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Blocking Time with Varying k To the aspect of blocking time in Figs. 8 and 9, the blocking time reduces with k in three approaches because the number of resulting blocks (n/k) becomes less as k gets bigger.
Blocking Time with Varying Database Sizes In Fig. 10, we compare the blocking time for three approaches with different dataset sizes.
When a candidate task starts to run, lines 1 and 2 recycle the free time between the maximal blocking time with the practical blocking time.
Two methods are proposed for PICARD to reduce the maximum blocking time.
However, this model is not correct and the blocking time is confusing.
We also measure the blocking time with different dataset sizes for the three approaches.
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We also obtain the distributions of blocking times and first failure times, respectively.
In this paper, we consider a three-stage AFSP with blocking times and sequence dependent setup times.
Finally, graded inhibition of pSTAT5 by TCR signaling may directly influence CD4+ T helper subtype differentiation by blocking time-sensitive cytokine signals (Yamane and Paul, 2012).
This model graphically depicts LOS rates, blocking times, assignment difference rates, and cost variance.
ICU flow is a dynamic process affected by constraints manifesting in large blocking times, assignment differences and significant cost variance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com