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To date, implementation of roller compaction models to experimental work has been limited because these models typically require large experimental data sets or obscure input parameters that are difficult to obtain experimentally.
Large experimental data sets are available to classify PPPs in terms of persistence.
Simulations are validated using a large experimental data base obtained with a wavefront sensor developed by Phasics™.
A large experimental data set of these joints is collected from published literature based on consistent criteria for data selection.
The challenge in corrosion modelling is rigorous calibration at the hand of sufficiently large experimental data sets, as well as field validation.
We compare predictions of the proposed approaches using a large experimental data basis, with different configurations of perforations, different stacking sequences and different carbon/epoxy materials.
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The development of more complex analytical instruments and the need to cope with larger experimental data sets have demanded for new approaches in data analysis, which have led to advanced methods in experimental design and data processing.
We suggest that there is an excellent basis and larger experimental data set for improvement of the cTP cleavage site predictor.
As such, the generalizability of these results will require the analysis of larger experimental data sets as they become available [ 38].
This article describes general terms used in statistical analysis of large, complex experimental data.
To address this complex phenomenon, several simple models were developed and tested against large scale experimental data for the past three decades.
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