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Since there are not only desirable measures but also undesirable measures in supply chains, the usual data envelopment analysis (DEA) model allows measuring performance for complete weight flexibility.
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The model allows the measure of LOC to depend on observable variables.
This novel model allows us to measure reward seeking under threat of adversity in a within-subjects design.
The resulting kinetic model allows us to measure how lipid molecular area changes during oxidation, as well as to determine the rate constants controlling how quickly oxidation products are formed.
These separate models allow for measuring the importance of distances in explaining inequalities in use.
While in many situations the linearization of the measuring model allows using the standard ISO GUM procedure, in complex cases it might be necessary to proceed with Monte Carlo simulations as per ISO GUM supplement 1.
Despite the fact that the main clinical measure is quality adjusted life years (QALY), the model allows to calculate other measures of outcomes, as the mean HAM-A and the number of weeks with no or minimal symptoms.
After fitting of parameters, the highly efficient model allows to predict accurately the measured quench sensitivity, the volume fractions of quench induced precipitates, enthalpy changes in the quenched sample and hardness values.
The integration of adequate indexes within the model allowed the measure of WLES performance, obtaining the necessary information to better understand their composition and trends.
Using the measured profile, the model allows for calculation of the liquid-phase axial dispersion coefficients.
In summary the MREM model allows to calculate different relevant measures of diagnostic test accuracies: The mean logit sensitivity and logit specificity along with their standard errors for any known threshold, j, are estimated by the model.
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