Over-expression of Arabidopsis DnaJ (Hsp40) contributes to NaCl-stress tolerance

DnaJ (Hsp40), a heat shock protein, is a molecular chaperones responsive to various environmental stress. To analyze the protective role of DnaJ, we obtained sense transgenic Arabidopsis plants that constitutively expressed elevated levels of DnaJ. In this study, sense transgenic plants show large thinner, fade color and malformed leaves, as well as less floss of back leaves. Plants with enhanced levels of DnaJ in their transgenic sense lines exhibited tolerance to NaCl stress. Under 120 mM NaCl, root length was higher in transgenic sense plants than wild-type plants. In vitro expression system, DnaJ protein shows tolerance to high NaCl. These results suggest that over-expression of DnaJ can confer NaCl-stress tolerance

Large deformation of spherical vesicle studied by perturbation theory and Surface evolver

With tangent angle perturbation approach the axial symmetry deformation of a spherical vesicle in large under the pressure changes is studied by the elasticity theory of Helfrich spontaneous curvature model.Three main results in axial symmetry shape: biconcave shape, peanut shape, and one type of myelin are obtained. These axial symmetry morphology deformations are in agreement with those observed in lipsome experiments by dark-field light microscopy [Hotani, J. Mol. Biol. 178, (1984) 113] and in the red blood cell with two thin filaments (myelin) observed in living state (see, Bessis, Living Blood Cells and Their Ultrastructure, Springer-Verlag, 1973). Furthermore, the biconcave shape and peanut shape can be simulated with the help of a powerful software, Surface Evolver [Brakke, Exp. Math. 1, 141 (1992) 141], in which the spontaneous curvature can be easy taken into account.Comment: 16 pages, 6 EPS figures and 2 PS figure

GAMAFlow: Estimating 3D Scene Flow via Grouped Attention and Global Motion Aggregation

The estimation of 3D motion fields, known as scene flow estimation, is an essential task in autonomous driving and robotic navigation. Existing learning-based methods either predict scene flow through flow-embedding layers or rely on local search methods to establish soft correspondences. However, these methods often neglect distant points which, in fact, represent the true matching elements. To address this challenge, we introduce GAMAFlow, a point-voxel architecture that models local motion and global motion to predict scene flow iteratively. In particular, GAMAFlow integrates the advantages of (i) the point Transformer with Grouped Attention and (ii) global Motion Aggregation to boost the efficacy of point-voxel correlation. Such an approach facilitates learning long-distance dependencies between current frame and next frame. Experiments illustrate the performance gains achieved by GAMAFlow compared to existing works on both FlyingThings3D and KITTI benchmarks

Nonlinear Spring-Mass-Damper Modeling and Parameter Estimation of Train Frontal Crash Using CLGAN Model

Due to the complexity of a train crash, it is a challenging process to describe and estimate mathematically. Although different mathematical models have been developed, it is still difficult to balance the complexity of models and the accuracy of estimation. ,is paper proposes a nonlinear spring-mass-damper model of train frontal crash, which achieves high accuracy and maintains low complexity. ,e Convolutional Long-short-term-memory Generation Adversarial Network (CLGAN) model is applied to study the nonlinear parameters dynamic variation of the key components of a rail vehicle (e.g., the head car, anticlimbing energy absorber, and the coupler buffer devices). Firstly, the nonlinear lumped model of train frontal crash is built, and then the physical parameters are deduced in twenty different cases using D’Alembert’s principle. Secondly, the input/output relationship of the CLGAN model is determined, where the inputs are the nonlinear physical parameters in twenty initial conditions, and the output is the nonlinear relationship between the train crash nonlinear parameters under other initial cases. Finally, the train crash dynamic characteristics are accurately estimated during the train crash processes through the training of the CLGAN model, and then the crash processes under different given conditions can be described effectively. ,e estimation results exhibit good agreement with finite element (FE) simulations and experimental results. Furthermore, the CLGAN model shows great potential in nonlinear estimation, and CLGAN can better describe the variation of nonlinear spring damping compared with the traditional model. ,e nonlinear spring-mass-damper modeling is involved in improving the speed and accuracy of the train crash estimation, as well as being able to offer guidance for structure optimization in the early design stage

A Review of 3D Point Clouds Parameterization Methods

3D point clouds parameterization is a very important research topic in the fields of computer graphics and computer vision, which has many applications such as texturing, remeshing and morphing, etc. Different from mesh parameterization, point clouds parameterization is a more challenging task in general as there is normally no connectivity information between points. Due to this challenge, the papers on point clouds parameterization are not as many as those on mesh parameterization. To the best of our knowledge, there are no review papers about point clouds parameterization. In this paper, we present a survey of existing methods for parameterizing 3D point clouds. We start by introducing the applications and importance of point clouds parameterization before explaining some relevant concepts. According to the organization of the point clouds, we first divide point cloud parameterization methods into two groups: organized and unorganized ones. Since various methods for unorganized point cloud parameterization have been proposed, we further divide the group of unorganized point cloud parameterization methods into some subgroups based on the technique used for parameterization. The main ideas and properties of each method are discussed aiming to provide an overview of various methods and help with the selection of different methods for various applications

A survey of parametric modelling methods for designing the head of a high-speed train

With the continuous increase of the running speed, the head shape of the high-speed train (HST) turns out to be a critical factor for further speed boost. In order to cut down the time used in the HST head design and improve the modelling efficiency, various parametric modelling methods have been widely applied in the optimization design of the HST head to obtain an optimal head shape so that the aerodynamic effect acting on the head of HSTs can be reduced and more energy can be saved. This paper reviews these parametric modelling methods and classifies them into four categories: 2D, 3D, CATIA-based, and mesh deformation-based parametric modelling methods. Each of the methods is introduced, and the advantages and disadvantages of these methods are identified. The simulation results are presented to demonstrate that the aerodynamic performance of the optimal models constructed by these parametric modelling methods has been improved when compared with numerical calculation results of the original models or the prototype models of running trains. Since different parametric modelling methods used different original models and optimization methods, few publications could be found which compare the simulation results of the aerodynamic performance among different parametric modelling methods. In spite of this, these parametric modelling methods indicate more local shape details will lead to more accurate simulation results, and fewer design variables will result in higher computational efficiency. Therefore, the ability of describing more local shape details with fewer design variables could serve as a main specification to assess the performance of various parametric modelling methods. The future research directions may concentrate on how to improve such ability

Train post-derailment behaviours and containment methods: a review

Railway accidents, particularly serious derailments, can lead to catastrophic consequences. Therefore, it is essential to prevent derailment escalation to reduce the likelihood of severe derailments. Train post-derailment behaviours and containment methods play a critical role in preventing derailment escalation and providing passive safety protection and accident prevention in the event of a derailment. However, despite the increasing attention on this field from academia and industry in recent years, there is a lack of systematic exploration and summarization of emerging applications and containment methods in train post-derailment research. For this reason, this paper presents a comprehensive review of existing studies on train post-derailment behaviours, encompassing various topics such as post-derailment contact–impact models, dynamic modelling and simulation techniques, and the primary factors influencing post-derailment behaviours. Significantly, this review introduces and elucidates substitute guidance mechanisms (SGMs), which serve as railway-specific passive safety protection and accident prevention measures. The various types of SGMs are depicted, and their ongoing developments and applications are explored in depth. The review additionally points out several unresolved challenges including the adverse effects of SGMs, and proposes future research directions to advance the theoretical understanding and practical application of train post-derailment behaviours and containment methods. This review seeks to be a valuable reference for railway industry professionals in preventing catastrophic derailment consequences through post-derailment containment methods

Genetic relationships within Brassica rapa as inferred from AFLP finterprints

Amplified fragment length polymorphism (AFLP) markers were employed to assess the genetic diversity amongst two large collections of Brassica rapa accessions. Collection A consisted of 161 B. rapa accessions representing different morphotypes among the cultivated B. rapa, including traditional and modern cultivars and breeding materials from geographical locations from all over the world and two Brassica napus accessions. Collection B consisted of 96 accessions, representing mainly leafy vegetable types cultivated in China. On the basis of the AFLP data obtained, we constructed phenetic trees using mega 2.1 software. The level of polymorphism was very high, and it was evident that the amount of genetic variation present within the groups was often comparable to the variation between the different cultivar groups. Cluster analysis revealed groups, often with low bootstrap values, which coincided with cultivar groups. The most interesting information revealed by the phenetic trees was that different morphotypes are often more related to other morphotypes from the same region (East Asia vs. Europe) than to similar morphotypes from different regions, suggesting either an independent origin and or a long and separate domestication and breeding history in both region

Data-driven train set crash dynamics simulation

© 2016 Informa UK Limited, trading as Taylor & Francis GroupTraditional finite element (FE) methods are arguably expensive in computation/simulation of the train crash. High computational cost limits their direct applications in investigating dynamic behaviours of an entire train set for crashworthiness design and structural optimisation. On the contrary, multi-body modelling is widely used because of its low computational cost with the trade-off in accuracy. In this study, a data-driven train crash modelling method is proposed to improve the performance of a multi-body dynamics simulation of train set crash without increasing the computational burden. This is achieved by the parallel random forest algorithm, which is a machine learning approach that extracts useful patterns of force–displacement curves and predicts a force–displacement relation in a given collision condition from a collection of offline FE simulation data on various collision conditions, namely different crash velocities in our analysis. Using the FE simulation results as a benchmark, we compared our method with traditional multi-body modelling methods and the result shows that our data-driven method improves the accuracy over traditional multi-body models in train crash simulation and runs at the same level of efficiency

Consistent as-similar-as-possible non-isometric surface registration

© 2017 The Author(s)Non-isometric surface registration, aiming to align two surfaces with different sizes and details, has been widely used in computer animation industry. Various existing surface registration approaches have been proposed for accurate template fitting; nevertheless, two challenges remain. One is how to avoid the mesh distortion and fold over of surfaces during transformation. The other is how to reduce the amount of landmarks that have to be specified manually. To tackle these challenges simultaneously, we propose a consistent as-similar-as-possible (CASAP) surface registration approach. With a novel defined energy, it not only achieves the consistent discretization for the surfaces to produce accurate result, but also requires a small number of landmarks with little user effort only. Besides, CASAP is constrained as-similar-as-possible so that angles of triangle meshes are preserved and local scales are allowed to change. Extensive experimental results have demonstrated the effectiveness of CASAP in comparison with the state-of-the-art approaches