Stata syntax used to implement the SMMs, please see the online supplementary appendices 1 and 2. Results from the linear regression model are presented as adjusted mean differences with associated 95% CIs.
The general solution of an inhomogeneous linear differential equation can be presented as the linear combination of a particular solution (any chosen solution of the equation) and the general solution of the associated homogeneous differential equation.
In cases where the magnitude of the stresses acting leads to relatively small changes in martensite start temperature, the general model can be simplified so that the shift in martensite start temperature can be presented as a linear function of maximum and minimum principal stresses.
33 The adopted LGC model can be presented as a piecewise linear trend model y t = η 1 + λ t η 2 + ε t, t = 1, 2, 3, 4, η 1 = α 1 + ζ 1, η 1 = α 1 + ζ 2, (1 where yt is the observed outcome, η1 is a latent level component and η2 is the latent slope component.
