Comparison of the retention behaviour of phenol derivatives on porous graphitized and octadecylsilica columns

Summary The retention of 22 ring-substituted phenol derivatives on porous graphitized carbon (PGC) (eluents: acetonitrile — water and methanol — water mixtures) and on octadecylsilica (ODS) (eluents: methanol — 0.025 M KH₂PO₄ mixtures) was determined, and the relationship between retention and physicochemical parameters were evaluated by principal component analysis followed by two-dimensional nonlinear mapping and by cluster analysis as well as by canonical correlation analysis. Calculations proved that marked differences can be detected between the retention characteristics of PGC and ODS columns, and the electronic parameters of phenol derivatives have the highest impact on their retention. The comparison of various multivariate mathematical-statistical methods indicated that principal component analysis followed by two dimensional non-linear mapping is the most appropriate method for the evaluation of large data matrices in RP-HPLC.     

Fuzzy theory in analytical chemistry

The concept of fuzzy theory is described in order to provide the analyst with the means for dealing with vague statements, uncertain observations or the fuzziness of human perception and interpretation, in general. In a theoretical part, basic notions of fuzzy theory are given, such as types of membership functions, operations with fuzzy sets, definitions of fuzzy numbers, points, functions, and relations, and the use of linguistic variables. The difference between fuzziness and probability is outlined. The applications section demonstrates advantages of fuzzy theory methods compared to common mathematical methods with respect to data handling for calibration of analytical methods, to classification of Chromatographie and spectroscopic patterns, to component identification and multicomponent analysis, and to designing fuzzy expert systems for selection of analytical procedures.     

Applications of maximum likelihood principal component analysis: incomplete data sets and calibration transfer

The application of a new method to the multivariate analysis of incomplete data sets is described. The new method, called maximum likelihood principal component analysis (MLPCA), is analogous to conventional principal component analysis (PCA), but incorporates measurement error variance information in the decomposition of multivariate data. Missing measurements can be handled in a reliable and simple manner by assigning large measurement uncertainties to them. The problem of missing data is pervasive in chemistry, and MLPCA is applied to three sets of experimental data to illustrate its utility. For exploratory data analysis, a data set from the analysis of archeological artifacts is used to show that the principal components extracted by MLPCA retain much of the original information even when a significant number of measurements are missing. Maximum likelihood projections of censored data can often preserve original clusters among the samples and can, through the propagation of error, indicate which samples are likely to be projected erroneously. To demonstrate its utility in modeling applications, MLPCA is also applied in the development of a model for chromatographic retention based on a data set which is only 80% complete. MLPCA can predict missing values and assign error estimates to these points. Finally, the problem of calibration transfer between instruments can be regarded as a missing data problem in which entire spectra are missing on the ‘slave’ instrument. Using NIR spectra obtained from two instruments, it is shown that spectra on the slave instrument can be predicted from a small subset of calibration transfer samples even if a different wavelength range is employed. Concentration prediction errors obtained by this approach were comparable to cross-validation errors obtained for the slave instrument when all spectra were available.     

Ligand-protein docking using a quantum stochastic tunneling optimization method

A novel hybrid optimization method called quantum stochastic tunneling has been recently introduced. Here, we report its implementation within a new docking program called EasyDock and a validation with the CCDC/Astex data set of ligand-protein complexes using the PLP score to represent the ligand-protein potential energy surface and ScreenScore to score the ligand-protein binding energies. When taking the top energy-ranked ligand binding mode pose, we were able to predict the correct crystallographic ligand binding mode in up to 75% of the cases. By using this novel optimization method run times for typical docking simulations are significantly shortened.     

Molekulares S=GeCl2 Matrix-IR-Untersuchungen und ab initio SCF-Rechnungen

The Molecule S?GeCl₂. Matrix IR Investigation and Ab initio SCF Calculation Molecular S?GeCl₂ is found in a matrix reaction between the high-temperature molecule Ge?S and Cl₂. A structure analog to that of phosgene can be derived from the isotopical shifts (⁷⁰Ge/⁷²Ge/⁷³Ge/⁷⁴Ge/⁷⁶Ge and ³⁵Cl/³⁷Cl) within the IR spectra. The normal coordinate analysis results for the Ge?S force constant a value of 4.21 mdyn/Å. The spectroscopic results are confirmed by ab initio SCF calculations.     

Optimisation of the connection between TA-MS systems together with improved data interpretation for TA-MS applications

TA-MS provides chemical and thermal response information. In order for this information to be meaningful sampling must be performed with minimum dead volume and without cold spots. Dead volume leads to response lag and peak broadening. Cold spots result in sample condensation, leading to inaccurate data and potentially inlet blockage. This paper describes a hot zone inlet system and 'in-situ' probe both of which provide fast response, zero dead volume sampling directly to the MS. The paper also discusses data interpretation methodologies and introduces 'soft ionisation' as a means of providing simplified analysis of complex mixtures. This revised version was published online in July 2006 with corrections to the Cover Date.     

Information content of data and variables and types of weighting in least-squares regression methods

Algorithms are given for evaluating the relative amount of useful information related to a particular parameter which is carried by individual data points and intervals of the variables. The algorithms provide an efficient means of using the information contained in a set of data. Applications to the optimization of weighting in regression methods are described. Several informational and combined informational-statistical types of weighting are studied as a means of improving the accuracy and precision of the parameters obtained by non-linear regression.     

Sampling Consensus of 2nd-Order Multi-Agent Systems Based on Time-Varying Topology北大核心CSCD

基于速度一致位移差保持不变的一致性概念,研究了二阶多智能体系统在时变拓扑下的采样一致性问题。首先,引入虚拟领导者,将具有时变拓扑结构的多智能体系统的采样一致性问题转换为误差系统的采样控制稳定性问题。其次,通过预估采样误差,研究采样误差对系统达到一致性的影响。最后,应用Lyapunov稳定性理论,分析所构造的误差系统的稳定性,并给出该误差系统最终稳定的充分条件。数值仿真结果验证了理论分析的有效性和正确性。     

带有随机初值的复值Ginzburg-Landau方程的弱平均动力学*

本文研究了无界域上的带有随机初值的复值Ginzburg-Landau方程.首先, 基于解过程的全局适定性, 建立了带有随机初值的Ginzburg-Landau方程的平均随机动力系统.然后, 证明了弱拉回平均随机吸引子的存在唯一性以及随机吸引子的周期性,并将其进一步推广到加权空间L²(?, L²_σ(R)).