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The parameters and variables used in this model are as follows: Parameters.
The notation is as follows: Parameters: j: Index of demand related to demand points (customers), j = 1, 2, …, n.
The parameters and variables used in this model are as follows: Parameters Q k): the set of legal paths for commodity k. orig k): the origin node for commodity k. dest k): the destination node for commodity k.
The General Search Parameters used were as follows: parameters automatically adjusted for short queries; maximum target sequences, 100; the E threshold was 10; word size, 3. The Scoring Algorithm Parameters were: matrix, BLOSUM62; gap costs, existence, 11 extension, 1; compositional adjustments, conditional compositional score matrix adjustment.
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(5.1) with two groups of parameters as follows: parameter I: (lambda=2), (mu=1), parameter II: (lambda=-20), (mu=5).
The rest of the network parameters are estimated as follows: Parameter learning: For each Bayesian network classifier C i, we use the maximum likelihood estimates calculated from frequency counts in the training dataset, D, to estimate the network parameters.
Following parameters were used to calculate saponification and iodine value.
Following parameters viz biochemical oxygen demand (BOD), chemical oxygen demand (COD), nitrogen, phosphorus, and potassium analyses were used to calculate grey water pollutant load.
The postoperatively followed parameters were Visual Analog Scale, arterial tension, heart rate, gas analysis, beginning of peristalsis, and pulmonary complications.
Following parameters were used: −-read-mismatches 5 read-gap-length 3—read-edit-dist 5 read-gap-lengthfr-firststrand—splice-mismatches 0 and GTF.
Following parameters were calculated to evaluate the effects of different concentrations of PCB 31 on the growth of the zebrafish:(Kerambrun et al. 2012).
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