SHORT COMMUNICATION COMMENTS ON THE RESIDUAL BILINEARIZATION METHOD

Through theoretical analysis and computer simulation,this short communication comments on theresidual bilinearization(RBL)method and compares it with non-bilinear rank annihilation(NBRA)forthe treatment of second-order calibration with non-bilinear data.It is found that these two methods aremathematically equivalent but have different noise propagation properties.The second-order advantage,namely quantitation in the presence of unknown interferences,can be carried over to non-bilinear dataonly if there exists a net analyte rank(NAR)for the analyte of interest.     

ADVANCES IN SECOND-ORDER CALIBRATION

Several multivariate methods are now available for the calibration of second-order or hyphenatedinstruments(e.g.GC/MS).When applied to bilinear data,it has been shown that calibration can beperformed in the presence of unknown interferences-a significant advantage over first-order calibration.In this paper,non-bilinear rank annihilation(NBRA),a method which has the potential of handling,second-order non-bi-linear data,is studied through theoretical analysis and computer simulation.It isfound that the second-order advantage can be carried over to non-bilinear data if a property defined asnet analyte rank(NAR)holds for the analyte of interest.The net analyte signal(NAS)is definedaccordingly for second-order calibration and the analogy to and difference from lower-order calibrationare discussed.With NAS,some analytical figures of merit such as signal-to noise ratio,selectivity,sensitivity and limit of determination can be calculated for second order calibration.An application toMS/MS data is also given.     

Display-sites selection by houbara bustard (Chlamydotis [undulata] macqueenii) in Mori, Xinjiang, People''s Republic of China

Display-site selection by houbara bustards was studied in Mori, Xinjiang, China during the breeding seasons from April to June 2000. Most of the habitats chosen for displaying were short sub-shrubby and open areas close to high shrub patches. The displaying males clearly prefer low covered areas and avoid densely covered and high vegetation sites. The vegetation density and number of plant species at display sites were significantly lower from that at randomly selected sites. The average distance to the closest shrub patch was significantly shorter at display sites than at random sites. Plant species richness, vegetative density, vegetative cover and distance to the shrub patches are possibly the most important factors that determined the display-sites selection of houbara bustard.     

RECENT DEVELOPMENTS IN MULTIVARIATE CALIBRATION

With the goal of understanding global chemical processes,environmental chemists have some of the mostcomplex sample analysis problems.Multivariate calibration is a tool that can be applied successfully inmany situations where traditional univariate analyses cannot.The purpose of this paper is to reviewmultivariate calibration,with an emphasis being placed on the developments in recent years.The inverseand classical models are discussed briefly,with the main emphasis on the biased calibration methods.Principal component regression(PCR)and partial least squares(PLS)are discussed,along with methodsfor quantitative and qualitative validation of the calibration models.Non-linear PCR,non-linear PLSand locally weighted regression are presented as calibration methods for non-linear data.Finally,calibration techniques using a matrix of data per sample(second-order calibration)are discussed briefly.     

RESIDUAL BILINEARIZATION.PART 1:THEORY AND ALGORITHMS

When using hyphenated methods in analytical chemistry,the data obtained for each sample are given asa matrix.When a regression equation is set up between an unknown sample (a matrix) and a calibrationset (a stack of matrices),the residual is a matrix R.The regression equation is usually solved by minimizing the sum of squares of R.If the sample containssome constituent not calibrated for,this approach is not valid.In this paper an algorithm is presentedwhich partitions R into one matrix of low rank corresponding to the unknown constituents,and onerandom noise matrix to which the least squares restrictions are applied.Properties and possibleapplications of the algorithm are also discussed.In Part 2 of this work an example from HPLC with diode array detection is presented and the resultsare compared with generalized rank annihilation factor analysis (GRAFA).     

跨国公司对华直接投资区位选择战略分析

区位因素是影响跨国公司制定对外直接投资战略的重要因素之一。本文以香港、澳门、日本、韩国、美国、德国等国家和地区为代表，分析了跨国公司对华直接投资的动机及区位选择战略，在此基础上提出加入WTO之后中国企业海外直接投资的区位选择战略。     

MIXTURE ANALYSIS USING FACTOR ANALYSIS.Ⅱ:SELF-MODELING CURVE RESOLUTION

One of the major applications of factor analysis in the chemical literature,self-modeling curve resolution(SMCR),is covered in this review,including a historical account of the inethods derived from Lawtonand Sylvestre's original method.Papers treating the theory or applications of SMCR are included.Qualitative and quantitative applications are described where appropriate.     

Inferring organic content of sediments by scanning reflectance spectroscopy (380–730 nm): applying a novel methodology in a case study from proglacial lakes in Norway

Reflectance spectroscopy in the visible spectrum (VIS-RS) is a method that has been successfully applied for inferring organic content of sediments. In this study, we test the applicability of VIS-RS to lake sediments in Norway. On the one hand we use conventional, established algorithms for inferring organic content of sediments, on the other hand we test the potential of multivariate calibration techniques to infer organic content. For absolute quantification of organic content, conventional C_org measurements are needed when using conventional algorithms as well as when employing multivariate calibration techniques. Both, conventional algorithms and multivariate calibrations, result in estimates of organic content closely mirroring loss-on-ignition measurements. When using multivariate calibration techniques, a conventional C_org measurement every 5 cm is sufficient to obtain estimates of organic matter that are more accurate than those obtained by means of conventional algorithms. Therefore, the potential of multivariate calibration techniques and VIS-RS to substitute measurements of more time consuming and costly sediment parameters (e.g. clay minerals) should be tested.     

基于时间序列遥感数据的西藏山南地区植被覆盖变化特征分析

基于2002-2013年长时间序列MODIS-NDVI遥感数据,采用Mann-Kendall检验、多元回归分析、GIS空间分析等方法,分析了西藏山南地区植被覆盖的时空变化特征,并结合地表温度和降雨量数据,采用植被-气候时空关系模型,定量分析了气候变化和人类活动对植被覆盖变化的相对贡献率。结果表明:(1)2002-2013年山南地区年均植被覆盖度在60.16%～63.26%波动,山南地区的高、中高、中等、中低、低植被覆盖度区域占比分别为40.96%、14.07%、14.93%、14.16%、15.88%。(2)近12年来,西藏山南地区植被覆盖的变化趋势大体处于平稳状态,植被覆盖度下降区域占总面积的11.8%,植被覆盖度上升区域占3.18%。(3)气候变化是影响植被生长的决定性因素,但人类活动可在短期内加快植被变化速率。在植被覆盖度下降区域,气候变化和人类活动的贡献分别为93.75%和6.25%;在植被覆盖度上升区域,气候变化和人类活动的贡献分别为76.57%和23.43%。     

传感器交叉辐射定标综述

 当前遥感应用逐渐从定性向定量化发展的趋势对辐射定标的可靠性提出了更高的要求。传统的场地替代定标法虽然定标精度较高,但是其对场地及测量过程有很严格的要求,能够提供的数据非常有限。交叉定标是国际上新近发展起来的一种无场地定标技术,它的应用弥补了场地定标的不足。对光线匹配交叉定标、光谱匹配交叉定标、基于辐射传输模型交叉定标以及基于瑞利散射交叉定标4种交叉定标方法原理进行了详细介绍,并针对每种方法分析了其适用性。     

SHORT COMMUNICATION MULTIVARIATE CALIBRATION IN UV-VIS SPECTROSCOPY

A multivariate calibration procedure based on principal component analysis is proposed.UV-vis spectraof ternary mixtures have been used to check the applicability of the procedure.     

NONPARAMETRIC FACTOR ANALYSIS IN URBAN GEOGRAPHY: METHOD AND VALIDATION

The present paper demonstrates that Kelly's (1955) method of hand factor analyzing the data matrices derived from repertory grids can be employed as a general method of multivariate analysis in geography. This brings the advantages of a non-parametric and noncomputer dependent approach to areas such as factorial ecology, classifications of towns and cities, and urban behavioral analyses, where multivariate techniques have customarily been employed. The method is initially explained by recourse to a simple hypothetical urban retailing data set. Subsequently, more complex real world examples involving multivariate analyses of housing data for Barbados, West Indies, and urban consumers' cognitions of a single store are presented. It is shown that the nonparametric method gives results that are virtually identical to those obtained from traditional computer-based factor analyses. Throughout the paper, the pedagogic and practical virtues of the nonparametric method are considered.     

AN IMPROVED ALGORITHM FOR THE GENERALIZED RANK ANNIHILATION METHOD

An improved algorithm for the generalized rank annihilation method(GRAM)is presented.GRAM isa method for multicomponent calibration using two-dimensional instruments,such as GC-MS.In thispaper an orthonormal base is first computed and used to project the calibration and unknown sampleresponse matrices into a lower-dimensional subspace.The resulting generalized eigenproblem is thensolved using the QZ algorithm.The result of these improvements is that GRAM is computationally morestable,particularly in the case where the calibration sample contains chemical constituents not present inthe unknown sample and the unknown contains constituents not present in the calibration(the mostgeneral case).     

制造业企业区位选择集聚经济指向的空间效应

集聚经济指向是中国制造业企业区位选择的重要特征,同时企业的选址决策通常受到集聚经济的空间效应影响。因此,尝试使用空间泊松模型探讨中国制造业企业区位选择集聚经济指向的空间效应。结果表明：① 地方化经济和城市化经济均存在显著的空间效应,但空间效应的作用要弱于本地效应。② 地方化经济和城市化经济的空间效应是不同的,前者存在溢出效应,后者则为阴影效应。③ 地方化经济和城市化经济的正向影响尺度也存在差异,前者正向影响尺度较大,而后者正向影响仅限于本地。④ 集聚经济的本地效应和空间效应存在行业差异,其强度存在正相关。     

新疆重点生态功能区生态补偿的空间选择研究

基于生态补偿空间选择的基本原理,从区域范畴、资金分配、生态价值3个方面,解析了新疆重点生态功能区的基本特征,并利用"效益成本比瞄准"法、"生态补偿优先等级"法定量分析新疆生态补偿优先等级.结果表明:"生态补偿优先等级"中,新疆生态补偿优先等级与转移支付资金分配基本一致,因忽略环境因素,使得优先补偿等级未获得充分的生态转...     

城市地表灰尘重金属研究进展及展望

城市地表灰尘是城市环境科学研究的对象之一,在对国内外有关城市灰尘概念界定研究成果进行总结的基础上,对城市地表灰尘提出新的概念和新的认识。城市地表灰尘是城市生态环境中具有环境指示作用的地表颗粒物,是含有重金属、多环芳烃等多种污染物的源与汇。对国内外地表灰尘重金属的含量水平、时空分布特征、迁移循环、赋存形态及生物有效性等方面的研究成果进行比较系统的阐述;灰尘中重金属的来源识别的方法主要有GIS空间分析,元素示踪技术,统计学方法等。并提出在今后的研究中应进一步完善城市地表灰尘中重金属的时间变化规律、地表灰尘在环境中的滞留时间、粒径的划分以及区域人体健康影响及风险评估模型优化四个方面的研究。     

A BOOTSTRAP RESAMPLING SCHEME FOR USING THE CANONICAL CORRELATION TECHNIQUE IN RANK ESTIMATION

Rank estimation by canonical correlation analysis in multivariate statistics has been proposed as analternative approach for estimating the number of components in a multicomponent mixture.Amethodological turning point of this new approach is that it focuses on the difference in structure ratherthan in magnitude in characterizing the difference between the signal and the noise.This structuraldifference is quantified through the analysis of canonical correlation,which is a well-established datareduction technique in multivariate statistics.Unfortunately,there is a price to be paid for having thisstructural difference:at least two replicate data matrices are needed to carry out the analysis.In this paper we continue to explore the potential and to extend the scope of the canonical correlationtechnique.In particular,we propose a bootstrap resampling method which makes it possible to performthe canonical correlation analysis on a single data matrix.Since a robust estimator is introduced to makeinference about the rank,the procedure may be applied to a wide range of data without any restrictionon the noise distribution.Results from real as well as simulated mixture samples indicate that when usedin conjunction with this resampling method,canonical correlation analysis of a single data matrix isequally efficient as of replicate data matrices.     

临沂城市湿地公园植物区域功能性选择与配置初探

阐述了临沂湿地公园植物的功能性选择及配置的原则和具体方法。介绍了临沂湿地基本情况和湿地公园规划,对临沂的适生湿地植物品种加以分析,并对湿地公园不同功能区的植物选择与合理配置进行了一些粗浅的研究。     

THE EFFECT OF MEAN CENTERING ON PREDICTION IN MULTIVARIATE CALIBRATION

Traditionally,one form of preprocessing in multivariate calibration methods such as principal componentregression and partial least squares is mean centering the independent variables(responses)and thedependent variables(concentrations).However,upon examination of the statistical issue of errorpropagation in multivariate calibration,it was found that mean centering is not advised for some datastructures.In this paper it is shown that for response data which(i)vary linearly with concentration,(ii)have no baseline(when there is a component with a non-zero response that does not change inconcentration)and(iii)have no closure in the concentrations(for each sample the concentrations of allcomponents add to a constant,e.g.100%)it is better not to mean center the calibration data.That is,the prediction errors as evaluated by a root mean square error statistic will be smaller for a model madewith the raw data than a model made with mean-centered data.With simulated data relativeimprovements ranging from 1% to 13% were observed depending on the amount of error in thecalibration concentrations and responses.     

REVIEW ARTICLE MULTIVARIATE EXPLORATORY DATA ANALYSIS AND GRAPHICS:A TUTORIAL

Exploratory data analysis(EDA)is a toolbox of data manipulation methods for looking at data to seewhat they seem to say,i.e.one tries to let the data speak for themselves.In this way there is hope thatthe data will lead to indications about'models'of relationships not expected a priori.In this respect EDAis a pre-step to confirmatory data analysis which delivers measures of how adequate a model is.In thistutorial the focus is on multivariate exploratory data analysis for quantitative data using linear methodsfor dimension reduction and prediction.Purely graphical multivariate tools such as 3D rotation andscatterplot matrices are discussed after having introduced the univariate and bivariate tools on which theyare based.The main tasks of multivariate exploratory data analysis are identified as'search for structure'by dimension reduction and'model selection'by comparing predictive power.Resampling is used tosupport validity,and variables selection to improve interpretability.