自我质疑机制下公共物品博弈模型的相变特性

公共物品博弈是研究群体相互作用的经典模型,广泛用于解释自私个体间合作的涌现和保持.本文从理论分析和蒙特卡罗模拟两个方面研究了二维正方格子上一个有偿惩罚机制下随自我质疑更新规则演化的公共物品博弈模型的相变特性.理论分析方面,将公共物品博弈模型转化为一个外场不为零的铁磁Ising模型.通过有效能量发现:不存在惩罚时,个体间的耦合强度为零,体系只有外场作用;存在惩罚时,个体间包含最近邻、次近邻和第三近邻相互作用且外场不为零.蒙特卡罗模拟方面,首先验证了理论分析的正确性,然后对公共物品博弈模型相关的一级相变和二级相变进行了有限尺度标度分析.研究发现:1)蒙特卡罗模拟所得结果与类Ising模型分析结果完全吻合;2)相比二维Ising模型,公共物品博弈的二级相变临界指数发生了变化;3)公共物品博弈的一级相变与二维Ising模型相同.     

二硼化镁颗粒超导体的特性研究

我们通过B2O3和Mg的置换反应制备了MgB2复合超导体，对其物理特性的研究发现：在超导转变过程中dR／dT～T曲线出现了两个转变峰，而在Ⅰ～Ⅴ测量中也观测到了电压的两次转变，我们认为这起源于二硼化镁晶界效应的增强．并利用二维渗流模型对这些现象进行了讨论．     

基于数字地图的二维电波传播问题仿真

为快速预测二维地理环境下的电磁环境特性,应用二维抛物方程模型对电磁环境进行仿真。为了得到真实有效的地形数据,研究了从GeoTiff中抽取网格点上地理信息的方法,并利用双线性插值法计算了任意位置处的高程值。同时研究了地球表面两点之间计算距离的方法,将该方法的计算结果与GIS软件结果进行对比,验证了该方法的可靠性。在标准大气环境下,利用二维抛物方程模型仿真分析了不同距离处不同高度的电波传播传播因子的变化情况,为预测真实地理环境中的电波传播特性提供了一种有效的方法。     

海洋声学传播新模型

海洋声学模型将我们对海洋中传播的声音在物理上的理解转换为数学表示,进而利用计算机进行模拟计算。海洋声学模型主要包括环境模型、传播模型、噪声模型、混响模型和声纳性能模型。水平变化传播模型研究,包括二维(距离和深度)和三维(距离、深度和方位角)模型研究,是目前海洋声学的重要研究方向之一。     

基于CNN的废旧纺织品在线NIR高效识别与自动分选技术研究

我国每年产生废旧纺织品超过2 600多万吨，且随着经济的发展呈现逐年增多的趋势，而其再生利用率不足10%。废旧纺织品组分的多样性和结构的复杂性是影响其准确分类、快速回收和高附加值再利用的最大障碍。人工识别分选既费时费力又不准确，而近红外光谱分析技术可对其进行快速无损高效识别分选。在前期探究的最佳测试条件下，利用自主研制的“纤维制品在线近红外高效识别与分选装置”对聚酯、棉、毛、锦纶、真丝、粘胶、腈纶、聚酯/毛、聚酯/棉、聚酯/锦纶、真丝/棉混纺和“特殊类”共计12类1 060个废旧纤维织物样本进行在线原始近红外光谱采集。基于采集的样本在线原始NIR谱图，利用卷积神经网络方法，依据输入的样本光谱数据及对应分类标签进行网络训练，建立了12类废旧纺织品在线NIR定性识别模型。对比一维、二维卷积神经网络模型，其二维模型较优，该模型是将901～2 500 nm的一维数组归一化后转化为40×40像素的二维灰度图像，再交替进行多次卷积和池化来进行光谱特征的提取、压缩和数据降维。通过模型计算得到每类废旧纺织品样本的类别概率值，取其最大值作为该类织物的最终分类。本模型训练过程设置为500轮，每次取32个样...     

一种可能存在的两分量任意子模型

本文提出了一个可能存在的两分量任意子模型,在玻色表象下给出了多体波函数的一般形式.我们分析了规范矢势引起的相互作用,通过将规范矢势类比于一个二维静电场,发现这个两分量任意子模型近似等价于一个二维两分量库仑气体,在低温下可能发生 Kosterlitz-Thouless 相变.     

二维量子随机行走及其物理实现

近年来量子随机行走相关课题因其非经典的特性,已经成为越来越多科研人员的研究热点。这篇文章中我们回顾了一维经典随机行走和一维量子随机行走模型,并且在分析两种二维经典随机行走模型的基础上,我们构建二维量子随机行走模型。通过对随机行走者的位置分布标准差的计算,我们可以证明基于这种二维量子随机行走模型的算法优于其他上述随机行走。除此之外,我们提出一个利用线性光学方法的实验方案,实现这种二维量子随机行走模型。     

二硼化镁颗粒超导体的特性研究

我们通过B2O3和Mg的置换反应制备了MgB2复合超导体,对其物理特性的研究发现:在超导转变过程中dR/dT～T曲线出现了两个转变峰,而在I～V测量中也观测到了电压的两次转变,我们认为这起源于二硼化镁晶界效应的增强.并利用二维渗流模型对这些现象进行了讨论.     

PML-FDTD法在分析负折射率材料中的应用

将时域有限差分(FDTD)法引入了对负折射率材料物理现象的仿真研究.给出了二维TM波在负折射率材料中的时域差分方程,并且在吸收边界处使用了理想匹配层(PML).为了避免在迭代过程中出现的不稳定现象,在差分方程的推导中引入了Drude模型, 并对Pendry提出的由负折射率材料构成的平板透镜具有的完美成像现象进行了数值仿真验证.由仿真结果发现,完美成像现象只在平板透镜的折射率n=－1时出现,当n≠－1时则会出现近轴聚焦效应.     

二维扫描反射镜的鲁棒脉冲-峰值控制应用研究

针对二维扫描反射镜控制时可能遇到的脉冲干扰问题,以及设备制造时误差带来的参数不确定性,结合鲁棒脉冲-峰值理论提出了一种计算脉冲响应保守界,并设计了一个状态反馈控制器。通过理论建模得到二维扫描反射镜方位电机伺服控制模型,引入参数不确定性后,对该模型采用LMI优化求得控制器参数及峰值响应保守界。经过仿真验证,该方法相对于普通的PID控制方法减少了20%的峰值响应和50%的恢复时间,对于二维扫描反射镜脉冲干扰具有较好的抑制效果。     

Spatiotemporal Characteristic of Epidemic Spreading in Mobile Individuals

We numerical simulate the propagation behaviour and people distribution trait of epidemic spreading in mobile individuals by using cellular automaton method. The simulation results show that there exists a critical value of infected rate fluctuating amplitude, above which the epidemic can spread in whole population. Moreover, with the value of infected rate fluctuating amplitude increasing, the spatial distribution of infected population exhibits the spontaneous formation of irregular spiral waves and convergence phenomena, at the same time, the density of different populations will oscillate automatically with time. What is more, the traits of dynamic grow clearly and stably when the time and the value of infected rate fluctuating amplitude increasing. It is also found that the maximal proportion of infected individuals is independent of the value of fluctuating amplitude rate, but increases linearly with the population density.     

Scaling behavior of threshold epidemics

We study the classic Susceptible-Infected-Recovered (SIR) model for the spread of an infectious disease. In this stochastic process, there are two competing mechanism: infection and recovery. Susceptible individuals may contract the disease from infected individuals, while infected ones recover from the disease at a constant rate and are never infected again. Our focus is the behavior at the epidemic threshold where the rates of the infection and recovery processes balance. In the infinite population limit, we establish analytically scaling rules for the time-dependent distribution functions that characterize the sizes of the infected and the recovered sub-populations. Using heuristic arguments, we also obtain scaling laws for the size and duration of the epidemic outbreaks as a function of the total population. We perform numerical simulations to verify the scaling predictions and discuss the consequences of these scaling laws for near-threshold epidemic outbreaks.     

Comparison of immunization strategies in geographical networks

The epidemic spread and immunizations in geographically embedded scale-free (SF) and Watts-Strogatz (WS) networks are numerically investigated. We make a realistic assumption that it takes time which we call the detection time, for a vertex to be identified as infected, and implement two different immunization strategies: one is based on connection neighbors (CN) of the infected vertex with the exact information of the network structure utilized and the other is based on spatial neighbors (SN) with only geographical distances taken into account. We find that the decrease of the detection time is crucial for a successful immunization in general. Simulation results show that for both SF networks and WS networks, the SN strategy always performs better than the CN strategy, especially for more heterogeneous SF networks at long detection time. The observation is verified by checking the number of the infected nodes being immunized. We found that in geographical space, the distance preferences in the network construction process and the geographically decaying infection rate are key factors that make the SN immunization strategy outperforms the CN strategy. It indicates that even in the absence of the full knowledge of network connectivity we can still stop the epidemic spread efficiently only by using geographical information as in the SN strategy, which may have potential applications for preventing the real epidemic spread.     

Effect of incubation period on epidemic spreading in complex networks

We study the effect of incubation period on epidemic spreading in the Barabasi--Albert scale-free network and the Watts--Strogatz small world network by using a Suspectable-Incubated-Infected-Suspectable model. Our analytical investigations show that the epidemic threshold is independent of incubation period in both networks, which is verified by our large-scale simulation results. We also investigate the effect of incubation period on the epidemic dynamics in a supercritical regime. It is found that with the increase of incubation period Ω , a damped oscillation evolution of ρ_T(the ratio of persons in incubated state) appears and the time needed to reach a saturation value increases. Moreover, the steady value of ρ_T increases and approaches to an asymptotic constant with the value of {\it\Omega} increasing. As a result, the infected ratio ρ_I decreases with the increase of Ω according to a power law.     

一种基于元胞自动机的自适应网络病毒传播模型

自适应网络是节点动力学和网络动力学相互作用和反馈的演化网络. 基于元胞自动机建立自适应网络中易感-感染-易感(susceptible-infected-susceptible)的病毒传播模型,研究节点为了规避病毒传播所采取的多种网络重连规则对病毒传播及网络统计特征的影响. 结果表明:自适应网络中的重连规则可以有效减缓病毒传播速度,降低病毒传播规模;随机重连规则使得网络统计特征趋于随机网络;基于元胞自动机建立的传播模型清晰地表达了病毒在传播过程中的双稳态现象. 关键词： 自适应网络 传播动力学 网络动力学 元胞自动机     

Epidemic dynamics behavior in some bus transport networks

We abstract bus transport networks (BTNs) to complex networks using the Space P approach. First, we select three actual BTNs in three major cities in China, namely, Beijing, Shanghai and Hangzhou. Using the SIS model, we simulate and study the epidemic spreading in the three BTNs. We obtain the density of infected vertices varying with time and the stationary density of infected vertices varying with infection rate. Second, we simulate and study the epidemic spreading in a recently introduced BTN evolution model, the network properties of which correspond well with those of actual BTNs. Third, we use mean-field theory to analyze the epidemic dynamics behavior of the BTN evolution model and obtain the theoretical epidemic threshold of this model. The theoretical value agrees well with the simulation results. Based on the work in this paper, we provide the following possible forecasts for epidemic dynamics in actual BTNs. An actual BTN should have a finite positive epidemic threshold. If the effective infection rate is above this threshold, the epidemic spread in the network and the density of infected vertices finally stabilizes in a balanced state. Below this threshold, the number of infected vertices decays exponentially fast and the epidemic cannot spread on a large scale.     

Effects of Vaccination Efficacy on Wealth Distribution in Kinetic Epidemic Models

The spread of the COVID-19 pandemic has highlighted the close link between economics and health in the context of emergency management. A widespread vaccination campaign is considered the main tool to contain the economic consequences. This paper will focus, at the level of wealth distribution modeling, on the economic improvements induced by the vaccination campaign in terms of its effectiveness rate. The economic trend during the pandemic is evaluated, resorting to a mathematical model joining a classical compartmental model including vaccinated individuals with a kinetic model of wealth distribution based on binary wealth exchanges. The interplay between wealth exchanges and the progress of the infectious disease is realized by assuming, on the one hand, that individuals in different compartments act differently in the economic process and, on the other hand, that the epidemic affects risk in economic transactions. Using the mathematical tools of kinetic theory, it is possible to identify the equilibrium states of the system and the formation of inequalities due to the pandemic in the wealth distribution of the population. Numerical experiments highlight the importance of the vaccination campaign and its positive effects in reducing economic inequalities in the multi-agent society.     

Modelling of discrete-time SIS models with awareness interactions on degree-uncorrelated networks

Many epidemic models ignored the impact of awareness on epidemics in a population, though it is not the case from the real viewpoints. In this paper, a discrete-time SIS model with awareness interactions on degree-uncorrelated networks is considered. We study three kinds of awareness, including local awareness and global awareness which are originated from the epidemic-dependent information, and individual awareness which is epidemic-independent and determined by the individual information. We demonstrate analytically that awareness of the epidemic-dependent information cannot change the epidemic threshold regardless of the global or local spreading information. In contrast, epidemic-independent awareness to individual information increases the epidemic threshold in finite scale-free networks, but cannot halt the absence of epidemic threshold in an infinite scale-free network. By numerical simulations, we find that local awareness has a stronger impact on epidemic prevalence than global awareness. Our findings explore the effects of various types of awareness on epidemic spreading and address their roles in the epidemic control.     

Variations of the epidemic distribution with some characteristic parameters

Considering the epidemic spread among a population of mobile agents which can get infected and maintain the infection for a period, we investigate the variation of the homogeneity of the epidemic distribution with the remaining time of infection τ, the velocity modulus of the agent v, and the infection rate α. We find that the distribution of the infected cluster size is always exponential. By analyzing the variation of the characteristic infected cluster size coefficient, we show that, the inhomogeneity of the epidemic distribution increases with the increase of τ for very low v, while decreases with the increase of τ for moderate v. And the epidemic distribution tends to a homogeneous state as both v and α increase.     

Variations in epidemic distribution with some characteristic parameters

Considering the spread of an epidemic among a population of mobile agents that can get infected and maintain the infection for a period, we investigate the variation in the homogeneity of the distribution of the epidemic with the remaining time of infection τ, the velocity modulus of the agent v, and the infection rate α. We find that the distribution of the infected cluster size is always exponential. By analyzing the variation of the characteristic infected cluster size coefficient, we show that the inhomogeneity of epidemic distribution increases with an increase in τ for very low v, while it decreases with an increase in τ for moderate v. The epidemic distribution also tends to a homogeneous state as both v and α increase.