HPLC method development and validation of cytotoxic agent phenyl‐heptatriyne in Bidens pilosa with ultrasonic‐assisted cloud point extraction and preconcentration

Extraction and pre‐concentration of a bioactive marker compound, phenyl‐1,3,5‐heptatriyne from Bidens pilosa, prior to HPLC has been demonstrated using both organic and ecofriendly solvents. Non‐ionic surfactants, viz. Triton X‐100, Triton X‐114 and Genapol X‐80, were used for extraction. No back‐extraction or liquid chromatographic steps were required to remove the target phytochemical from the surfactant‐rich extractant phase. The optimized cloud point extraction procedure has been shown to be a potentially useful methodology for the preconcentration of the target analyte, with a preconcentration factor of 4–99. Moreover, the method is simple, sensitive, rapid and consumes lesser solvent than traditional methods. An isocratic chromatographic separation and quantitation was accomplished on a C₁₈ column with acetonitrile–acidified aqueous as mobile phase at a flow rate of 1.0 mL/min, UV detection at 254 nm and specificity with photo diode‐array detector (PDA) and MS. Under the optimum experimental conditions recovery was satisfactory (99.18–100.33%) without interference from the surfactant. The method seems to be reliable with intraday precision and interday precision below 2.0%. Good linearity was obtained in the working range from 7.5 to 30 µg/mL with correlation coefficient >0.99. The limits of detection and quantitation were 1.84 and 6.13 µg/mL, respectively. The method was validated following international guidelines and successfully applied for quantitative assays of cytotoxic compound phenyl‐1,3,5‐heptatriyne in Bidens pilosa. Copyright © 2010 John Wiley & Sons, Ltd.     

利用三维激光扫描数据和KNNS-ICP算法进行变形分析

利用KNNS-ICP算法对扫描物体的变形量进行分析,提取目标物变形量的6个参数,即3个旋转变量和3个平移变量,充分利用了扫描点云中的几何信息,通过对目标物点云的整体分析来提取变形量,避免了利用单点进行变形分析的偶然性.ICP算法主要从不同目标物点云数据的获取、变形前和变形后点云的整体配准、对全局配准后的每个目标物点云进行局部匹配来获取变形量等3个阶段来进行.通过理想状况下的实验模型及非理想状况下的实验模型进行实例分析,论证了利用该方法进行变形监测的可行性.     

云计算环境下政府信息资源开发利用研究

简述了云计算的概念及内涵,分析了云计算环境下政府信息资源开发利用的经济可行性和技术可行性,在此基础上提出了云计算环境下政府信息资源开发利用的新模式。     

The synthesis and self‐assembly of ABA amphiphilic block copolymers containing styrene and oligo(ethylene glycol) methyl ether methacrylate in dilute aqueous solutions: Elevated cloud point temperatures for thermoresponsive micelles

A series of ABA amphiphilic triblock copolymers possessing polystyrene (PS) central hydrophobic blocks, one group with “short” PS blocks (DP = 54–86) and one with “long” PS blocks (DP = 183–204) were synthesized by atom transfer radical polymerization. The outer hydrophilic blocks were various lengths of poly(oligoethylene glycol methyl ether) methacrylate, a comb‐like polymer. The critical aggregation concentrations were recorded for certain block copolymer samples and were found to be in the range circa 10⁻⁹ mol L⁻¹ for short PS blocks and circa 10⁻¹² mol L⁻¹ for long PS blocks. Dilute aqueous solutions were analyzed by transmission electron microscopy (TEM) and demonstrated that the short PS block copolymers formed spherical micelles and the long PS block copolymers formed predominantly spherical micelles with smaller proportions of cylindrical and Y‐branched cylindrical micelles. Dynamic light scattering analysis results agreed with the TEM observations demonstrating variations in micelle size with PS and POEGMA chain length: the hydrodynamic diameters (D_H) of the shorter PS block copolymer micelles increased with increasing POEGMA block lengths while maintaining similar PS micellar core diameters (D_C); in contrast the values of D_H and D_C for the longer PS block copolymer micelles decreased. Surface‐pressure isotherms were recorded for two of the samples and these indicated close packing of a short PS block copolymer at the air–water interface. The aggregate solutions were demonstrated to be stable over a 38‐day period with no change in aggregate size or noticeable precipitation. The cloud point temperatures of certain block copolymer aggregate solutions were measured and found to be in the range 76–93 °C; significantly these were ∼11 °C higher in temperature than those of POEGMA homopolymer samples with similar chain lengths. © 2008 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 46: 7739–7756, 2008     

一种基于 Pareto 云隶属度的 MOPSO 算法

为定量解决非支配解排序问题,并兼顾多目标粒子群优化算法（multi-objective particle swarm optimization,MOPSO）的收敛性和多样性,提出了一种基于 Pareto 云隶属度的 MOPSO 算法。利用 Logistic 混沌映射优化种群的初始空间分布并融合布谷鸟搜索（cuckoo search,CS）指导粒子跳出局部陷阱,以增强算法的全局寻优能力。首次提出云向量评价 Pareto 最优解集方法,采用云隶属度评价准则对粒子适应度值进行量化评价。依据云隶属度选取个体最优和群体最优,平衡全局开发与开采,进而实现外部档案维护。测试函数集 ZDT 的实验结果表明,改进算法在收敛性和多样性方面较 MOPSO 和 NSGA-Ⅱ有一定优势。     

机器学习在车载激光点云分类中的应用研究

点云信息分类提取与利用是车载移动测量系统的关键技术,提高车载激光点云分类的智能化已成为当代信息科学技术发展所面临的重要问题.从分析点云的特征入手,采用机器学习方法对车载激光点云的行道树提取进行了大量实验研究.首先,在点云原始特征的基础上,根据其局部几何特征及空间分布,构造了由三维空间位置、回波强度、颜色值、法向量、单位投影密度、残差及回波强度维度内的残差等17个特征值组成的点云高维特征向量,然后采用支持向量机和人工神经网络两种机器学习方法分别对行道树点云进行提取实验.实验中采用了粒子群优化算法对支持向量机进行参数寻优,采用所构造的由17个特征值构成的特征向量对点云进行机器学习,两种学习方法的行道树点云分类精度分别可以达到99.75%和99.25%.实验结果表明,采用机器学习的方法对于提高车载激光点云分类自动化程度和智能化水平具有重要意义和作用.     

基于三维激光扫描的岩体结构面信息提取

以地下矿山工程中的岩体结构面信息为研究对象,应用三维激光扫描技术与地质构造分析相结合的研究手段,将岩体结构面信息提取和三维激光扫描技术进行系统集成.利用Z+Flasercontrol点云数据处理程序和Geomagic Studio逆向工程软件对围岩点云信息进行预处理.基于Java语言开发了由点云数据获取结构面信息的点云数据处理系统,并应用Dips地质构造分析软件分析结果,结合人工测量数据验证此系统的准确性与可行性.研究结果表明,和传统方法相比,该方法具有快速、自动、高精度和远距离测量的优点.     

云计算中虚拟网络服务的性能测量及运营实践

在云计算中,虚拟网络服务是核心的服务之一,用户可以在云环境中创建属于自己的虚拟网络部件,而不用考虑底层的物理网络环境.OpenStack作为目前最流行的开源云计算平台,可以通过不同的方法和技术来实现虚拟网络服务.本文针对当前的OpenStack版本,对其虚拟网络服务不同的实现方法和技术进行了详细的分析与性能测量,并介绍了上海交通大学云计算平台的运营实践现状,为高校建设云计算虚拟网络服务提供了参考.     

基于卡通纹理分解和NSCT的卫星云图边缘提取

为方便提取云图的缓变边缘,提出了基于卡通纹理分解和无下采样轮廓波变换(NSCT)的卫星云图边缘特征提取算法。首先使用基于张量扩散的卡通纹理分解对卫星云图进行预处理,减小云图中噪声和纹理的影响,将云的缓变边缘转化为阶跃边缘;然后依据NSCT域中高频系数的正负关系和相关性进行边缘特征提取。通过实验,将文中算法分别与基于投影卡通纹理分解的预处理算法、小波多尺度积、NSCT模极大值和NSCT多尺度积等边缘特征提取算法进行比较。结果表明,本算法可更准确地提取云的边缘,且受噪声和纹理的影响小,边缘的连续性更好,为准确地进行云分类提供了保障。     

基于云计算安全体系架构的研究

随着计算机与互联网的发展,云计算与物联网、智慧地球等概念一样备受关注。目前云计算已从产生到实质发展阶段,因此云计算的安全问题备受关注,近年来成为计算机学科领域的研究热点。在此背景下,首先介绍了云计算的概念、特点及服务模式,并在此基础上总结了云计算所面临的安全隐患,最后提出了云计算中确保信息安全的解决方案。