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Finally, we also show the efficiency of our new methods with a large set of experiments.
Calculations are compared to a large set of experiments available from the literature.
A large set of experiments confirm that the proposed models improve the prediction accuracy with respect to existing algorithms and they show stable results for different workload scenarios.
This device is used to carry out a large set of experiments investigating various mixtures with optical on-line diagnostics (FTIR for HI and H2O, UV visible for I2).
A large set of experiments was executed for both the first and second step, varying temperature (20 60 °C for the first and 100 120 °C for the second step), reactant and catalyst concentrations, as well as the presence of the first-step reactant (4CEBr and AA) in the second, whereas the stepwise multi-regression modelling with consequent sensitivity analysis ensued.
Numerical predictions lead to a quantitative evaluation of the threshold load for indentation fracture, and an improved method for the evaluation of material toughness from the indentation load, crack size, hardness, elastic constants, and indenter geometry, which compare favorably to a large set of experiments in the literature.
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Through a large set of computational experiments, we compare the performance of these formulations.
DFI can identify distinctive gene features across a large set of diverse experiments without any direct inter-sample normalization.
While co-expression across a large set of microarray experiments has been previously used in bioinformatic studies [ 17- 20], the method presented here differs from other approaches.
Here, we introduce Differential Feature Index (DFI) to identify distinctive features across a large set of diverse experiments using read counts without any direct inter-sample normalization.
Over a large set of stress experiments, δOAT mRNA levels are in much closer correlation to P5CDH mRNA than to P5CR mRNA (data not shown).
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CEO of Professional Science Editing for Scientists @ prosciediting.com