液膜富集－原子吸收法测定碱金属盐中的铜、钴、镍

液膜富集－原子吸收法测定碱金属盐中的铜、钴、镍于惠芬，王爱霞，王云平，徐书绅，杨光强（长春师范学院化学系长春　１３００３２）关键词液膜富集，铜，钴，镍，原子吸收法应用乳状液膜富集痕量金属，在分析化学领域是一种新方法，已有一些文献报道［１－３］。本文在...     

Total synthesis of (+/-)-galanthamine

[reaction: see text] A practical and efficient total synthesis of (+/-)-galanthamine was achieved from commercially available materials through a novel approach, in which the construction of its core structure and the special allylic alcohol group were based on a successive semipinacol rearrangement/desilyation/cyclization and Saegusa-Ito oxidation, respectively.     

用分光计测量三棱镜的折射率的几个问题

本文对分光计的调整、三棱镜的折射率的测量及数据处理过程中常出现的几个问题作详细的分析,提出了最简单的调节方法,澄清了一些易混淆的问题,可以避免盲目的操作,达到该实验的训练目的。     

双修饰电极零电流示波双电位滴定的研究——混合酸的连续滴定

本文采用双聚苯胺修饰电极进行示波双电位滴定,用示波器直接指示△E的变化,以示波器荧光屏上荧光点最大位移指示终点。方法简便、快速、直观,用于乙二醛母液中混合酸的连续滴定,结果满意。     

Synthesis and application of amberlite XAD-2 functionalized with dithizone for field preconcentration and separation of trace cadmium in seawater.

A chelating resin coupling Amberlite XAD-2 functionalized with dithizone is synthesized and characterized. Dissolved cadmium is field-preconcentrated using a minicolumn packed with the synthesized resin and determined by flame atomic absorption spectrometry. Five experimental variables are evaluated. The enrichment factor of 416 is obtained for 50 mL of sampling volume, and the detection limit (3 sigma) of the procedure is 6.7 ng L(-1). The precision (RSD) for 11 independent determinations is 1.97%. This method has been successfully applied to the determination of cadmium in natural seawater samples.     

Human cognitive paradigm and its application in semi-supervised learning

In many practical data mining applications such as web page classification, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning algorithms such as Tri-training have attracted much attention. However, mislabeling the unlabeled data during the learning process is an inevitable problem and harms the performance improvement of the hypothesis. To solve this problem, a novel human cognitive paradigm is constructed for semi-supervised learning in this paper. In detail, based on local distribution of feature space, the majority voting scheme is substituted by an estimation of the probability of sample to belong to a certain class as an efficient strategy for data editing. It considers the form of the underlying probability distribution in the neighborhood of a point to identify and remove the mislabeled data. Validation of the proposed method is performed with extensive experiments. Results demonstrate that compared with Tri-training method, our method can more effectively and stably exploit unlabeled data to enhance the learning performance.