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The WWB model shows significant skill in predicting the interannual variability of the characteristics of WWBs, while the prediction skill of the WWB seasonal cycle is limited by the record length of available data.
The buoy-driven wave model shows significant skill at most validation sites, but prediction errors for individual swell or sea events can be large.
But the subsequent comparisons of measured and predicted extreme value exceedance probabilities up to a maximum normalized amplitude ratio=10·0, using the same calibrated SDOF model, shows significant differences, suggesting the occurrence of considerable non-linear coupling of beam vibrations.
The resistivity model shows significant contrasts (~ 1 10,000 Ω-m) from west to east, indicating a rather heterogeneous subsurface structure.
Testing the hierarchical model against an OLS model shows significant variation across countries in immigrants' belonging (with an intra-class coefficient of.13), which confirms the appropriateness of the model choice.
The model shows significant associations between travel time to the facility and education, age, site and type of facility (p<0.05).
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The model showed significant differences among the three population groups as well as between genders.
When benign prostate hyperplasia was chosen as controls, both TT versus CC and recessive model showed significant difference.
The results of the model show significant COPh and COPL improvement with respect to existing traditional CHP systems.
The deep learning model showed significant improvement over the traditional machine learning model, Support Vector Machines (SVM), with the overall accuracy increased by 15% from 65.40% to 80.82%.
Experimental results using this model show significant promise in reflecting the performance of guide-wires and catheters measuring the stress exerted upon relevant anatomical structures.
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