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When the working parameters were set to their standard values, the processor time spent by our algorithm was 11.8%% higher than the one consumed by CCVC and 8.9%% higher than the one spent by CC, also consuming 12.8 and 11.1 % respectively more time to perform the necessary memory accesses.
The total number of papers retrieved and considered by our algorithm was 37996.
The cumulative incidence of infection estimated by our algorithm was approximately 40% higher than reported by the DHR and NRP for the entire study period (Table 2 and Figure 4).
The second observation is that the runtime required by our algorithm was <2 seconds in all cases, whereas the algorithm by Bordewich and colleagues took a number of days in some cases, without completion.
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Results produced by our algorithm are demonstrated using a variety of examples.
The results produced by our algorithm are almost the same as Rules in most of the cases.
We show that the approximate solutions obtained by our algorithm are better than those produced by other spline and domain decomposition methods.
The experimental results show that the predictive accuracy obtained by our algorithm is statistically higher than that of the compared targets.
Extensive tests verify that the states generated by our algorithm are stable states and show the exponential storage capacity of a HNN.
The results obtained by our algorithm are compared with five other state-of the-art algorithmstate-of the-artratestate-of the-artithm outperforms those stalgorithms-art algorithms in terms of accuracy and robustness.
We finally demonstrate that the set of potential hexes H built by our algorithm is significantly larger than those built using predefined patterns of subdivision of a hexahedron in tetrahedral elements.
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