To better investigate the relationships among the model parameters (as the inputs) and the pressure derivative data points (as the model responses), the number of experiments could be increased and more number of factor levels may be examined.
AcidMiner takes raw data in the form of multiple alignments and Newick protein and species trees, processes it to produce derivative data such as parsimony-based polarization of substitutions and stores the result in a relational database structure.
The dataset presented here permits full open access both to μCT-derived raw data (here: the projection images) as well as derivative data (here: the reconstructed image stacks).
With a fair quotation EULA, users are granted the right to distribute source and derivative data, possibly including published map products, as long as the original EULA and metadata which accompany the distribution have not been revised.
A three-stage procedure for assessing the effectiveness of different smoothing techniques on pressure derivative data is adopted in this work as follows.
Normalized pressure derivative data points are considered as inputs to the networks, while the well-testing variables according to the equations provided in Table 2 are used as the targets.
After proper manipulations and required preprocessing including sampling, dimensionality reduction and normalization (similar to the works described in "fractional factorial design (second phase of DOE)" section), the preprocessed derivative data points are fed as inputs into the neural network models including MLPs, PNNs and GRNNs.
The normalized derivative data points are then used as inputs to the neural networks.
Intake, apparent total-tract digestibility, N balance, urinary concentration of urea, and purine derivatives data were analyzed as repeated measurements taken over the 28-d period (122, 150, 178, 206, 234, and 262 d of gestation).
