Regarding to a fiber-reinforced material formed by deforming a reinforcing material composed of a plurality of fiber layers from an initial shape and molding into a predetermined shape, an identification device, an identification method, and an identification program generate a first data in which a physical quantity distribution inside the fiber-reinforced material is mapped to the initial shape, perform binarization of the first data to generate a second data in which a label identifying the fiber layer is mapped to the initial shape, and map the second data to a predetermined shape, based on a deformation data.

Provided is a method for estimating abrasion resistance of polymer composite materials. The present disclosure relates to a method for estimating abrasion resistance, the method including: irradiating a sulfur compound-containing polymer composite material with high intensity X-rays; measuring an X-ray absorption in a small region of the polymer composite material while varying an energy of the X-rays, whereby a dispersion state and a chemical state of the sulfur compound are analyzed; and quantifying an inhomogeneous state of cross-link degradation in the polymer composite material based on the dispersion state and the chemical state.

A computing platform is configured to (1) extract first, second, third, fourth, and fifth source datasets from a first database containing data about a set of product-level ingredients, a second database containing data about food products, a third database containing data about manufacturing plants, a fourth database containing data about ingredient source locations, and a fifth database containing data about ingredient transportation modes, respectively, (2) merge the first, second, third, fourth, and fifth source datasets into a first merged dataset, (3) update the first merged dataset by inserting a column representing distance measurements between source locations and plant locations for product-level ingredients, (4) extract a sixth source dataset from a sixth database containing environmental-impact values for ingredients, (5) merge the updated first merged dataset and the sixth source dataset into a second merged dataset, and (6) determine a group of environmental-impact indicators for each product-level ingredient in the set.