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The utilization of Laws' textural measures improved the system classification performance, providing an overall classification accuracy of 94.4%.
These measures improved the similarity of the signals generated by the particles' impact, significantly, and allowed the use of cross-correlation.
Surprisingly, neither of these measures improved the performance of children in any of the age groups, suggesting that, contrary to our prediction, inhibitory control might not be a key performance-limiting factor in the advance planning paddle-box task.
The inclusion of health status measures improved the explanatory power considerably.
Indeed, combining 2 centrality measures improved the overall significant predictions (see Figure 2).
Compared with the baseline model, all three comorbidity measures improved the model prediction for having higher c-statistic values.
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These two simple measures improve the circulation of air within a kiln, and thus the process of combustion.
The obtained performance measures improve the results of similar systems presented in earlier work.
The experimental results show that optimal integration weights estimated using multi-normalization and ancillary measures improves the performance of the multibiometric systems even under low SNR conditions.
Using valid and reliable measures improves the application of psychosocial constructs to interventions that successfully increase adherence to colonoscopy.
We measured outcomes on two occasions primarily to examine the time of maximum effect, and also because repeated measures improves the power to test the main objective.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com