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In this paper, we present a new formal test, which is based on mean squares, for analyzing three-level orthogonal saturated designs.
Table 3 Analysis of variance (mean squares) for various growth features of S. halepense seedlings grown at different alkaloid concentrations Characters MS Datura s Significance MSConc Significance MSInter Significance Germination 247.80 N.S 38.90 N.S. 141.09 N.S.
Relative mean square was calculated as the mean square of each factor normalized by the sum of mean squares for each spot [33].
The p-values are based on testing the ratio of mean squares for a factor (like 'mask') and the mean square of errors (random fluctuations), assuming that ratio is F-distributed.
The mean squares for FA and individual variation exceeded the error components by more than 41-fold (Table 1), indicating that measurement error was negligible relative to the biological shape variation.
Infit mean squares for all remaining items fell below the 1.3 criterion.
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Good comparisons are shown with the experimental data mean and root mean square for both the gas phase and spray droplets profiles.
The purpose of this problem is to design a state feedback controller such that the closed-loop system is exponentially stable (or exponentially ultimately bounded) in the mean square, for all admissible nonlinearities and time-delays.
We aim at designing a full-order filter such that the dynamics of the estimation error is guaranteed to be stochastically, exponentially, ultimately bounded in the mean square, for all admissible nonlinearities and time delays.
Using stochastic counterparts of Lyapunov stability theory, we present adaptive state and parameter estimators with ultimately exponentially bounded estimator errors in the sense of mean square for both continuous-time and discrete-time nonlinear stochastic systems.
That is, the methods proposed in [15, 16] are ineffective for the exponential stability in mean square for such systems.
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