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We discuss the implementation of established COP scoping mechanisms using our approach and present new scoping mechanisms developed for our enhancements to Lively Kernel.
The average recovery time on double failures can be reduced from more than 10s to only approximately 100 ms with our enhancements.
We describe our enhancements to the Chaff learning algorithm that have enabled an efficient distributed implementation, and detail the technological approach we have taken to realizing this implementation.
We suggest that our enhancements to the CPOE system (ie, forced checks of all medication parameters and clearly visible verification status) acted as a cueing function that encouraged task resumption by reminding nurses of outstanding verification items after being interrupted.
As a result, our enhancements can achieve a significant speedup of the A*-based protein design algorithm by four orders of magnitude on large-scale test data through pre-computation and parallelization, while still maintaining an acceptable memory overhead.
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Hingis's quotes just needed to be written in stone and presented like a mantra to all young athletes in our enhancement-crazed culture.
The enhancement model used in our enhancement algorithm is proposed by A.F. Laine in 1996 [14].
We also show "visually better" results while comparing our enhancement results with those from other enhancement techniques and from commercial software packages.
Our enhancement is the link between adoption and IT Requirements, which we represent with a bold, dotted arrow.
As seen in Figures 1 and 2, our enhancement of DISpro to allow for variable threshold does in fact allow the user to modulate specificity and sensitivity values as they see fit.
Interestingly, around the K-edge of iodine, SER10% slightly increases by crossing the edge (from 1.25 to 1.64) but our enhancement ratios remain low in comparison with some other published data (Laster et al, 1993).
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