Machine learning materials physics: Multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures

Many important multi-component crystalline solids undergo mechanochemical spinodal decomposition: a phase transformation in which the compositional redistribution is coupled with structural changes of the crystal, resulting in dynamically evolving microstructures. The ability to rapidly compute the macroscopic behavior based on these detailed microstructures is of paramount importance for accelerating material discovery and design. Here, our focus is on the macroscopic, nonlinear elastic response of materials harboring microstructure. Because of the diversity of microstructural patterns that can form, there is interest in taking a purely computational approach to predicting their macroscopic response. However, the evaluation of macroscopic, nonlinear elastic properties purely based on direct numerical simulations (DNS) is computationally very expensive, and hence impractical for material design when a large number of microstructures need to be tested. A further complexity of a hierarchical nature arises if the elastic free energy and its variation with strain is a small-scale fluctuation on the dominant trajectory of the total free energy driven by microstructural dynamics. To address these challenges, we present a data-driven approach, which combines advanced neural network (NN) models with DNS to predict the homogenized, macroscopic, mechanical free energy and stress fields arising in a family of multi-component crystalline solids that develop microstructure. The hierarchical structure of the free energy's evolution induces a multi-resolution character to the machine learning paradigm: We construct knowledge-based neural networks (KBNNs) with either pre-trained fully connected deep neural networks (DNNs), or pre-trained convolutional neural networks (CNNs) that describe the dominant characteristic of the data to fully represent the hierarchically evolving free energy.Comment: 24 pages, 15 figure

An Improved ResNet-50 for Garbage Image Classification

In order to solve the classification model\u27s shortcomings, this study suggests a new trash classification model that is generated by altering the structure of the ResNet-50 network. The improvement is divided into two sections. The first section is to change the residual block. To filter the input features, the attention module is inserted into the residual block. Simultaneously, the downsampling process in the residual block is changed to decrease information loss. The second section is multi-scale feature fusion. To optimize feature usage, horizontal and vertical multi-scale feature fusion is integrated to the primary network structure. Because of the filtering and reuse of image features, the enhanced model can achieve higher classification performance than existing models for small data sets with few samples. The experimental results show that the modified model outperforms the original ResNet-50 model on the TrashNet dataset by 7.62% and is more robust. In the meanwhile, our model is more accurate than other advanced methods

Motion and Metal Artifact Correction for Enhancing Plaque Visualization in Coronary Computed Tomography Angiography

Atherosclerosis detection remains challenging in coronary CT angiography due to motion and metal artifacts. Motion artifacts arising from rapid coronary artery displacement occurred over the acquisition window may lead to intensity reduction and feature doubling or distortion, severely hindering the visualization of a plaque of interest. Similarly, for patients with cardiac implants, pacing electrodes or implant lead components can create substantial blooming and streak artifacts in the heart region, obscuring the background anatomy adjacent to the component. In this work we presented an image-based compensation framework exploiting a rigid and linear motion model for correcting motion artifacts, and a novel reconstruction method incorporating a deformable model for metal leads to eliminate metal artifacts to improve plaque visualization. The feasibility of both correction methods is validated with simulation and experimental studies. We found a dramatic improvement in the ability to visualize fine details in the coronary artery plaque after the application of the proposed motion compensation method. Similarly, anatomy visualization even near the boundary of the component has greatly improved after reconstruction with the deformable known-component model. Both proposed methods have the potential to improve plaque visualization and characterization in coronary CT angiography

An overview of the Spanish translation course of the Hispanic Philology Degree in China

Nowadays, due to the increasingly frequent exchanges between China and the Spanishspeaking countries, there has been a huge demand for Chinese-Spanish translators. Regarding the training of professionals of Spanish translators, the Spanish translation course of the Hispanic Philology degree constitutes the main solution. This research focuses on the panorama of this course in China. The objective of this work is to present an iceberg of the current situation of the course in Spanish translation in China and offer an updated and real perspective on the subject. It is essential and urgent to adapt this subject in order to train more qualified professionals. This study provides resources and pragmatic information, that will help improve this course.Hoy en día, debido a los intercambios cada vez más frecuentes entre China y los países de habla española, se ha producido una enorme demanda de los traductores de chino-español. En cuanto a la formación de los profesionales de traductores de español, la asignatura de traducción de español de la licenciatura en Filología Hispánica constituye la vía principal. Esta investigación se enfoca en el análisis de esta asignatura en China. El objetivo de este trabajo consiste en presentar un iceberg de la situación actual de la asignatura de la traducción de español en China y ofrecer una perspectiva actualizada y real de la materia. Es imprescindible y urgente llevar a cabo la reforma de esta asignatura a fin de formar a más profesionales cualificados. Este estudio proporciona recursos e informaciones pragmáticas, las cuales ayudarán a la mejora de esta asignatura

Análisis de la Asignatura de Traducción de Español de la Licenciatura en Filología Hispánica en China

Resumen: Hoy en día, debido a los intercambios cada vez más frecuentes entre China y los países de habla española, se ha producido una enorme demanda de los traductores de chino-español. En cuanto a la formación de los profesionales de traductores de español, la asignatura de traducción de español de la licenciatura en Filología Hispánica constituye la vía principal. Esta investigación se enfoca en el análisis de esta asignatura en China. El objetivo de este trabajo consiste en presentar un iceberg de la situación actual de la asignatura de la traducción de español en China y ofrecer una perspectiva actualizada y real de la materia. Es imprescindible y urgente llevar a cabo la reforma de esta asignatura a fin de formar a más profesionales cualificados. Este estudio proporciona recursos e informaciones pragmáticas, las cuales ayudarán a la mejora de esta asignatura.Abstract: Nowadays, due to the increasingly frequent exchanges between China and the Spanish-speaking countries, there has been a huge demand for Chinese-Spanish translators. Regarding the training of professionals of Spanish translators, the Spanish translation course of the Hispanic Philology degree constitutes the main solution. This research focuses on the panorama of this course in China. The objective of this work is to present an iceberg of the current situation of the course in Spanish translation in China and offer an updated and real perspective on the subject. It is essential and urgent to adapt this subject in order to train more qualified professionals. This study provides resources and pragmatic information, that will help improve this course