遗传梯度法在水质数学模型参数估值的应用

介绍一种混合遗传算法－－遗传梯度法，并将其应用于水质数学模型参数估值，较一般优化算法能稳定得到的全局最优解。     

Nonparametric Ranked-set Sampling Confidence Intervals for Quantiles of a Finite Population

Ranked-set sampling from a finite population is considered in this paper. Three sampling protocols are described, and procedures for constructing nonparametric confidence intervals for a population quantile are developed. Algorithms for computing coverage probabilities for these confidence intervals are presented, and the use of interpolated confidence intervals is recommended as a means to approximately achieve coverage probabilities that cannot be achieved exactly. A simulation study based on finite populations of sizes 20, 30, 40, and 50 shows that the three sampling protocols follow a strict ordering in terms of the average lengths of the confidence intervals they produce. This study also shows that all three ranked-set sampling protocols tend to produce confidence intervals shorter than those produced by simple random sampling, with the difference being substantial for two of the protocols. The interpolated confidence intervals are shown to achieve coverage probabilities quite close to their nominal levels. Rankings done according to a highly correlated concomitant variable are shown to reduce the level of the confidence intervals only minimally. An example to illustrate the construction of confidence intervals according to this methodology is provided.     

垃圾填埋场水分迁移模型的应用研究

以渗滤液回灌为核心的填埋场生化反应器是当今国际固体废物研究的新方向 ,其具有减少渗滤液处理难度和加速填埋场稳定化的作用 ,其中控制填埋场水分是关键。本文通过对填埋场水分运移特征的分析 ,建立了渗滤液回灌条件下 ,生化反应器填埋场水分迁移的饱和 -非饱和三维非稳定数学模型 ,并求其有限单元数值解 ,定量模拟和预报不同回灌条件下填埋场水分的时空分布规律并进行实用研究。针对重庆市长生桥卫生填埋场设计情况和实际条件 ,运移模拟模型分析了水平沟和竖式井回灌条件下填埋场内水分的分布规律 ,证明了协同回灌方式的有效性     

A set of associated statistical tests for spatial clustering

The purpose of this paper is to develop a set of associated statistical tests for spatial clustering. In particular, a set of three associated tests will be developed; these will correspond to the three types of tests set out by Besag and Newell (general tests, focused tests, and tests for the detection of clustering). The associated tests draw primarily, though not exclusively, upon existing tests and results. The principal contributions are based upon the score statistic for focused tests, which has been an important approach to testing for clustering around environmental hazards. The first contribution consists of the formulation of a global statistic for general tests that corresponds to focused score statistics, along with an assessment of the distribution of the statistic under the null hypothesis of no raised incidence. The local score statistics used for focused tests will have the property of summing to the global statistic used for the corresponding general test. Attention is also given to the maximum local score statistic for the “test for the detection of clustering”. The critical values of this statistic which are required for testing the null hypothesis are described. Application of the methods is made to leukemia data for central New York State.     

Some statistical and conceptual issues in the detection of low-level environmental pollutants

With increasing concern over chemicals that are potential health hazards at low levels, determination of limits of detection have undergone considerable scrutiny. Most traditional detection limit estimators suffer from extensive statistical and/or conceptual limitations. In this paper, traditional detection limit estimators are described and critically evaluated. Using the terminology of Currie (1968), methods are categorized into decision limits versus detection limits. The methods are further categorized into single concentration design versus calibration design methodologies. While the single concentration design methods are useful for fixing ideas and clarifying definitions, they are shown to be extremely limited in practice since dependence of variability on concentration can neither be estimated or incorporated. Calibration-based detection limit estimators are described, compared and contrasted. Generalizations to non-constant variance, multiple future detection decisions and simultaneous control of Type I and II errors are provided. The various calibration-based methods are illustrated using real data and experimental design issues for detection limit studies discussed.     

Genetic Effects of Habitat Fragmentation on Common Species of Swiss Fen Meadows

Abstract:  The area of Caricion davallianae alliance in Switzerland has been considerably reduced and fragmented during the last 150 years. We assessed the genetic variability, inbreeding level, and among-population differentiation of two common habitat-specific plant species, Carex davalliana SM. and Succisa pratensis Moench, in 18 Caricion davallianae fen meadows subjected to fragmentation. We used a spatial field design of fen systems (six systems total), each consisting of one large habitat island and two small habitat islands. We used allozyme electrophoresis to derive standard genetic parameters ( A, P, H_O, H_E, F_IS, F_ST ). In Carex we identified a consistently lower A in isolated habitat islands; furthermore, H_E was lower in small habitat islands than in large habitat islands. In Succisa we identified a lower H_O in small habitat islands than in larger ones. Small habitat islands were marginally significantly differentiated (  F_ST ) from large islands for Succisa . For both species, no effects were evident for F_IS ; therefore, we argue that genetic drift rather than inbreeding is the main cause of the observed differences. The genetic structure of Carex and Succisa in small habitat islands differed from that in large habitat islands, but differences were small. It appears that the observed differences in genetic variability among fen meadows correspond to observed differences in fitness and demographic traits. We show that habitat fragmentation affects not only the rare species in an ecosystem but also reduces the survival probabilities of common species. One of the main goals of conservation should be to mitigate fragmentation of natural habitats in order to increase population sizes and connectivity.     

Detection and delineation of critical areas using echelons and spatial scan statistics with synoptic cellular data

Geographical surveillance for hotspot detection and delineation has become an important area of investigation both in geospatial ecosystem health and in geospatial public health. In order to find critical areas based on synoptic cellular data, geospatial ecosystem health investigations apply recently discovered echelon tools. In order to find elevated rate areas based on synoptic cellular data, geospatial public health investigations apply recently discovered spatial scan statistic tools. The purpose of this paper is to conceptualize a joint role for these together in the spirit of a cross-disciplinary cross-fertilization to accomplish more effective and efficient geographical surveillance for hotspot detection and delineation, and early warning system.     

Bayesian areal wombling via adjacency modeling

Recently, public health professionals and other geostatistical researchers have shown increasing interest in boundary analysis, the detection or testing of zones or boundaries that reveal sharp changes in the values of spatially oriented variables. For areal data (i.e., data which consist only of sums or averages over geopolitical regions), Lu and Carlin (Geogr Anal 37: 265–285, 2005) suggested a fully model-based framework for areal wombling using Bayesian hierarchical models with posterior summaries computed using Markov chain Monte Carlo (MCMC) methods, and showed the approach to have advantages over existing non-stochastic alternatives. In this paper, we develop Bayesian areal boundary analysis methods that estimate the spatial neighborhood structure using the value of the process in each region and other variables that indicate how similar two regions are. Boundaries may then be determined by the posterior distribution of either this estimated neighborhood structure or the regional mean response differences themselves. Our methods do require several assumptions (including an appropriate prior distribution, a normal spatial random effect distribution, and a Bernoulli distribution for a set of spatial weights), but also deliver more in terms of full posterior inference for the boundary segments (e.g., direct probability statements regarding the probability that a particular border segment is part of the boundary). We illustrate three different remedies for the computing difficulties encountered in implementing our method. We use simulation to compare among existing purely algorithmic approaches, the Lu and Carlin (2005) method, and our new adjacency modeling methods. We also illustrate more practical modeling issues (e.g., covariate selection) in the context of a breast cancer late detection data set collected at the county level in the state of Minnesota.     

AGA法及其在非线性数学模型参数估计中的应用

介绍了在基因算法基础上改进的加速基因算法（ＡＧＡ），并通过环境非线性数学模型参数估计实例对该法与传统参数估计方法进行了比较。初步结果表明，ＡＧＡ法直观、简便、拟合精度高、通用性强，易为广大环境工程技术人员理解和使用。     

The National Resources Inventory: a long-term multi-resource monitoring programme

Interest in natural resources and the environment has led to the development of new federal monitoring efforts, the expansion of existing federal inventory programmes, and discussions of inter-agency collaboration for natural resource assessment data collection. As federal programmes evolve, knowledge gained from existing long-term survey programmes can provide valuable contributions to statistical and operational aspects of survey efforts. This paper describes the National Resources Inventory (NRI), which has been conducted by the US Department of Agriculture's Natural Resources Conservation Service in cooperation with the Iowa State University Statistical Laboratory for several decades. The current NRI is a longitudinal survey of soil, water, and related environmental resources designed to assess conditions and trends every five years on non-federal US lands. An historical overview is provided highlighting the development of the survey programme. Sample design, data collection, and estimation procedures used in the 1992 NRI are described, and statistical issues related to long-term monitoring are discussed.