风云三号C星全球导航卫星掩星探测仪首次实现北斗掩星探测

全球导航卫星掩星探测仪(GNOS)是国际首台北斗系统(BDS)和全球定位系统(GPS)双系统兼容掩星探测仪, 2013年9月23日随风云三号卫星C星(FY3 C)发射入轨, 目前已测得大量掩星数据. 介绍了FY3 C-GNOS的组成; 统计了2013年10月12日全天的FY3 C-GNOS掩星事件及其全球分布情况; 通过与GPS精密定轨结果进行对比分析, 测试了BDS在轨实时定位精度, 检验了BDS掩星产品的可靠性和一致性. 初步分析结果表明: 14颗BDS卫星在轨运营的条件下, BDS和GPS兼容掩星探测仪可将掩星事件数提高约33.3%; BDS实时定位平均偏差优于6 m, 标准偏差优于7 m; 5-25 km高度范围内, BDS与GPS内符合精度大气折射率优于2%, 温度优于2 K, 湿度优于1.5 g/kg, 压强优于2%, 电离层峰值密度优于15.6%. GNOS的在轨正常运行及BDS与GPS掩星定位精度及反演产品的一致性为北斗掩星探测的业务化运行奠定了基础.     

The Effective Gluon Propagator in the Global Color Symmetry Model

It is shown that the effective gluon propagator in the global color symmetry model can be calculated in the instanton dilute liquid approximation. The calculated effective gluon propagator is consistent with the general command on the qualitative features of the gluon propagator, i.e., (i) the gluon propagator is significantly enhanced at small space-like k², and (ii) for k² > 1.5 GeV² the perturbative results are quantitatively reliable.     

复杂气候系统和全球变暖

两位气候学家和一位理论物理学家共同荣获2021年诺贝尔物理学奖。诺贝尔物理学奖委员会在背景介绍材料中指出,他们在“理解复杂物理系统领域做出了开创性贡献”。相信很多人会对两位气候学家获奖而感到惊讶,物理学奖委员会进一步具体指出,人类活动导致全球变暖这一论断建立在坚实的科学基础之上,两位气候学家获奖是因为他们基于物理原理模拟地球气候、量化气候变率、可靠地预测全球变暖所做出的杰出贡献。物理学奖委员会将诺贝尔奖授予两位气候学家,表明对基于物理理论解决现实世界复杂物理问题的高度重视,尤其是对人类身处其中的气候环境问题的重视。文章将解读两位气候学家的学术贡献,尤其是如何基于基础物理理论建立预测全球变暖的模型以及检测和归因人类活动导致全球变暖的方法。     

An extended improved global structure model for influential node identification in complex networks

Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses. Many classical approaches have been proposed by researchers regarding different aspects. To explore the impact of location information in depth, this paper proposes an improved global structure model to characterize the influence of nodes. The method considers both the node's self-information and the role of the location information of neighboring nodes. First, degree centrality of each node is calculated, and then degree value of each node is used to represent self-influence, and degree values of the neighbor layer nodes are divided by the power of the path length, which is path attenuation used to represent global influence. Finally, an extended improved global structure model that considers the nearest neighbor information after combining self-influence and global influence is proposed to identify influential nodes. In this paper, the propagation process of a real network is obtained by simulation with the SIR model, and the effectiveness of the proposed method is verified from two aspects of discrimination and accuracy. The experimental results show that the proposed method is more accurate in identifying influential nodes than other comparative methods with multiple networks.     

Learnable three-dimensional Gabor convolutional network with global affinity attention for hyperspectral image classification

Benefiting from the development of hyperspectral imaging technology, hyperspectral image (HSI) classification has become a valuable direction in remote sensing image processing. Recently, researchers have found a connection between convolutional neural networks (CNNs) and Gabor filters. Therefore, some Gabor-based CNN methods have been proposed for HSI classification. However, most Gabor-based CNN methods still manually generate Gabor filters whose parameters are empirically set and remain unchanged during the CNN learning process. Moreover, these methods require patch cubes as network inputs. Such patch cubes may contain interference pixels, which will negatively affect the classification results. To address these problems, in this paper, we propose a learnable three-dimensional (3D) Gabor convolutional network with global affinity attention for HSI classification. More precisely, the learnable 3D Gabor convolution kernel is constructed by the 3D Gabor filter, which can be learned and updated during the training process. Furthermore, spatial and spectral global affinity attention modules are introduced to capture more discriminative features between spatial locations and spectral bands in the patch cube, thus alleviating the interfering pixels problem. Experimental results on three well-known HSI datasets (including two natural crop scenarios and one urban scenario) have demonstrated that the proposed network can achieve powerful classification performance and outperforms widely used machine-learning-based and deep-learning-based methods.     

Dynamics and intermittent stochastic stabilization of a rumor spreading model with guidance mechanism in heterogeneous network

We propose a novel rumor propagation model with guidance mechanism in heterogeneous complex networks. Firstly, the sharp threshold of rumor propagation, global stability of the information-equilibrium and information-prevailing-equilibrium under R₀ <1 and R₀> 1 is carried out by Lyapunov method and LaSalle's invariant principle. Next, we design an aperiodically intermittent stochastic stabilization method to suppress the rumor propagation. By using the Itô formula and exponential martingale inequality, the expression of the minimum control intensity is calculated. This method can effectively stabilize the rumor propagation by choosing a suitable perturb intensity and a perturb time ratio, while minimizing the control cost. Finally, numerical examples are given to illustrate the analysis and method of the paper.     

Noise-induced global asymptotic stability

We prove analytically that additive and parametric (multiplicative) Gaussian distributed white noise, interpreted in either the Itô or Stratonovich formalism, induces global asymptotic stability in two prototypical dynamical systems designated as supercritical (the Landau equation) and subcritical, respectively. In both systems without noise, variation of a parameter leads to a switching between a single, globally stable steady state and multiple, locally stable steady states. With additive noise this switching is mirrored in the behavior of the extrema of probability densities at the same value of the parameter. However, parametric noise causes a noise-amplitude-dependent shift (postponement) in the parameter value at which the switching occurs. It is shown analytically that the density converges to a Dirac delta function when the solution of the Fokker-Planck equation is no longer normalizable.     

一个全球气候非线性振荡模型的近似解

研究了一个全球气候的非线性模型. 利用摄动理论和方法, 构造了相关问题解的渐近展开式, 该问题解的渐近展开式具有较高的近似度. 摄动渐近方法是一个解析方法, 得到的解还能够继续进行解析运算. 关键词： 非线性 摄动 全球气候     

Duffing-van der Pol振子随机分岔的全局分析

应用广义胞映射方法研究了参激和外激共同作用的Duffing-van der Pol振子的随机分岔.以 系统参数通过某一临界值时，如果系统的随机吸引子或随机鞍的形态发生突然变化，则认为 系统发生随机分岔为定义，分析了参激强度和外激强度的变化对于随机分岔的影响.揭示了 随机分岔的发生主要是由于系统的随机吸引子与系统的随机鞍碰撞产生的.分析表明，广义 胞映射方法是分析随机分岔的有力工具，这种全局分析方法可以清晰地给出随机分岔的发生 和发展. 关键词： 随机分岔 全局分析 广义胞映射方法 随机吸引子 随机鞍     

复眼透镜提高液晶投影照明系统的能量利用率

照明系统是液晶投影仪的重要组成部分．它将直接影响到投影仪的光能利用率以及色彩等系统性能。对于复眼照明系统,复眼透镜的良好设计可以大人提高系统的能量利用率。在对液晶投影照明系统的原理进行分析的基础上,提出了两种复眼透镜的设计方案。两种方案均采用矩形子复眼,便于设计和加工。通过对复眼透镜中每个子复眼进行偏心以及改变其形状大小来提高系统能量利用率。计算机模拟结果表明,两种设计方案与传统设汁相比,在光能利用率上分别提高了16．5％和26．3%,并且具有较高的均匀性。最后,还对两种复眼透镜的设计方案进行了分析和比较。