L-模糊弱伪理想及其性质

给出了L-fuzzy弱伪理想的概念及其刻画,并研究它的运算性质.在二次弱理想的基础上,借助于以前研究者所得出的一些成果,将弱伪理想进一步推广到L-模糊集论中,提出了L-fuzzy弱伪理想的概念以及L-fuzzy弱伪理想的4种水平截集式刻画,并进一步研究了它在L-模糊环同态下的不变性与逆不变性,以及它的和与积等运算性质.     

新型L-乳酸酸菜工艺流程和质量标准

主要介绍新型L-乳酸酸菜工艺流程.该工艺用纯乳酸菌和专用不锈钢大型发酵罐发酵,获优质乳酸酸菜,并提出一个规范的酸菜质量企业标准.是用生物技术提升传统发酵食品质量的成功实例.     

两参数指数分布模型多重模糊假设检验的贝叶斯方法

模糊假设检验是统计决策中处理模型概念的一种非常重的情形，对于双参数指数分布尺度参数的多重模糊假设检验，本文在定数截尾样本情形，研究其贝叶斯解，先验分布考虑了Jeffreys先验和共扼先验。     

L-缬氨酸生产菌的选育及基于遗传算法的发酵培养基优化

采用以黄色短杆菌TV10为出发菌株,经紫外线、硫酸二乙酯逐级诱变处理,在含磺胺胍(SG)、α-氨基丁酸(α-AB)、2-噻唑丙氨酸(2-TA)结构类似物平板上定向筛选,获得L-缬氨酸高产菌株TV230。运用遗传算法,对L-缬氨酸发酵培养基的优化进行了研究,用10组实验完成了8因素20水平的优化任务。实验结果表明,采用优化后的发酵培养基能使缬氨酸的产量提高19.4%。     

基于支持向量机的模式识别方法

介绍了由Vapnik等人提出的统计学习理论和由此发展的支持向量机，分析了其应用前景和研究方向，两个算例表明，在模式识别领域中，采用支持向量机这一新方法，具有其他传统方法不可比拟的优势。     

L-谷氨酸氧化酶高产菌株的快速筛选及诱变育种

用谷氨酸氧化酶测试液浸湿白卷纸置于菌落上,根据白卷纸上显示红色快慢、深浅,就能大致测得该菌落菌株产酶能力高低。该法可靠性大、工作量小、操作简便,显示结果快。利用该法,配合亚硝酸诱变,在短暂三个月内,将L-谷氨酸氧化酶产生菌株产酶能力提高了约25倍。     

金属膜对L-缬氨酸发酵液过滤的研究

研究了金属膜过滤的各种操作参数对L-缬氨酸过滤效果的影响，综合各方面的因素确定了最适工艺条件：进出膜压力分别为4．0MPa和3．0MPa，过滤温度为25℃，过滤液pH为3．0。在此操作条件下，蛋白质去除率达到87．36％以上，色素去除率达48．61％，L-缬氨酸收率达90．2％。     

洛仑兹曲线与基尼系数的应用

基于洛仑兹曲线详细地分析了收入分配差别的基尼系数及其计算方法。结合统计年鉴的数据，计算了我国目前的基尼系数，指出了基尼系数的不足并介绍了收入分配差别变化的一般规律。     

Prediction and setup of phytoplankton statistical model of Qiandaohu Lake

This research considers the mathematical relationship between concentration of Chla and seven environmental factors, i.e. Lake water temperature (T), Secci-depth (SD), pH, DO, CODMn, Total Nitrogen (TN), Total Phosphorus (TP). Stepwise linear regression of 1997 to 1999 monitoring data at each sampling point of Qiandaohu Lake yielded the multivariate regression models presented in this paper. The concentration of Chla as simulation for the year 2000 by the regression model was similar to the observed value. The suggested mathematical relationship could be used to predict changes in the lakewater environment at any point in time. The results showed that SD, TP and pH were the most significant factors affecting Chla concentration.     

Empirical study on mutual fund objective classification

Mutual funds are usually classified on the basis of their objectives. If the activities of mutual funds are consistent with their stated objectives, investors may look at the latter as signals of their risks and incomes. This work analyzes mutual fund objective classification in China by statistical methods of distance analysis and discriminant analysis; and examines whether the stated investment objectives of mutual funds adequately represent their attributes to investors. That is, if mutual funds adhere to their stated objectives, attributes must be heterogeneous between investment objective groups and homogeneous within them. Our conclusion is to some degree, the group of optimized exponential funds is heterogeneous to other groups. As a whole, there exist no significant differences between different objective groups; and 50% of mutual funds are not consistent with their objective groups.