An important application of synchronization is in adaptive parameter estimation methods where parameters in a model are adjusted dynamically in order to minimize the synchronization error [13 15].
a Models were adjusted for age and number of sexual partners.
Depending on the shape of the tail in the BTCs, a CDE with retardation or a mobile/immobile model was adjusted successfully.
Initial regression models were unadjusted; a subsequent model was adjusted for age, race-ethnicity, and sex, while a final multivariable model adjusted for age, race-ethnicity, sex, education, income, smoking, pack years smoked, alcohol consumption, body mass index, and C-reactive protein.
Additionally, a mathematical model was adjusted to the experimental release profiles in order to quantitatively describe the drug release and elucidate the underlying drug release mechanisms.
Due to the restrictions in the aleatorisation procedure, a mixed model was adjusted, i.e., aleatory effects were used for the factor "day in which the experiment was carried out" and fixed effects for the polynomial coefficients.
A different model was adjusted for each dependent variable.
A second model was adjusted for age and employment grade to account for the role of socioeconomic position.
A transmission model was adjusted to the dataset of active tuberculosis cases reported in Portugal between 2002 and 2009.
A SARIMA model was adjusted to the data of dengue incidence from 2000 to 2006, using the Box and Jenkins approach developed in 1974 [ 32, 34, 35, 42, 43].
