B－P网络用于水质综合评价方法的研究

本文应用误差反向传播（Ｂ—Ｐ）算法的人工神经网络建立了水质综合评价模型。该模型应用于实例的水质评价结果表明Ｂ—Ｐ网络用于水质综合评价具有客观性和实用性。     

Modeling and spatial prediction of pre-settlement patterns of forest distribution using witness tree data

At the time of European settlement, land surveys were conducted progressively westward throughout the United States. Outside of the original 13 colonies, surveys generally followed the Public Land Survey system in which trees, called witness trees, were regularly recorded at 1 mi by 1 mi grid intersections. This unintentional sampling provides insight into the composition and structure of pre-European settlement forests, which is used as baseline data to assess forest change following settlement. In this paper, a model for the Public Land Surveys of east central Alabama is developed. Assuming that the locations of trees of each species are realized from independent Poisson processes whose respective log intensities are linear functions of environmental covariates (i.e., elevation, landform, and physiographic province), the species observed at the survey grid intersections are independently sampled from a generalized logistic regression model. If all 68 species found in the survey were included, the model would be highly over-parameterized, so only the distribution of the most common taxon, pines, will be considered at this time. To assess the impact of environmental factors not included in the model, a hidden Gaussian random field shall be added as a random effect. A Markov Chain Monte Carlo algorithm is developed for Bayesian inference on model parameters, and for Bayes posterior prediction of the spatial distribution of pines in east central Alabama. Received: June 2004 / Revised: November 2004     

Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters

Two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data. Four major aquatic insect orders (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e. EPTC), and four environmental variables (elevation, stream order, distance from the source, and water temperature) were used to implement the models. The data were collected and measured at 155 sampling sites on streams of the Adour–Garonne drainage basin (South-western France). The modelling procedure was carried out following two steps. First, a self-organizing map (SOM), an unsupervised ANN, was applied to classify sampling sites using EPTC richness. Second, a backpropagation algorithm (BP), a supervised ANN, was applied to predict EPTC richness using a set of four environmental variables. The trained SOM classified sampling sites according to a gradient of EPTC richness, and the groups obtained corresponded to geographic regions of the drainage basin and characteristics of their environmental variables. The SOM showed its convenience to analyze relationships among sampling sites, biological attributes, and environmental variables. After accounting for the relationships in data sets, the BP used to predict the EPTC richness with a set of four environmental variables showed a high accuracy (r=0.91 and r=0.61 for training and test data sets respectively). The prediction of EPTC richness is thus a valuable tool to assess disturbances in given areas: by knowing what the EPTC richness should be, we can determine the degree to which disturbances have altered it. The results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables.     

A capture-recapture model with heterogeneity and behavioural response

We develop the non-parametric maximum likelihood estimator (MLE) of the full M_bh capture-recapture model which utilizes both initial capture and recapture data and permits both heterogeneity (h) between animals and behavioural (b) response to capture. Our MLE procedure utilizes non-parametric maximum likelihood estimation of mixture distributions (Lindsay, 1983; Lindsay and Roeder, 1992) and the EM algorithm (Dempsteret al., 1977). Our MLE estimate provides the first non-parametric estimate of the bivariate capture-recapture distribution.Since non-parametric maximum likelihood estimation exists for submodels M_h (allowing heterogeneity only), Mb (allowing behavioural response only) and M₀ (allowing no changes), we develop maximum likelihood-based model selection, specifically the Akaike information criterion (AIC) (Akaike, 1973). The AIC procedure does well in detecting behavioural response but has difficulty in detecting heterogeneity.     

水环境数值模拟—SIMPLE算法研究与进展

在查阅了众多国内外有关水环境数值模拟文献的基础上,重点考察应用SIMPLE类算法进行数值模拟并同时提出改进的研究。从算法的收敛速度;校正方程;所采用的网格形式三个方面对SIMPLE算法以及在该算法的基础上经改进得到的SIMPLER、SIM-PLEC、SIMPLEX等SIMPLE类算法的研究状况进行了论述。最后,在以上论述的基础上,对SIMPLE类算法的发展趋势进行了分析预测。     

Simulation on an optimal combustion control strategy for 3-D temperature distributions in tangentially pc-fired utility boiler furnaces

The control of 3-D temperature distribution in a utility boiler fumace is essential for the safe, economic and clean operation of pc-fired fumace with multi-burner system. The development of the visualization of 3-D temperature distributions in pcfired fumaces makes it possible for a new combustion control strategy directly with the fumace temperature as its goal to improve the control quality for the combustion processes. Studied in this paper is such a new strategy that the whole fumace is divided into several parts in the vertical direction, and the average temperature and its bias from the center in every cross section can be extracted from the visualization results of the3-D temperature distributions. In the simulation stage, a computational fluid dynamics(CFD) code served to calculate the 3-D temperature distributions in a furnace, then a linear model was set up to relate the features of the temperature distributions with the input of the combustion processes, such as the flow rates of fuel and air fed into the furnaces through all the bumers. The adaptive genetic algorithm was adopted to find the optimal combination of the whole input parameters which ensure to form an optimal 3-D temperature field in the fumace desired for the operation of boiler. Simulation results showed that the strategy could soon find the factors making the temperature distribution apart from the optimal state and give correct adjusting suggestions.     

浅谈县级生态农业建设评价指标体系框架

我省自八十年代初始,已开展了生态农业试点建设,在不同区域条件下取得了明显的经济、生态、社会三大效益。“八五”期间试点范围还将会扩大,在此基础上,提出评价指标为生态农业建设规范化提供依据。本文拟写评价指标体系及其标准原则、评价方法、指标体系的解释和评价单项指标群体。通过指标评价方法,既可找出影响生态经济功能的限制因子,又可找到问题的突破口及配套措施。将有效地促进县级生态农业的健康发展。     

Predicting the Range of Chinese Mitten Crabs in Europe

Abstract:  Ecological niche modeling provides a means for predicting the potential future distribution of a nonindigenous species based on environmental characteristics of the species' native range. We applied this method to the Chinese mitten crab (Eriocheir sinensis) , a catadromous crustacean with a long history of invasion in Europe. We used genetic algorithm for rule-set prediction to predict the potential European distribution of mitten crab based on its distribution in 42 locations in its native Asia. The climatic variables, air temperature, number of days, amount of precipitation, and wetness index, contributed significantly to predictions of native distribution limits. Although the genetic algorithm for rule-set prediction model was developed for the native range, the species' extensive distribution in Europe ( n = 434) allowed independent validation of the predictions. Application of the model to Europe was successful, with 84% of occurrences in regions predicted to be suitable by >80% of the models and <4% of occurrences in areas predicted suitable by <50% of the models (mainly along the northern range). At the watershed scale, areas with established mitten crab populations had significantly higher habitat matching than sites that were not invaded. The independent validation of the Asian-based model by the European distribution revealed that predictions were highly accurate. The model also identified large areas of Europe, particularly along the Mediterranean coast, as vulnerable to future invasion. These predictions can be used to develop strategies to control the spread of mitten crab by preventing introductions into vulnerable areas.     

Noninvasive prenatal diagnosis of β-thalassaemia using individual fetal erythroblasts isolated from maternal blood after enrichment

Single nucleated red blood cells (NRBCs) isolated from maternal circulation were used for prenatal diagnosis of β-thalassaemia. The study included 22 pregnant women in the first trimester, 6 carriers at risk for β-thalassaemia and 16 noncarriers. Methodology involved enrichment of NRBCs by magnetic cell sorting (MACS) and microdissection of single NRBCs with a laser micromanipulation system. Single-cell genotyping based on nested real-time PCR for genotyping β-globin gene mutations was performed followed by a multiplexed minifingerprinting to confirm the origin of the isolated cells and possible contamination. Two polymorphic markers (D13S314 and GABRB3) facilitated the identification of fetal NRBCs through comparison of allele sizes found in the respective parents. In this study, 224 single NRBCs were detached and transferred into individual PCR tubes. Allele amplification in at least one microsatellite marker was achieved in 128/224 cells. Minifingerprinting analysis showed that 22 cells were fetal, 26 maternal and 80 were noninformative due to ADO or homozygosity. In 6 NRBCs the β-globin gene was amplified and in 2, coming from the same pregnancy, only the paternal mutation was detected. The low PCR success when genotyping isolated NRBCs was possibly due to the poor quality of fetal NRBCs and the relatively large size of the β-globin gene product. Copyright © 2007 John Wiley & Sons, Ltd.