Progress of Soil Research in China

Soil,as a survival natural resource for the existent of human beings,is always highly concerned by contemporary scientists.Being a tag to symbolize the development level of soil science,research in soil classification is a focus in today's international soil science.     

结构动力分析一种简易的直接积分法

本文利用方块脉冲函数优良的运算性质,提出一种结构动力分析简易的直接积分法——方块脉冲函数法,具有无条件稳定的积分格式,并表明方法的可行和有效.     

植物钙营养与贮存关系的研究

多年多点试验表明：钙在４种石灰性土壤以及一些植物体内的运输缓慢；在生殖生长为主期间，根外喷施０．４％ＣａＣｌ２溶液，可改变果实表皮细胞形状，有效地延长果品贮存期，ｔ检验处理均高于对照，差异均达极显著。     

污泥对地下水质影响的模拟研究

施用工业污泥和生活污泥作肥料，会影响棕壤和褐土两种不同土壤地下的水质．用土柱来模拟实际土壤，对土柱进行淋洗．通过测定土柱淋洗液，分析了两种污泥在棕壤和褐土上的淋洗特性及对地下水质的影响．测定项目主要包括：EC、pH、DOC、重金属元素(Zn、Cu、Pb、Cd)．实验结果表明，褐土比棕壤更适合施用污泥．     

玻璃钢地下输水管的生产工艺

阐述了生产玻璃钢地下输水管道的工艺流程及管壁各层的设计内容及其作用,分析了各工艺中的工艺要点,并给出了生产工艺数据。     

营口地区公路抗盐碱栽植新技术的研究

以辽宁省营口地区作为研究实例,通过土壤全盐量的测定,对营口地区抗盐碱栽植新技术进行了分析,并对结果进行了评价。     

结构力学计算机辅助教学模式的应用研究

针对结构力学这门课程,从教学改革的需要出发,分析了计算机辅助教学的必要性,介绍了CAI课件的研制与课堂应用效果。     

我国土地资源可持续发展与利用

通过对我国土地资源特征及利用现状 ,分析了我国土地利用中存在的主要问题 ,进一步提出了实施土地资源可持续利用的具体对策     

都匀阶地土的承载特性研究

分析了阶地的形成与土层序列阶地土的判属以及持力层和承载。     

Samples Selection for Artificial Neural Network Training in Preliminary Structural Design

An artificial neural network (ANN) is applied in the preliminary structural design of reticulated shells. Major efforts are made to enhance the generalization ability of networks through well-selected training samples. Number-theoretic methods (NTMs) are adopted to generate samples with low discrepancy, i.e.uniformly scattered in the domain, where discrepancy is a quantitative measurement of the uniformity. The discrepancy of the NTM-based sample set is 1/6-1/7 that of samples with equal spacing. In a case study,networks trained by NTM-based samples are compared with those trained by equal-spaced samples in generalizing performance. The results show that both the computational precision and stability of the former ANNs are more satisfactory than those of the latter. It is concluded that the flexibility of ANNs in generalizing can be effectively increased by use of uniformly distributed training samples rather than simply piling data.More reliable uniformity should be obtained, however, through NTMs instead of equal-spaced samples.