混合溶剂对Ru(Ⅳ)萃取的研究

一、前言 在一定浓度盐酸介质中,Ru(Ⅳ)以RuCl_6~(2-)络阴离子状态存在,可被C类溶剂TOA(三辛胺)以离子缔合机制定量地萃入有机相。由实验得到在相同条件下,TOA对Ru(Ⅳ)的萃取率随稀释剂介电常数的增加而下降。     

NH4SCN从ROH-CTMAB-C12H26载金有机相中反萃金

研究了硫氰酸铵（NH4SCN）从混合醇（ROH）－十六烷基三甲基溴化铵（CTMAB）－正十二烷（C12H26）载金有机相中反萃金，考察了平衡时间，PH值，反萃剂浓度，温度等因素对反萃率的影响及有机相的循环使用，绘制了反萃等温曲线，结果表明，NH4SCN对金具有较高反萃率，可顺利实现金的反萃。     

MiR-10a in Pancreatic Juice as a Biomarker for Invasive Intraductal Papillary Mucinous Neoplasm by miRNA Sequencing

New biomarkers are needed to further stratify the risk of malignancy in intraductal papillary mucinous neoplasm (IPMN). Although microRNAs (miRNAs) are expected to be stable biomarkers, they can vary owing to a lack of definite internal controls. To identify universal biomarkers for invasive IPMN, we performed miRNA sequencing using tumor-normal paired samples. A total of 19 resected tissues and 13 pancreatic juice samples from 32 IPMN patients were analyzed for miRNA expression by next-generation sequencing with a two-step normalization of miRNA sequence data. The miRNAs involved in IPMN associated with invasive carcinoma were identified from this tissue analysis and further verified with the pancreatic juice samples. From the tumor-normal paired tissue analysis of the expression levels of 2792 miRNAs, 20 upregulated and 17 downregulated miRNAs were identified. In IPMN associated with invasive carcinoma (INV), miR-10a-5p and miR-221-3p were upregulated and miR-148a-3p was downregulated when compared with noninvasive IPMN. When these findings were further validated with pancreatic juice samples, miR-10a-5p was found to be elevated in INV (p = 0.002). Therefore, three differentially expressed miRNAs were identified in tissues with INV, and the expression of miR-10a-5p was also elevated in pancreatic juice samples with INV. MiR-10a-5p is a promising additional biomarker for invasive IPMN.     

Reciprocal bi-directional generative adversarial network for cross-modal pedestrian re-identification

To improve the accuracy of cross-modal pedestrian re-identification,a reciprocal bi-directional generative adversarial network-based method is proposed.First,we build two generative adversarial networks to generate cross-modal heterogeneous images.Second,an associated loss is designed to pull close the distribution of features in latent space during the image translation between visible and infrared images so as to help the networks generate fake heterogeneous images that have high similarity with the real images.Finally,by concatenating the original and generated heterogeneous pedestrian images into the discriminative feature extraction network,images from different modalities can be unified into a common modality,thus suppressing the cross-modal gap.Representation learning and metric learning are utilized to achieve more discriminative pedestrian features.Comparative experiments are conducted on SYSU-MM01 and RegDB datasets to analyze the accuracy with different loss functions.Compared with other state-of-the-art cross-modal pedestrian re-identification methods,the proposed method achieves a higher accuracy and stronger robustness.     

Method for discrimination of false targets in multistation radar systems based on the deep neural network

For the existing jamming discrimination methods for multistation radar systems,only the single feature of target echo space correlation is utilized as the metric,which leads to insufficient comprehensiveness of feature extraction,so that effectiveness and universality are insufficient for the discrimination algorithm.In this paper,an identification method in multistatic radar systems based on the deep neural network is proposed.This method combines the characteristics of multistatic radar systems cooperative detection technology,which has many available resources and strong scheduling ability in space,time and frequency domain,with the strong model learning and feature representation ability in the process of information processing on the deep neural network,so that it can effectively apply to the field of anti-deception jamming.Full use is made of unknown information about echo data to obtain more multi-dimensional,more comprehensive,more complete and deeper feature differences besides correlation,so as to achieve a better jamming discrimination effect.Simulation results show that the proposed method can effectively reduce the influence of noise and pulse number on the jamming discrimination performance.At the same time,the limitation of the target echo correlation coefficient on anti-jamming technology under nonideal conditions is alleviated,which broadens the boundary conditions of the application process.     

银耳多糖超声波提取工艺优化及抗BV2细胞炎症的作用研究

以银耳为原料, 采用超声波辅助热水浸提法提取银耳多糖, 以多糖提取率为指标, 以超声波功率、提取料液比、提取温度和提取时间为影响因素, 设计单因素试验, 并通过正交试验得到较佳提取工艺。通过用TRPV1(Transient Receptor Potential Vanilloid1)试剂盒检测了小胶质细胞TRPV1的释放水平, 与脂多糖(Lipopolysaccharide, LPS)对照组相比, 不同质量浓度的银耳多糖组(62.5, 250, 1000 μg/mL)均能不同程度地抑制TRPV1的释放量。说明了银耳多糖对于BV2小胶质细胞炎症具有良好的抑制作用。     

面向对象轮廓约束GGVF Snake的建筑物边界优化方法

分析高分辨率遥感影像中建筑物的特征和常用方法提取建筑物存在边界漏检误检导致的边界不规则等问题,提出面向对象轮廓约束广义梯度矢量流（GGVF）Snake模型的建筑物边界优化方法. 在利用分类法获取建筑物轮廓的初始结果基础上,自动提取每个建筑物轮廓线作为GGVF Snake的初始轮廓线,获取各轮廓外接矩形进行对象裁剪,提取每个建筑物的子图对象. 对每个子图对象进行Canny边缘检测,结合Hough变换提取直线特征,输入到广义梯度矢量流模型的迭代求解中快速最小化能量函数,实现对象级建筑物轮廓的精确优化. 实验结果表明,利用提出的方法能够自动获取初始建筑物的轮廓信息,提高优化的自动化程度;基于对象级的边缘检测与直线特征的加入,有助于GGVF Snake快速拟合,准确地平滑建筑物边缘且准确度高. 相对于其他对比方法,本文方法的轮廓优化总体精度和综合值均有提升,可以作为有效提升分类原理获取的建筑物轮廓的优化后处理手段,提高了建筑物提取的精度.     

PSO-IIIA(3)亚砜萃取Pt(IV)的机理

研究表明,在酸性介质中,亚砜萃取Pt(Ⅳ)的主要机理为酸性缔合,其萃合物组成为H_2PtCl_6·6H_2O·2PSO。并计算了亚砜萃取Pt(Ⅳ)的萃取平衡常数。     

图像中的文本定位技术研究综述

介绍了图像中文本定位技术的研究现状,依据定位所利用的特征进行分类,分析了其中几种典型算法,并对文本定位算法的性能评价标准进行了深入讨论。     

多信息源下本体自动抽取的实现*

以关系型数据库、XML文件、HTML文件、一般文档为信息源,运用不同的方法分别将多信息源映射为概念图,并按照拟定的概念逻辑结构进行统一存储,最后运用抽取算法实现本体的自动抽取.