Low‐memory iterative density fitting

A new low‐memory modification of the density fitting approximation based on a combination of a continuous fast multipole method (CFMM) and a preconditioned conjugate gradient solver is presented. Iterative conjugate gradient solver uses preconditioners formed from blocks of the Coulomb metric matrix that decrease the number of iterations needed for convergence by up to one order of magnitude. The matrix‐vector products needed within the iterative algorithm are calculated using CFMM, which evaluates them with the linear scaling memory requirements only. Compared with the standard density fitting implementation, up to 15‐fold reduction of the memory requirements is achieved for the most efficient preconditioner at a cost of only 25% increase in computational time. The potential of the method is demonstrated by performing density functional theory calculations for zeolite fragment with 2592 atoms and 121,248 auxiliary basis functions on a single 12‐core CPU workstation. © 2015 Wiley Periodicals, Inc.     

A consensus successive projections algorithm – multiple linear regression method for analyzing near infrared spectra

The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named “consensus SPA-MLR” (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques.     

Development of models for prediction of the antioxidant activity of derivatives of natural compounds

Antioxidants are important for maintaining the appropriate balance between oxidizing and reducing species in the body and thus preventing oxidative stress. Many natural compounds are being screened for their possible antioxidant activity. It was found that a mushroom pigment Norbadione A, which is a pulvinic acid derivative, shows an antioxidant activity; the same was found for other pulvinic acid derivatives and structurally related coumarines. Based on the results of in vitro studies performed on these compounds as a part of this study quantitative structure–activity relationship (QSAR) predictive models were constructed using multiple linear regression, counter-propagation artificial neural networks and support vector regression (SVR). The models have been developed in accordance with current QSAR guidelines, including the assessment of the models applicability domains. A new approach for the graphical evaluation of the applicability domain for SVR models is suggested. The developed models show sufficient predictive abilities for the screening of virtual libraries for new potential antioxidants.     

Modulating ion current rectification generating high energy output in a single glass conical nanopore channel by concentration gradient

Inspired by biological systems that have the inherent skill to generate considerable bioelectricity from the salt content in fluids with highly selective ion channels and pumps on cell membranes,herein,a fully abiotic,single glass conical nanopores energy-harvesting is demonstrated.Ion current rectification(ICR)in negatively charged glass conical nanopores is shown to be controlled by the electrolyte concentration gradient depending on the direction of ion diffusion.The degree of ICR is enhanced with the increasing forward concentration difference.An unusual rectification inversion is observed when the concentration gradient is reversely applied.The maximum power output with the individual nanopore approaches10~4pW.This facile and cost-efficient energy-harvesting system has the potential to power tiny biomedical devices or construct future clean-energy recovery plants.     

基于内源性致香物质和化学计量学的烟草感官评价研究

采用主成分分析法结合遗传算法和神经网络,建立了基于烟草内源性致香物质的感官质量评价预测模型。利用气相色谱-质谱(GC-MS)技术对超临界萃取-分子蒸馏所得烟草精油中的内源性致香组分进行定性定量分析,汇总各类致香指标后,对其进行主成分分析;以提取所得5个主成分的得分作为输入变量,感官评吸分数作为输出变量,分别使用标准BP神经网络和遗传算法(GA)优化的BP神经网络建立预测模型。对比实验结果表明,GA优化后的模型预测效果更优,其预测值与实验值间的相关系数为0.96,预测均方根误差为1.81,说明GA-BP模型具有更好的拟合能力和预测能力,该模型能有效地预测烟草精油的感官品质。     

Gradient polymer networks formed by photopolymerization with self‐floating polysiloxane‐containing nanogel

Polysiloxane‐containing nanogels can be used as a fast, convenient and environmentally friendly method to control gradient photopolymerization and to obtain gradient polymer network because of its self‐floating feature. The chain length of polysiloxane is a key factor that influences the self‐floating capability of the polysiloxane‐containing nanogel. This paper reports a series of nanogels compositions synthesized with methacrylate‐modified polysiloxanes with different chain lengths, urethane dimethacrylate (UDMA) and isobornyl methacrylate (IBMA) at a molar ratio of 10:20:70 in the presence of a thiol chain transfer agent. The effect of polysiloxane chain length on self‐floating capability of the nanogel and gradient polymer network was researched. The results show that polysiloxane chain length is the main driving force for the self‐floating capability of the nanogels. The nanogel with long polysiloxane chain length exhibits good self‐floating capability in the monomer–polymer matrix because of the lower surface tension of polysiloxane. Furthermore, the gradient polymer network containing the nanogel with long polysiloxane chain length presents lower dispersion surface energy and greater hardness and thermostability. Copyright © 2016 John Wiley & Sons, Ltd.     

极长与极短的铜铜距离-吡唑环三核亚铜配合物亲铜作用本质及发光调控

本文结合近年来亲金属作用研究领域的进展,针对本课题组在环三核亚铜配合物方面的最新研究成果,讨论了两个铜(I)–铜(I)作用相差悬殊的体系。一是通过构筑具有环三核单元的三棱柱笼状配合物,确认在正堆积模式下即使环三核单元之间亲铜作用极弱依然可以在磷光发射态中产生强亲铜作用,且通过配体的预留配位点与Cu_2I_2簇连结从而得到自校准大范围发光温度计;二是通过与亲铜作用正交的Br―Br卤键,实现环三核亚铜配合物前所未有的极短铜(I)–铜(I)距离,通过各种电子结构分析方法研究其本质。结果表明即使当铜(I)–铜(I)距离很接近铜的范德华半径和时,其本质依然为闭壳层作用,而Br―Br作用总为闭壳层作用,且该体系中最强的Br―Br作用很好地体现出一个Br原子的σ穴和另一个Br原子的负静电势区域的匹配性。     

高选择性高效液相色谱-电化学检测法测定丹参中5种酚类物质

发展了一种高灵敏度的高效液相色谱-电化学检测（HPLC-ECD）方法,用于丹参中5种酚类物质的定量分析。为了获得较高的检测灵敏度,对流动相的pH、缓冲溶液类型和浓度、有机相种类和梯度以及流速做了系统研究。在较低的pH（2.8）、较低的缓冲盐浓度（20 mmol/L NaH₂PO₄）和较缓的乙腈梯度下,以0.2 mL/min流速可为5种酚类的分离检测提供较好的分离度和较高的检测灵敏度。在获得优化的分离参数后,将其用于14批丹参药材中5种酚类物质的定量分析。结果表明,该方法可获得较好的回收率（>95%）、较宽的线性范围（高达4个数量级）、较好的重复性（RSD<4.01%）和较高的灵敏度（咖啡酸的LOQ低至1.5μg/L）。与紫外检测方法相比,ECD检测方法具有更高的选择性,可减少非抗氧化活性的物质的干扰。     

基于高通量解析算法的复杂样品重叠气相色谱-质谱信号的快速分析

化学计量学算法为重叠气相色谱-质谱(GC-MS)信号的解析提供了有效手段,但其在计算过程中一般需要将数据进行分段处理,然后只对信号的某些区间进行解析,难以实现真正意义上的高通量分析。该文结合移动窗口目标转换因子分析(MWTTFA)和非负免疫算法(NNIA),建立了一种高通量解析方法。首先,根据所有可能存在的目标组分的标准质谱信息,利用MWTTFA检验复杂信号中存在的组分,并确定目标组分的质谱信息和洗脱时间区域。以得到的质谱信息作为后续计算的输入值,利用NNIA解析得到相应的色谱信息。采用快速升温程序对17种和42种农药混合标准样品的GC-MS信号进行分析,利用所建立的方法可在10 min内得到全部组分的色谱和质谱信息。     

Computational method for optimal control of switched systems with input and state constraints

In this paper, we consider an optimal control problem of switched systems with input and state constraints. Since the complexity of such constraint and switching laws, it is difficult to solve the problem using standard optimization techniques. In addition, although conjugate gradient algorithms are very useful for solving nonlinear optimization problem, in practical implementations, the existing Wolfe condition may never be satisfied due to the existence of numerical errors. And the mode insertion technique only leads to suboptimal solutions, due to only certain mode insertions being considered. Thus, based on an improved conjugate gradient algorithm and a discrete filled function method, an improved bi-level algorithm is proposed to solve this optimization problem. Convergence results indicate that the proposed algorithm is globally convergent. Three numerical examples are solved to illustrate the proposed algorithm converges faster and yields a better cost function value than existing bi-level algorithms.