基于SEM-AHP方法的核电厂定量人为因素评价研究

文章基于人为因素分析与分类系统,分析了115起核电厂执照运行事件报告(Licensed Operation Event Report, LOER),并对影响核电厂事件发生的人为因素进行分类,采用结构方程模型方法得出各影响因素的路径系数,两两比较得到层次分析模型的判断矩阵,进而确定各因素的权重。同时对核电厂人为因素的重要性做排序分析,找到核电厂运行过程中的安全管理薄弱环节,从而促进核电厂系统平稳运行。     

论我国违宪审查程序

我国宪法与立法法对违宪审查程序已有原则性规定,而法规备案审查工作程序、司法解释备案审查工作程序的修订增加了违宪审查的工作程序,使我国违宪审查开始具有可操作性。但是我国违宪审查程序仍有待进一步完善。     

互联网之火：网络舆情和时代焦虑 ——从南京的突发事件谈起

社会转型时期各种矛盾凸显激化,以门为表征的突发事件集中爆发。这其中,互联网既是真相披露者,也是情绪煽动者;既有为建构所必须的解构,也有勇猛不能掩饰的粗暴。十几年来,互联网不断颁布新的尺度,强迫传统媒体调整竞合,从而逐渐形成新的传播语境,宣告新的价值体系。在新语境下,精英与草根的位格关系和互动结构,都发生了颠覆式的变化。他们之间的话语冲突源自于精英对网络的隔膜和草根的群体盲目,这种双重误区,使得社会焦虑指数交替升级,政府与群众、草根与精英、中心与边缘之间不断发生进退博弈,比照自适。网络话语的冲突也正是时代焦虑的媒体映像,网络舆情既是民意的自然体现,也可为官方决策提供参考。     

论信息系统——ERP项目监理

信息系统工程监理在我国目前还是一个新生事物,无论是在理论研究上,还是在社会实践中,都还处于一个探索和起步阶段.要真正发挥ERP工程监理的作用,解决ERP成功率不高的问题,必须按照ERP工程的特点,建立相应的监理机制和实施模式.文章力图从ERP项目的特点和存在的问题出发,探讨ERP项目监理的有关问题.     

Bayesian nonparametric modelling of the link function in the single-index model using a Bernstein–Dirichlet process prior

This paper proposes the use of the Bernstein–Dirichlet process prior for a new nonparametric approach to estimating the link function in the single-index model (SIM). The Bernstein–Dirichlet process prior has so far mainly been used for nonparametric density estimation. Here we modify this approach to allow for an approximation of the unknown link function. Instead of the usual Gaussian distribution, the error term is assumed to be asymmetric Laplace distributed which increases the flexibility and robustness of the SIM. To automatically identify truly active predictors, spike-and-slab priors are used for Bayesian variable selection. Posterior computations are performed via a Metropolis-Hastings-within-Gibbs sampler using a truncation-based algorithm for stick-breaking priors. We compare the efficiency of the proposed approach with well-established techniques in an extensive simulation study and illustrate its practical performance by an application to nonparametric modelling of the power consumption in a sewage treatment plant.     

A dimensionality reduction method of continuous dependent variables based supervised Laplacian eigenmaps

Dimensionality reduction is one of the important preprocessing steps in high-dimensional data analysis. In this paper we propose a supervised manifold learning method, it makes use of the information of continuous dependent variables to distinguish intrinsic neighbourhood and extrinsic neighbourhood of data samples, and construct two graphs according to these two kinds of neighbourhoods. Following the idea of Laplacian eigenmaps, we reveal that on the low-dimensional manifold the neighbourhood structure can be preserved or even improved. Our approach has two important characteristics: (i) it uses dependent variables to find an informative low-dimensional projection which is robust to noisy independent variables and (ii) the objective function simultaneously enlarges the distance between dissimilar samples and pushes similar samples close to each other according to the graph constructed with the help of continuous dependent variables. Our experiments demonstrate that the effectiveness of our method is over their traditional rivals.     

The nested joint clustering via Dirichlet process mixture model

This article focuses on the clustering problem based on Dirichlet process (DP) mixtures. To model both time invariant and temporal patterns, different from other existing clustering methods, the proposed semi-parametric model is flexible in that both the common and unique patterns are taken into account simultaneously. Furthermore, by jointly clustering subjects and the associated variables, the intrinsic complex shared patterns among subjects and among variables are expected to be captured. The number of clusters and cluster assignments are directly inferred with the use of DP. Simulation studies illustrate the effectiveness of the proposed method. An application to wheal size data is discussed with an aim of identifying novel temporal patterns among allergens within subject clusters.     

Estimation of geodesic tortuosity and constrictivity in stationary random closed sets

We investigate the problem of estimating geodesic tortuosity and constrictivity as two structural characteristics of stationary random closed sets. They are of central importance for the analysis of effective transport properties in porous or composite materials. Loosely speaking, geodesic tortuosity measures the windedness of paths, whereas the notion of constrictivity captures the appearance of bottlenecks resulting from narrow passages within a given materials phase. We first provide mathematically precise definitions of these quantities and introduce appropriate estimators. Then, we show strong consistency of these estimators for unboundedly growing sampling windows. In order to apply our estimators to real data sets, the extent of edge effects needs to be controlled. This is illustrated using a model for a multiphase material that is incorporated in solid oxide fuel cells.