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An observer-based output feedback controller with an inter-sample model predictor is initially designed.

To compensate for the lack of continuous measurements, an inter-sample model predictor is included within the control system to provide the observer with an estimate of the output when measurements are not available from the sensors.

In multiple linear regression analyses, P < 0.05 was considered significant.> -wrap-foot> * Controlling for other predictors in model, predictor is significant at α = 0.05 Characteristics of the patients are presented in Table  1.

The visualizations (Figure 3) show that the variation unexplained by the fixed model predictors is very large, both among stands and among seedlings within a stand.

The model predictors were related to (i) the distance separating the patches, (ii) the characteristics of the landscape between the patches and (iii) the characteristics of the departure and arrival patches, including the number of individuals marked in each patch.

Model predictors were based on 300 ms events convolved with a hemodynamic response gamma function [25].

Our model predictors are consistent with previous findings.

The effects of model predictors were measured using odds ratios (95% CI) and P values.

Potential model predictors were recoded as required by collapsing sparsely populated levels.

Coefficients for the model predictors are fitted to data from hybridizations to targets with known genome sequences.

To the final model, predictors were selected by forcing all those variables that were significant in pulmonary tuberculosis, and from other variables selection was by forward stepwise method.