Search

Numerical methods for analyzing diffusion phenomena involving strong discontinuities and complicated interfaces are of great scientific and technical importance. In this paper, we extend the existing step-balanced random walk, which overcomes the problem of detailed balance of a random walker in three-dimensional (3D), diffusion-tensor discontinuous systems, to particle density (ρ) discontinuous systems, and introduce volumetric heat capacity C and temperature T by considering ρ as heat density, i.e., ρ = C T , to apply it to thermal problems. Two types of thermophysical simulations are demonstrated: one is steady-state heat flow due to a temperature difference at the end surfaces on a two-phase slab model unit cell with periodic boundary conditions; the other is the time variation of temperature distribution due to heat diffusion from a point heat source in a repeated two-phase slab model with no periodic boundary conditions. We see the correct behavior of heat and temperature expected in 3D discontinuous systems composed of two phases with anisotropic thermal conductivity. The applicability to problems other than pure diffusion and the limitations of the method are also discussed. [ABSTRACT FROM AUTHOR]

The fracture toughness of silicon nitride (Si3N4) ceramics was evaluated directly from their microstructures via deep learning using convolutional neural network models. Totally 156 data sets containing microstructural images and relative densities were prepared from 45 types of Si3N4 samples as input feature quantities (IFQs) and were correlated to the fracture toughness as an objective variable. The data sets were divided into two groups. One was used for training, resulting in the creation of regression models for two kinds of IFQs: the microstructures only and a combination of the microstructures and the relative densities. The other group was used for testing the validity of the created models. As a result, the determination coefficient was approximately 0.8 even when using only the microstructures as the IFQs and was further improved when adding the relative densities. It was revealed that the fracture toughness of Si3N4 ceramics was well evaluated from their microstructures. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) models such as a convolutional neural network (CNN) are powerful tools for predicting the properties of materials from their microstructural images, etc. It is, however, critically essential to understand how the AI models use images and information to predict the target properties. In this study, we tried to gain insight into the inner workings of two AI models trained to predict bending strength (BS) and thermal conductivity (TC) of silicon nitride ceramics. Focusing on the intermediate feature representation of the microstructural images in the networks, the high‐dimensional data points corresponding to sample images were mapped onto a two‐dimensional plane using t‐distributed stochastic neighbor embedding (t‐SNE). The maps demonstrated that the AI models predicted BS and TC primarily based on the porosity and grain sizes of the samples. The result indicates that t‐SNE is a useful technique for making the basis of models' predictions more understandable and well founded. [ABSTRACT FROM AUTHOR]

Acer mono is thought to comprise seven varieties, but its classification and nomenclature are controversial. A recent molecular phylogenetic study suggested that A. mono varieties glabrum (G) and mayrii (M) do not interbreed, despite occurring syntopically (same location) and having a common genetic origin. However, if these are separate, reproductively isolated species, the basis for that isolation remains unclear. Here, we tested whether G and M can be considered separate species that differ in multiple ecological characteristics. We compared G and M in terms of distribution, habitats, drought tolerance of leaves, and flowering phenology of trees, and we verified their reproductive isolation (RI) in Japan. G showed higher drought and salt tolerance than M, which is consistent with the distribution of G in salt‐stressed coastal areas with relatively low precipitation. In contrast, M occurs in snowy, mesic, and mountain habitats. Even in neighboring M and G trees, the onset of flowering in M is earlier than that of G. Similarly, flower drop in M is completed sooner than in G. Although the presence of post‐zygotic reproductive barriers was not tested, the high calculated RI index probably contributes to RI. Two varieties of A. mono with a common genetic origin but different drought tolerance should be considered separate species. They likely shifted their climatic niches, involving local adaptation to different climatic conditions. This is roughly supported by an ENM‐based niche analysis and significant differences in flowering time. These findings help to understand the physiological diversification of A. mono. [ABSTRACT FROM AUTHOR]