农机投入对农业GTFP影响及门槛效应的区域差异分析

按照粮食的产销情况,将全国划分为3类粮食功能区域,利用固定效应模型和门槛效应模型分析了农机投入对农业绿色全要素生产率（GTFP）的影响及门槛效应的区域差异。结果表明：农机投入对农业GTFP的影响程度存在区域异质性,农机投入对粮食主销区农业GTFP提升的促进作用最大,粮食产销平衡区次之,粮食主产区最小;农机投入对农业GTFP的影响路径亦存在区域差异,粮食主产区和产销平衡区的农机投入通过促进农业技术效率和农业技术进步进而推动农业GTFP提升,粮食主销区的农机投入通过促进农业技术进步推动农业GTFP提升;农机投入对粮食主产区农业GTFP的影响存在双门槛效应,对粮食主销区和产销平衡区农业GTFP的影响存在单门槛效应。     

区域创新政策评价的框架、测度与检验

构建区域创新政策评价的理论框架,在宏观知识生产函数中引入信息化作为创新技术进步的替代变量,基于DEA与Malmquist指数,用全要素生产率指数存量来衡量区域创新政策。并以省际高技术产业为例进行了实证,研究结果表明：区域创新政策的特征和评价维度决定评价的多样性,基于政策效果与政策交互作用的区域创新政策评价具有重要意义;区域创新政策总体上处于稳步提高阶段,较低地区以西部中部地区为主;区域创新政策测度模型系统性较好,其检验方法有待进一步深化;采用政府研发经费投入作为区域创新政策的替代变量值得商榷。     

Analysis of resistance factors for LRFD of soil nail pullout limit state using default FHWA load and resistance models

Abstract

Resistance factors for load and resistance factor design (LRFD) of pullout limit state of both permanent and temporary soil nails are calibrated against a wide design space using the current Federal Highway Administration (FHWA) nail load and resistance models. The calculated resistance factors were shown to scatter broadly among design scenarios that differ in wall face batter, soil friction angle, nail ultimate bond strength, and surcharge live load. An important lesson learned from the analysis results is that the current practice of using a single resistance factor for LRFD of nail pullout limit state could not result in uniform reliabilities across different design scenarios. Simple artificial neural network (ANN) models were developed for computation of resistance factors. Design examples demonstrated the ability of the ANN models in providing resistance factors that yield satisfactory and consistent reliabilities in different nail pullout designs.     

Post-earthquake economic resilience and recovery efficiency in the border areas of the Tibetan Plateau: A case study of areas affected by the Wenchuan Ms 8.0 Earthquake in Sichuan,China in 2008

Journal of Geographical Sciences - The border areas of the Tibetan Plateau and the neighboring mountainous areas have a high incidence of earthquakes with a magnitude greater than Ms 5.0, as well...     

Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data

Most studies have the achieved rapid and accurate determination of soil organic carbon (SOC) using laboratory spectroscopy; however, it remains difficult to map the spatial distribution of SOC. To predict and map SOC at a regional scale, we obtained fourteen hyperspectral images from the Gaofen-5 (GF-5) satellite and decomposed and reconstructed the original reflectance (OR) and the first derivative reflectance (FDR) using discrete wavelet transform (DWT) at different scales. At these different scales, as inputs, we selected the 3 optimal bands with the highest weight coefficient using principal component analysis and chose the normalized difference index (NDI), ratio index (RI) and difference index (DI) with the strongest correlation with the SOC content using a contour map method. These inputs were then used to build regional-scale SOC prediction models using random forest (RF), support vector machine (SVM) and back-propagation neural network (BPNN) algorithms. The results indicated that: 1) at a low decomposition scale, DWT can effectively eliminate the noise in satellite hyperspectral data, and the FDR combined with DWT can improve the SOC prediction accuracy significantly; 2) the method of selecting inputs using principal component analysis and a contour map can eliminate the redundancy of hyperspectral data while retaining the physical meaning of the inputs. For the model with the highest prediction accuracy, the inputs were all derived from the wavelength range of SOC variations; 3) the differences in prediction accuracy among the different prediction models are small; and 4) the SOC prediction accuracy using hyperspectral satellite data is greatly improved compared with that of previous SOC prediction studies using multispectral satellite data. This study provides a highly robust and accurate method for predicting and mapping regional SOC contents.     

基于吸力量测确定膨胀土活动带和裂隙深度

采用M itchell公式和裂隙扩展深度方程两种吸力法确定安康地区膨胀土大气影响深度和裂隙开展深度。其一通过对安康地区两处天然边坡开挖观测井,利用张力计进行不同深度处吸力值的现场量测,根据M itchell提出公式计算大气影响深度;其二根据非饱和土抗拉强度公式,建立膨胀土裂隙扩展深度方程,利用基质吸力量测结果求其理论解。结果表明,安康地区膨胀土吸力变化曲线随深度增加变幅减小,呈波浪式推移。M itchell公式确定安康地区膨胀土的大气影响深度为3.35m以内,裂隙深度方程确定裂隙开展深度为3.063.14m。利用M itchell公式计算大气影响深度与膨胀土断裂理论公式确定的裂隙开展深度结果接近。     

土结构性本构模型研究现状综述

土本构模型的建立是一个重要而又复杂的问题,到目前为止,国内外学者们已提出数以百计的土本构模型,诸多文献也对这些模型进行了评述和归纳。然而这些土本构模型多是在扰动土或砂土的基础上发展和建立起来的,它们难以描述由于土结构性引起的各种非线性行为,其计算结果与实际情况相差甚远。天然土体一般都具有一定的结构性,所以有必要建立考虑土结构性影响的土本构模型。针对这个现实,目前有些学者已基于各种理论和方法,提出了一些可以考虑土结构性影响的土本构模型,并得了较好的应用。但在目前的文献中还很少有对土的结构性本构模型研究进行归纳,基于此,本文简要介绍了一下目前土的结构性本构模型研究现状,并提出了这些本构模型在应用中所存在的问题。     

广西农业干旱动态评估模型

以自然条件下大气-作物-土壤系统的水量平衡方程为基础,结合广西干旱灾害特点,建立广西农业干旱评估模型。与广西历史农业旱情实况对比分析表明,该模型能够较客观、定量地对农业干旱的发生、发展过程进行逐日动态监测和灾中、灾后评估。     

基于不同区域层级的文化产业集聚研究

利用企业层面数据,从市域和省域两个尺度研究中国文化产业的集聚特征,并揭示集聚对文化企业生产率的影响。应用一致性方法估计了中国文化企业的生产函数,并计算企业层面全要素生产率估计值,并从专业化和城市化两个角度研究集聚对于文化企业的影响。结果表明：① 地级市尺度下,文化产业专业化经济并不显著,但却存在城市化经济;② 在更大的省域层面,则呈现出专业化经济显著,多样化经济弱化的特征;③ 行业和区域异质性回归显示了结果的稳健性,核心文化产业的产业集聚经济更显著;④ 在知识溢出效应占主导的文化集聚中,通信基础设施良好的省份被赋予了创意交流的便利性,因而拥有更强的集聚经济。     

土壤粒径的光谱响应特性研究

以实验室制备的5个不同粒径水平的土壤样本和室内高光谱数据为基础,通过对光谱数据进行重采样、数学变换等预处理并进行单因素方差分析、相关性分析和回归分析,探讨土壤粒径的高光谱特性,建立了光谱数据预测土壤粒径的校正模型。结果表明,土壤粒径对反射光谱有显著的影响,波长越长影响越大;在全波段范围内土壤粒径和光谱数据都呈负相关关系,对原始光谱数据进行微分变换能增加其与土壤粒径的相关性;以反射率一阶微分建立的回归模型为反演土壤粒径的最佳模型,其建模决定系数■、预测决定系数■、预测相对偏差RPD分别为0.666,0.653,2.043,预测均方根误差RMSE为0.175。