Topological Anderson Insulators by homogenization theory

A central property of (Chern) topological insulators is the presence of robust asymmetric transport along interfaces separating two-dimensional insulating materials in different topological phases. A Topological Anderson Insulator is an insulator whose topological phase is induced by spatial fluctuations. This paper proposes a mathematical model of perturbed Dirac equations and shows that for sufficiently large and highly oscillatory perturbations, the systems is in a different topological phase than the unperturbed model. In particular, a robust asymmetric transport indeed appears at an interface separating perturbed and unperturbed phases. The theoretical results are based on careful estimates of resolvent operators in the homogenization theory of Dirac equations and on the characterization of topological phases by the index of an appropriate Fredholm operator

THE QUALITY WATER ENVIRONMENT HAPPENING OF THE HUONG RIVER IN THE HUE CITY, PERIOD OF 2003-2006

Joint Research on Environmental Science and Technology for the Eart

Explicit corrector in homogenization of monotone operators and its application to nonlinear dielectric elastomer composites

This paper concerns the rigorous periodic homogenization for a weakly coupled electroelastic system of a nonlinear electrostatic equation with an elastic equation enriched with electrostriction. Such coupling is employed to describe dielectric elastomers or deformable (elastic) dielectrics. It is shown that the effective response of the system consists of a homogeneous dielectric elastomer described by a nonlinear weakly coupled system of PDEs whose coefficients depend on the coefficients of the original heterogeneous material, the geometry of the composite and the periodicity of the original microstructure. The approach developed here for this nonlinear problem allows obtaining an explicit corrector result for the homogenization of monotone operators with minimal regularity assumptions. Two $L^p-$gradient estimates for elastic systems with discontinuous coefficients are also obtained.Comment: We provide a new proof to extend the explicit first-order corrector result in the first version of this paper. The new explicit corrector result (cf. Theorem 1) holds globally and unifies the previous classical correct results in homogenization of the divergence equation (both linear and nonlinear). New references are added. Comments are welcome

Pedagogy undergraduates’ perception on twenty-first century skills

Teachers head up their students to the bright future, their role is indispensable, especially in the 21st century, which expects them to be energetic and flexible to apply knowledge to the daily life and carrier task. Examining the perception on 21st century skills teaching of pedagogy teacher-to-be undergraduates - plays a vital role in identifying deficits in teachers’ professional development; as well as organizing training programs to enhance their knowledge and skills. To the best of our knowledge, no study to date has examined pedagogy undergraduates’ perception in Vietnam. This study aimed at examining Vietnamese undergraduates' perception on teaching the 21st century skills. Our crosssectional study used the 21st Century Skills Teaching Scale. Descriptive analysis and ANOVA were performed in this research. The results showed that: (1) Vietnamese pedagogy students had a high level of perception on teaching the 21st century skills; (2) there was no gender difference in their perception; and (3) there was no significant difference in their perception regard to their school years and (4) there was significant difference between those having joined soft skill courses at their university and those having not joined anyone

Surface-based protein domains retrieval methods from a SHREC2021 challenge

publication dans une revue suite à la communication hal-03467479 (SHREC 2021: surface-based protein domains retrieval)International audienceProteins are essential to nearly all cellular mechanism and the effectors of the cells activities. As such, they often interact through their surface with other proteins or other cellular ligands such as ions or organic molecules. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence similar 3D surface properties (shape, physico-chemical properties, …). The protein surfaces are therefore of primary importance for their activity. In the present work, we assess the ability of different methods to detect such similarities based on the geometry of the protein surfaces (described as 3D meshes), using either their shape only, or their shape and the electrostatic potential (a biologically relevant property of proteins surface). Five different groups participated in this contest using the shape-only dataset, and one group extended its pre-existing method to handle the electrostatic potential. Our comparative study reveals both the ability of the methods to detect related proteins and their difficulties to distinguish between highly related proteins. Our study allows also to analyze the putative influence of electrostatic information in addition to the one of protein shapes alone. Finally, the discussion permits to expose the results with respect to ones obtained in the previous contests for the extended method. The source codes of each presented method have been made available online

Calibration of a passive sampling device for the determination of nitrogen dioxide in ambient air

Nitrogen dioxide (NO2), a common air pollutant, has been widely admitted to be harmful to both the environment and human health, demanding its well-control procedure and corresponding quantification. In this study, NO2 in ambient air was collected by a passive sampling method using the Willems badge diffusive sampler, followed by a derivatization step with the Griess-Saltzman solution, and analyzed by ultraviolet-visible (UV–vis) spectroscopy at 543 nm. The device can be utilized for 168 h of continuous field sampling. The experimental sampling rate (Ke) of (4.02 ± 0.29) × 10−3 m3 h − 1 with a relative standard deviation (% RSD) of 9.6 % was determined by conducting parallel experiments between an active sampling method (ISO 6768:1998) and the Willems samplers. After exposure time, samplers could be stored for two weeks in a refrigerator at 4 °C before analyzing. The studied passive diffusive sampler was simple, low-cost, easy to reuse; permitted determining the average concentration of NO2 in ambient air. The average NO2 concentrations for 2-hour to 4-hour sampling periods at different studied sampling sites in Ho Chi Minh city (Vietnam) were ranged from 11.5 to 189 μg m − 3

SHREC 2021: surface-based protein domains retrieval

Oral. 3DOR is the dedicated workshop series for methods, applications and benchmark-based evaluation of 3D object retrieval, classification, and similarity-based object processing. The workshop also includes the 2021 edition of the 3D Shape Retrieval Challenge (SHREC). Accepted full papers will be published in Computers & Graphics Journal (Elsevier), and accepted short papers will appear in the Eurographics Digital Library.International audienceProteins are essential to nearly all cellular mechanism, and often interact through their surface with other cell molecules, such as proteins and ligands. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence surface, which is therefore of primary importance for their activity. In the present work, we assess the ability of five methods to retrieve similar protein surfaces, using either their shape only (3D meshes), or their shape and the electrostatic potential at their surface, an important surface property. Five different groups participated in this challenge using the shape only, and one group extended its pre-existing algorithm to handle the electrostatic potential. The results reveal both the ability of the methods to detect related proteins and their difficulties to distinguish between topologically related proteins

SHREC 2021 Track:Retrieval and classification of protein surfaces equipped with physical and chemical properties

This paper presents the methods that have participated in the SHREC 2021 contest on retrieval and classification of protein surfaces on the basis of their geometry and physicochemical properties. The goal of the contest is to assess the capability of different computational approaches to identify different conformations of the same protein, or the presence of common sub-parts, starting from a set of molecular surfaces. We addressed two problems: defining the similarity solely based on the surface geometry or with the inclusion of physicochemical information, such as electrostatic potential, amino acid hydrophobicity, and the presence of hydrogen bond donors and acceptors. Retrieval and classification performances, with respect to the single protein or the existence of common sub-sequences, are analysed according to a number of information retrieval indicators

SHREC\u2717: RgB-D to CAD Retrieval With ObjectNN Dataset

© 2017 The Eurographics Association. The goal of this track is to study and evaluate the performance of 3D object retrieval algorithms using RGB-D data. This is inspired from the practical need to pair an object acquired from a consumer-grade depth camera to CAD models available in public datasets on the Internet. To support the study, we propose ObjectNN, a new dataset with well segmented and annotated RGB-D objects from SceneNN [HPN∗16] and CAD models from ShapeNet [CFG∗15]. The evaluation results show that the RGB-D to CAD retrieval problem, while being challenging to solve due to partial and noisy 3D reconstruction, can be addressed to a good extent using deep learning techniques, particularly, convolutional neural networks trained by multi-view and 3D geometry. The best method in this track scores 82% in accuracy

RGB-D to CAD retrieval with objectNN dataset

The goal of this track is to study and evaluate the performance of 3D object retrieval algorithms using RGB-D data. This is inspired from the practical need to pair an object acquired from a consumer-grade depth camera to CAD models available in public datasets on the Internet. To support the study, we propose ObjectNN, a new dataset with well segmented and annotated RGB-D objects from SceneNN [HPN*16] and CAD models from ShapeNet [CFG*15]. The evaluation results show that the RGB-D to CAD retrieval problem, while being challenging to solve due to partial and noisy 3D reconstruction, can be addressed to a good extent using deep learning techniques, particularly, convolutional neural networks trained by multi-view and 3D geometry. The best method in this track scores 82% in accuracy