基于Global/Local法的螺栓连接结构静强度渐进破坏

为了研究螺栓连接结构静强度渐进破坏特点,应用Global/Local有限元分析技术,分两级建立了螺栓连接结构有限元模型,并对其进行了带EWK断裂子程序的材料/几何非线性有限元分析。结果表明:螺栓连接结构的断裂部位位于螺栓孔边缘,当加载到最大载荷的42%时开始出现破坏单元,在加载到最大载荷的48%之前裂纹扩展是一个稳态的过程,之后便迅速扩展直至整体结构失效。Global/Local分析技术提高了整个过程的分析效率。

STRAIGHTFORWARD MULTI-SCALE BOUNDARY ELEMENT METHOD FOR GLOBAL/LOCAL MECHANICAL ANALYSIS OF ELASTIC HETEROGENEOUS MATERIAL

A straightforward multi-scale boundary element method is proposed for global and local mechanical analysis of heterogeneous material.The method is more accurate and convenient than finite element based multi-scale method.The formulations of this method are derived by combining the homogenization approach and the fundamental equations of boundary element method.The solution gives the convenient formulations to compute global elastic constants and the local stress field.Finally,two numerical examples of porous material are presented to prove the accuracy and the efficiency of the proposed method.The results show that the method does not require the iteration to obtain the solution of the displacement in micro level.

Studying the co-evolution of information diffusion,vaccination behavior and disease transmission in multilayer networks with local and global effects

Today,with the rapid development of the internet,a large amount of information often accompanies the rapid transmission of disease outbreaks,and increasing numbers of scholars are studying the relationship between information and the disease transmission process using complex networks.In fact,the disease transmission process is very complex.Besides this information,there will often be individual behavioral measures and other factors to consider.Most of the previous research has aimed to establish a two-layer network model to consider the impact of information on the transmission process of disease,rarely divided into information and behavior,respectively.To carry out a more in-depth analysis of the disease transmission process and the intrinsic influencing mechanism,this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamic co-evolution of information diffusion,vaccination behavior,and disease transmission.This is achieved by considering four influential relationships between adjacent layers in multilayer networks.In the information layer,the diffusion process of negative information is described,and the feedback effects of local and global vaccination are considered.In the behavioral layer,an individual's vaccination behavior is described,and the probability of an individual receiving a vaccination is influenced by two factors:the influence of negative information,and the influence of local and global disease severity.In the disease layer,individual susceptibility is considered to be influenced by vaccination behavior.The state transition equations are derived using the micro Markov chain approach(MMCA),and disease prevalence thresholds are obtained.It is demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior,and is mainly influenced by global vaccination behavior;vaccination behavior is mainly influenced by local disease conditions,and is less influenced by global disease conditions;the disease transmission threshold increases with the increasing vaccination rate;and the scale of disease transmission increases with the increasing negative information diffusion rate and decreases with the increasing vaccination rate.Finally,it is found that when individual vaccination behavior considers both the influence of negative information and disease,it can increase the disease transmission threshold and reduce the scale of disease transmission.Therefore,we should resist the diffusion of negative information,increase vaccination proportions,and take appropriate protective measures in time.

Deep Global Multiple-Scale and Local Patches Attention Dual-Branch Network for Pose-Invariant Facial Expression Recognition

Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inconsistent from one view to another.This study develops a deep global multiple-scale and local patches attention(GMS-LPA)dual-branch network for pose-invariant FER to weaken the influence of pose variation and selfocclusion on recognition accuracy.In this research,the designed GMS-LPA network contains four main parts,i.e.,the feature extraction module,the global multiple-scale(GMS)module,the local patches attention(LPA)module,and the model-level fusion model.The feature extraction module is designed to extract and normalize texture information to the same size.The GMS model can extract deep global features with different receptive fields,releasing the sensitivity of deeper convolution layers to pose-variant and self-occlusion.The LPA module is built to force the network to focus on local salient features,which can lower the effect of pose variation and self-occlusion on recognition results.Subsequently,the extracted features are fused with a model-level strategy to improve recognition accuracy.Extensive experimentswere conducted on four public databases,and the recognition results demonstrated the feasibility and validity of the proposed methods.

Integrated multi-scale approach combining global homogenization and local refinement for multi-field analysis of high-temperature superconducting composite magnets

Second-generation high-temperature superconducting(HTS)conductors,specifically rare earth-barium-copper-oxide(REBCO)coated conductor(CC)tapes,are promising candidates for high-energy and high-field superconducting applications.With respect to epoxy-impregnated REBCO composite magnets that comprise multilayer components,the thermomechanical characteristics of each component differ considerably under extremely low temperatures and strong electromagnetic fields.Traditional numerical models include homogenized orthotropic models,which simplify overall field calculation but miss detailed multi-physics aspects,and full refinement(FR)ones that are thorough but computationally demanding.Herein,we propose an extended multi-scale approach for analyzing the multi-field characteristics of an epoxy-impregnated composite magnet assembled by HTS pancake coils.This approach combines a global homogenization(GH)scheme based on the homogenized electromagnetic T-A model,a method for solving Maxwell's equations for superconducting materials based on the current vector potential T and the magnetic field vector potential A,and a homogenized orthotropic thermoelastic model to assess the electromagnetic and thermoelastic properties at the macroscopic scale.We then identify“dangerous regions”at the macroscopic scale and obtain finer details using a local refinement(LR)scheme to capture the responses of each component material in the HTS composite tapes at the mesoscopic scale.The results of the present GH-LR multi-scale approach agree well with those of the FR scheme and the experimental data in the literature,indicating that the present approach is accurate and efficient.The proposed GH-LR multi-scale approach can serve as a valuable tool for evaluating the risk of failure in large-scale HTS composite magnets.

Methodology for local correction of the heights of global geoid models to improve the accuracy of GNSS leveling

At present,one of the methods used to determine the height of points on the Earth’s surface is Global Navigation Satellite System(GNSS)leveling.It is possible to determine the orthometric or normal height by this method only if there is a geoid or quasi-geoid height model available.This paper proposes the methodology for local correction of the heights of high-order global geoid models such as EGM08,EIGEN-6C4,GECO,and XGM2019e_2159.This methodology was tested in different areas of the research field,covering various relief forms.The dependence of the change in corrected height accuracy on the input data was analyzed,and the correction was also conducted for model heights in three tidal systems:"tide free","mean tide",and"zero tide".The results show that the heights of EIGEN-6C4 model can be corrected with an accuracy of up to 1 cm for flat and foothill terrains with the dimensionality of 1°×1°,2°×2°,and 3°×3°.The EGM08 model presents an almost identical result.The EIGEN-6C4 model is best suited for mountainous relief and provides an accuracy of 1.5 cm on the 1°×1°area.The height correction accuracy of GECO and XGM2019e_2159 models is slightly poor,which has fuzziness in terms of numerical fluctuation.

Global Existence and Decay of Solutions for a Class of a Pseudo-Parabolic Equation with Singular Potential and Logarithmic Nonlocal Source

This article investigates the well posedness and asymptotic behavior of Neumann initial boundary value problems for a class of pseudo-parabolic equations with singular potential and logarithmic nonlinearity. By utilizing cut-off techniques and combining with the Faedo Galerkin approximation method, local solvability was established. Based on the potential well method and Hardy Sobolev inequality, derive the global existence of the solution. In addition, we also obtained the results of decay.

Oruxmaps软件结合Global Mapper软件在矿产资源行业并购中的应用

纸质地质图结合手持GPS,采用手工方式野外记录的传统地质调查工作方式已不能适应数字化和信息化的要求,在矿产资源并购项目中,运用Oruxmaps、Global Mapper等软件,可以制作适应地质调查工作所特需的离线地质图,实现地质图、GPS定位、路线轨迹记录、文字记录、影像记录等的有机结合,形成了一套行之有效的工作流程,极大地提高了路线地质调查工作的质量和效率。

Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization

Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.

Loop Closure Detection via Locality Preserving Matching With Global Consensus

A critical component of visual simultaneous localization and mapping is loop closure detection(LCD),an operation judging whether a robot has come to a pre-visited area.Concretely,given a query image(i.e.,the latest view observed by the robot),it proceeds by first exploring images with similar semantic information,followed by solving the relative relationship between candidate pairs in the 3D space.In this work,a novel appearance-based LCD system is proposed.Specifically,candidate frame selection is conducted via the combination of Superfeatures and aggregated selective match kernel(ASMK).We incorporate an incremental strategy into the vanilla ASMK to make it applied in the LCD task.It is demonstrated that this setting is memory-wise efficient and can achieve remarkable performance.To dig up consistent geometry between image pairs during loop closure verification,we propose a simple yet surprisingly effective feature matching algorithm,termed locality preserving matching with global consensus(LPM-GC).The major objective of LPM-GC is to retain the local neighborhood information of true feature correspondences between candidate pairs,where a global constraint is further designed to effectively remove false correspondences in challenging sceneries,e.g.,containing numerous repetitive structures.Meanwhile,we derive a closed-form solution that enables our approach to provide reliable correspondences within only a few milliseconds.The performance of the proposed approach has been experimentally evaluated on ten publicly available and challenging datasets.Results show that our method can achieve better performance over the state-of-the-art in both feature matching and LCD tasks.We have released our code of LPM-GC at https://github.com/jiayi-ma/LPM-GC.

SLGC: Identifying influential nodes in complex networks from the perspectives of self-centrality, local centrality, and global centrality

Identifying influential nodes in complex networks and ranking their importance plays an important role in many fields such as public opinion analysis, marketing, epidemic prevention and control. To solve the issue of the existing node centrality measure only considering the specific statistical feature of a single dimension, a SLGC model is proposed that combines a node’s self-influence, its local neighborhood influence, and global influence to identify influential nodes in the network. The exponential function of e is introduced to measure the node’s self-influence;in the local neighborhood,the node’s one-hop neighboring nodes and two-hop neighboring nodes are considered, while the information entropy is introduced to measure the node’s local influence;the topological position of the node in the network and the shortest path between nodes are considered to measure the node’s global influence. To demonstrate the effectiveness of the proposed model, extensive comparison experiments are conducted with eight existing node centrality measures on six real network data sets using node differentiation ability experiments, susceptible–infected–recovered(SIR) model and network efficiency as evaluation criteria. The experimental results show that the method can identify influential nodes in complex networks more accurately.

El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023

In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.

GLOBAL SOLUTIONS TO 1D COMPRESSIBLE NAVIER-STOKES/ALLEN-CAHN SYSTEM WITH DENSITY-DEPENDENT VISCOSITY AND FREE-BOUNDARY

This paper is concerned with the Navier-Stokes/Allen-Cahn system,which is used to model the dynamics of immiscible two-phase flows.We consider a 1D free boundary problem and assume that the viscosity coefficient depends on the density in the form ofη(ρ)=ρ^(α).The existence of unique global H^(2m)-solutions(m∈N)to the free boundary problem is proven for when 0<α<1/4.Furthermore,we obtain the global C^(∞)-solutions if the initial data is smooth.

Global hybrid simulations of soft X-ray emissions in the Earth’s magnetosheath

Earth’s magnetopause is a thin boundary separating the shocked solar wind plasma from the magnetospheric plasmas,and it is also the boundary of the solar wind energy transport to the magnetosphere.Soft X-ray imaging allows investigation of the large-scale magnetopause by providing a two-dimensional(2-D)global view from a satellite.By performing 3-D global hybrid-particle-in-cell(hybrid-PIC)simulations,we obtain soft X-ray images of Earth’s magnetopause under different solar wind conditions,such as different plasma densities and directions of the southward interplanetary magnetic field.In all cases,magnetic reconnection occurs at low latitude magnetopause.The soft X-ray images observed by a hypothetical satellite are shown,with all of the following identified:the boundary of the magnetopause,the cusps,and the magnetosheath.Local X-ray emissivity in the magnetosheath is characterized by large amplitude fluctuations(up to 160%);however,the maximum line-of-sight-integrated X-ray intensity matches the tangent directions of the magnetopause well,indicating that these fluctuations have limited impact on identifying the magnetopause boundary in the X-ray images.Moreover,the magnetopause boundary can be identified using multiple viewing geometries.We also find that solar wind conditions have little effect on the magnetopause identification.The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission will provide X-ray images of the magnetopause for the first time,and our global hybrid-PIC simulation results can help better understand the 2-D X-ray images of the magnetopause from a 3-D perspective,with particle kinetic effects considered.

Background removal from global auroral images:Data-driven dayglow modeling

Global images of auroras obtained by cameras on spacecraft are a key tool for studying the near-Earth environment.However,the cameras are sensitive not only to auroral emissions produced by precipitating particles,but also to dayglow emissions produced by photoelectrons induced by sunlight.Nightglow emissions and scattered sunlight can contribute to the background signal.To fully utilize such images in space science,background contamination must be removed to isolate the auroral signal.Here we outline a data-driven approach to modeling the background intensity in multiple images by formulating linear inverse problems based on B-splines and spherical harmonics.The approach is robust,flexible,and iteratively deselects outliers,such as auroral emissions.The final model is smooth across the terminator and accounts for slow temporal variations and large-scale asymmetries in the dayglow.We demonstrate the model by using the three far ultraviolet cameras on the Imager for Magnetopause-to-Aurora Global Exploration(IMAGE)mission.The method can be applied to historical missions and is relevant for upcoming missions,such as the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)mission.

Record-breaking High-temperature Outlook for 2023: An Assessment Based on the China Global Merged Temperature(CMST) Dataset

According to the latest version(version 2.0) of the China global Merged Surface Temperature(CMST2.0) dataset, the global mean surface temperature(GMST) in the first half of 2023 reached its third warmest value since the period of instrumental observation began, being only slightly lower than the values recorded in 2016 and 2020, and historically record-breaking GMST emerged from May to July 2023. Further analysis also indicates that if the surface temperature in the last five months of 2023 approaches the average level of the past five years, the annual average surface temperature anomaly in 2023 of approximately 1.26°C will break the previous highest surface temperature, which was recorded in 2016of approximately 1.25°C(both values relative to the global pre-industrialization period, i.e., the average value from 1850 to1900). With El Ni?o triggering a record-breaking hottest July, record-breaking average annual temperatures will most likely become a reality in 2023.

Globalization and Glocalization in the History of Chinese Medicine

The globalization of Chinese medicine, forged through successive waves of migration, cultural exchanges, and economic imperatives, constitutes a nuanced and intricate process with historical roots extending over millennia. It stands as the culmination of interconnected historical events that reverberated beyond the confines of China, emerging as a phenomenon characterized by the adjustment of Chinese medical theories, clinical practices, and materia medica to indigenous customs and healthcare traditions prevalent in both proximate and distant regions. In these glocalized processes, the global and the local intersect and mix. The frameworks of globalization and glocalization allow a critical interpretation of the many hybridizations that have shaped overseas Chinese medicine's history and present.

Manufacturing Localization,Technology Backfire,and Economic De-globalization

Since the global financial crisis,global value chain(GVC)have been contracting amid the trend toward economic de-globalization.With GVC participation rate in mind as the core indicator of economic de-globalization,in this paper we create a multi-country general equilibrium model to characterize the mechanism by which manufacturing localization affects GVC participation rate.Our theoretical derivation shows that changes in the local manufacturing status of final products in various countries directly influence the GVC participation rate of those countries.When the local proportion of a country’s final products reaches a certain level,rising local proportion of intermediate inputs,economic growth below the world average level,and technology progress all cause the country’s GVC participation rate to decline,giving rise to de-globalization at the manufacturing and trade levels.We further provide a comprehensive interpretation based on an empirical test of the deep-seated causes of economic de-globalization in relation to such economic phenomena as increasing trade concentration,the“technology backlash”effect of the new industrial revolution,and economic growth driven by the combined forces of trade protectionism and quantitative easing.

基于Ka波段的海事卫星Global Xpress系统

文章主要介绍了基于Ka波段的海事卫星Global Xpress系统的技术特点,着重与传统Vsat业务进行了对比,简单介绍了GX的海事业务。

Simplified Penman-Monteith Equation Determined by Temperature-Based Global Radiation Data and Its Multilocal Validation under Subhumid Climatic Conditions in Hungary

The extent to which specific climatic factors influence evapotranspiration under subhumid conditions in Hungary was investigated. The reference evapotranspiration, calculated with the internationally accepted Penman-Monteith equation proposed by FAO, was considered. The results show that the influence of radiation, which provides energy for evaporation, is the strongest factor and that the influence of global radiation alone is very strong. Taking into account that radiation was measured under rather limited conditions in space and time, global radiation was calculated using the Hargreaves method based on temperature. Accordingly, we have defined a formula based on temperature-based global radiation and verified the data obtained with the Penman-Monteith formula calculated for 14 meteorological stations. The verification gave good results, therefore the method can be used for practical purposes in the subhumid conditions of Hungary based on the data of the nearest meteorological station.