An architectural model of spring wheat: Evaluation of the effects of population density and shading on model parameterization and performance

ADELwheat is an architectural model that describes development of wheat in 3D. This paper analyzes the robustness of the parameterization of ADELwheat for spring wheat cultivars in relation to plant population density and shading. The model was evaluated using data from two spring wheat experiments with three plant population densities and two light regimes. Model validation was done at two levels of aggregation: (a) by comparing parameterization functions used as well as parameter values to the data (leaf and tiller appearance, leaf number, blade dimensions, sheath length, internode length) and (b) by comparing ground cover (GC) and leaf area index (LAI) of simulated virtual wheat plots with GC and LAI calculated from data. A sensitivity analysis was performed by modulating parameters defining leaf blade dimensions and leaf or tiller appearance rate.In contrast to population density, shading generally increased phyllochron and delayed tiller appearance. Both at the level of the organ and at the level of the canopy the model performed satisfactorily. Parameterization functions in the model that had been established previously applied to independent data for different conditions; GC and LAI were simulated adequately at three population densities. Sensitivity analysis revealed that calibration of phyllochron and blade area needs to be accurate to prevent disproportional deviations in output.The robustness of the model parameterization and the simulation performance confirmed that the model is a complete architectural model for aboveground development of spring wheat. It can be used in studies that require simulation of spring wheat structure, such as studies on plant–insect interaction, remote sensing, and light interception.     

突变级数法在采空区塌陷预测中的应用

基于突变级数理论,分析影响采空区稳定性的因素(包括采空区体积率、距地表的垂深、地质构造复杂程度、煤层倾角、覆盖层厚度、覆盖层类型等),研究采空区塌陷的预测方法.对各影响因素进行排序并确定各因素指标体系,得到各样本的突变级数,从而对采空区稳定性进行预测.实例验证表明,突变级数法的判断结果符合实际,与神经网络分级法的判断结果一致.可见,基于突变级数理论的采空区塌陷预测方法具有科学性和有效性.     

考虑人因差错影响的结构可靠度计算方法

通过对单个或任意多个人因差错影响下构件截面失效概率计算方法的分析讨论,推导出在理想已知条件下的数学解析求解公式;在考虑人因差错对结构参数影响程度的空间离散化后,得出了相应的简化计算方法;并以一个单筋梁的正截面为例进行了失效概率计算和人因差错影响程度分析.在目前人因差错的发生及其影响规律的研究不完善、缺乏必要的数学模型的现实条件下,该方法对人因差错影响下构件截面失效概率的分析计算有一定的实用价值.     

Elite cues,media coverage,and public concern: an integrated path analysis of public opinion on climate change, 2001–2013

To analyze the factors affecting US public concern about the threat of climate change between January 2002 and December 2013, data from 74 separate surveys are used to construct quarterly measures of public concern over global climate change. Five factors should account for changes in levels of concern: extreme weather events, public access to accurate scientific information, media coverage, elite cues, and movement/countermovement advocacy. Structural equation modeling indicates that elite cues, movement advocacy efforts, weather, and structural economic factors influence the level of public concern about climate change. While media coverage exerts an important influence, it is itself largely a function of elite cues and economic factors. Promulgation to the public of scientific information on climate change has no effect. Information-based science advocacy has had only a minor effect on public concern, while political mobilization by elites and advocacy groups is critical in influencing climate change concern.     

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.     

对"十一五"期间加快环保产业结构调整的有关思考

通过介绍“十一五”期间加快进行我国环保产业结构调整的迫切性,提出了应当采取的措施及要处理好的几个关系。     

某厂化学装置爆炸对设备房结构的损坏——调查与模拟分析

在对某厂化学爆炸所作的现场调查的基础上，对设备房钢筋混凝土框架结构构件的受损状况进行了分类描述，采用有限元方法对框架结构在静力和爆炸冲击荷载下的反应进行了模拟分析，分析结果与现场调查情况基本一致，给出了一些有意义的结论。     

地震作用下结构失稳诱发的塌陷和地裂缝机理分析

分析了地震诱发的地表塌陷和地裂缝机制。通过地裂缝微观机理分析研究发现:(1)地震造成了大量的地裂缝和塌陷,这些破坏的出现与地震发生时造成的瞬间应力变化和结构破坏密切相关;(2)利用结构突变失稳理论来研究岩土体内部结构是可行的。当应力状态满足孔隙结构失稳判别式时,结构元的变形状态将产生一个“跳跃”;(3)地震情况下岩土体颗粒之间有效接触力的增加,使得结构的变形能增大;同时导致颗粒间连接刚度的降低,导致结构的失稳,这些变化又是在瞬间发生和完成的,这就造成了在地震发生时总是伴随着大量的裂缝和塌陷的出现。     

广州市地面塌陷的形成原因与时空分布

地面塌陷是珠江三角洲城市地质灾害的主要类型之一。广州市地面塌陷灾害频繁发生,近10年来呈波动式上升趋势。其塌陷类型分为岩溶塌陷和工程地面塌陷两种,岩溶塌陷主要分布在广花盆地的花都区、白云区,工程地面塌陷主要分布在中心城区。地面塌陷灾害孕灾环境复杂、致灾因子多样,承灾体脆弱,灾害后果严重。人为因素是地面塌陷的主要诱发因素。岩溶塌陷主要是由于过量抽取地下水或矿山疏干排水、地下采空、暴雨触发所致;工程塌陷主要是人类工程行为所致,其主要致灾因子包括排水疏干与突水(突泥)作用、人工加载、人工振动、人工开挖桩基、地表渗水、地铁等地下工程盾构掘进等。