非常规突发事件中影响当事人状态的要素分析与数理描述

本文界定了突发事件、非常规突发事件的概念,分析了在非常规突发事件中影响当事人的情绪、心理、行为状态的三大类因素:当事人周围物质条件巨变的四方面因素,周围人群情绪与行为两方面因素,以及主导信息的作用;进而,分析了主导信息与当事人心理、氛围耦合、交互的机制,用生灭过程描述了非常规突发事件中当事人状态演变的模型。

近代小说及其研究的数理描述

据统计,近代小说共有2671种。由时间分布看,明清两代共544年,其中,从鸦片战争到辛亥革命后72年问世的小说为前472年的3倍。在这72年中,小说创作的分布也不均衡,前55年的速率基本与前数百年无异,自甲午战争起陡变;就空间分布看,又集中于上海。其中自有政治、经济、文化上的原因。

复杂光学曲面数理描述和设计方法研究

为满足高性能光学系统的设计要求,需要研究面形灵活可控的光学曲面数理描述方法和其相应的分析及设计方法。具有更多自由度的复杂光学曲面可以有效提升光学系统的性能。简要讨论了光学曲面的数理描述方法;概括性地介绍了光学曲面数理描述方法的研究现状与进展,介绍了局部和整体面形控制方法,归纳总结了新的复杂光学曲面数理描述方程,并通过实例证明相关曲面应用的可行性和有效性。最后对曲面数理描述方法的未来发展进行了讨论和展望。

论董仲舒的“馀义”言说

《春秋繁露》是典型的融合性文本。董仲舒能够将不同学派的思想资源融为一体,与其"馀义"言说方式有关。馀义言说是"以比贯类、以辨付赘"的方法,亦即通过连环问答、数理描述、天人对话揭示人情与辞义、天志与名号之间的同一性。不同学派的思想资源能够融为一体,正是借助于"比例法"与"正义法"。

Multi-sensor image registration using multi-resolution shape analysis

Multi-sensor image registration has been widely used in remote sensing and medical image field, but registration performance is degenerated when heterogeneous images are involved. An image registration method based on multi-resolution shape analysis is proposed in this paper, to deal with the problem that the shape of similar objects is always invariant. The contours of shapes are first detected as visual features using an extended contour search algorithm in order to reduce effects of noise, and the multi-resolution shape descriptor is constructed through Fourier curvature representation of the contour’s chain code. Then a minimum distance function is used to judge the similarity between two contours. To avoid the effect of different resolution and intensity distribution, suitable resolution of each image is selected by maximizing the consistency of its pyramid shapes. Finally, the transformation parameters are estimated based on the matched control-point pairs which are the centers of gravity of the closed contours. Multi-sensor Landsat TM imagery and infrared imagery have been used as experimental data for comparison with the classical contour-based registration. Our results have been shown to be superior to the classical ones.

ROLE OF META-MODEL IN ENGINEERING DATA WAREHOUSE

Engineering data are separately organized and their schemas are increasingly complex and variable. Engineering data management systems are needed to be able to manage the unified data and to be both customizable and extensible. The design of the systems is heavily dependent on the flexibility and self-description of the data model. The characteristics of engineering data and their management facts are analyzed. Then engineering data warehouse (EDW) architecture and multi-layer metamodels are presented. Also an approach to manage anduse engineering data by a meta object is proposed. Finally, an application flight test EDW system (FTEDWS) is described and meta-objects to manage engineering data in the data warehouse are used. It shows that adopting a meta-modeling approach provides a support for interchangeability and a sufficiently flexible environment in which the system evolution and the reusability can be handled.

The Seasickness as Marine Problem-Case Study

Motion sickness is one of the main reasons why travelling by sea becomes a bad memory for a number of people, mainly due to a nauseating symptom. The aim of this paper is to characterize a condition called seasickness that has affected many people from a mathematical and psychological point of view. Based on the mathematical characterization, an existing model will be reviewed and described. In the psychological aspects, a statistical study of two hundred and thirteen people was performed in order to provide a comprehensive view of the different psychological factors affecting the passengers. Motion sickness also impacts onboard safety, as it reduces the effectiveness and the operating capability of the crew when responding to hazardous situations.

Simple groups with orders 2~a3~b5~cp^d, 2~a3~b7~cp^d and 2~a3~b5~c7~d

This work deals with the power exponent 1rand 2r respectively of the maximal and second-maximal prime factors of the order of simple K4-group, and the classification for simple 4{5,7}K--group G (i.e. G can not be divided by 5 nor by 7 or ()Gp= 4 ), simple 5 -4K-group G (i.e. G can not divided by 5 and ()Gp=4) and simple 7-4K-group G (i.e. G can not divided by 7 and ()Gp= 4). It is derived that 1r =1, 2 and 4, and 2r is not greater than 4. All the simple 4K-groups with order 235,237abcdabcdpp and 2357abcd are obtained.

From projective representations to quasi-quantum groups

This is a contribution to the project of quiver approaches to quasi-quantum groups.We classify Majid bimodules over groups with 3-cocycles by virtue of projective representations.This leads to a theoretic classification of graded pointed Majid algebras over path coalgebras,or equivalently cofree pointed coalgebras,and helps to provide a projective representation-theoretic description of the gauge equivalence of graded pointed Majid algebras.We apply this machinery to construct some concrete examples and obtain a classification of finite-dimensional graded pointed Majid algebras with the set of group-likes equal to the cyclic group of order 2.

Wild dogma II： The role and implications of wild dogma for wild dog management in Australia

The studies of Allen （2011） and Allen et al. （2011） recently examined the methodology underpinning claims that dingoes provide net benefits to biodiversity by suppressing foxes and cats. They found most studies to have design flaws and/or observational methods that preclude valid interpretations from the data, describing most of the current literature as ＇wild dogma＇. In this short supplement, we briefly highlight the roles and implications of wild dogma for wild dog management in Australia. We discuss nomenclature, and the influence that unreliable science can have on policy and practice changes related to apex predator management

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

A local discriminant regularized soft k-means （LDRSKM） method with Bayesian inference is proposed for multimode process monitoring. LDRSKM extends the regularized soft k-means algorithm by exploiting the local and non-local geometric information of the data and generalized linear discriminant analysis to provide a better and more meaningful data partition. LDRSKM can perform clustering and subspace selection simultaneously, enhancing the separability of data residing in different clusters. With the data partition obtained, kernel support vector data description （KSVDD） is used to establish the monitoring statistics and control limits. Two Bayesian inference based global fault detection indicators are then developed using the local monitoring results associated with principal and residual subspaces. Based on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode correlations, and local geometric information can be exploited to enhance monitoring performances for nonlinear and non-Gaussian processes. The effectiveness and efficiency of the proposed method are evaluated using the Tennessee Eastman benchmark process.