因素交互作用分析的广义线性模型

为了估计疾病暴露因素之间的交互作用,提出了一种分析疾病暴露因素的交互作用的有效方法.该方法建立一个广义线性模型,通过估计模型中的参数及方差、协方差,定量的分析交互作用的大小和类型,不仅适用于队列研究资料,同时也适用用于病例-对照研究资料.     

基于多模块模糊贝叶斯网络的恐怖袭击事件量化分析

近年来,全球范围内恐怖袭击事件的发生日益频繁,已经成为许多国家和地区所面临的主要安全问题.首先建立了全球恐怖袭击事件危险性分级指标体系,并基于多模块模糊贝叶斯网络来建立一个基于观测事件"后验概率"的推理模型,从而解决信息不确定时恐怖袭击事件的危险性分级.其次,利用FCM聚类模型对恐怖袭击事件的危险度进行聚类分析,从而依据事件特征对犯罪嫌疑人进行准确锁定.然后,建立了贝叶斯网络恐怖活动预测模型,为全球反恐态势提供有效预测,并利用ArcGIS热点图分析各地区恐怖袭击事件的时空特性.最后,基于VAR模型揭示了不同区域之间在恐怖袭击事件发生时跨区域间的相互影响关系,并提出了相应的反恐建议.     

失业型贫困群体的创业意向-行为转化研究——应用半参数Cox风险模型的触发效应分析

研究应用事件史分析方法跟踪失业型贫困群体创业意向到创业行为转化的过程,解释是什么因素影响着这一独特群体创业行为的触发.在研究中,构建了包含情景因素的"意向一行为"解释框架,估计"意向一行为"的转化时间,将转化的时间间隔作为创业状态转换事件的"生存时间",应用事件史分析中的半参数Cox风险模型,考察外驱力和内驱力因素对这种时间间隔的触发效用差异.研究结果表明,"意向一行为"的转化时间在地区和性别之间的差异显著,并受到体制工作经验背景、创业培训、创业行为态度的多重影响.基于以上研究结论,针对缩短创业意向转化时间、维持创业者创业热情、鼓励女性创业等提出政策建议.     

博弈分析在环境污染群体性事件中的应用研究

从管理学的角度出发,环境群体性事件的发生是各方利益博弈非均衡的结果.在污染项目建设纠纷中,作为强势方的项目企业,存在着强制建设和协商建设等可选策略;由于经济状况和认知水平等诸多差异,地方居民则存在利益受损下同意建设或过激行为等响应类型.通过将污染项目建设问题的演化博弈模型与不同策略成本下的情景预设相结合,研究多情景下企业和地方居民之间的博弈演化过程及其影响因素.研究表明,污染项目建设诱发环境群体性事件的概率取决于一系列复杂因素,它们和事件发生概率间并不是简单的线性关系.最后,数值仿真验证了理论分析的正确性.     

利用数值进制的方法快速预测事件发生的模型及其应用

通过对影响事件发生的因素的分析,提出了因素数字化的简单方法,利用数值进制的方法建立了因素与事件发生的快速预测模型,可以在社会各个领域得到广泛的应用.     

在有交互作用下上海、伦敦期货市场铜期货价格引导关系的研究

传统的两变量引导关系模型一般仅仅考虑到自变量（包括即时与滞后因子）对因变量独立的引导作用，往往忽略了因素之间（自变量与自变量，自变量与因变量之间）交互作用对因变量产生的影响，本文提出了一种改进的引导关系模型，在传统模型的基础上添加一个交互项来刻画因素之间的交互作用对因变量所产生的影响，并对上海期货交易所和伦敦金属交易所铜期货价格之间的引导关系做了实证分析，得到一些有意义的结果，并且改进后的模型较之传统模型检验的拟合度和精确度都有一定的提高。     

基于推广的回归模型的风险管理方法

首先,利用Choquet积分将多元线性回归模型进行推广;然后,基于推广的非线性回归模型,将若干交互作用的风险因素抽象成一个数学模型,采用定量分析的方法,在给定风险损失和风险发生概率的历史数据的基础上,计算出各个风险在风险集合中的重要程度及其风险之间交互作用的大小,从而进行有效的风险管理.     

基于扩展事件图的因果分析方法及在仿真中的应用

通过因果分析对仿真结果作出因果解释是仿真非常重要的一个环节.为了能够支持因果分析,首先提出扩展事件图,以重构仿真中发生的事件以及事件之间的因果关系;然后根据扩展事件图提出若干分析算法,通过对图模型的概率参数的可能性和主要性分析,获取因果知识;最后以一个攻防对抗仿真为例,对以上方法进行了必要的验证.     

战略联盟的冲突问题研究

战略联盟为成员企业带来大量机遇的同时,也隐含着各种各样的冲突.通常,协商是解决冲突问题的有效方法,最终结局是在战略联盟伙伴之间实现一个最优策略.如果决策制定者了解解决冲突事件的整个演化过程,一个合理的结局将会实现.本文根据战略联盟冲突问题的特点,建立了冲突分析模型,并通过实例说明其应用.     

关于相互独立事件的几点错误认识

“相互独立事件同时发生的概率”,是高中数学必修课的内容,但我们在教学调查中发现,不少教师在理解“事件的独立性”这一概念时,还存在一些偏差.现分析如下.1概念什么是事件的独立性?课本给出的定义是:事件A(或B)是否发生对事件B(或A)发生的概率没有影响,这样的两个事件叫做相互独立事件.这里说的不是“对事件B(或A)发生没有影响”,而是“对事件B(或A)发生的概率没有影响”.但很多人并没有对“概率”一词引起注意.特别地,在对两个具体事件事件进行判断时,往往用直观的方法,这也容易导致对“概率”一词的忽略.事实上,“概率”一词在这个…     

A multi-level Taguchi-factorial two-stage stochastic programming approach for characterization of parameter uncertainties and their interactions: An application to water resources management

This paper presents a multi-level Taguchi-factorial two-stage stochastic programming (MTTSP) approach for supporting water resources management under parameter uncertainties and their interactions. MTTSP is capable of performing uncertainty analysis, policy analysis, factor screening, and interaction detection in a comprehensive and systematic way. A water resources management problem is used to demonstrate the applicability of the proposed approach. The results indicate that interval solutions can be generated for the objective function and decision variables, and a variety of decision alternatives can be obtained under different policy scenarios. The experimental data obtained from the Taguchi’s orthogonal array design are helpful in identifying the significant factors affecting the total net benefit. Then the findings from the multi-level factorial experiment reveal the latent interactions among those important factors and their curvature effects on the model response. Such a sequential strategy of experimental designs is useful in analyzing the interactions for a large number of factors in a computationally efficient manner.     

Regularities in data from factorial experiments

This article documents a meta‐analysis of 113 data sets from published factorial experiments. The study quantifies regularities observed among factor effects and multifactor interactions. Such regularities are known to be critical to efficient planning and analysis of experiments and to robust design of engineering systems. Three previously observed properties are analyzed: effect sparsity, hierarchy, and heredity. A new regularity is introduced and shown to be statistically significant. It is shown that a preponderance of active two‐factor interaction effects are synergistic, meaning that when main effects are used to increase the system response, the interaction provides an additional increase and that when main effects are used to decrease the response, the interactions generally counteract the main effects. © 2006 Wiley Periodicals, Inc. Complexity 11: 32–45, 2006     

Models and estimation methods for clinical HIV-1 data

Clinical HIV-1 data include many individual factors, such as compliance to treatment, pharmacokinetics, variability in respect to viral dynamics, race, sex, income, etc., which might directly influence or be associated with clinical outcome. These factors need to be taken into account to achieve a better understanding of clinical outcome and mathematical models can provide a unifying framework to do so. The first objective of this paper is to demonstrate the development of comprehensive HIV-1 dynamics models that describe viral dynamics and also incorporate different factors influencing such dynamics. The second objective of this paper is to describe alternative estimation methods that can be applied to the analysis of data with such models. In particular, we consider: (i) simple but effective two-stage estimation methods, in which data from each patient are analyzed separately and summary statistics derived from the results, (ii) more complex nonlinear mixed effect models, used to pool all the patient data in a single analysis. Bayesian estimation methods are also considered, in particular: (iii) maximum posterior approximations, MAP, and (iv) Markov chain Monte Carlo, MCMC. Bayesian methods incorporate prior knowledge into the models, thus avoiding some of the model simplifications introduced when the data are analyzed using two-stage methods, or a nonlinear mixed effect framework. We demonstrate the development of the models and the different estimation methods using real AIDS clinical trial data involving patients receiving multiple drugs regimens.     

Modulus‐based multigrid methods for linear complementarity problems

By employing modulus‐based matrix splitting iteration methods as smoothers, we establish modulus‐based multigrid methods for solving large sparse linear complementarity problems. The local Fourier analysis is used to quantitatively predict the asymptotic convergence factor of this class of multigrid methods. Numerical results indicate that the modulus‐based multigrid methods of the W‐cycle can achieve optimality in terms of both convergence factor and computing time, and their asymptotic convergence factors can be predicted perfectly by the local Fourier analysis of the corresponding modulus‐based two‐grid methods.     

Nonlinear correlations in the hydrophobicity and average flexibility along the glycolytic enzymes sequences

Nonlinear methods widely used for time series analysis were applied to glycolytic enzyme sequences to derive information concerning the correlation of hydrophobicity and average flexibility along their chains. The 20 sequences of different types of the 10 human glycolytic enzymes were considered as spatial series and were analyzed by spectral analysis, detrended fluctuations analysis and Hurst coefficient calculation. The results agreed that there are both short range and long range correlations of hydrophobicity and average flexibility within investigated sequences, the short range correlations being stronger and indicating that local interactions are the most important for the protein folding. This correlation is also reflected by the fractal nature of the structures of investigated proteins.     

基于ISM与M-F模型的公务员工资普调频率研究

以普调频率为核心的工资增长机制,是我国公务员工资制度改革悬而未决的难点问题,其关键在于分析影响因素并确定普调周期。文章采用ISM、M-F等方法,对我国公务员工资普调频率影响因素进行量化分析,旨在通过研究各因素影响时效的衍变规律,寻求长期稳态下最适宜的普调周期。研究发现,基于时效周期的工资普调影响指标体系包含24项因素,其中CPI等8项流量因素构成短期时效层,自身变化及影响时效短,易波动,综合影响期为1.55年;平均任职时间等13项存量因素构成中期时效层,指标变动具有长期累积性,综合影响期为3.11年;工资差异结构等3项支配因素构成长期时效层,反映工资供求矛盾状态,综合影响期为4.53年;在24项因素综合作用下,系统于2.34年达到了长期稳态水平,各项影响因素实现了最佳平衡,可近似将2.50年设计为普调周期。     

On the non-ideal vibrating structures

In this work, analysis of the response to vibrations of a small building equipped with an electromechanical vibration absorber is investigated. The case of harmonic excitation with constant or time dependent frequencies is considered. The interaction between the structure and the energy source is analyzed via the Sommerfeld effect inside the resonance region. The resonance capture and the vibration reduction are displayed by the time history displacement of the last story. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

供应链交互作用对牛鞭效应的影响分析

考虑两平行供应链系统,建立了需求依赖于两种产品价格的需求函数模型,分析了平行供应链交互作用对牛鞭效应的影响。研究表明:(1)供应链交互作用可能增加或减弱牛鞭效应。(2)对于具有产品可替代性的两竞争型供应链系统,若产品价格交互敏感性不强,则较大协方差的引入可以抑制牛鞭效应。(3)对于具有产品互补性的两合作型供应链系统,若产品价格交互敏感性较强,则较小协方差的引入可以抑制牛鞭效应。     

Time and resonance patterns in chaotic piece-wise linear systems

The aim of this paper is to characterize time and resonant behavior in a class of piece-wise linear systems evolving chaotically. To this end, statistical methods are used to reveal interactions of different parts of the system. The system under study comprises a continuous time subsystem and a switching rule that induces an oscillatory path by switching alternately between stable and unstable conditions. Since the system is not continuous, the principal oscillation frequency depends on the switching regime and linear subsystem parameters; therefore, many time and resonant patterns can be observed. It is shown that the system may display resonance produced by the action of a external signal (switching law), as well as internal and combinational resonance. The effect of system parameters on time evolution and resonance is studied. It is shown that the nature of subsystems eigenvalues plays a crucial role in the type of resonance observed, producing in some cases complex interaction of resonance modes.     

Integration of topological measures for eliminating non-specific interactions in protein interaction networks

High-throughput protein interaction assays aim to provide a comprehensive list of interactions that govern the biological processes in a cell. These large-scale sets of interactions, represented as protein–protein interaction networks, are often analyzed by computational methods for detailed biological interpretation. However, as a result of the tradeoff between speed and accuracy, the interactions reported by high-throughput techniques occasionally include non-specific (i.e., false-positive) interactions. Unfortunately, many computational methods are sensitive to noise in protein interaction networks; and therefore they are not able to make biologically accurate inferences.In this article, we propose a novel technique based on integration of topological measures for removing non-specific interactions in a large-scale protein–protein interaction network. After transforming a given protein interaction network using line graph transformation, we compute clustering coefficient and betweenness centrality measures for all the edges in the network. Motivated by the modular organization of specific protein interactions in a cell, we remove edges with low clustering coefficient and high betweenness centrality values. We also utilize confidence estimates that are provided by probabilistic interaction prediction techniques. We validate our proposed method by comparing the results of a molecular complex detection algorithm (MCODE) to a ground truth set of known Saccharomyces cerevisiae complexes in the MIPS complex catalogue database. Our results show that, by removing false-positive interactions in the S. cerevisiae network, we can significantly increase the biological accuracy of the complexes reported by MCODE.