基于改进的非线性GM(1,1)模型的职业病预测研究

为了更准确地预测职业病,在传统GM(1,1)模型的基础上,提出改进的非线性GM(1,1)模型,在传统的GM(1,1)模型中引入弱化算子,将紧邻均值与原始数据之间的线性假设改为非线性假设,提高曲线的拟合度。以2005—2014年的全国职业病例数为研究对象,进行数据拟合和预测分析,其中以2014年职业病例数作为验证数据,并利用后验差比值和小误差概率2个参数,检验该改进模型的预测精度。由应用实例的分析结果可知:在职业病发病趋势的预测方面,改进的非线性GM(1,1)模型的预测精度提高到一级,曲线拟合度较高,预测得到2015年的职业病例数为34 900例。     

采用支持向量机算法优化电化学处理油田污水的工艺参数

采用支持向量机(SVM)算法,将Box-Behnken设计法与支持向量回归算法(SVR)实验参数优化软件相结合,优化电化学去除油田污水COD的工艺参数。通过量子粒子群算法对SVM算法参数进行优化,从建立的回归模型中找到工艺参数的全局最佳点:电解时间60 min,电解电流3 A,三维电极填充料中石英砂质量695 g。模型得到的COD理论最优去除率为92.48%,验证实验得到的COD去除率为91.43%。     

基于宜居视角的居住城市化水平测度——以大连市为例

从宜居的视角出发,结合大连市发展现状建立居住城市化水平测度指标体系,利用模糊层次分析法（AHP）确定指标权重,计算居住城市化水平测度综合指数。通过整理样本社区测度综合指数的数据,建立指标数据库,利用地理信息系统软件MapInfo 10进行格网赋值,构建基于空间插值法的居住城市化水平测度模型,绘制大连居住城市化水平测度专题图,直观展现大连市居住城市化水平发展的时空变化特征。结果表明,1995—2010年大连市居住城市化发展呈现出由整体发展水平低、内部差异小到发展速度提高、地域发展不均衡,再到发展速度减慢,日趋平衡,最后实现发展速度再次提高、均衡发展、差异减小的阶段特征。     

甘肃省第三产业对经济增长贡献的实证研究

第三产业的加快发展是生产力提高和社会进步的必然结果。大力发展第三产业有利于提高工农业生产的社会化和专业化水平,有利于优化生产结构,促进市场充分发育,缓解就业压力,从而促进整个经济持续、快速和健康发展。利用1991-2009年数据,以向量自回归模型为基础,采用单位根检验、协整分析和格兰杰因果检验等方法对甘肃省第三产业与经济增长之间的关系进行了实证研究。结果表明,1991-2009年甘肃省第三产业总值对GDP的长期弹性为0．35035,第三产业对经济增长具有积极的促进作用。     

An improved method to study the impact of pesticide sprays on small song birds

Abstract

Four test groups of small songbirds (Zebra Finch, Poephila guttata) were sprayed in a chamber with varying concentrations of fenitrothion. Exposure levels were assessed by monitoring air concentrations, deposits of the active ingredient (AI) on glass plates and droplets/cm² on Kromekote® cards. All indices of exposure were linearly correlated and the mean AI deposit on glass plates for the four groups tested were equivalent to 38, 51, 139 and 255 g/ha or 14%, 18%, 50% and 91% of the highest permissible emitted rate for broadscale forest spraying in Canada. Significant depression in body weights and brain acetylcholinesterase levels were noted only for the highest exposure group. Fenitrothion residues in blood were detectable only at the highest exposure level, and in liver at the two higher levels. Carcass and feather residues were much higher than those in blood and liver, and were detectable at all exposure levels but the residues did not increase linearly with exposure. For one of the spray groups, we were able to compute an equivalent acute oral dose based on matching acetylcholinesterase inhibition.     

Novel Organisms: Comparing Invasive Species, GMOs, and Emerging Pathogens

Invasive species, range-expanding species, genetically modified organisms (GMOs), synthetic organisms, and emerging pathogens increasingly affect the human environment. We propose a framework that allows comparison of consecutive stages that such novel organisms go through. The framework provides a common terminology for novel organisms, facilitating knowledge exchange among researchers, managers, and policy makers that work on, or have to make effective decisions about, novel organisms. The framework also indicates that knowledge about the causes and consequences of stage transitions for the better studied novel organisms, such as invasive species, can be transferred to more poorly studied ones, such as GMOs and emerging pathogens. Finally, the framework advances understanding of how climate change can affect the establishment, spread, and impacts of novel organisms, and how biodiversity affects, and is affected by, novel organisms.     

吉林省农业病虫害发生规律与防治效果的定量分析

本文以定量分析为主要手段，详细研究了吉林省农业病虫害发生的时间过程规律和空间分布规律，深入探讨了环境因子对农业病虫害的影响，综合评价了病虫害防治工作的效果。     

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1

ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.     

Water supply and sanitation services for all: global progress during the 1990s

The Earth may be largely covered with water, but over one billion people are estimated to be without safe drinking water and almost 2.5 billion (40% of the world's population) without adequate sanitation at the outset of the new millennium. The provision of safe water and sanitation for all poses several serious institutional and economic challenges at international, national and local levels. Despite the various political commitments made from the late 1970s onwards, these commitments have remained largely unfulfilled. Even though some efforts to expand coverage have been made over the past two decades, much of those efforts have been undermined by socioeconomic problems and growing population, particularly in the urban areas of developing countries. The water supply and sanitation sector is actually in acute need of new investments for expansion and maintenance of facilities. Nonetheless, some positive trends can be discerned, such as, for example, the increasing recognition of integrated water resources management, environmental sanitation, public-private partnerships and women as a key for improvement and expansion of services.     

DEVELOPMENT OF WATERSIIED MODELS FOR TWO SIERRA NEVADA BASINS USING A GEOGRAPHIC INFORMATION SYSTEM1

ABSTRACT: Techniques were developed using vector and raster data in a geographic information system (GIS) to define the spatial variability of watershed characteristics in the north-central Sierra Nevada of California and Nevada and to assist in computing model input parameters. The U.S. Geological Survey's Precipitation-Runoff Modeling System, a physically based, distributed-parameter watershed model, simulates runoff for a basin by partitioning a watershed into areas that each have a homogeneous hydrologic response to precipitation or snowmelt. These land units, known as hydrologic-response units (HRU's), are characterized according to physical properties, such as altitude, slope, aspect, land cover, soils, and geology, and climate patterns. Digital data were used to develop a GIS data base and HRIJ classification for the American River and Carson River basins. The following criteria are used in delineating HRU's: (1) Data layers are hydrologically significant and have a resolution appropriate to the watershed's natural spatial variability, (2) the technique for delineating HRU's accommodates different classification criteria and is reproducible, and (3) HRU's are not limited by hydrographic-subbasin boundaries. HRU's so defined are spatially noncontiguous. The result is an objective, efficient methodology for characterizing a watershed and for delineating HRU's. Also, digital data can be analyzed and transformed to assist in defining parameters and in calibrating the model.