徐闻南山镇红树林沉积物及红树植物中重金属的累积特征与迁移规律

为探究重金属在红树林沉积物及红树植物中的分布累积及迁移规律，选取了徐闻南山镇红树林为研究对象，通过测定红树林沉积物及红树植物不同部位（根、茎、叶）的重金属质量分数，运用富集因子、生物富集系数、转移系数及相关性分析等方法进行分析。结果表明：1）红树林沉积物重金属质量分数表现为铬（Cr）＞锌（Zn）＞镍（Ni）＞铜（Cu）＞铅（Pb）＞砷（As）＞汞（Hg）＞镉（Cd），为中等变异程度；除了镍（Ni）元素外，其余7种重金属未超过国家一级标准，除了铅（Pb）元素外，其余7种重金属均超过广东省土壤环境背景值，说明研究区沉积物中重金属具有一定的积累效应。2）沉积物中砷（As）、铜（Cu）、锌（Zn）、汞（Hg）、镍（Ni）、铬（Cr）富集因子值均＞1.5，说明受到轻微人为活动影响；各站位镍（Ni）富集因子值均＞5，结合研究区背景，反映了镍（Ni）受到自然和人为输入的共同影响。3）白骨壤体内重金属主要集中在根部，而红海榄体内重金属在根茎叶中分布相对均匀。白骨壤根茎叶部位的大多数重金属质量分数远高于红海榄，说明白骨壤对重金属的吸附能力比红海榄强。汞（Hg）集中分布在植物的叶片部位，且与其他重金属之间相关性不明显；推测汞（Hg）主要通过叶片吸收进入植物体内，与交通运输污染有关。4）不同红树植物对不同重金属富集能力各异，白骨壤对重金属的富集能力表现为：镉（Cd）＞砷（As）＞铜（Cu）＞锌（Zn）＞汞（Hg）＞铅（Pb）＞镍（Ni）＞铬（Cr），红海榄表现为：镉（Cd）＞铜（Cu）＞汞（Hg）＞锌（Zn）＞铅（Pb）＞砷（As）＞镍（Ni）＞铬（Cr）。白骨壤和红海榄对汞（Hg）的运移能力都较强；红海榄对镉（Cd）的富集能力和转运能力都较强，而白骨壤对镉（Cd）富集能力较强，转运能力却较弱，这说明红树植物对重金属元素的富集能力与转运能力不存在正比关系。     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.     

污水-海水混合过程中磷转移的动力学

模拟研究厦门市污水排海时污水-海水混合过程中磷含量和形态的变化,及其在海水-颗粒物界面交换的动力学过程;提供转移模式,确定转移速率,进行磷的容量校正,为厦门西海域环境容量估算和水质控制提供定量参数和理论依据。     

Measurements of higher harmonic wave forces on a vertical truncated circular cylinder

Wave forces on a vertical truncated circular cylinder in Stokes waves with the wave slopes ranging from 0.06 to 0.24, are measured in a wave tank. The higher harmonic wave forces are compared with the available values from theories of the FNV (Faltisen–Newman–Vinje) model and Varyani solution. The first harmonic horizontal forces measured are much larger than the theoretical values from the FNV model, while the first harmonic vertical forces are well predicted by the Varyani theory. It was also found that the FNV model significantly overpredicts the second harmonic horizontal forces in high frequency waves, but under predicts the third harmonic forces. The differences between the actual measurement and the theory, in the second and third harmonic horizontal forces, become smaller at low wave frequencies as the wave slope increases. In addition, the transverse instabilities in the incoming waves with high wave slope were observed, which is due to the nonlinear modulation. Measurements were, thus, carried out before the instability occurred.     

水下智能潜器的神经网络运动控制

本文介绍一种基于神经网络的水下智能潜器的运动控制方法，该方法通过在线学习，融控制与滤波为一体。计算机仿真与水池实验验证表明，该方法的控制与滤波性能良好，对环境的学习与适应能力强。该方法事实上可用于一般动力系统的控制。     

流形上的空间密度聚类算法研究

研究流形上的聚类分析，针对基于密度的空间聚类引入了流形概念，提出1种基于流形的密度聚类算法，该方法将流形的概念与聚类相结合，可以适用于样本为复杂分布的聚类。文中通过实例证明此算法的有效性。     

Transfer-function modelling between environmental variation and mesozooplankton in the Baltic Sea

Time series of freshwater runoff, seawater salinity, temperature and oxygen were used in transfer functions (TF) to model changes of mesozooplankton taxa in the Baltic Sea from the 1960’s to the 1990’s. The models were then compared with long term zooplankton monitoring data from the same period. The TF models for all taxa over the whole Baltic proper and at different depth layers showed statistically significant estimates in t-tests. TF models were further compared using parsimony as a criterion. We present models showing 1) r² > 0.4, 2) the smallest residual standard error with the combination of exploratory variables, 3) the lowest number of parameters and 4) the highest proportional decrease in error term when the TF model residual standard error was compared with those of the univariate ARIMA model of the same response variable. Most often (7 taxa out of a total of 8), zooplankton taxa were dependent on freshwater runoff and/or seawater salinity. Cladocerans and estuarine copepods were more conveniently modelled through the inclusion of seawater temperature and oxygen data as independent variables. Our modelling, however, explains neither the overall increase in zooplankton abundance nor a simultaneous decrease found in the neritic copepod, Temora longicornis. Therefore, biotic controlling agents (e.g. nutrients, primary production and planktivore diets) are suggested as independent variables for further TF modelling. TF modelling enabled us to put the controlling factors in a time frame. It was then possible, despite the inherent multiple correlation among parameters studied to deduce a chain-of-events from the environmental controls and biotic feedback mechanisms to changes in zooplankton species. We suggest that the documented long-term changes in zooplankton could have been driven by climatic regulation only. The control by climate could be mediated to zooplankton through marine chemical and physical factors, as well as biotic factors if all of these were responding to the same external control, such as changes in the freshwater runoff. Increased runoff would explain both the increasing eutrophication, causing the overall increase of zooplankton, and the changes in selective predation, contributing to decline of Temora.