Interfacial activity and rate of palladium(II) extraction with decyl pyridine monocarboxylates

Decyl isonicotiniate and decyl nicotiniate permit a rapid extraction of palladium(II) from its hydrochloride solutions. Decyl picoliniate is not a suitable extractant. 4-Octylphenylamine can act as a phase transfer catalyst when its concentration in the organic phase is at least 2·5 × 10^?4 M . It seems that the interfacial adsorption of pyridine carboxylates and of 4-octylphenylamine is not the decisive phenomenon in palladium(II) extraction. The negligible protonation of the pyridine-type extractants and the phase transfer catalysis explains well the quick extraction of palladium(II) with the commercial and highly hydrophobic extractant ACORGA CLX-50.     

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.``     

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。     

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。     

基于启发式规则优化的网页元素提取方法

网页信息提取方法中的启发式规则,是识别网页标签信息、利用网页节点分析结果、针对网页不同内容、完成信息提取的重要手段。本研究在对现有启发式规则进行研究分析的基础上,提出了几种优化的启发式规则,实现对网页标题、发布时间、来源以及正文内容等元素信息的精准提取。本研究进一步提出了运用编辑距离算法实现正文内容提取准确率的判定,并提出阙值优化方法,克服了正文提取中噪声节点多、内容识别不完全的缺陷,大大提高了提取的准确度。     

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.     

LearningADD: Machine learning based acoustic defect detection in factory automation

Defect inspection of glass bottles in the beverage industrial is of significance to prevent unexpected losses caused by the damage of bottles during manufacturing and transporting. The commonly used manual methods suffer from inefficiency, excessive space consumption, and beverage wastes after filling. To replace the manual operations in the pre-filling detection with improved efficiency and reduced costs, this paper proposes a machine learning based Acoustic Defect Detection (LearningADD) system. Moreover, to realize scalable deployment on edge and cloud computing platforms, deployment strategies especially partitioning and allocation of functionalities need to be compared and optimized under realistic constraints such as latency, complexity, and capacity of the platforms. In particular, to distinguish the defects in glass bottles efficiently, the improved Hilbert-Huang transform (HHT) is employed to extend the extracted feature sets, and then Shuffled Frog Leaping Algorithm (SFLA) based feature selection is applied to optimize the feature sets. Five deployment strategies are quantitatively compared to optimize real-time performances based on the constraints measured from a real edge and cloud environment. The LearningADD algorithms are validated by the datasets from a real-life beverage factory, and the F-measure of the system reaches 98.48 %. The proposed deployment strategies are verified by experiments on private cloud platforms, which shows that the Distributed Heavy Edge deployment outperforms other strategies, benefited from the parallel computing and edge computing, where the Defect Detection Time for one bottle is less than 2.061 s in 99 % probability.