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It is well known that the Kaleva-Seikkala's type fuzzy metric space is an important generalization of a Menger probabilistic metric space and a metric space as well (see [1, 4, 8]) and possesses rich structure with suitable choices of binary operations.
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This motivates our choice of binary valued function to approximate protein concentrations in time.
The choice of binary versus textual protocols is a trade-off of efficiency against simplicity.
The choice of the binary solution (LiBr/H2O) of this second system is based on the following states: Open image in new window Fig. 1 Combined system ORC and ACS scheme.
The choice of a binary tree is due to the possibility of exploiting the similarity between groups of partitions, using the decision tree to narrow down the partitions that the encoder has to check, but not deciding the mode of the MB exactly. .
Another issue about the choice of a binary or weighted network needs addressing.
A simple solution for this problem is introduction of prior probability in the choice of unit binary classifier.
The choice of a binary variable was due to the lack of instruments that aggregated the extent of trouble caused by each type of no healthy appearance.
One may argue that our assumption that the choice of unit binary classifier is an independent process (see Materials and Methods) is too simplified.
The choice of a binary outcome and the use of an adapted and largely applied regression model were motivated by our will to overcome a biostatistical issue that has been raised by one of our clinical question.
They are the reason for the choice of the binary distance variables x, as these constraints cannot be modeled with integer variables directly taking the distance values (i.e., d uv ).
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