A modification of the successive projections algorithm for spectral variable selection in the presence of unknown interferents

This work proposes a modification to the successive projections algorithm (SPA) aimed at selecting spectral variables for multiple linear regression (MLR) in the presence of unknown interferents not included in the calibration data set. The modified algorithm favours the selection of variables in which the effect of the interferent is less pronounced. The proposed procedure can be regarded as an adaptive modelling technique, because the spectral features of the samples to be analyzed are considered in the variable selection process. The advantages of this new approach are demonstrated in two analytical problems, namely (1) ultraviolet–visible spectrometric determination of tartrazine, allure red and sunset yellow in aqueous solutions under the interference of erythrosine, and (2) near-infrared spectrometric determination of ethanol in gasoline under the interference of toluene. In these case studies, the performance of conventional MLR-SPA models is substantially degraded by the presence of the interferent. This problem is circumvented by applying the proposed Adaptive MLR-SPA approach, which results in prediction errors smaller than those obtained by three other multivariate calibration techniques, namely stepwise regression, full-spectrum partial-least-squares (PLS) and PLS with variables selected by a genetic algorithm. An inspection of the variable selection results reveals that the Adaptive approach successfully avoids spectral regions in which the interference is more intense.     

一个由咪唑衍生物构筑的二重穿插的锌配位聚合物的合成、结构及荧光性质

以ZnCl2、H3IDC(咪唑-4,5-二羧酸)和bix(1,4-双(咪唑基-1-甲基)-苯)为原料,在水热条件下得到了1个新的二重穿插的三维层-柱状金属-有机框架结构的配位聚合物{[Zn3(IDC)(bix)1.5Cl3].0.25H2O}n(1),并通过元素分析、红外光谱、热重分析以及单晶X-射线结构分析对其组成和结构进行了表征。单晶X-射线结构分析表明,配合物1的晶体属于单斜晶系,P21/c空间群,a=1.158 44(17)nm,b=1.088 75(16)nm,c=2.391 7(3)nm,β=96.835(2)°,V=2.995 1(8)nm3,Z=4,Dc=1.813 g.cm-3,F(000)=1 638,对于4 772个可观测点(I>2σ(I)),最终残差因子R1=0.037 9,wR2=0.089 6。在配合物1中,每个IDC3-分别桥联4个锌(Ⅱ)离子形成一维锯齿形链状结构,一维链通过cis-μ2-bix的2个氮原子连接形成二维网状结构,相邻的二维层之间再通过trans-μ2-bix的2个氮原子进一步连接形成了二重穿插的三维层-柱状金属-有机框架结构。固体室温荧光测试结果表明,配合物1在波长为400 nm的光激发下于468 nm处出现强烈的荧光发射。     

Kinetic study of oxidative coupling of methane over Mn and/or W promoted Na_2SO_4/SiO_2 catalysts

A comprehensive kinetic model for oxidative coupling of methane(OCM)over Mn and/or W promoted Na2SO4/SiO2 catalysts was developed based on a micro-catalytic reactor data.The methane conversion and ethylene,ethane,carbon monoxide and carbon dioxide selectivities were obtained in a wide operating condition range of 750 - 825-C,CH4/O2=2.5 - 10 and contact time=267 - 472 kg s m-3.Reaction networks of six models with different rate equation types were compared together.The kinetic rate parameters of each reaction network were estimated using linear regression or genetic algorithm optimization method(GA).A reaction network suggested by Stansch et al.for OCM was found to be the best one and was further used in this work.The suggested model could predict the experimental results of OCM reaction within a deviation range of ± 20%.     

三维荧光二阶校正法用于复杂基体样中川芎嗪和替米沙坦的快速定量分析

采用三维荧光光谱法,结合化学计量学中基于交替归一加权残差(ANWE)算法的二阶校正方法,实现了人血浆样品中川芎嗪和替米沙坦的同时快速定量分析.当组分数N取4时,ANWE算法解析获得的川芎嗪和替米沙坦的平均回收率分别为99.3士3.5%和96.8±2.0%.本文同时给出了此方法的检测限(LOD)与定量下限(LOQ),获得...     

Study on tribological behaviors of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites via a d‐optimal mixture design

This study analyzed variations of tribological behaviors that depend on the injection molding techniques during the blending of short glass fiber (SGF) and polytetrafluoroethylene (PTFE) reinforced polycarbonate (PC) composites. The proposed planning of blending experiments is to use a D‐optimal mixture design (DMD). The tribological behaviors of friction coefficient and wear mass loss were selected for discussion. Nine experimental runs, based on a DMD method, utilized to train the back‐propagation neural network (BPNN) and then the simulated annealing algorithm (SAA) approach is applied to search for an optimal mixture ratio setting. In addition, the result of BPNN integrating SAA was also compared with response surface methodology (RSM) approach. The results of confirmation experiment show that DMD, RSM, and BPNN integrating SAA method are effective tools for the optimization of reinforced process. Furthermore, the scanning electron microscope (SEM) images show that the abundant debris are peeled off from the matrix materials and predominant delamination mechanisms and plastic deformation are shown on the worn surface after tribological behavior tests. Copyright © 2010 John Wiley & Sons, Ltd.     

A hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization

Numerous optimization methods have been proposed for the solution of the unconstrained optimization problems, such as mathematical programming methods, stochastic global optimization approaches, and metaheuristics. In this paper, a metaheuristic algorithm called Modified Shuffled Complex Evolution (MSCE) is proposed, where an adaptation of the Downhill Simplex search strategy combined with the differential evolution method is proposed. The efficiency of the new method is analyzed in terms of the mean performance and computational time, in comparison with the genetic algorithm using floating-point representation (GAF) and the classical shuffled complex evolution (SCE-UA) algorithm using six benchmark optimization functions. Simulation results and the comparisons with SCE-UA and GAF indicate that the MSCE improves the search performance on the five benchmark functions of six tested functions.     

Finding the shortest paths by node combination

By repeatedly combining the source node’s nearest neighbor, we propose a node combination (NC) method to implement the Dijkstra’s algorithm. The NC algorithm finds the shortest paths with three simple iterative steps: find the nearest neighbor of the source node, combine that node with the source node, and modify the weights on edges that connect to the nearest neighbor. The NC algorithm is more comprehensible and convenient for programming as there is no need to maintain a set with the nodes’ distances. Experimental evaluations on various networks reveal that the NC algorithm is as efficient as Dijkstra’s algorithm. As the whole process of the NC algorithm can be implemented with vectors, we also show how to find the shortest paths on a weight matrix.     

Hybrid evolutionary algorithms in a SVR traffic flow forecasting model

Accurate urban traffic flow forecasting is critical to intelligent transportation system developments and implementations, thus, it has been one of the most important issues in the research on road traffic congestion. Due to complex nonlinear data pattern of the urban traffic flow, there are many kinds of traffic flow forecasting techniques in literature, thus, it is difficult to make a general conclusion which forecasting technique is superior to others. Recently, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. This investigation presents a SVR traffic flow forecasting model which employs the hybrid genetic algorithm-simulated annealing algorithm (GA-SA) to determine its suitable parameter combination. Additionally, a numerical example of traffic flow data from northern Taiwan is used to elucidate the forecasting performance of the proposed SVRGA-SA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA), back-propagation neural network (BPNN), Holt-Winters (HW) and seasonal Holt-Winters (SHW) models. Therefore, the SVRGA-SA model is a promising alternative for forecasting traffic flow.     

Solving quadratic convex bilevel programming problems using a smoothing method

In this paper, we present a smoothing sequential quadratic programming to compute a solution of a quadratic convex bilevel programming problem. We use the Karush-Kuhn-Tucker optimality conditions of the lower level problem to obtain a nonsmooth optimization problem known to be a mathematical program with equilibrium constraints; the complementary conditions of the lower level problem are then appended to the upper level objective function with a classical penalty. These complementarity conditions are not relaxed from the constraints and they are reformulated as a system of smooth equations by mean of semismooth equations using Fisher-Burmeister functional. Then, using a quadratic sequential programming method, we solve a series of smooth, regular problems that progressively approximate the nonsmooth problem. Some preliminary computational results are reported, showing that our approach is efficient.     

Function-valued Padé-type approximant via E-algorithm and its applications in solving integral equations

The function-valued Padé-type approximant (FPTA) was defined in the inner product space [8]. In this work, we choose the coefficients in the Neumann power series to make the inner product with both sides a function-valued system of equations to yield a scalar system. Then we express an FPTA in the determinant form. To avoid the direct computation of the determinants, we present the E-algorithm for FPTA based on the vector-valued E-algorithm given by Brezinski [4]. The method of FPTA via E-algorithm (FPTAVEA) not only includes all previous methods but overcomes their essential difficulties. The numerical experiment for a typical integral equation [1] illustrates that the method of FPTAVEA is simpler and more effective for obtaining the characteristic values and the characteristic functions than all previous methods. In addition, this method is also applicable to other Fredholm integral equations of the second kind without explicit characteristic values and characteristic functions. A corresponding example [12] is given and the numerical result is the same as that in [12].