Energy flow and community structure in freshwater ecosystems

The goal of this article is twofold: 1) It aims at providing an overview on some major results obtained from energy flow studies in individuals, populations, and communities, and 2) it will also focus on major mechanisms explaining community structures. The basis for any biological community to survive and establish a certain population density is on the one hand energy fixation by primary producers together with adequate nutrient supply and the transfer of energy between trophic levels (bottom-up effect). On the other hand, predator pressures may strongly control prey population densities one or more trophic levels below (top-down effect). Other interpopulation effects include competition, chemical interactions and evolutionary genetic processes, which further interact and result in the specific structuring of any community with respect to species composition and population sizes.     

用于瞳孔序列图像分析的实时轮廓检测和参数提取

本文介绍一种医用图像分析系统．该系统连续获取瞳孔图像,根据对图像序列的某些先验知识自动确定门限．在此基础上进行快速边缘检测,并自动修补缺损的目标轮廓．在判定所得轮廓有效后计算目标面积．若数据无效则自动重新采样．序列图像观测结束后对间隔不均匀的数据进行内插校正．系统所提供的分析结果可用于临床和医学研究     

平均色差计算机配色方法的研究

本文提出了Ｅ＾２Ｄ６５＋Ｅ＾２Ａ→ｍｉｎ计算机配色方法。实验表明计算机配色方法在不同的光源照明时色差趋于平均。     

多维双曲型方程一般差分逼近时空网格同时加密的稳定性

讨论多维常系数双曲型方程式在一个中介面两侧的区域中,关于时间空间网格同时加密或稀疏情况下,分别应用不同的一般的柯西稳定差分逼近式的耦合稳定条件,推广了Ｂｅｒｇｅｒ只用于一维情形及Ｌ－Ｗ格式的结果．在多维情形下,应用Ｍｉｃｈｅｌｓｏｎ方法,利用傅里叶变换,得到包含对偶参数的特征方程式．再应用Ｍｉｃｈｅｌｓｏｎ的Ｕ．Ｋ．Ｃ判别条件,得到了多维常系数双曲型方程在中介面两边应用一般的分别为柯西稳定的格式并关于时空网格同时加密或稀疏问题的耦合稳定条件．     

整（0,m）插值的推广

引进了一类新的指数型整函数插值,推广了（０,ｍ）插值,同时也考虑了该插值算子的收敛性。     

一类二维非线性对流扩散问题的特征—差分法

研究了一类具有Ｄｉｒｉｃｈｌｅｔ边界的二维非线性对流扩散问题的特征—差分法,建立了基于双线性插值的特征—差分格式,给出了其最大模估计。     

模糊聚类在松属种间生理差异中的应用

对松属８个树种的净光合速度,蒸腾速率,气孔阻力和叶温等生理指标进行了测定。结果表明,某些树种存在相似的生理特性;具有相似生理特性的树种对环境有相同的生理需求。采用模糊聚类分析对参试树种按生理特性分成三类;第一类,Ｐｎ和Ｒｓ较大,Ｅ较小,宜生长于光照强,二氧化碳浓度高,水分要求不高的立地;第二类与第一类相反,第三类为居间类型。居此,在造林生产中根据树种的生理特性,适地适树,创造良好的经济效益。     

含两参数的抛物型方程混合问题的差分解法

研究含２个小参数的抛物型方程的奇异摄动问题,构造了指数拟合差分格式并证明了差分格式的解一阶一致收敛于微分方程的解,提高了一致收敛的精度．     

抛物型方程的一个新的高精度显格式

本文给出了解抛物型方程的一个新的显式差分格式,截断误差达０（Δｔ３＋Δｘ４）,是同类的显格式中精度最高的．     

Forecasting in the presence of level shifts

This article addresses the problem of forecasting time series that are subject to level shifts. Processes with level shifts possess a nonlinear dependence structure. Using the stochastic permanent breaks (STOPBREAK) model, I model this nonlinearity in a direct and flexible way that avoids imposing a discrete regime structure. I apply this model to the rate of price inflation in the United States, which I show is subject to level shifts. These shifts significantly affect the accuracy of out‐of‐sample forecasts, causing models that assume covariance stationarity to be substantially biased. Models that do not assume covariance stationarity, such as the random walk, are unbiased but lack precision in periods without shifts. I show that the STOPBREAK model outperforms several alternative models in an out‐of‐sample inflation forecasting experiment. Copyright © 2005 John Wiley & Sons, Ltd.