Objective To investigate machine learning for linking image content, human perception, cognition, and error in the diagnostic interpretation of mammog

Three-dimensional (3D) scanners are used in more and more applications, and range accuracy is the main challenge for manufacturers. The knowledge of this accuracy helps to better exploit the results and the information from the scanners. Usually when the manufacturer does not provide enough information, the user has to characterize the scanner before using it. This letter describes some preliminary results we have obtained during the characterization of a commercial 3D scanner based on structured light.¹ More precisely, we will focus on the study of range accuracy with respect to an illuminant that our reproducible experimental setup has provided.

Neutron imaging is presented as a tool for quantifying the diffusion of ions inside porous materials, such as carbon electrodes used in the desalination process via capacitive deionization and in electrochemical energy-storage devices. Monolithic mesoporous carbon electrodes of ∼10 nm pore size were synthesized based on a soft-template method. The electrodes were used with an aqueous solution of gadolinium nitrate in an electrochemical flow-through cell designed for neutron imaging studies. Sequences of neutron images were obtained under various conditions of applied potential between the electrodes. The images revealed information on the direction and magnitude of ion transport within the electrodes. From the time-dependent concentration profiles inside the electrodes, the average value of the effective diffusion coefficient for gadolinium ions was estimated to be 2.09 ± 0.17 × 10⁻¹¹ m² s⁻¹ at 0 V and 1.42 ± 0.06 × 10⁻¹⁰ m² s⁻¹ at 1.2 V. The values of the effective diffusion coefficient obtained from neutron imaging experiments can be used to evaluate model predictions of the ion transport rate in capacitive deionization and electrochemical energy-storage devices.