一类带有非线性爆炸的粒子增长的数学模型

所建立的数学模型是由可数无穷多个彼此相互关联的非线性常微分方程所组成的自治系统,它刻划了在只有基本粒子和i-粒子(i≥1 ) 进行碰撞反应的系统里,粒子增长过程中密度随时间的变化规律.研究了这一自治系统解的存在性、唯一性、密度守恒以及解的渐近性质.     

离散的非线性爆炸方程的密度守恒解

离散的非线性爆炸方程是刻划粒子增长动力学的数学模型，这一模型反映了一类粒子反应系统中各种粒子密度随时间变化的规律，它是由可数无限多个彼此相互关联的非线性常微分方程所组成的自治系统。本文研究了这一无限维系统的密度守恒解的存在性。     

一类粒子反应系统数学模型解的研究

所研究的数学模型实质上是由可数无穷多个彼此相互关联的非线性常微分方程所组成的自治系统,它刻划了在只有基本粒子与i-粒子(i≥1)进行碰撞反应的系统里,粒子增长过程中密度随时间的变化规律.本文证明了在爆炸占优的条件下,这一系统解的ω极限集只含有平衡点;在更强的条件下ω极限集只含有唯一的平衡点,并且当时间t→∞时,该系统的解强收敛于这一平衡点.     

一类离散的非线性爆炸方程平衡点的稳定性

本所研究的非线性爆炸方程实质上是由可数无穷多个彼此相互关联的非线性常微分方程所组成的自治系统，它刻划了在只有基本粒子与i-粒子(i≥1)进行碰撞反应的系统里，粒子增长过程中密度随时间变化规律。本证明了如果系数满足一定的假设，那么在爆炸占优的条件下，这一系列的平衡点在Lyapunov意义下是稳定的．     

基于广义反射函数与自治系统等价的非自治系统

自治系统的研究比非自治系统要容易得多,以广义反射函数为前提,给出了等价于自治系统的非自治系统,从而将非自治系统的研究转化为自治系统的研究,并研究了该类系统的周期解及稳定性.     

连续非自治系统的有限时间稳定性及其充分条件（英文）

本文研究了连续非自治系统的有限时间稳定性问题.从一维连续非自治系统的有限时间稳定性分析入手,本文通过使用比较原理,获得了一些判定一般n维连续非自治系统的有限时间稳定性的充分条件,这些条件改善了已有的连续非自治系统有限时间稳定性的判定条件.     

连续非自治系统的有限时间稳定性及其充分条件

本文研究了连续非自治系统的有限时间稳定性问题. 从一维连续非自治系统的有限时间稳定性分析入手, 本文通过使用比较原理, 获得了一些判定一般n维连续非自治系统的有限时间稳定性的充分条件,这些条件改善了已有的连续非自治系统有限时间稳定性的判定条件.     

具有拟周期外力的非自治时滞发展方程的近似惯性流形

该文研究了一类具有拟周期外力的非自治时滞发展方程, 通过延伸相平面将非自治系统转化为自治系统, 再证明相应的自治系统的时滞惯性流形的存在性, 并在时滞惯性流形的基础上构造了非自治发展方程的近似惯性流形.     

参数不确定非自治混沌系统的自适应指数同步

针对参数不确定非自治混沌系统,研究了指数同步问题。给出了自适应控制器的构造方法,并运用Lyapunov稳定性定理证明了在该控制器下的误差系统是指数稳定的,且可以通过调整控制参数控制同步时间。最后,利用MATLAB软件对两个含有不确定参数的非自治混沌系统进行了数值仿真,验证了所提出方法的有效性和正确性.     

可化为对角形二维非自治系统轨线性态

本文给出了二阶函数矩阵可化为对角形函数矩阵的充要条件 ,并讨论了相应的非自治系统轨线性态 ,其中解轨线的遍历性是平面自治系统所没有的     

Incremental Model-Based Clustering for Large Datasets With Small Clusters

Clustering is often useful for analyzing and summarizing information within large datasets. Model-based clustering methods have been found to be effective for determining the number of clusters, dealing with outliers, and selecting the best clustering method in datasets that are small to moderate in size. For large datasets, current model-based clustering methods tend to be limited by memory and time requirements and the increasing difficulty of maximum likelihood estimation. They may fit too many clusters in some portions of the data and/or miss clusters containing relatively few observations. We propose an incremental approach for data that can be processed as a whole in memory, which is relatively efficient computationally and has the ability to find small clusters in large datasets. The method starts by drawing a random sample of the data, selecting and fitting a clustering model to the sample, and extending the model to the full dataset by additional EM iterations. New clusters are then added incrementally, initialized with the observations that are poorly fit by the current model. We demonstrate the effectiveness of this method by applying it to simulated data, and to image data where its performance can be assessed visually.     

Performance analysis of a hybrid MPI/OpenMP application on multi-core clusters

The mixing of shared memory and message passing programming models within a single application has often been suggested as a method for improving scientific application performance on clusters of shared memory or multi-core systems. DL_POLY, a large scale molecular dynamics application programmed using message passing programming has been modified to add a layer of shared memory threading and the performance analysed on two multi-core clusters. At lower processor numbers, the extra overheads from shared memory threading in the hybrid code outweigh performance benefits gained over the pure MPI code. On larger core counts the hybrid model performs better than pure MPI, with reduced communication time decreasing the overall runtime.     

产业集群的分形结构—基于演化的观点

以宏观的视角来研究企业的地理分布,并在地理空间与社会网络结构的基础上建立一个产业集群模型;该模型显示出产业集群的分形结构进而揭示出产业集群是一种自组织系统,即在生产交易过程中自发形成有序的结构或状态的现象.根据该模型,运输成本或者禀赋只是形成产业集群的所有充分条件中的一个;影响产业集群的最重要指标是关系网络空间的分形维度,它显示了经济系统的层次结构性.网络密度很大的集群的关系网络可能是接近紊乱的,即分形维度接近于零;而紊乱将会导致这个集群效益下降,甚至促使集群崩溃.     

Obviating some of the theoretical barriers of data envelopment analysis-discriminant analysis: an application in predicting cluster membership of customers

Data envelopment analysis-discriminant analysis (DEA-DA) has been used for predicting cluster membership of decision-making units (DMUs). One of the possible applications of DEA-DA is in the marketing research area. This paper uses cluster analysis to cluster customers into two clusters: Gold and Lead. Then, to predict cluster membership of new customers, DEA-DA is applied. In DEA-DA, an arbitrary parameter imposing a small gap between two clusters (η) is incorporated. It is shown that different η leads to different prediction accuracy levels since an unsuitable value for η leads to an incorrect classification of DMUs. We show that even the data set with no overlap between two clusters can be misclassified. This paper proposes a new DEA-DA model to tackle this issue. The aim of this paper is to illustrate some computational difficulties in previous DEA-DA approaches and then to propose a new DEA-DA model to overcome the difficulties. A case study demonstrates the efficacy of the proposed model.     

A cluster-based optimization approach for the multi-depot heterogeneous fleet vehicle routing problem with time windows

This paper presents a novel three-phase heuristic/algorithmic approach for the multi-depot routing problem with time windows and heterogeneous vehicles. It has been derived from embedding a heuristic-based clustering algorithm within a VRPTW optimization framework. To this purpose, a rigorous MILP mathematical model for the VRPTW problem is first introduced. Likewise other optimization approaches, the new formulation can efficiently solve case studies involving at most 25 nodes to optimality. To overcome this limitation, a preprocessing stage clustering nodes together is initially performed to yield a more compact cluster-based MILP problem formulation. In this way, a hierarchical hybrid procedure involving one heuristic and two algorithmic phases was developed. Phase I aims to identifying a set of cost-effective feasible clusters while Phase II assigns clusters to vehicles and sequences them on each tour by using the cluster-based MILP formulation. Ordering nodes within clusters and scheduling vehicle arrival times at customer locations for each tour through solving a small MILP model is finally performed at Phase III. Numerous benchmark problems featuring different sizes, clustered/random customer locations and time window distributions have been solved at acceptable CPU times.     

Regional Prediction of COVID-19 in the United States Based on the Difference Equation Model

The novel coronavirus pneumonia 2019 (COVID-19) has swept the globe in just a few months with negative social and psychological consequences for public health. So far, the United States has been one of the countries most affected by the epidemic. In this study, 51 states in the United States are divided into 10 state clusters according to relevant factors, and a difference equation model with spatio-temporal dynamic characteristics is established to predict the transmission dynamics of COVID-19 in the 10 state clusters and obtain data on regional aggregation levels (the United States). The study showed that the Pearson Correlation Coefficient between the actual data and the predicted data in the 10 state clusters is between 0.6 and 0.96 (mean R$^{2}$=0.8448), and the mean absolute error (MAE) of the newly confirmed cases in each cluster is between 300 and 1650 (mean MAE=878) and the average forecasting error rate (AFER) of the total confirmed cases in each cluster is between 0.9$\%$ and 3$\%$ (mean AFER=1.57$\%$). These results show that the difference equation model can well predict the changes in the recent confirmed cases of infectious diseases such as COVID-19.     

基于传染病模型的集群内异质企业间知识传播机理分析与仿真

知识传播是产业集群内知识资源动态累积的过程,是新时期产业集群竞争优势的重要源泉之一。本文根据企业规模及其在产业集群内地位的差异将集群企业划分大小两类异质企业,构建了基于传染病模型的产业集群内异质企业间的知识传播模型,通过模型分析推导出了知识传播再生数的一般表达式,验证了知识传播平衡点的存在性与稳定性。进一步结合仿真分析发现,企业间接触率、学习成功率、大小企业比例均会影响到知识传播的再生率,进而影响集群知识传播的平衡点,且在知识传播再生数大于1的情况下,随着集群内大企业占比的上升,知识传播再生数将相应下降,集群内知识交流氛围的活跃性也将下降。     

Microscopical investigation of wave propagation phenomena in residual saturated porous media

Wave propagation is used in many fields for measurement and characterization. Corresponding multiphase models usually use a continuous approach. Nevertheless, systems like wetted rocks may be saturated residually in certain situations. In such cases, one fluid is distributed as clusters, each different in size and shape. One single, continuous phase cannot account for a variety of fluid clusters, either disconnected from each other or connected only about thin liquid films. Therefore, we present a model that considers a heterogeneous distribution of disconnected fluid clusters in the form of harmonic oscillators. These oscillators are described and distinguished by their mass, damping and eigenfrequency. Hence, the model allows to characterize different clusters and includes an additional damping mechanism due to oscillations of the fluid clusters. (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)