微波高压提取快速测定饲料含氟量的探讨

采用微波高压制样技术,快速提取饲料中的氟元素,结合氟离子选择性电极标准添加和电位极差测试方法,能够快速、准确地测试饲料中的含氟量     

Cyanex有机膦类萃取剂在贵金属溶剂萃取中的研究和应用

介绍了近年来Cyanex有机膦类萃取剂在贵金属溶剂萃取中的研究状况及应用进展，展望了该类萃取剂在贵金属分离提取中的应用前景。     

Influence of solvents on extraction of U(Ⅵ) by N,N'-didecanoylpiperazine

Extraction behavior of N, N'-didecanoylpiperazine (DDPEZ) for U(Ⅵ)in a series of solvents from aqueous nitric acid media was investigated for the first time.The dependence of distribution ratios on the concentration of aqueous nitric acid.extractant and temperature has been discussed. The increasing sequence of extractiveability of DDPEZ is given: chloroform, carbon tetrachloride, dimethylbenzene, toluene,benzene.``     

Extraction of fission palladium by Aliquat 336 and electrochemical studies on direct recovery from ionic liquid phase

The wide electrochemical window and ion exchange properties of a room-temperature ionic liquid (RTIL) have been exploited for the extraction of palladium from nitric acid medium into ionic liquid phase — followed by direct electrodeposition of the metal from the organic phase. Extraction of palladium by commercial Aliquat 336 ionic liquids, tri-n-octylmethylammonium chloride (TOMAC) and tri-n-octylmethylammonium nitrate (TOMAN), was studied as a function of [HNO₃], [NO₃⁻] and [TOMAN]. The distribution ratio (D_Pd(II)) of palladium in TOMAN increased with an increase in the concentration of nitric acid and passed through a maximum at 1.0 M nitric acid. In contrast, the D_Pd(II) value of palladium in TOMAC decreased continuously with an increase in the concentration of nitric acid. Substantial amounts of water and nitric acid were also co-extracted into the organic phase with palladium.     

碳/环氧复合材料的冲击拉伸

该文研究了T300碳纤维单向增强的环氧复合材料,在应变率从10~(-3)/s到10~3/s范围内的冲击拉伸行为.通过对实验数据进行拟合,得出该范围内材料对应变率具有弱的敏感性,表现在破坏强度及破坏应变随应变率增加不显著变化,平均模量几乎不受应变率的影响.分析了试件的几何尺寸效应,讨论了应力波作用对破坏形态的影响以及实验中观察到的拔出现象.从应变率在10~2～10~3/s附近材料行为某些非确定性,指出在更宽范围内了解其性能的必要性.     

基于自组织神经网络的实时统计特征抽取方法

应用人工神经网络讨论统计了特征抽取的原理，提出了相应的抽取方法。该方法具有实时性，容错性，高数据压缩率和良好的特征分辨率。通过计算机仿真和桥梁索力状态的实例计算分析证实了该技术具有广泛的应用价值。     

钇、钪分离工艺的新进展

本文简要介绍中国科学院长春应用化学研究所近年来在钇、钪分离工艺研究方面的新进展。     

KNOWLEDGE-BASED FEATURE DISCOVERY FOR EVALUATION FUNCTIONS

Since Samuel's work on checkers over thirty years ago, much effort has been devoted to learning evaluation functions. However, all such methods are sensitive to the feature set chosen to represent the examples. If the features do not capture aspects of the examples significant for problem solving, the learned evaluation function may be inaccurate or inconsistent. Typically, good feature sets are carefully handcrafted and a great deal of time and effort goes into refining and tuning them. This paper presents an automatic knowledge-based method for generating features for evaluation functions. The feature set is developed iteratively: features are generated, then evaluated, and this information is used to develop new features in turn. Both the contribution of a feature and its computational expense are considered in determining whether and how to develop it further.
This method has been applied to two problem-solving domains: the Othello board game and the domain of telecommunications network management. Empirical results show that the method is able to generate many known features and several novel features and to improve concept accuracy in both domains.     

On-Line Shape recognition with incremental training using binary synaptic weights algorithm

Recognition of hand drawn shapes is beneficial in drawing packages and automated sketch entry in handheld computers. In this paper, we propose a new approach to on-line geometric shape recognition with incremental training function, which utilizes a heuristic function to reduce noise and a neural network for classification and on-line training. Instead of recognizing segments of a drawing and then performing syntactical analysis to match with a predefined shape, which is weak in terms of generalization and dealing with noise, we examine the shape as a whole. The main concept of the recognition method is derived from the fact that internal angles are very important in the perceived shape of outlines. Our application's aim is to recognize elliptic, rectangular, and triangular shapes in a way similar to human cognition of these shapes. Human beings recognize such basic shapes regardless of the variations in size, noise on the shape border, rotation and in the case of triangles, regardless of the type of the triangle. The key concept is that the neural network learns the relationships between the internal angles of a shape and its classification, therefore only a few training samples which represent the class of the shape are sufficient. Fast meremental training, which is performed on-line, is accomplished by the use of the Binary Synaptic Weights algorithm, a one pass, feedforward neural network training algorithm. Incremental training offers the advantage of adjusting the recognition capability of the system to the user's drawings. the results are very successful, such that the neural network correctly classified shapes that did not have any resemblance to the shapes in the initial training set.     

用熔融还原的方法从浸钒渣中提镓——碳饱和情况下的熔融还原过程

用通用旋转组合设计的方法得到了浸钒渣在饱和碳的熔融还原过程中镓的还原提取率与温度,时间及CaO的加入量关系的非线性数学模型。用非线性优化的方法并综合考虑Fe,Mn的回收,得到了最优的工艺条件及结果;用Langmuir—Knudsen方程推算了镓在铁液中的理论挥发量与时间的关系,与由本模型得到的关系式是一致的。