Experimental results on data analysis algorithms for extracting and interpreting edge feature data for duct tape and textile physical fit examinations.

A physical fit is an important observation that can result from the forensic analysis of trace evidence as it conveys a high degree of association between two items. However, physical fit examinations can be time-consuming, and potential bias from analysts may affect judgment. To overcome these shortcomings, a data analysis algorithm using mutual information and a decision tree has been developed to support practitioners in interpreting the evidence.

Using convolutional neural networks to support examiners in duct tape physical fit comparisons.

This paper describes the construction and use of a machine-learning model to provide objective support for a physical fit examination of duct tapes. We present the ForensicFit package that can preprocess and database raw tape images. Using the processed tape image, we trained a convolutional neural network to compare tape edges and predict membership scores (i.

Vickers hardness prediction from machine learning methods.

The search for new superhard materials is of great interest for extreme industrial applications. However, the theoretical prediction of hardness is still a challenge for the scientific community, given the difficulty of modeling plastic behavior of solids. Different hardness models have been proposed over the years.

Boosting Photocatalytic Hydrogen Production by Modulating Recombination Modes and Proton Adsorption Energy.

Solar-driven production of renewable energy (e.g., H) has been investigated for decades.

A Machine-Driven Hunt for Global Reaction Coordinates of Azobenzene Photoisomerization.

Azobenzene is a very important system that is often studied for better understanding light-activated mechanical transformations via photoisomerization. The central C-N═N-C dihedral angle is widely recognized as the primary reaction coordinate for changing cis- to trans-azobenzene and vice versa. We report on a global reaction coordinate (containing all internal coordinates) to thoroughly describe the reaction mechanism for azobenzene photoisomerization.