In addition to organic pollutants, PROXIMAL could also be used for predicting transformations of other types of chemicals that contain substructures recognizable by Phase I and Phase II enzymes.
After obtaining a predicted transformation, the source points are transformed and these steps are performed, iterated until the transformation is negligible.
Framework for combining the predicted transformation error with other uncertainties was introduced to determine the overall uncertainty in shear strength parameters.
Framework for combining the predicted transformation error with other uncertainties was introduced that can be used to determine the overall uncertainty of shear strength parameters.
In this study, we demonstrate that a statistical learning approach using three features or material descriptors related to the chemical bonding and atomic radii of the elements in the alloys, provides a means to predict transformation temperatures.
The degree of variant selection was determined by comparing the predicted transformation texture during heating and cooling based on the Burgers relationship and the assumption of no variant selection with the measured textures.
The use of electronic theory to predict transformation products also could be expanded and validated to better characterize contaminant fate.
The third step ranks the predicted transformation products using available data on the activity and abundance of the enzymes associated with the transformations.
Figure 4 shows only the predicted transformation products corresponding to the unique molecular structures that are unrelated to any others by symmetry.
On the other hand, this also limits the scope of products that can be predicted, as the predicted transformation will be similar to the reactions cataloged in the databases used to build the look-up tables.
