爆炸荷载作用下RC单向简支板的优化设计

目前,对爆炸荷载作用下的钢筋混凝土(RC)结构和构件的设计主要采用控制其动力响应最大位移的方法,例如TM-5和ASCE的试算法,类似于静力作用下的极限状态设计法,虽然过程简单,却无法获得最优化的设计.由RC板的抗力-挠度(R-y)曲线分析可知,曲线下方面积代表板在爆炸荷载作用下可能吸收的能量,直接体现RC板抵抗爆炸荷载的能力.采用TM-5和ASCE推荐的单自由度体系简化RC简支单向板,选用双折线模型计算RC板的R-y曲线,分析不同配筋率的R-y曲线下方面积,面积最大时的配筋率即为该设计的最佳配筋率,改变板的几何尺寸,按上述方法分别得出不同设计方案的最佳配筋率.爆炸荷载作用下必须考虑应变率对混凝土和钢筋的应力影响和材料非线性,不能直接应用静力计算中的矩形等效原则简化计算,研究采用分层法迭代求解R-y双折线的特征值.研究成果和分析方法对爆炸荷载作用下的RC板的结构设计和加固设计具有较高的指导价值.     

中国蚁科昆虫三新纪录属、种记述

记述中国蚁科昆虫三新纪录及其新纪录种;隐居瘤胸蚁Ｄａｃａｔｒｉｓ ｔｅｍｐｌａｒｉｓＲｉｇａｔｏ,斜塔形蚁ＭａｙｒｉｅｌｌａｔｒａｎｓｆｕｇａＢａｒｏｎｉＵｒｂａｎｉ和威氏叉唇蚁Ｃａｌｙｐｔｏｍｙｒｍｅｘ ｗｉｔｔｍｅｒｉ ＢａｒｏｎｉＵｒｂａｎｉ简述三新纪录属的特征,并对三新纪录作了描述。     

The evolution of sales forecasting management: a 20‐year longitudinal study of forecasting practices

This paper presents results of a survey designed to discover how sales forecasting management practices have changed over the past 20 years as compared to findings reported by Mentzer and Cox (1984) and Mentzer and Kahn (1995). An up‐to‐date overview of empirical studies on forecasting practice is also presented. A web‐based survey of forecasting executives was employed to explore trends in forecasting management, familiarity, satisfaction, usage, and accuracy among companies in a variety of industries. Results revealed decreased familiarity with forecasting techniques, and decreased levels of forecast accuracy. Implications for managers and suggestions for future research are presented. Copyright © 2006 John Wiley & Sons, Ltd.     

A non‐Gaussian generalization of the Airline model for robust seasonal adjustment

In their seminal book Time Series Analysis: Forecasting and Control, Box and Jenkins (1976) introduce the Airline model, which is still routinely used for the modelling of economic seasonal time series. The Airline model is for a differenced time series (in levels and seasons) and constitutes a linear moving average of lagged Gaussian disturbances which depends on two coefficients and a fixed variance. In this paper a novel approach to seasonal adjustment is developed that is based on the Airline model and that accounts for outliers and breaks in time series. For this purpose we consider the canonical representation of the Airline model. It takes the model as a sum of trend, seasonal and irregular (unobserved) components which are uniquely identified as a result of the canonical decomposition. The resulting unobserved components time series model is extended by components that allow for outliers and breaks. When all components depend on Gaussian disturbances, the model can be cast in state space form and the Kalman filter can compute the exact log‐likelihood function. Related filtering and smoothing algorithms can be used to compute minimum mean squared error estimates of the unobserved components. However, the outlier and break components typically rely on heavy‐tailed densities such as the t or the mixture of normals. For this class of non‐Gaussian models, Monte Carlo simulation techniques will be used for estimation, signal extraction and seasonal adjustment. This robust approach to seasonal adjustment allows outliers to be accounted for, while keeping the underlying structures that are currently used to aid reporting of economic time series data. Copyright © 2006 John Wiley & Sons, Ltd.