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Flexibility is an essential characteristic of the HMS in order to manufacture high-variety low-volume artifacts.
In this paper, we aim to analyse the resilience of Human-Machine Systems (HMS) in order to improve it from learning process.
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In order to use HM and link adaptive relaying, we now allow each user to be served for β = 2 time slots.
Four fuel cycle schemes are evaluated in order to investigate the effect of heavy metal (HM) recycling.
In order to speed up the intra prediction process, HM [26] adopted a simplified intra prediction algorithm.
We combined this information and HM expression profiles with the phenotype of tomato introgression lines (ILs) in order to identify candidate genes involved in epigenetically regulated processes.
In order to evaluate the efficiency of the proposed methods, HM 9.0 reference software was utilized.
In order to confirm these data for our E. histolytica HM-1 IMSS HM-1 IMSSe performed a bistraine reaction followed by strand-sperformedCR of the EHsp100 gene.
In order to create an SC message, we make use of hierarchical modulation (HM) [11, 12].
And so, in order to improve the decoding at the RS, we propose the use of HM.
In order to calculate this, we use the Kaplan Meier method outlined above to estimate the HM-free survival probability, that is, the probability that an FA patient will not develop HM.
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