コネキシン43はアメロブラスチンの発現および歯原性上皮の分化に必要である

要約のみTohoku University福本敏課

Prediction of Yield Surface of Single Crystal Copper from Discrete Dislocation Dynamics and Geometric Learning

A yield surface of a material is a set of critical stress conditions beyond which macroscopic plastic deformation begins. For crystalline solids, plastic deformation occurs by the motion of dislocations, which can be captured by discrete dislocation dynamics (DDD) simulations. In this paper, we predict the yield surfaces and strain-hardening behaviors using DDD simulations and a geometric manifold learning approach. The yield surfaces in the three-dimensional space of plane stress are constructed for single-crystal copper subjected to uniaxial loading along the $[100]$ and $[110]$ directions, respectively. With increasing plastic deformation under $[100]$ loading, the yield surface expands nearly uniformly in all directions, corresponding to isotropic hardening. In contrast, under $[110]$ loading, latent hardening is observed, where the yield surface remains nearly unchanged in the orientations in the vicinity of the loading direction itself, but expands in other directions, resulting in an asymmetric shape. This difference in hardening behaviors is attributed to the different dislocation multiplication behaviors on various slip systems under the two loading conditions

Psychological contract’s effect on job mobility: Evidence from Chinese construction worker

The subject of this study is that the psychological contract (PC) approaches to job mobility within the construction industry with special reference to migrant construction workers in China. Using a semi-structured interview to elicit a full range of the PC’s con- tent of construction worker, we unravel the mechanism of such contract to influence the informal job mobility of workers through the lens of the evolutionary game framework. The results demonstrate that, in the case of fulfilling PC, the informal job mobility of workers is under control, and both workers and employers benefit from this situation. This study deepens the understanding of the PC’s effect on the job mobility of construction workers in China during the course of economic change. The theoretical and practical implications are discusse

Geometric prior of multi-resolution yielding manifolds and the local closest point projection for nearly non-smooth plasticity

Elastoplasticity models often introduce a scalar-valued yield function to implicitly represent the boundary between elastic and plastic material states. This paper introduces a new alternative where the yield envelope is represented by a manifold of which the topology and the geometry are learned from a set of data points in a parametric space (e.g. principal stress space, -plane). Here, deep geometric learning enables us to reconstruct a highly complex yield envelope by breaking it down into multiple coordinate charts. The global atlas that consists of these coordinate charts in return allows us to represent the yield surface via multiple overlapping patches, each with a specific local parametrization. This setup provides several advantages over the classical implicit function representation approach. For instance, the availability of coordinate charts enables us to introduce an alternative stress integration algorithm where the trial stress may project directly on a local patch and hence circumvent the issues related to non-smoothness and the lack of convexity of yield surfaces. Meanwhile, the local parametric approach also enables us to predict hardening/softening locally in the parametric space, even without complete knowledge of the yield surface. Comparisons between the classical yield function approach on the non-smooth plasticity and anisotropic cam-clay plasticity model are provided to demonstrate the capacity of the models for highly precise yield surface and the feasibility of the implementation of the learned model in the local stress integration algorithm

Comparative genomics of five Valsa species gives insights on their pathogenicity evolution

Valsa is a genus of ascomycetes within the Valsaceae family. This family includes many wood destructive pathogens such as the well known Valsa mali and Valsa pyri which cause canker diseases in fruit trees and threaten the global fruit production. Lack of genomic information of this family is impeding our understandings about their evolution and genetic basis of their pathogenicity divergence. Here, we report genome assemblies of Valsa malicola, Valsa persoonii, and Valsa sordida which represent close relatives of Valsa mali and Valsa pyri with different host preferences. Comparative genomics analysis revealed that segmental rearrangements, inversions, and translocations frequently occurred among Valsa spp. genomes. Gene families that exhibited gene copy expansions tended to be associated with secondary metabolism, transmembrane transport, and pyrophosphatase activities. Orthologous genes in regions lost synteny exhibited significantly higher rate of synonymous substitution (KS) than those in regions retained synteny. Moreover, among these genes, membrane transporter families associated with antidrug (MFS, DHA) activities and nutrient transportation (SP and APCs) activities were significantly over-represented. Lineage specific synonymous substitution (KS) and nonsynonymous substitution (KA) analysis based on the phylogeny constructed from 11 fungal species identified a set of genes with selection signatures in Valsa clade and these genes were significantly enriched in functions associated with fatty acid beta-oxidation, DNA helicase activity, and ATPase activity. Furthermore, unique genes that possessed or retained by each of the five Valsa species are more likely part of the secondary metabolic (SM) gene clusters. SM gene clusters conserved across five Valsa species showed various degrees of diversification in both identity and completeness. All 11 syntenically conserved SM clusters showed differential expression during the infection of apple branch with Valsa mali suggesting involvements of secondary metabolism in the pathogenicity of Valsa species

Simultaneous Multiple Object Detection and Pose Estimation using 3D Model Infusion with Monocular Vision

Multiple object detection and pose estimation are vital computer vision tasks. The latter relates to the former as a downstream problem in applications such as robotics and autonomous driving. However, due to the high complexity of both tasks, existing methods generally treat them independently, which is sub-optimal. We propose simultaneous neural modeling of both using monocular vision and 3D model infusion. Our Simultaneous Multiple Object detection and Pose Estimation network (SMOPE-Net) is an end-to-end trainable multitasking network with a composite loss that also provides the advantages of anchor-free detections for efficient downstream pose estimation. To enable the annotation of training data for our learning objective, we develop a Twin-Space object labeling method and demonstrate its correctness analytically and empirically. Using the labeling method, we provide the KITTI-6DoF dataset with $\sim7.5$K annotated frames. Extensive experiments on KITTI-6DoF and the popular LineMod datasets show a consistent performance gain with SMOPE-Net over existing pose estimation methods. Here are links to our proposed SMOPE-Net, KITTI-6DoF dataset, and LabelImg3D labeling tool

DP-MPM: Domain partitioning material point method for evolving multi-body thermal–mechanical contacts during dynamic fracture and fragmentation

We propose a material point method (MPM) to model the evolving multi-body contacts due to crack growth and fragmentation of thermo-elastic bodies. By representing particle interface with an implicit function, we adopt the gradient partition techniques introduced by Homel and Herbold (2017) to identify the separation between a pair of distinct material surfaces. This treatment allows us to replicate the frictional heating of the evolving interfaces and predict the energy dissipation more precisely in the fragmentation process. By storing the temperature at material points, the resultant MPM model captures the thermal advection–diffusion in a Lagrangian frame during the fragmentation, which in return affects the structural heating and dissipation across the frictional interfaces. The resultant model is capable of replicating the crack growth and fragmentation without requiring dynamic adaptation of data structures or insertion of interface elements. A staggered algorithm is adopted to integrate the displacement and temperature sequentially. Numerical experiments are employed to validate the diffusion between the thermal contact, the multi-body contact interactions and demonstrate how these thermo-mechanical processes affect the path-dependent behaviors of the multi-body systems

Formal Modeling and Verification for MVB

Multifunction Vehicle Bus (MVB) is a critical component in the Train Communication Network (TCN), which is widely used in most of the modern train techniques of the transportation system. How to ensure security of MVB has become an important issue. Traditional testing could not ensure the system correctness. The MVB system modeling and verification are concerned in this paper. Petri Net and model checking methods are used to verify the MVB system. A Hierarchy Colored Petri Net (HCPN) approach is presented to model and simulate the Master Transfer protocol of MVB. Synchronous and asynchronous methods are proposed to describe the entities and communication environment. Automata model of the Master Transfer protocol is designed. Based on our model checking platform M3C, the Master Transfer protocol of the MVB is verified and some system logic critical errors are found. Experimental results show the efficiency of our methods