多元时间序列复杂网络流型动力学分析

针对气液两相流流动特性,利用有限元分析方法设计变曲率对壁式电导传感器.采用设计加工的传感器在多相流装置上进行气液两相流动态实验,并测得多组对应于不同流型的电导波动信号. 基于测量数据,采用多元时间序列复杂网络构建算法构建对应于不同流型的复杂网络.在此基础上, 对网络的社团特性进行了分析, 研究发现,不同的社团结构对应于不同的流型,而社团内部网络特征可有效刻画不同流型内在动力学特性.多元时间序列复杂网络分析可为两相流流型演化动力学特性研究及流型识别提供新理论、开拓新途经.     

基于簇相似度的网络社团结构探测算法

社团结构对复杂系统的结构特性和动力学特性有重要影响.提出了一个度量社团相似度的模型,称为簇相似度.该模型能够度量两个社团的相似度大小,为研究社团间的作用机制提供帮助.而且基于该模型,设计了一个社团划分算法.算法采用层次聚类的思想,每次合并两个相似度最大的社团,并通过一个评价函数选择最优社团划分.数值实验以及与CNM,GN,EigenMod等主流算法做比较,表明本算法的精度和效率都比较高,尤其对于边密度较高的网络,性能非常理想.     

基于联合矩阵分解的节点多属性网络社团检测

发现复杂网络中的社团结构在社会网络、生物组织网络和在线网络等复杂网络中具备十分重要的意义. 针对社交媒体网络的社团检测通常需要利用两种信息源: 网络拓扑结构特征和节点属性特征, 丰富的节点内容属性信息为社团检测的增加了灵活性和挑战. 传统方法是要么仅针对这两者信息之一进行单独挖掘, 或者将两者信息得到的社团结果进行线性叠加判决, 不能有效进行信息源的融合. 本文将节点的多维属性特征作为社团划分的一种有效协同学习项进行研究, 将两者信息源进行融合分析, 提出了一种基于联合矩阵分解的节点多属性网络社团检测算法CDJMF, 提高了社团检测的有效性和鲁棒性. 实验表明, 本文所提的方法能够有效利用节点的属性信息指导社团检测, 具备更高的社团划分质量.     

Sustainable management of an alpine national park: handling the two-edged effect of tourism

Attracting visitors to an alpine national park can open up additional sources of funding for species conservation. However, tourism also brings ecologically negative impacts to the park and, in particular, to endangered species. In this paper, we discuss the handling of this two-edged effect of nature-based tourism within the context of a national park’s management decision. We develop a stylized model which frames the interaction of a representative largely unknown species, its habitat, and park visitors in an alpine ecosystem. In applying this to the protection of a rock partridge population in the Hohe Tauern National Park (Austria), we illustrate that a combined visitor and species protection policy can maximize steady state net benefits from tourism and conservation, while ensuring that the endangered species reaches its conservation target in the long run. Thus, even for a small, largely unknown species such as the rock partridge, and not only for popular species like the golden eagle, it is possible to endogenously generate a conservation budget by attracting visitors.     

Airport noise modelling and aircraft scheduling so as to minimize community annoyance

A mathematical model of the annoyance created at an airport by aircraft operations is developed. The model incorporates population distribution considerations around an airport and the annoyance caused by aircraft noise. The objective function of this model corresponds to seeking to minimize total population annoyance created by all aircraft operations in a 24-hour period. Several factors are included in this model as constraint relationships. Aircraft operations by type and time period are upper bounded. Demand for flight services is incorporated by including lower bounds on the number of operations by type of aircraft, runway used and time period. Also upper bounds on the number of operations for each runway are included. The mathematical model as formulated is recognized as corresponding to a nonlinear integer mathematical programming problem.The solution technique selected makes use of a successive linear approximation optimization algorithm. An especially attractive feature of this solution algorithm is that it is capable of obtaining solutions to large problems. For example, it would be feasible to attempt the solution of problems involving several thousand variables and over 500 linear constraints. This suggested solution algorithm was implemented on a computer and computational results obtained for example problems.     

复杂网络上集群行为与自旋模型

复杂系统中的集群行为(collective behavior)是复杂系统涌现性的重要表现之一, 它通过微观个体之间的相互作用, 在宏观上表现出一定的时空或功能有序结构．已有研究表明, 个体之间相互作用的网络结构对复杂系统的集群行为有重要影响．基于复杂网络上的自旋模型, 介绍了复杂网络上的集群行为的部分研究进展． 首先简要介绍了自旋模型的相变现象以及网络结构对相变行为的影响; 其次, 介绍了以自旋模型为基础应用到社会经济等领域中的工作, 包括舆论形成、羊群效应以及分隔现象(segregation)等, 尤其关注网络结构对集群行为的影响, 最后介绍了基于自旋模型的网络社团划分方法．对进一步的发展方向进行了初步的讨论．     

Detection of community overlap according to belief propagation and conflict

Most existing methods for detection of community overlap cannot balance efficiency and accuracy for large and densely overlapping networks. To quickly identify overlapping communities for such networks, we propose a new method that uses belief propagation and conflict (PCB) to occupy communities. We first identify triangles with maximal clustering coefficients as seed nodes and sow a new type of belief to the seed nodes. Then the beliefs explore their territory by occupying nodes with high assent ability. The beliefs propagate their strength along the graph to consolidate their territory, and conflict with each other when they encounter the same node simultaneously. Finally, the node membership is judged from the belief vectors. The PCB time complexity is nearly linear and its space complexity is linear. The algorithm was tested in extensive experiments on three real-world social networks and three computer-generated artificial graphs. The experimental results show that PCB is very fast and highly reliable. Tests on real and artificial networks give excellent results compared with three newly proposed overlapping community detection algorithms.     

基于多方博弈的村落遗产地旅游开发模式形成机理研究——以开平碉楼与村落为例

借助博弈论的思想和方法,以开平碉楼与村落为例,对村落遗产地旅游开发中的核心利益相关者为实现各自利益诉求所实施的博弈行为进行分析,建立两阶段博弈模型,试图找出博弈均衡,通过典型的博弈关系来剖析他们之间的相互关系和作用机制,从而揭示村落遗产地旅游开发模式的形成机理.     

绿脓素对降解原油的微生物菌群的影响

铜绿假单胞菌WJ-1是从蒙古林油田油水样中分离得到的一株能够以原油为唯一碳源, 通过合成某些代谢产物增加其环境竞争力和存活率的烃降解菌. 用氯仿从WJ-1LB培养液中萃取绿脓素, 气相色谱-质谱法(Gas chromatograph-Mass Spectrometry, GC-MS)分析得该物质为1-羟基吩嗪. 通过在原油培养基中加入和未加入0.2 g/L绿脓素, 培养32 d来研究绿脓素对烃降解菌群T2的群落结构和原油降解能力的影响. 变性梯度凝胶电泳(Denaturing Gradient Gel Electrophoresis, DGGE)分析结果表明: 在32 d的原油降解中, 含有绿脓素的培养液中T2菌群多样性减少. 气相色谱和棒薄层层析分析得: 含有绿脓素的培养液中原油的芳香族烃等非烃类和正构烷烃中长链组分的降解率比未加入绿脓素的要低. 超氧化物歧化酶(SOD)和过氧化氢酶(catalase)活性检测结果表明: 不同菌体的过氧化氢酶和超氧化物歧化酶含量与活性不同, 引起对绿脓素抗性的差异. 可见绿脓素对烃降解菌群的结构和原油降解能力有负面影响.     

Weighted Evolving Networks with Self-organized Communities

In order to describe the self-organization of communities in the evolution of weighted networks, we propose a new evolving model for weighted community-structured networks with the preferential mechanisms functioned in different levels according to community sizes and node strengths, respectively. Theoretical analyses and numerical simulations show that our model captures power-law distributions of community sizes, node strengths, and link weights, with tunable exponents of v ≥ 1, γ 〉 2, and α 〉 2, respectively, sharing large clustering coefficients and scaling clustering spectra, and covering the range from disassortative networks to assortative networks. Finally, we apply our new model to the scientific co-authorship networks with both their weighted and unweighted datasets to verify its effectiveness.