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We demonstrate that the logarithm of the light transmitted through the sample solution, referred to as pseudo-absorbance, can suffice to determine macromolecular transport parameters of interest, with little loss of precision.
In this paper, we propose a new particle swarm optimization algorithm incorporating the best human learning strategies for finding the optimum solution, referred to as a Self Regulating Particle Swarm Optimization (SRPSO) algorithm.
By realising the present process configuration, yields about 27% for lactic acid enriched solution and up to 73% for the amino acid solution referred to the silage juice raw material were gained, respectively.
The proposed solution, referred to as the heterogeneous defect prediction (HDP) approach, is to first select the important features from the source domain using a feature selection method to eliminate redundant and irrelevant features.
The solution, referred to as the sparse heterogeneous feature representation (SHFR), is implemented by creating a binary classifier for each class in the source and the target domains separately.
The new Rao-Blackwellized version of the ReDif-PF was compared to an exact broadcast implementation of the optimal centralized PF solution, referred to as the DcPF algorithm, and to two equivalent, fully distributed PFs using respectively iterative minimum consensus (CbPFa) and flooding (CbPFb).
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The numerical solution refers to Figure 1.
Every non-dominated solution refers to a UC scheme.
The solution refers to paddle impellers and flat-blade turbines operating in a laminar mixing regime.
The Garvin Alterman Loewenthal solution refers to the problem of a line blast load suddenly applied in the interior of an elastic half-space.
In the above theorem, a normal optimal solution refers to an optimal solution at which an appropriate second-order constraint qualification is satisfied.
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