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Schemas, quantification, modeling, and experiments?
Parameter variations have become essential in the design of micro- and nano-electronic (-mechanical) systems as well as of coupled electro-thermal problems, since in many analyses such as optimization and uncertainty quantification, modeling and simulation at many values of the parameters are unavoidable.
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The framework includes a field-scale compositional reservoir flow model, an uncertainty quantification model and a neural network optimization process.
The framework includes a field-scale compositional reservoir multiphase flow model, an uncertainty quantification model and a neural network optimization process.
A systematic model validation procedure including uncertainty quantification, model update and prediction is described based on a non-probabilistic interval model.
The combination of image quantification, model building, and computer simulation is illustrated here using the example of diffusion in the endoplasmic reticulum.
Secondly, a predictive API quantification model was developed and validated by calculating the accuracy profile based on the analysis results of validation experiments.
A Complexity Quantification Model (CQM) is proposed and relationship from design parameters to complexity factors is defined in accordance with its anticipated influence and importance.
Based on a Kalman filtering structure, the proposed measurement and uncertainty quantification models explicitly take into account several important sources of errors in the travel time estimation/prediction process, such as the uncertainty associated with prior travel time estimates, measurement errors and sampling errors.
Data analysis was performed with qBASE Browser which employs a Δ-Ct relative quantification model with PCR efficiency correction and single reference gene normalization (β-actin: 5'-GGACTTCGAGCAAGAGATGG-3', and 5'-AGCACTGTGTTGGCGTACAG-3') [28].
Data analysis was performed using GenEx 5.0 (MultiD, Göteborg, Sweden), which uses the ΔΔCt relative quantification model with PCR efficiency correction and reference gene normalisation.
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CEO of Professional Science Editing for Scientists @ prosciediting.com