基于二叉树的簇内分层密钥管理方案

通过对基于分簇的数据融合隐私保护算法CPDA的分析,提出了一种基于簇内分层随机密钥管理方案.它使用二叉树的逻辑结构,对簇内节点进行重新组织后,传输各自的切片数据,再通过建立密钥树的逻辑层次结构.对整个组密钥进行管理最终实现了在数据融合的同时,保护数据安全通过实验分析,相比于分簇隐私数据融合协议CPDA在具有更好的隐私保护性的同时,更低的数据通信量以及良好的可扩展性.     

Strongly enhanced quadrupole deformation in a class of N≈Z nuclei driven by large-scale clustering?

The formation of large size clusters,and/or their relative motion as a possible excitation mode,are sugge sted to be closely related to the origin of deformation in specific cases,namely the case of two doubly-magic clusters or two clusters with nearby characterization.New lifetime data in N=Z ~(76)Sr and ~(80)Zr leading to large B(E2) values are reproduced consistently and well within this approach,along with data for a few neighboring N≈Z nuclei.These results are compared to previous studies of ~(32)S and ~(20)Ne and all of them support the ideas of the large-scale cluster approach.     

基于不同条件的旅游路线规划问题研究

运用2015年全国研究生数学建模竞赛F题的数据资料,针对旅游路线合理规划问题的第一问展开研究.以F题的问题一为起点进行了分析研究,是因为第一问的完成是解决后续问题的关键.首先通过地图搜集并补全了缺失数据并对数据进行合理的处理,然后采用将旅游年数最少目标转化为该最小生成树的"最少圈覆盖"方法来进行对问题一的求解,也可以理解为一个广义的多旅行商问题,以旅行商的人数(即年数)最少为目标.采用Dijkstra算法、最少圈覆盖法、智能算法和图论聚类等方法,通过这些方法建立了单目标优化模型,并运用旅行商问题和模型之间的转换来对问题进行分析与求解.     

基于灰色聚类分析的风险评价方法研究——以江苏民间资本进入金融领域风险评价为例

运用灰色系统理论,结合AHP方法,建立灰色聚类分析模型,对风险进行评价,并以江苏民间资本进入金融领域风险评价为例进行研究.结果表明,民间资本进入金融领域的风险属于高风险范畴,其中进入风险中的产业风险和社会风险属于高风险,信用风险属于较高风险,政治风险属于中等风险.并针对上述风险的等级,提出了相应的政策建议.模型具有一定的实用价值,它对于正确认识民间资本进入金融领域的风险和有针对性地制定相应的政策措施具有一定的理论和现实意义.     

Intensity‐based estimation of extreme loss event probability and value at risk

We develop a methodology for the estimation of extreme loss event probability and the value at risk, which takes into account both the magnitudes and the intensity of the extreme losses. Specifically, the extreme loss magnitudes are modeled with a generalized Pareto distribution, whereas their intensity is captured by an autoregressive conditional duration model, a type of self‐exciting point process. This allows for an explicit interaction between the magnitude of the past losses and the intensity of future extreme losses. The intensity is further used in the estimation of extreme loss event probability. The method is illustrated and backtested on 10 assets and compared with the established and baseline methods. The results show that our method outperforms the baseline methods, competes with an established method, and provides additional insight and interpretation into the prediction of extreme loss event probability. Copyright © 2012 John Wiley & Sons, Ltd.     

Generation of linear and nonlinear waves in numerical wave tank using clustering technique-volume of fluid method

A two-dimensional (2D) numerical model is developed for the wave simulation and propagation in a wave flume.The fluid flow is assumed to be viscous and incompressible,and the Navier-Stokes and continui...     

Hierarchical Model-Based Clustering for Large Datasets

In recent years, hierarchical model-based clustering has provided promising results in a variety of applications. However, its use with large datasets has been hindered by a time and memory complexity that are at least quadratic in the number of observations. To overcome this difficulty, this article proposes to start the hierarchical agglomeration from an efficient classification of the data in many classes rather than from the usual set of singleton clusters. This initial partition is derived from a subgraph of the minimum spanning tree associated with the data. To this end, we develop graphical tools that assess the presence of clusters in the data and uncover observations difficult to classify. We use this approach to analyze two large, real datasets: a multiband MRI image of the human brain and data on global precipitation climatology. We use the real datasets to discuss ways of integrating the spatial information in the clustering analysis. We focus on two-stage methods, in which a second stage of processing using established methods is applied to the output from the algorithm presented in this article, viewed as a first stage.     

Argentophilic Interactions

The decade 1990–2000 saw a growing interest in aurophilic interactions in gold chemistry. These interactions were found to influence significantly a variety of structural and other physical characteristics of gold(I) compounds. The attention paid to this unusual and counterintuitive type of intra‐ and intermolecular bonding between seemingly closed‐shell metal centers has rapidly been extended to also include silver chemistry. Hundreds of experimental and computational studies have since been dedicated to the argentophilicity phenomenon. The results of this development are reviewed herein focusing on molecular systems where two or more silver(I) centers are in close contact leading to specific structural characteristics and a variety of novel physical properties. These include strongly modified ligand‐to‐metal charge‐transfer processes observed in absorption and emission spectroscopy, but also colossal positive and negative thermal expansion on the one hand and unprecedented negative linear compressibility of crystal parameters on the other.     

MOEPGA: A novel method to detect protein complexes in yeast protein–protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm

The identification of protein complexes in protein–protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.     

Detection of the support damage of spatial steel structures under earthquakes based on shaking table test

There are always some local damages in spatial steel structures induced by strong earthquakes, such as welding cracks of steel nodes, anchor loose of supports etc. If these local damages of spatial steel structures occur, there will be serious dangers to the structural safety. In order to detect the position of local damage under earthquake quickly and accurately, the method of support damage diagnosis of spatial steel structures under earthquakes is studied by using wavelet packet decomposition, data fusion and cluster analysis. Furthermore, a scale model of spatial steel structure was tested on a seismic simulation shaking table to detect the position of damaged support. Results show that the damaged support position can be detected accurately by using the method proposed in this paper, which has practical applications to the damage detection of spatial steel structures.