连续函数序列收敛的几个充要条件

讨论连续函数序列在[a,b]上一致收敛的充要条件;连续函数序列的收敛函数仍为连续函数的充要条件;连续函数序列的收敛函数不连续的充要条件。     

复变函数共轭解析的充要条件

在共轭解析函数定义基础上,讨论指数形式表示的函数共轭解析的充要条件.     

单输出非线性系统的自适应观测器

对1个非线性系统通过状态空间坐标变换.变换为1个特定自适应观测器的充分必要条件进行了讨论和证明.     

关于极限函数连续性的一个充分必要条件

众所周知,连续函数列的极限函数不一定是连续的,这在理论与应用上均造成许多障碍。本文讨论了度量空间(X,d)到(Y,p)的连续函数,给出并证明了连续函数列的极限函数是连续函数的一个充分必要条件。     

一类(h,)—— 意义下非光滑规划解的充分条件

在Ben—Tal广义代数运算的基础上引进了广义(h,(?))—方向导数及广义(h,(?))—梯度的概念,对非光滑函数提出了几个非凸概念,然后在比较弱的条件下给出了非光滑非凸规划的几个充分条件。     

关于I．Schur问题的一个推广证明

本文利用微分方法证明了函数单调递增的充要条件为α＜０，单调递减的充要条件为；数列。调递增的充要条件为α≤（２１ｎ３－３１ｎ２）／（２１ｎ２－ｌｎ３），而且单调递减的充要条件为。     

等权Hanoi puzzle解的充分必要条件

提出等权 Hanoi Puzzle 的基本概念,并且推导出 Hanoi Puzzle 解的充分必要条件。     

Managing job seekers' organizational image beliefs: The role of media richness and media credibility.

In this article, the authors assessed job seekers' organizational image beliefs before and after they experienced 3 recruitment media. The authors examined whether perceptions of media richness and credibility were related to improvements in the correspondence between job seekers' image beliefs and firms' projected images. Both media richness and credibility perceptions were associated with correspondence between job seekers' image beliefs and firms' projected images. However, results revealed that richness and credibility perceptions were likely to enhance job seekers' initial beliefs about firms' images when their beliefs were positive but did not diminish job seekers' beliefs about firms' images when their initial impressions were too positive. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Patterning and predicting aquatic macroinvertebrate diversities using artificial neural network

A counterpropagation neural network (CPN) was applied to predict species richness (SR) and Shannon diversity index (SH) of benthic macroinvertebrate communities using 34 environmental variables. The data were collected at 664 sites at 23 different water types such as springs, streams, rivers, canals, ditches, lakes, and pools in The Netherlands. By training the CPN, the sampling sites were classified into five groups and the classification was mainly related to pollution status and habitat type of the sampling sites. By visualizing environmental variables and diversity indices on the map of the trained model, the relationships between variables were evaluated. The trained CPN serves as a 'look-up table' for finding the corresponding values between environmental variables and community indices. The output of the model fitted SH and SR well showing a high accuracy of the prediction (r>0.90 and 0.67 for learning and testing process, respectively) for both SH and SR. Finally, the results of this study, which uses the capability of the CPN for patterning and predicting ecological data, suggest that the CPN can be effectively used as a tool for assessing ecological status and predicting water quality of target ecosystems.     

Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery

Remote sensing represents a powerful tool to derive quantitative and qualitative information about ecosystem biodiversity. In particular, since plant species richness is a fundamental indicator of biodiversity at the community and regional scales, attempts were made to predict species richness (spatial heterogeneity) by means of spectral heterogeneity. The possibility of using spectral variance of satellite images for predicting species richness is known as Spectral Variation Hypothesis. However, when using remotely sensed data, researchers are limited to specific scales of investigation. This paper aims to investigate the effects of scale (both as spatial and spectral resolution) when searching for a relation between spectral and spatial (related to plant species richness) heterogeneity, by using satellite data with different spatial and spectral resolution. Species composition was sampled within square plots of 100 m² nested in macroplots of 10,000 m². Spectral heterogeneity of each macroplot was calculated using satellite images with different spatial and spectral resolution: a Quickbird multispectral image (4 bands, spatial resolution of 3 m), an Aster multispectral image (first 9 bands used, spatial resolution of 15 m for bands 1 to 3 and 30 m for bands 4 to 9), an ortho-Landsat ETM+ multispectral image (bands 1 to 5 and band 7 used; spatial resolution, 30 m), a resampled 60 m Landsat ETM+ image.Quickbird image heterogeneity showed a statistically highly significant correlation with species richness (r = 0.69) while coarse resolution images showed contrasting results (r = 0.43, r = 0.67, and r = 0.69 considering the Aster, Landsat ETM+, and the resampled 60 m Landsat ETM+ images respectively). It should be stressed that spectral variability is scene and sensor dependent. Considering coarser spatial resolution images, in such a case even using SWIR Aster bands (i.e. the additional spectral information with respect to Quickbird image) such an image showed a very low power in catching spectral and thus spatial variability with respect to Landsat ETM+ imagery. Obviously coarser resolution data tend to have mixed pixel problems and hence less sensitive to spatial complexity. Thus, one might argue that using a finer pixel dimension should inevitably result in a higher level of detail. On the other hand, the spectral response from different land-cover features (and thus different species) in images with higher spectral resolution should exhibit higher complexity.Spectral Variation Hypothesis could be a basis for improving sampling designs and strategies for species inventory fieldwork. However, researchers must be aware on scale effects when measuring spectral (and thus spatial) heterogeneity and relating it to field data, hence considering the concept of scale not only related to a spatial framework but even to a spectral one.