Self-organized Criticality in a Modified Evolution Model on Generalized Barabasi-Albert Scale-Free Networks

A modified evolution model of self-organized criticality on generalized Barabasi-Albert （GBA）.scale-free networks is investigated. In our model, we find that spatial and temporal correlations exhibit critical behaviors. More importantly, these critical behaviors change with the parameter b, which weights the distance in comparison with the degree in the GBA network evolution.

价值链模型下我国乡村体育赛事研究--以贵州“村BA”“村超”为例

贵州“村BA”“村超”的爆火,引发了社会的广泛关注与深入思考。以两项赛事为研究对象,借鉴波特价值链模型,构建了“村BA”“村超”的价值链模型,进而从需求产生、组织运营、政策条件、文化氛围等方面分析了赛事的核心竞争力。两项赛事的价值主要体现在赋能体育产业融合发展,助推乡村振兴,以及促进群众体育与竞技体育协调发展等方面。两项赛事的成功具有重要的启示意义,认为我国的乡村体育赛事发展需要政府发挥主导作用,提供政策支持与保障;坚持以人民为中心,满足乡村居民体育需求;加强赛事组织管理,切实保障赛事品质;打造特色赛事IP,积极推进融合发展。

Malicious Code Modeling and Analysis in Weighted Scale-Free Networks

We study the detailed malicious code propagating process in scale-free networks with link weights that denotes traffic between two nodes. It is found that the propagating velocity reaches a peak rapidly then decays in a power-law form, which is different from the well-known result in unweighted network case. Simulation results show that the nodes with larger strength are preferential to be infected, but the hierarchical dynamics are not clearly found. The simulation results also show that larger dispersion of weight of networks leads to slower propagating, which indicates that malicious code propagates more quickly in unweighted scale-free networks than in weighted scale-free networks under the same condition. These results show that not only the topology of networks but also the link weights affect the malicious propagating process.

A Novel Neighbor-Preferential Growth Scale-Free Network Model and its Properties

In this paper, we propose a novel neighbor-preferential growth (NPG) network model. Theoretical analysis and numerical simulations indicate the new model can reproduce not only a scale-free degree distribution and its power exponent is related to the edge-adding number m, but also a small-world effect which has large clustering coefficient and small average path length. Interestingly, the clustering coefficient of the model is close to that of globally coupled network, and the average path length is close to that of star coupled network. Meanwhile, the synchronizability of the NPG model is much stronger than that of BA scale-free network, even stronger than that of synchronization-optimal growth network.

A new evolutional model for institutional field knowledge flow network

Purpose:This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model(IKM).The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.Design/Methodology/Approach:The IKM model enhances the preferential attachment and growth observed in scale-free BA networks,while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network.To compare its performance,the BA and DMS models are also employed for simulating the network.Pearson coefficient analysis is conducted on the simulated networks generated by the IKM,BA and DMS models,as well as on the actual network.Findings:The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network.It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm.The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.Research Limitations:This study has some limitations.Firstly,it primarily focuses on the evolution of knowledge flow networks within the field of physics,neglecting other fields.Additionally,the analysis is based on a specific set of data,which may limit the generalizability of the findings.Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.Practical Implications:The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions.It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations.The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.Originality/value:This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks.The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions.Moreover,the model has the potential to be applied to other knowledge networks,which are formed by knowledge organizations as node units.

Modelling the spread of sexually transmitted diseases on scale-free networks

In this paper a new model for the spread of sexually transmitted diseases （STDs） is presented. The dynamic behaviors of the model on a heterogenons scale-free （SF） network are considered, where the absence of a threshold on the SF network is demonstrated, and the stability of the disease-free equilibrium is obtained. Three immunization strategies, uniform immunization, proportional immunization and targeted immunization, are applied in this model. Analytical and simulated results are given to show that the proportional immunization strategy in the model is effective on SF networks.

Integrated systemic inflammatory response syndrome epidemic model in scale-free networks

Based on the scale-free network, an integrated systemic inflammatory response syndrome model with artificial immunity, a feedback mechanism, crowd density and the moving activities of an individual can be built. The effects of these factors on the spreading process are investigated through the model. The research results show that the artificial immunity can reduce the stable infection ratio and enhance the spreading threshold of the system. The feedback mechanism can only reduce the stable infection ratio of system, but cannot affect the spreading threshold of the system. The bigger the crowd density is, the higher the infection ratio of the system is and the smaller the spreading threshold is. In addition, the simulations show that the individual movement can enhance the stable infection ratio of the system only under the condition that the spreading rate is high, however, individual movement will reduce the stable infection ratio of the system.

BAS-PSO算法优化的无传感器轮毂电机转速控制

针对无位置传感器轮毂电机比例积分(Proportion Integration,PI)控制中常见的抗干扰能力差、计算量大、控制精度不高等问题,提出一种粒子群-天牛须(Beetle Antennae Search-Particle Swarm Optimization,BAS-PSO)算法,在模型参考自适应系统(Model Reference Adaptive System,MRAS)观测电机转速与位置的基础上,实现转速控制器的PI参数自整定。建立永磁同步轮毂电机(Permanent Magnet Synchronous Motor,PMSM)的数学模型,以转速环传递函数的时间绝对误差积分指标(Integrated Time Absolute Error,ITAE)作为适应度函数,通过BAS-PSO算法优化转速控制器的PI参数;MRAS将电机本身作为参考模型,将含有待估计参数的定子电流方程作为可调模型,基于Popov超稳定理论,设计合适的自适应律,估计电机的转速与位置;利用Matlab/Simulink进行仿真对比,并在HIL平台进行硬件测试验证。结果表明:与传统方法相比,BAS-PSO算法能够提高电机的运行响应速度,降低转速超调量,提高系统的鲁棒性。

Epidemic spreading in scale-free networks including the effect of individual vigilance

In this paper, we study the epidemic spreading in scale-flee networks and propose a new susceptible-infected- recovered （SIR） model that includes the effect of individual vigilance. In our model, the effective spreading rate is dynamically adjusted with the time evolution at the vigilance period. Using the mean-field theory, an analytical result is derived. It shows that individual vigilance has no effect on the epidemic threshold. The numerical simulations agree well with the analytical result. Purthermore, we investigate the effect of individual vigilance on the epidemic spreading speed. It is shown that individual vigilance can slow the epidemic spreading speed effectively and delay the arrival of peak epidemic infection.

Nucleon—Pair Shell Model：Magnetic Excitations for Ba Isotopes

Magnetic excitations for Ba isotopes are discussed within the nucleon-pair shell model truncated in the SD subspace. With the SD pair determined by a surface- interaction, M1 transitions for are well fitted. The M1 and M3 transitions for and are also predicted. It is shown that the statement, the collective magnetic properties are due to the orbital motion of nucleons, is approximately valid.

Application of Hydrological Model to Simulate Rainfall-Runoff into An Khe Reservoir in the Ba River Basin, Vietnam

The BR （Ba River） basin is one of 9 main river basins in Vietnam. In the past 20 years, natural hazards such as flood andinundation have been complex and increased dramatically in both frequency and intensity in the BR basin. Recently, there have beenapproximately 198 large and small operating reservoirs which lead to increase natural hazards in the river basin. An Khe reservoir,one of big reservoirs in the upstream of the Ba river, impacts significantly on flooding in the downstream. This paper useshydrological model to simulate the flows as a basic for the safety operation of An Khe reservoir in order to prevent the downstreamfloods. The results indicate the Nash-Sutcliffe coefficient higher than 0.8 is stable and reliable parameter.

自适应BAS优化RBF神经网络的短时交通流预测

为提高短时交通流预测精度,针对传统径向基函数(radial basis function,RBF)神经网络短时交通流预测模型中心值固定、易受漂移数据干扰问题,提出自适应天牛须搜索算法(beetle antennae search algorithm,BAS)优化RBF神经网络的短时交通流预测模型。模型采用自适应步长提高BAS算法迭代速度和寻优能力,结合DBSCAN聚类确定RBF神经网络隐含层径向基函数网络中心,进而优化神经网络结构。通过路网真实交通流数据进行训练,选择常用于短时交通流预测的BP神经网络,RBF神经网络,广义RBF神经网络进行对比。结果表明:优化后的模型预测结果相较BP神经网络平均绝对误差降低了1.87%、平均绝对百分比误差降低了15.96%、均方根误差降低了3.24%,拟合度提高了3.96%;相较广义RBF神经网络平均绝对误差降低1.36%、平均绝对百分比误差降低了5.01%、均方根误差降低了2.19%,拟合度提高了2.5%。改进后的短时交通流预测模型能够为智能交通诱导提供可靠的预测值。

Random walk immunization strategy on scale-free networks

A novel immunization strategy called the random walk immunization strategy on scale-free networks is proposed. Different from other known immunization strategies, this strategy works as follows： a node is randomly chosen from the network. Starting from this node, randomly walk to one of its neighbor node; if the present node is not immunized, then immunize it and continue the random walk; otherwise go back to the previous node and randomly walk again. This process is repeated until a certain fraction of nodes is immunized. By theoretical analysis and numerical simulations, we found that this strategy is very effective in comparison with the other known immunization strategies.

基于改进BAS算法的配电网多目标优化重构

天牛须搜索(BAS)算法存在早熟收敛、计算效率低下等缺点,文中提出一种改进BAS算法,并应用于解决配电网优化重构问题。首先建立以网络损耗、负荷均衡度、电压偏差以及系统供电可靠性为目标的数学模型;然后针对传统天牛须搜索算法易陷入局部最优,采用模拟退火的蒙特卡洛准则进行改进,提高了算法的稳定性和可重复性,并采用改进后的天牛须搜索算法对数学模型进行迭代求解,给出配电网最优重构方案;最后结合IEEE 69总线系统进行实例仿真分析,与未进行系统重构配置进行对比,所提算法减少了网络损耗,提高了系统运行的经济稳定性。

基于BA网络的突发事件下静态局部路由策略研究

为解决突发事件发生时,城市路网受到较大影响,甚至造成拥堵,文章基于无标度BA网络对路网建模,分析突发事件对节点通行能力的影响,进而通过对网络应用静态局部路由策略,分析不同程度突发事件下的最优路由策略参数,从而得出最佳路由策略。经仿真得出,优化后的模型提高网络临界信息包产生率,网络中数据包的分布更加均匀,路径选择更优,从而减少数据包平均传输时间,能够有效地解决突发事件下节点拥堵问题,使突发事件对网络通行能力的影响减小。

基于EI-BA模型的文本情感分析

为深入挖掘微博文本的情感特征、充分考虑表情符号和标点符号对情感表达的作用,提出一种结合情感信息的深度神经网络模型。该模型利用余弦相似性计算表情符号和标点符号的情感得分,通过双向长短期记忆网络提取文本的上下文语义特征,引入注意力机制进一步提取与任务相关的情感特征,由Softmax分类器计算最终分类结果。模型应用在新型冠状病毒暴发初期的微博文本数据集上,结果表明其性能优于相关优异的基线方法。

基于天牛须优化算法的相关向量机边坡稳定性分析

为了快速、准确地预测边坡稳定性,及时控制边坡危害,提出了一种基于天牛须(beetle antennae search,BAS)优化算法的相关向量机(relevance vector machine,RVM)边坡稳定性分析模型。基于RVM模型,建立了边坡影响因素与稳定性的非线性映射关系;采用BAS算法对RVM模型参数进行优化,提出了基于BAS算法的RVM边坡稳定性分析优化模型;并将该模型应用于京新高速公路的边坡稳定性分析。分析结果表明:与实际值相比,基于BAS-RVM模型的最大绝对值相对误差为3.90%;在相同学习样本下,与RVM模型、支持向量机(support vector machine,SVM)模型和径向基函数(radical basis function,RBF)模型的预测值相比,BAS-RVM模型预测结果的可信度和拟合度更好、精度更高,其平均绝对值误差(mean absolute error,EMA)、均方根误差(root mean square error,ERMS)、相对均方误差(relative root mean square error,ERRMS)远低于其他3种模型。

BA模型的三种扩展

无标度网络的发现,使人类对于复杂网络的认识进入了一个新的天地.BA模型是对于这种新认识的第1个抽象模型.由于考虑了系统的成长性和连接的择优性,BA模型给人们带来了很多启发.但是BA模型的两个基本假定,对于解释许多现实中的现象来说过于简单,与现实的网络还有较大的距离.所以,作者试图对BA模型进行扩展,即根据现实中的网络,增添某些假定,并通过计算机模拟进行观察,以便进一步探索复杂网络系统的规律.对BA模型的扩充考虑了3个因素:择优选择的成本;边的重新连接;网络的初始状态.实验结果表明,在考虑了这些因素之后,BA模型的基本结论并没有变化,它们的影响在于演化过程的某些方面,例如在某些极端条件下的演变趋势等.

焦点企业成长视角的集群创新网络无标度特征研究——BA模型的改进及其模拟分析

无标度网络的发现,为人类认识复杂网络提供了一个全新的思路,而BA模型的建立为研究复杂网络提供了一个很好的方法。在BA模型的择优连接机制中加入适应度和度饱和值来模拟集群创新网络演进过程,进而对此类集群创新网络的无标度性特征进行模拟分析。研究得出,基于焦点企业成长的集群创新网络是一类异质性的网络,并具有集聚性、稳定性和脆弱性的特征。