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.

基于跨模态交叉注意力网络的多模态情感分析方法

挖掘不同模态内信息和模态间信息有助于提升多模态情感分析的性能,本文为此提出一种基于跨模态交叉注意力网络的多模态情感分析方法。首先,利用VGG-16网络将多模态数据映射到全局特征空间;同时,利用Swin Transformer网络将多模态数据映射到局部特征空间;其次,构造模态内自注意力和模态间交叉注意力特征;然后,设计一种跨模态交叉注意力融合模块实现不同模态内和模态间特征的深度融合,提升多模态特征表达的可靠性;最后,通过Softmax获得最终预测结果。在2个开源数据集CMU-MOSI和CMU-MSOEI上进行测试,本文模型在七分类任务上获得45.9%和54.1%的准确率,相比当前MCGMF模型,提升了0.66%和2.46%,综合性能提升显著。

多视野精细分析下的弱监督目标定位算法

针对多尺度目标定位精度较差,难以捕获完整目标边界的问题,设计一种多视野精细分析模块并融入通道与空间注意力机制抑制背景噪声的干扰,获取多尺度目标的高分辨率特征。利用随机特征选取模块获取特征图随机位置的组合,聚合多个位置图获取最具辨别性的位置及其它位置的信息,融合浅层生成的类激活图与聚合类激活图获取细粒度位置信息,捕获完整的目标边界。与现有的弱监督定位方法相比,在解决多尺度目标定位效果差和局部最优问题上具有一定的优势。

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.

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。

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.

在线社交网络竞争信息传播研究与稳定性分析

[目的/意义]当前,在线社交网络已成为人们获取和传播信息的重要途径,在线社交网络上的谣言发酵很快就会引发群体效应,为了应对谣言传播,政府机构等也会发布辟谣信息,所以正确把握两种意思相反的竞争信息传播的特点,有利于对谣言的管控。[方法/过程]基于竞争信息在在线社交网络中的传播关系,提出一种竞争信息扩散模型SC2I2R来描述两类信息在社会网络中的扩散过程,分析无病平衡点的局部稳定性和全局稳定性,并通过数值模拟研究扩散过程。[结果/结论]被接收和转发概率大的信息并不一定传播更广泛,信息传播还会受社交网络结构、初始传播节点数量等的影响。[局限]为了简化模型,没有考虑在线社交平台的推荐机制、热搜机制等可以跨越拓扑结构传播的机制以及各个平台之间的影响,也没有考虑其他影响传播的因素。

全球化背景下的跨国企业本土化路径分析

在全球经济一体化的进程中,跨国公司的角色逐渐凸显,特别是在贸易全球化、生产全球化和金融资本全球化的背景下。这一趋势不仅加速了全球范围内的经济整合,还显著提升了跨国公司在全球经济格局中的重要性。具体来说,跨国公司的全球战略已经使得单个国家的市场成为其全球市场布局的重要组成部分。在这个过程中,跨国公司不仅在全球经济建设中起到了关键的推动作用,而且其在世界经济中的地位也得到了显著的增强。文章旨在通过分析跨国公司在贸易中的本土化策略,探讨本土化策略对其经营成效的影响。

局部饱和层地下水重力效应的水文地质参数全局敏感性分析

为了分析局部水文重力效应对哪些参数敏感,基于虚拟仿真抽水试验模拟具有解析解的局部水文模型,进行水文地质参数的全局敏感性分析,可定量研究各个参数对模拟水文重力效应的直接影响及参数间相互耦合作用的间接影响。研究使用Sobol法,针对特定水文场景对局部饱和层地下水重力效应的水文地质参数进行全局敏感性分析,结果表明:1)研究案例中,储水系数对于局部饱和层地下水重力效应影响最大,渗透率次之,仪器距离初始水平面的距离影响最小。各参数间交互敏感度较低,“异参同效”性较低。2)参数的变化区间选择应根据现实情况选取适当的范围,选择不当的变化范围对敏感性分析具有较大影响。3)当样本达到一定数量时,样本量的增加对其全局敏感性分析结果并无较大影响,应选择合适的样本量以提高工作效率。研究结果为后续挑选重力敏感参数组,进行基于合成重力观测约束下的水文地质多参数反演以及模拟不同多参数组合的反演结果提供了基础。

应用AIM/Local中国模型定量分析减排技术协同效应对气候变化政策的影响

IPCC评价报告全面分析了温室气体减排政策措施所产生的对改善当地环境的协同效应 ,但目前国际上对国内环境保护政策措施所产生的减少温室气体排放的协同效应的研究还很缺乏 ,而后者更符合广大发展中国家的实际情况。应用区域能源环境经济综合评价模型 ,考虑现行技术及未来技术发展、当地环境保护政策和未来经济发展等因素 ,并结合对北京市的案例研究 ,来定量分析减排技术的这两种协同效应对我国气候变化政策的影响 。

基于NIRS和Local PLS算法的堆肥关键参数实时动态分析

为对不同堆肥工艺堆肥全过程关键参数进行实时动态分析,该研究以牛粪便和玉米秸秆为原料,进行规模化槽式和膜覆盖好氧堆肥,采集堆肥全过程样本,分析了2种堆肥技术堆肥全过程中含水率、有机质含量和碳氮比等关键参数的变化,并结合Local PLS算法建立了2种堆肥技术堆肥全过程中上述参数的通用速测模型,得出以下结果:1)2种主要工艺关键参数数值及变化规律均不同,且在整个堆肥过程中有显著性变化(P<0.05);2)所建立的Local PLS模型的RPD(Ratio of Prediction to Deviation)为4.47,RSD(Relative Standard Deviation)为3.37%,可达到很好的预测效果;有机质含量和碳氮比的R_P^2分别为0.74和0.77,RPD大于1.5,RSD小于10%,模型可用于定量预测;近红外预测值与实测值随堆肥时间的变化趋势具有较好的一致性,可实现规模化堆肥过程中关键参数的实时分析。

THE GLOBAL AND LOCAL C^(2)-SOLUTIONS FOR THE WAVE EQUATION □u = G(u_t, Du) IN THREE SPACE DIMENSIONS

Kunio Hidano[4] has shown that the global and local C2-solutions for semilinear wave equations with spherical symmetry in three space dimensions. This paper studies the global and local C2-solutions for the semilinear wave equations without spherical symmetry in three space dimensions. A problem put forward by Hiroyuki Takamura[2] is partially answered.

Feature extension and matching for mobile robot global localization

This paper introduces an indoor global localization method by extending and matching features. In the proposed method, the environment is partitioned into convex subdivisions. Local extended maps of the subdivisions are then built by exten- ding features to constitute the local extended map set. While the robot is moving in the environment, the local extended map of the current local environment is established and then matched with the local extended map set. Therefore, global localization in an indoor environment can be achieved by integrating the position and ori- entation matching rates. Both theoretical analysis and comparison experimental result are provided to verify the effectiveness of the proposed method for global localization.

Influential nodes identification in complex networks based on global and local information

Identifying influential nodes in complex networks is essential for network robust and stability,such as viral marketing and information control.Various methods have been proposed to define the influence of nodes.In this paper,we comprehensively consider the global position and local structure to identify influential nodes.The number of iterations in the process of k-shell decomposition is taken into consideration,and the improved k-shell decomposition is then put forward.The improved k-shell decomposition and degree of target node are taken as the benchmark centrality,in addition,as is well known,the effect between node pairs is inversely proportional to the shortest path length between two nodes,and then we also consider the effect of neighbors on target node.To evaluate the performance of the proposed method,susceptible-infected(SI)model is adopted to simulate the spreading process in four real networks,and the experimental results show that the proposed method has obvious advantages over classical centrality measures in identifying influential nodes.

A GLOBAL LINEAR AND LOCAL QUADRATIC SINGLE-STEP NONINTERIOR CONTINUATION METHOD FOR MONOTONE SEMIDEFINITE COMPLEMENTARITY PROBLEMS

A noninterior continuation method is proposed for semidefinite complementarity problem （SDCP）. This method improves the noninterior continuation methods recently developed for SDCP by Chen and Tseng. The main properties of our method are： （i） it is well d.efined for the monotones SDCP; （ii） it has to solve just one linear system of equations at each step; （iii） it is shown to be both globally linearly convergent and locally quadratically convergent under suitable assumptions.

GLOBAL BLOW-UP FOR A HEAT SYSTEM WITH LOCALIZED SOURCES AND ABSORPTIONS

In this paper there are established the global existence and finite time blow-up results of nonnegative solution for the following parabolic system ut = △u ＋ v^P（x0, t） - au^τ, x ∈ Ω, t 〉 0, △u ＋ v^P（x0, t） - bu^τ, x ∈ Ω, t 〉 0 subject to homogeneous Dirichlet conditions and nonnegative initial data, where x0 ∈ Ω is a fixed point, p, q, r, s ≥ 1 and a, b 〉 0 are constants. In the situation when nonnegative solution （u, v） of the above problem blows up in finite time, it is showed that the blow-up is global and this differs from the local sources case. Moreover, for the special case r = s = 1, lim t→T＊（T＊-t）^p＋1/pq-1u（x,t）=（p＋1）^1/pq-1（q＋1）^p/pq-1（pq-1）^-p＋1/pq-1, lim t→T＊（T＊-t）^q＋1/pq-1u（x,t）=（p＋1）^1/pq-1（q＋1）^p/pq-1（pq-1）^-p＋1/pq-1 are obtained uniformly on compact subsets of/2, where T＊ is the blow-up time.

Numerical simulation of the welding deformation for the side sill of the bogie frame based on local-global method

Considering the limitation of computational capacity, a new finite element solution is used to simulate the welding deformation of the side sill of railroad car＇ s bogie frame based on the local-global method. Firstly, a volumetric heat source defined by a double ellipsoid is adopted to simulate the thermal distributions of the arc welding process. And then, the local models extracted from the global model are computed with refined meshes. On these bases, the global distortions of the subject studied are ascertained by transferring the inner forces of computed local models to the global model. It indicates that the local-global method is feasible for simulating the large welded structures by comparing the computed results with the corresponding actual measured values. The work provides basis for optimizing the welding sequence and clamping conditions, and has theoretical values and engineering significance in the integral design, manufacturing technique selection of the bogie frame, as well as other kinds of large welded structures.

DNAPL场地污染通量升尺度预测的敏感性分析

重非水相液体(DNAPL)污染问题日益严重。为评估DNAPL污染场地的环境风险,常采用升尺度模型推估DNAPL污染源区溶解相的质量通量(溶解通量)。由于升尺度模型中的参数较多,调查成本较高,因此需筛选模型中的关键参数,指导实际污染场地设计合理的观测数据采集方案。首先对升尺度模型中6个参数(地下水平均流速q、标准化浓度C_(0)/C^(eq)、离散状DNAPL质量比例GF_(0)、初始时刻离散状DNAPL贡献的通量比例f_(g)、拟合参数β1及β2)开展全局敏感性分析,识别其中关键参数,进而采用局部敏感性分析定量化关键参数的变化对通量预测的影响。研究结果表明,参数q、C_(0)/C^(eq)、GF_(0)和f_(g)对通量预测有较大影响。q和C_(0)/C^(eq)在整个衰减过程中敏感性均相对较高,GF_(0)和f_(g)随着衰减过程的进行,敏感性不断增高,分别在衰减中后期和后期达到峰值;对于不同结构的污染源区,q或C_(0)/C^(eq)增大时,通量的增幅基本不变。随着污染源区中离散状DNAPL和池状DNAPL间的质量比例(GTP)增大,GF_(0)或f_(g)增大时,其对通量预测的影响不断增大或减小。因此在预测溶解通量时需将调查成本重点应用于q和C_(0)/C^(eq);在合理设计污染源区修复方案时,应重点调查GF_(0);在预测污染源区寿命时,f _(g)为重要调查对象;对于所有结构的污染源区,q和C_(0)/C^(eq)均为重要调查对象,对于GTP较大的污染源区,应将调查成本重点应用于GF_(0),对于GTP较小的污染源区,应重点调查f _(g)。