浙江省不同演替阶段的低海拔次生林植物功能群结构的比较研究

选取浙江省低海拔地区的16个次生林样地并调查其中胸径（DBH）≥1 cm的木本植物.通过观察群落外貌,结合数量分类方法（TWINSPAN）和排序方法（DCA）,将这些样地划分为4种演替阶段的森林类型（马尾松林,马尾松针阔叶混交林,演替早期常绿阔叶林,演替中期常绿阔叶林）.分析了不同演替阶段的次生林群落物种多样性和植物功能群结构组成变化.结果显示：（1） 适度择伐进入采伐期的马尾松可加速整个群落向常绿阔叶林演替;（2） 随着演替的进行,物种丰富度、Shannon Wiener指数、Simpson指数和Pielou均匀度指数均呈现增加趋势;（3） 按生活型、生长型、叶的生活期和对光的耐受性这4种功能群划分,所有样地分别以小高位芽植物和中高位芽植物、乔木和灌木或小乔木、常绿植物和耐阴植物占优;（4） 随着演替的进行,功能群结构比例呈现先降后升再降的是矮高位芽植物,先降后升的为小高位芽植物,呈现先升后降的为大高位芽植物、灌木、乔木、小乔木、常绿植物、阴性植物呈现上升趋势,落叶植物和阳性植物呈现下降趋势,而中高位芽植物、灌木或小乔木、耐阴植物则维持相对恒定的比例.     

A weight’s agglomerative method for detecting communities in weighted networks based on weight’s similarity

This paper proposes the new definition of the community structure of the weighted networks that groups of nodes in which the edge's weights distribute uniformly but at random between them. It can describe the steady connections between nodes or some similarity between nodes' functions effectively. In order to detect the community structure efficiently, a threshold coefficient κ to evaluate the equivalence of edges' weights and a new weighted modularity based on the weight's similarity are proposed. Then, constructing the weighted matrix and using the agglomerative mechanism, it presents a weight's agglomerative method based on optimizing the modularity to detect communities. For a network with n nodes, the algorithm can detect the community structure in time O(n²log₂ⁿ). Simulations on networks show that the algorithm has higher accuracy and precision than the existing techniques. Furthermore, with the change of κ the algorithm discovers a special hierarchical organization which can describe the various steady connections between nodes in groups.     

Community detection in signed networks based on discrete-time model

Community detection in signed networks has been studied widely in recent years. In this paper, a discrete difference equation is proposed to imitate the consistently changing phases of the nodes. During the interaction, each node will update its phase based on the difference equation. Each node has many different nodes connected with it, and these neighbors have different influences on it. The similarity between two nodes is applied to describe the influences between them. Nodes with high positive similarities will get together and nodes with negative similarities will be far away from each other.Communities are detected ultimately when the phases of the nodes are stable. Experiments on real world and synthetic signed networks show the efficiency of detection performance. Moreover, the presented method gains better detection performance than two existing good algorithms.     

运用层次分析法对水利旅游资源进行定量评价

本文通过对水利旅游资源的定性分析,选取重要的评价指标构建层次分析图,运用层次分析法,建立水利旅游资源综合评价体系,统一定量评价水利旅游资源,以便于决策机构科学、有效地开发水利旅游资源。     

民族旅游社区妇女的时间利用方式——以四川省桃坪羌寨为例

西部民族社区在旅游开发中由传统封闭的生活空间迅速转变为开放的旅游接待场所.在该转变过程中,当地妇女的时间利用方式受到旅游开发的深远影响,而现有旅游研究并未给予过多关注.以四川桃坪羌寨为例,利用时间日志和半结构访谈法获取当地妇女的时间利用数据,在与未参与旅游妇女的对比中,研究参与旅游妇女的时间利用行为.结果表明,参与旅游妇女劳动时间的增长挤压了其生活和休闲时间;旅游开发使得参与旅游妇女扮演“双重角色”,进而承担“双重劳动”;社区旅游为参与旅游妇女协调家庭传统角色和新角色提供了有效途径.     

Detecting community structure using label propagation with consensus weight in complex network

Community detection is a fundamental work to analyse the structural and functional properties of complex networks.The label propagation algorithm(LPA) is a near linear time algorithm to find a good community structure. Despite various ubsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the asic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these onsensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the dge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number f partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps,y computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter o adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an pproach named the label propagation algorithm with consensus weight(LPAcw), and the experimental results showed that he LPAcw could enhance considerably both the stability and the accuracy of community partitions.     

小世界网络与无标度网络的社区结构研究

模块性(modularity)是度量网络社区结构(community structure)的主要参数.探讨了Watts和Strogatz的小世界网络(简称W-S模型)以及Barabàsi 等的B-A无标度网络(简称B-A模型)两类典型复杂网络模块性特点.结果显示，网络模块性受到网络连接稀疏的影响，W-S模型具有显著的社区结构，而B-A模型的社区结构特征不明显.因此，应用中应该分别讨论网络的小世界现象和无标度特性.社区结构不同于小世界现象和无标度特性，并可以利用模块性区别网络类型，因此网络复杂性指标应该包括 关键词： 模块性 社区结构 小世界网络 无标度网络     

Love Dynamics Between Secure Individuals: A Modeling Approach

A mathematical model composed of two differential equations, which qualitatively describe the dynamics of love between secure individuals, is presented in this paper with two goals. The general goal is to show how dynamic phenomena in the field of social psychology can be analyzed following the modelling approach traditionally used in all other fields of sciences. The specific goal is to derive, from very general assumptions on the behavior of secure individuals, a series of rather detailed properties of the dynamics of their feelings. The analysis shows, in particular, why couples can be partitioned into fragile and robust couples, how romantic relationships are influenced by behavioral parameters and in which sense individual appeal creates order in a community.     

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