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The predictions also demonstrate the existence of a critical tunnel length.
Comparisons between measurements and predictions also demonstrate that the mathematical model described provides a reasonable simulation of explosions within obstacle containing enclosures of the type investigated, with rapid turbulent combustion being predicted with sufficient accuracy to yield reasonable results for the overpressures generated.
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The importance of considering stress gradient effects in notched specimen life prediction is also demonstrated.
A stage 3 AKI risk prediction score also demonstrated good discrimination (AUC = 0.78 (95% CI 0.75, 0.80)) as did the four comparison risk scores, but stage 3 AKI scores were less well calibrated.
Finally, we compare our predictions with preliminary experiments and also demonstrate the utility of our approach in modeling complex structural adhesives.
We also demonstrate how probabilistic predictions can be generated and mapped to visualize spatial patterns in co-occurrences.
These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data.
These data also demonstrate that expression pattern-based prediction of gene functions can be misleading.
The prediction results on pCR endpoint, shown in Figure 2, also demonstrate the advantage of RFA methods over other compared methods.
The online experiments also demonstrate that the proposed algorithm achieves higher prediction accuracy and reliability compared with the existing method.
We also demonstrate how this information can be used for structure prediction.
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