Sentence examples for different degrees of robustness from inspiring English sources

The different degrees of robustness can be traced back to the different mechanisms of generating ultrasensitivity.

We also show that different shapes exhibit different degrees of robustness with respect to its recovery.

Here, we address the relationship between robustness and evolvability by directly comparing the ability of genotypes with different degrees of robustness to adapt to novel environments.

The simulation results show that each model has different degrees of robustness against variations in the number of recurrent events and correlation.

Furthermore, because these various devices calculate stoke volume differently, the threshold values for each parameter in predicting preload responsiveness may be different between devices, and may exhibit different degrees of robustness under varying clinical conditions.

To be able to compare the evolvability of strains with different degree of robustness, the empirical studies mentioned above had to repeatedly select for robustness.

Finally, robustness and identification experiments are shown in Section 5 and the whole paper is concluded in Section 5. Recently, a variety of audio fingerprinting algorithms have been proposed in the literature, each with a different degree of robustness.

Moreover, in a system with imperfect synchronization conditions, a waveform that is designed with different weights towards time and frequency localization will provide different degree of robustness against the time and frequency residual synchronization errors as well [4, 5].

We believe this may be attributed to disagreement in P-values reported as well as that different methods have different degree of robustness (internal consistency).

When applied to a synthetic benchmarking signal corrupted by Gaussian white noise, the three transforms show different degrees of performance and robustness for different signal-to-noise ratios.

This mode of operation compares the goodness of fit of a Gaussian noise model with t-distributions of different degrees of freedom and infers the appropriate robustness level required for analyzing a microarray dataset.