Voltammetric determination of hafnium with calconcarboxylic acid

The interaction of Hf(IV) with calconcarboxylic acid (1-(2-hydroxy-4-sulfo-1-naphtylazo)-3-naphtolic acid) was investigated by cyclic voltammetry varying various factors (pH, ionic strength, contents of ethanol and scan rate). Optimal conditions of Hf(IV) determination in the presence of calconcarboxylic acid were found: acetic buffer solution pH 2.6, scan rate 0.5 Vs⁻¹. The detection limit of Hf(IV) concentration was 2.46 × 10⁻⁷ mol L⁻¹. The influence of foreign ions on the Hf(IV) determination was studied. It was established that some metals like cadmium, nickel, zinc, copper and titanium could be determined with Hf(IV) simultaneously. The method of voltammetric determination of hafnium was tested on model solutions and used in the determination of Hf(IV) in terbium-base alloy.     

Luminaolide, a novel metamorphosis-enhancing macrodiolide for scleractinian coral larvae from crustose coralline algae

A new metamorphosis-enhancing macrodiolide, luminaolide (1), was isolated from the crustose coralline algae (CCA) Hydrolithonreinboldii. Its structure was determined by spectroscopic analysis. A fraction (1.30 μg/mL) eluted with 80% aqueous MeOH by ODS gel column chromatography of the same CCA extract induced larval metamorphosis (25.9 ± 7.4%) against Leptastrea purpurea, and its metamorphosis-inducing activity was further enhanced to 92.6 ± 2.9% with the addition of 1 (25.6 ng/mL).     

黄河三角洲自然保护区植物与土壤因子关系及生态位分析

以黄河三角洲自然保护区36个样地22个植物种的重要值(IV)为基础, 使用双向指示种分析法(TWINSPAN)将其分为6个群落类型: 翅碱蓬群落、柽柳?翅碱蓬群落、柽柳?芦苇+碱蓬群落、芦苇群落、旱柳?芦苇群落、人工林群落(刺槐林和白腊林)。在群落演替梯度上, 土壤pH逐渐增加, 土壤盐分呈下降趋势, 土壤含水量最大和最小值分别出现在芦苇群落和人工林群落, 土壤有机碳和总氮总体逐渐增加, 土壤碳氮比呈下降趋势。通过典范对应分析(CCA)发现, 影响黄河三角洲植物种分布的关键土壤环境因子是土壤盐分和pH, 植物物种多样性与土壤盐分呈显著负相关, 与土壤pH有正相关关系。在土壤pH和盐分梯度上的生态 位分析发现, 优势种柽柳、芦苇和翅碱蓬的生态位宽度较大, 植物种之间的生态位重叠一般较小, 说明物种在土壤pH和盐分梯度上出现了生态位分化。     

Mn-CCA-CPC浊点萃取火焰原子吸收法测定水环境中锰

使用浊点萃取的方法分离富集水环境中痕量的锰离子,创新性地使用钙羧酸(CCA)与阳离子表面活性剂氯代十六烷基吡啶(CPC)为络合剂与锰离子形成更稳定的三元络合物,以TritonX-114为萃取剂浊点萃取分离富集水环境中的痕量锰,同时采用火焰原子吸收法进行测定,建立了测定水环境中痕量锰的新方法。对影响浊点萃取的主要因素如pH、TritonX-114的用量、CCA用量、CPC用量、NaCl用量、共存离子影响、加热温度、加热时间、离心时间、冰浴时间进行了优化。在优化的实验条件下,Mn2+-CCA-CPC体系分离富集锰有很好地效果,能够很好的克服基体干扰,浊点萃取体系在锰含量0.3～1.5 mg·L-1时呈现良好的线性关系。方法的灵敏度为1.939 mg·L-1,精密度为0.39%,检出限为(3σ)0.27 μg·L-1。测定自来水与井水中的锰含量的结果为33.5和64.5 μg·L-1,同时做加标回收实验,回收率在99.1%～101.5%之间。符合国际标准,因此该方法能够成功应用到水环境中的锰含量的测定,结果令人满意。     

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.     

台北地区冬季降水相似预报试验

为研究相似预报技术在低纬度地区的应用问题,利用典型相关分析方法,对台北冬季降水与降水发生前12h西风带上游关键区各高度层气象要素场的相关性进行了分析,定量化地确定了台北冬季降水的预报因子。在此基础上,根据综合相似离度构造多级相似方法,建立了台北冬季有无降水的预报模型;同时利用该模型对1995年冬季降水作了试报。结果表明,该模型可以综合考虑因子相关问题,预报效果较好。     

近年来CCA在气候分析与气候预测中的应用

详细论述了CCA方法应用于气候分析与预测中所取得的成果。着重分析了CCA方法在理论上和实践上所存在的优缺点。给出了CCA在我国汛期预报中应用的实例。     

内蒙古草原植物群落分布格局及其主导环境因子解释

基于内蒙古地区草原植物群落样带调查,采用双向指示种分析(TWINSAPN)、除趋势对应分析(DCA)、多元回归树(MRT)和典范对应分析(CCA)等方法,探讨了群落组成、分布格局及影响其分异的主导环境因子.结果如下:1)TWINSPAN将273个群落调查样方划分为8个群系级草原类型,各自代表并反映出该地区草原群落的特点;2)DCA排序结果间接地反映出群落类型与环境梯度之间的关系,CCA排序结果较好地展示了各群落类型中优势物种的生境特征,并进一步展示了该地区草原类型、主要物种与环境因子的响应关系;3)DCA、MRT及CCA结果均表明,气候因素对于该地区草原植被的地域分异影响作用较大,其中水分因子的影响十分突出,特别是最湿月、最暖季节与最冷季节降水,是构成内蒙草原植被类型区域分异的决定性因素;4)MRT与CCA结合使用能进一步挖掘出更多的环境信息,为群落分布格局的形成原因提供更深入更全面的环境解释.     

基于CCA排序的霍山森林植物功能型划分

采用群落生态学的调查方法,在霍山七里峪设置80个样方并采集土壤样品.通过计算重要值和测定土壤理化性质(pH值、含水量、有机质含量、N、P、K含量),建立样方×物种矩阵和样方×环境矩阵,用CCA排序划分植物功能型.结果表明,随着海拔等环境因子的变化,可以将七里峪天然次生林划分为:F1山核桃Carya cathayensi...     

New Abnormal Cervical Cell Detection Method of Multi-Spectral Pap Smears

Considering the problem of traditional cervical cancer detection method that brings high false negative rate （FNR） and high false positive rate （FPR）, a new abnormal cervical cells detection method of multi-spectral Pap smear is proposed in this thesis, on the basis of multi-spectral microscopic imaging technology and computer automotive recognition technology. At first, image in a specific wave band is segmented according to the relationship between intensity and spectrum of each pixel. Then, multi-spectral features of each pixel are extracted making use of improved cosine correlation analysis （CCA） algorithm. Combined with the characteristic of each cell＇s area, final definition is made. Experiments have proved the new approach could identify abnormal cells efficiently as well as lower FNR and FPR.