A New Discipline of Science-The Study of Open Complex Giant System and Its Methodology

This paper introduces the conception of open complex giant system and the methodology for dealing with the system, with stress on its profound significance in development of science and technology. The authors conclude that the reductionism underlying the exact science is not suitable to open complex giant system, and the only feasible alternative is the meta-synthetic engineering from the qualitative to the quantitative.     

PASCAL—SPECPK—酸碱离解常数的确定

完成了用分光光度数据确定二元质子酸碱重叠离解常数的程序设计,可快速、准确测定 pK_1和 pK_2,并给出模拟反算的离差及标准误差.程序用 TurboPascal(V5.0)编制.     

面向对象的MIS重构

在MIS(Management Information System)的开发中,软件重用是提高开发效率的一条重要途径。本文通过引入对象的表示、变换等,提出了软件重构的概念和方法,它是对软件重用的重要扩充。     

助力法在智能训练中的作用

就如何馒体操运动知觉由模糊状态转化为明确状态提出了助力法智化训练。通过实验性研究，取得了比较显著的效果。     

高校生物学教学中进行创新教育的探讨

本就如何在高校生物学教学过程中进行创新教育，培养具有创新意识、创新精神、创新能力的人才作了论述。     

暂态电路的试验设计

在电工电子技术暂态电路的试验设计中，因电路本身响应时间短，用常规仪器很难对其响应的过程进行直接测量。本文结合暂态电路的工作原理及常规仪器的特点，给出三种简单、实用的试验电路设计方法。     

关于如何办好我院安全工程专业的几点思考

为办好安全工程专业，应尽快成立该专业的建设指导委员会，围绕社会需求确定人才培养方案，加强专业建设和学科建设，同时要与建设高水平的安全生产基地相结合，做到相互促进，共同发展。     

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.