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The set of weights was generated by running the reference scenario described above, which was designed to represent the conditions under which the data were collected, but once the final weights have been generated it is possible to run alternative scenarios, such as those with control present, and hence to form weighted prevalence distributions for different control options.
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These weights were generated from a large sample of the general population in the UK using the time-trade off (TTO) technique [ 29].
The initial weights were generated randomly between −1 and +1.
After that, weights are generated by using the probabilities of fingertips from Equation 4.
The individual survey weights were generated separately for adults and children.
In the proposed methodology the criteria weights are generated by a fuzzy AHP procedure.
In neural networks, a stochastic pattern of synaptic weights is generated after a learning session is completed which can be used as keys.
The "atomic weights" are generated by removing the bits belonging to the corresponding atom and comparing the resulting similarity with the similarity of the unmodified fingerprint.
In the simulation, continuous heterogeneous services with different weights are generated, including the full buffer, VoIP, video, HTTP, and FTP services.
However, group decision making is the recommended approach in the case of selection where priority weights are generated from the evaluators.
For children (aged 0 to 15), the weights were generated from the household weights and the child selection weights – the selection weights corrected for only including a maximum of two children in a household.
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CEO of Professional Science Editing for Scientists @ prosciediting.com