人工神经元网络辅助催化剂生产

本文提出了一种探索催化剂生产优惠操作条件的新方法，从而使最终产品的质量得以提高，并能节约人力、物力、寻优是依据数学模型离线进行的。模型采用人工神经元网络ＡＮＮ以实际上并不完全的历史数据训练后自动产生。     

智能模拟技术在生产计划优化中的应用

在延时Ｐｅｔｒｉ网的基础上，提出一种更适合生产计划模拟的扩展延时Ｐｅｔｒｉ网。将它与专家系统相结合，设计并建立了计划专家模拟系统，借助于该系统对生产计划进行模拟运行和优化，以得到较优的利用调整的计划。     

多目标交互式分解法及其应用

本文针对工业系统中多目标决策模型的典型形式,研究基于ISTM 方法的交互式分解方法的基本原理,计算步骤和应用.此外,根据大型工业企业生产过程的特点,提出了按生产装置划分子系统的建模方法.文中讨论的应用实例表明:采用分解方法便于在微型计算机上实现大型多目标问题的优化计算和决策分析.     

均匀电场中Dirac方程的严格解和真空中正负电子对的产生率

基于在一均匀恒定电场中Dirac方程的严格解,本文导出了真空中正负电子对产生率的一个解析表达式,用抛物圆柱函数表示.此表达式在弱场极限下与熟知的结果一致.     

论浦东工业园区加强小企业安全生产监管的新思路

在对上海浦东工业园区内小企业建立模糊评价数学模型结合层次分析法进行综合分析的基础上提出对小企业实施分类分级监管、运行应急计划的全过程安全生产监管新思路,探索适应面广的安全长效管理模式。     

农产品的价格和需求对无公害植保技术使用的影响

分析了影响无公害植保技术使用的因素,从生产者具有追求利润最大化的动机入手,建立了农产品价格和需求对无公害植保技术使用的分析模型。得出了农产品在各种需求状况下支持使用无公害植保技术所必须具有的价格;提出了推广使用无公害植保技术若干建议措施。图3,参14。     

Hard Templating Synthesis of Mesoporous and Nanowire SnO_2 as Lithium Battery Anode Materials

1 Results Recently,Ryoo‘s group reported the preparation of ordered mesoporous carbon using highly ordered mesoporous silica[1-2]. Mesoporous and nanowire SnO2 anode materials for lithium batteries were prepared using KIT-6 and SBA-15 SiO2 templates. The as-prepared SnO2 nanowires had a diameter of 6 nm and a length of ≈3 μm and Brunauer-Emmett-Teller (BET) surface area of 80 m2/g while mesoporous SnO2 showed a pore size of 3.8 nm and a BET surface area of 160 m2/g. The charge capacities of these two an...     

Klebsiella oxytoca HP1乙醇脱氢酶基因敲除对产氢的影响

Klebsiella oxytoca HP1是从温泉筛选得到的很有潜力的产氢菌株。为了进一步提高产氢能力,应用同源重组技术构建了乙醇途径缺少菌株,并分析和比较了野生菌和工程菌的代谢流量分布。     

The State of the Kenyan Cotton Growing Industry

From the early 1960s to late 1980s, the Kenyan cotton growing industry played a vital role in the Kenyan economy in terms of provision of employment and creation of wealth in the rural areas. It also played a central role in the textile industry which was thriving during the above mentioned period. Over the years, cotton production in Kenya has fallen steadily, such that by the year 2000, the country experienced a severe cotton fiber deficit. This study was undertaken to investigate the trend of the cotton growing industry in Kenya. Selected aspects of the industry like cost of production, cotton seed distribution, the operation of cotton gins and the quality of cotton lint were considered. Kohonen Self Organizing Maps （SOM） and K-means clustering techniques were used in data analysis. The results of this study show that Kenyan cotton farmers produced seed cotton at a break-even price of US $ 0.31 per kilogram, while the price offered was US $ 0.29 per kilogram.