基于分层贝叶斯时空模型的雾霾天气过程计数分析

本文以北京市8个行政区(东城区、西城区、石景山区、海淀区、朝阳区、昌平区、顺义区、怀柔区)的PM2.5指数计算各区逐月雾霞天气过程计数频数为研究对象,选择考虑包括地表温度、相对湿度、平均风速、SO_2质量浓度和NO_2质量浓度在内的5个影响因素。本文定义雾霾天气过程,构建分层贝叶斯时空模型,在一个统计模型中对诸多影响因素进行分析,并从计数分析的角度对北京市雾霾天气现象的时空分布、影响因素进行深入讨论。通过分析得出,温度、湿度、污染物浓度对于雾霾天气过程发生具有促进作用,平均风速对于雾霾天气过程发生具有抑制作用。从时空角度分析,从时间维度上看雾霾天气过程的发生具有明显的季节性特征,冬季(1月、2月)以及3月雾霾天气过程发生次数最高,春季(4月、5月)发生次数最低,秋季发生次数略高于夏季。从空间维度上来看,中心城区(东城区、西城区、石景山、海淀区、朝阳区)雾霾天气过程发生次数明显高于郊区(顺义、昌平、怀柔),以东城区、西城区和朝阳区最为严重。     

Sharp dual estimates for conductance of an optimal circular cooling fin

A cooling fin attached to a cylinder of circular cross section is considered. We obtain sharp upper and lower estimates for the maximum heat dissipation under the constraint that the total weight of the fin should not exceed a given bound. It is assumed that the boundary of the cylinder has a constant temperature and that Newton's law of cooling holds.This research was supported by the International Atomic Energy Agency and the United Nations Educational, Scientific, and Cultural Organization, International Center for Theoretical Physics, Miramare, Trieste, Italy.The author wishes to thank Professor Abdus Salam, the International Atomic Energy Agency, and UNESCO for hospitality at the International Center for Theoretical Physics, Trieste, Italy. Thanks are also due to the University Grants Commission, New Delhi, India, for a travel Grant, and the University of Mysore, Mysore, India, for granting the necessary leave.On leave of absence from the University of Mysore, Mysore, India.     

Octonion short-time linear canonical transform

We investigate the octonion short-time linear canonical transform (OCSTLCT) in this paper. First, we propose the new definition of the OCSTLCT, and then several important properties of newly defined OCSTLCT, such as bounded, shift, modulation, time-frequency shift, inversion formula, and orthogonality relation, are derived based on the spectral representation of the octonion linear canonical transform (OCLCT). Second, by the Heisenberg uncertainty principle for the OCLCT and the orthogonality relation property for the OCSTLCT, the Heisenberg uncertainty principle for the OCSTLCT is established. Finally, we give an example of the OCSTLCT.     

基于共同配送的快递企业末端服务网点定价博弈研究

在末端共同配送背景下,以快递共配联盟为研究对象,建立了分散决策和集中决策的定价博弈模型。对分散决策情景考虑了固定支付、按件支付、收益共享、服务水平补偿、收益共享与成本共担五种不同的利润分配方法。在构建模型时,考虑了快递员提成比例、同行竞争、服务水平、宣传水平对需求和成本的影响,给出了快递企业与合作企业的最佳决策。最后,分析在不同模式下联盟的服务水平、价格、利润变化。结果显示,集中决策下联盟的总利润最高,分散决策下使用收益共享与成本共担模式最佳。     

非常规井数值模拟技术研究进展与发展趋势

与常规井数值模拟相比,非常规井数值模拟侧重于刻画垂直、水平井筒内的多相流动,解释由于摩擦、静水力、加速度以及滑移造成的各相之间的速度差及压力损失,模拟井下流量控制设备及复杂现象.从复杂结构井的流量方程、多相井筒管流与油藏渗流耦合模拟、多段井模型、井下流量控制设备模拟、近井区域粗化、井下复杂现象模拟和嵌入式裂缝模型7个方面出发总结了非常规井数值模拟技术研究进展和主要缺陷.基于多段井模型的井筒 油藏耦合数值模拟、扩展井模型与油藏模型耦合模拟、流固耦合数值模拟、对井下监测调控设备的数值模拟以及多段压裂复杂结构井的数值模拟是未来的发展趋势.     

Relationship between strength characteristics of unidirectional glass-reinforced plastics and their composition

The article discusses the effect of the amount of binder present and the porosity of the glass reinforced plastic on the usable strength of the glass fibers, the tensile strength, and the elasticity module in stretching. It is shown that a relationship exists over the whole range tested between the volume ratio of the components (binder and glass fiber), certain strength characteristics, and porosity. A method is suggested for the comparison of glass-reinforced plastics according to their usable fiber strength which can be used for the selection of the best binder, the glass material, the glass composition, glass fibers, the lubricant, the technological scheme, etc.Moscow. Translated from Mekhanika Polimerov, No. 3, pp. 477–480, May–June, 1970.     

大学数学教育的偏差与调整对策

大学数学教育与当今遍及全球的数学教育改革方向有较大的偏差.教学内容囿于课本,固守形式演绎体系,重视技能,忽略模式建构;传递、接受的观念根深蒂固,教学方法单一,以教材的逻辑线索代替认知线索;导致学生对内容本质缺乏理解,能力发展不均衡,情感态度发展有欠缺.调整对策是:追求全面的教育目标;提供包括知识背景、理论体系、应用、内容本质、思想方法等完整有机整体的学习内容;多种教学方法综合运用,设计合理的教学线索,让学生主动经历学习过程等.     

Contribution Biplots

To interpret the biplot, it is necessary to know which points—usually variables—are the ones that are important contributors to the solution, especially when there are many variables involved. This information can be calculated separately as part of the biplot's numerical results, but this means that a table has to be consulted along with the graphical display. We propose a new scaling of the display, called the contribution biplot, which incorporates this diagnostic information directly into the display itself, showing visually the important contributors and thus facilitating the biplot interpretation and often simplifying the graphical representation considerably. The contribution biplot can be applied to a wide variety of analyses, such as correspondence analysis, principal component analysis, log-ratio analysis, and various forms of discriminant analysis, and, in fact, to any method based on dimension reduction through the singular value decomposition. In the contribution biplot, one set of points, usually the rows of a data matrix, optimally represents the spatial positions of the cases or sample units, according to an appropriate distance measure. The other set of points, usually the columns of the data matrix, is represented by vectors that are related to their contributions to the low-dimensional solution. A fringe benefit is that often only one common scale for the row and column points is needed on the principal axes, thus avoiding the problem of enlarging or contracting the scale of one set of points to make the biplot legible. Furthermore, the contribution biplot also solves the problem in correspondence analysis and log-ratio analysis of low-frequency categories that are located on the periphery of the map, giving the false impression that they are important, when they are in fact contributing minimally to the solution. This article has supplementary materials online.     

Wind identification along a flight trajectory,part 3: 2D-dynamic approach

This paper deals with the identification of the wind profile along a flight trajectory by means of a two-dimensional dynamic approach. In this approach, the wind velocity components are computed as the difference between the inertial velocity components and the airspeed components. The airspeed profile as well as the nominal thrust, drag, and lift profiles are obtained from the available DFDR measurements. The actual values of the thrust, drag, and lift are assumed to be proportional to the respective nominal values via multiplicative parameters, called the thrust, drag, and lift factors. The thrust, drag, and lift factors plus the inertial velocity components at impact are determined by matching the flight trajectory computed from DFDR data with the flight trajectory available from ATCR data. This leads to a least-square problem which is solved analytically under the additional requirement of closeness of the multiplicative factors to unity. Application of the 2D-dynamic approach to the case of Flight Delta 191 shows that, with reference to the last 180 sec before impact, the values of the multiplicative factors were 1.09, 0.84, and 0.89; this implies that the actual values of the thrust, drag, and lift were 9% above, 16% below, and 11% below their respective nominal values. For the last 60 sec before impact, the aircraft was subject to severe windshear, characterized by a horizontal wind velocity difference of 123 fps and a vertical wind velocity difference of 80 fps. The 2D-dynamic approach is applicable to the analysis of windshear accidents in take-off or landing, especially for the case of older-generation, shorter-range aircraft which do not carry the extensive instrumentation of newer-generation, longer-range aircraft. The same methodology can be extended to the investigation of aircraft accidents originating from causes other than windshear (e.g., icing, incorrect flap position, engine malfunction), above all if its precision is further increased by combining the 2D-dynamic approach and the 2D-kinematic approach.     

一类极小本原对称图的指数集

运用有向图方法完全确定出顶点带环的n阶极小本原对称有向图的本原指数集,所得的结论是:1)顶点全部自带环的n阶极小本原对称有向图所成的子图类之本原指数集E1={2,3,…,n-1};2)顶点不全带环的n阶极小本原对称有向图所成的子图类之本原指数集E2={2,3,…,2n-2}\S,其中S是{n,n+1,…,2n-2}中的所有奇数之集;3)顶点带环的n阶极小本原对称有向图所成的特殊图类之本原指数集En=E1∪E2={2,3,…,2n-2}\S.