杆和梁在锁定过程的响应

对杆和梁在锁定过程的响应进行了详细分析，用波传播理论解释了部分结果的物理含义，对用Ｅｕｌｅｒ梁理论分析锁定问题的可行性进行了探讨。     

Genetic algorithm‐based wavelength selection method for spectral calibration

In this paper, we propose a genetic algorithm‐based wavelength selection (GAWLS) method for visible and near‐infrared (Vis/NIR) spectral calibration. The objective of GAWLS is to construct robust and predictive regression models by selecting informative wavelength regions. To demonstrate the ability of the proposed method, regression models for soil properties and sugar content of apples are constructed by using GAWLS and other variable selection methods. Copyright © 2010 John Wiley & Sons, Ltd.     

Partitioned partial least squares regression with application to a batch fermentation process

The performance of Partial Least Squares regression (PLS) in predicting the output with multivariate cross‐ and autocorrelated data is studied. With many correlated predictors of varying importance PLS does not always predict well and we propose a modified algorithm, Partitioned Partial Least Squares (PPLS). In PPLS the predictors are partitioned into smaller subgroups and the important subgroups with high prediction power are identified. Finally, regular PLS analysis using only those subgroups is performed. The proposed Partitioned PLS (PPLS) algorithm is used in the analysis of data from a real pharmaceutical batch fermentation process for which the process variables follow certain profiles during a specific fermentation period. We observed that PPLS leads to a more accurate prediction of the yield of the fermentation process and an easier interpretation, since fewer predictors are used in the final PLS prediction. In the application important issues such as alignment of the profiles from one batch to another and standardization of the predictors are also addressed. For instance, in PPLS noise magnification due to standardization does not seem to create problems as it might in regular PLS. Finally, PPLS is compared to several recently proposed functional PLS and PCR methods and a genetic algorithm for variable selection. More specifically for a couple of publicly available data sets with near infrared spectra it is shown that overall PPLS has lower cross‐validated error than PLS, PCR and the functional modifications hereof, and is similar in performance to a more complex genetic algorithm. Copyright © 2011 John Wiley & Sons, Ltd.     

Tris(4-anisyl)phosphine as an Efficient and Practical Reagent for the Synthesis of (E)-2-Benzylidenesuccinates

Condensation of maleic anhydride or dimethyl maleate with benzylaldehydes controlled by tris(4‐anisyl)‐phosphine to synthesize dimethyl (E)‐2‐benzylidenesuccinates has been systematically investigated. The protocol gives the product with high stereoselectivity in moderate to good yields under mild conditions. A plausible mechanism has been proposed.     

TiO_2纳米管阵列薄膜制备及生长机理的研究

本论文采用阳极氧化的方法,在NH4HF2+NH4H2PO4的混合水溶液中于室温下以金属钛为基体原位合成氧化钛纳米管阵列薄膜。讨论了电解液成分、外加电压、溶液的pH值对氧化钛纳米管阵列薄膜微观结构及形貌的影响,并建立了阳极氧化钛纳米管阵列薄膜的生长模型。氧化钛纳米管的结构与外加电压有很大的关系,只有电压在5～35V范围内才能制备出二氧化钛纳米管阵列薄膜,其管径随着电压的升高而增加,且管径范围为30～160nm。而薄膜的厚度与电解液有关,通过控制电解液的成分及pH值,可获得厚度为6.5μm的氧化钛纳米管阵列薄膜。     

Non-isothermal Decomposition Mechanism and Kinetics of LiClO4 in Nitrogen

The non-isothermal decomposition kinetics of LiClO4 in flow N2 atmosphere was studied.TG-DTA curves show that the decomposition proceeded through two well-defined steps below 900°C,and the mass loss was in agreement with the theoretica1 value.XRD profile demonstrates that the product of the thermal decomposition at 500°C is LiCl.For the decomposition kinetics study,the activation energies calculated with the Friedman method were considered as the initial values for non-linear regression and were used for verifying the correctness of the fitted models.The decomposition process was fitted by a two-step consecutive reaction:extended Prout-Tompkins equation[Bna,f (α) is (1?α)nα a] followed by a 1th order reaction(F1).The activation energies were (215.6±0.2) and (251.6±3.6) kJ/mol,respectively.The exponentials n and a for Bna reaction were (0.25±0.05) and (0.795±0.005),respectively.The reaction types and activation energies were in agreement with those obtained from the isothermal method,but the exponentials were optimized for better fitting and prediction.     

肾上腺素电化学氧化生成肾上腺素红反应机理的薄层光谱电化学研究

应用长光程薄层电解池(石墨电极和铂片电极),使用循环伏安法(CV)和单电位阶跃计时吸收法(SPS/CA),研究了肾上腺在pH=2.5～7.0的McIlvaine缓冲溶液中电氧化生成肾上腺素红的反应机理,测定了表观速率常数kobs.结果表明:溶液pH值对肾上腺素的电氧化反应有很大影响,肾上腺素生成肾上腺素红的kobs随溶液pH值的增加而增大.     

泡沫液膜的分子动力学模拟及泡沫析液机制的研究

采用分子动力学MD方法模拟表面活性剂稳定的泡沫液膜,通过分析表面活性剂头基与水分子的径向分布函数,分析泡沫液膜中水分子的状态,对结合水、捕获水进行定量;采用电导法测定不同表面活性剂稳定的泡沫的析液曲线,结合分子模拟结果,分析泡沫析液的微观机制,建立泡沫析液量随时间变化的物理模型,给出了模型参数的物理意义.     

Chemometrical strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification

The possibility provided by Chemometrics to extract and combine (fusion) information contained in NIR and MIR spectra in order to discriminate monovarietal extra virgin olive oils according to olive cultivar (Casaliva, Leccino, Frantoio) has been investigated.Linear discriminant analysis (LDA) was applied as a classification technique on these multivariate and non-specific spectral data both separately and jointly (NIR and MIR data together).In order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers), LDA was preceded either by feature selection or variable compression. For feature selection, the SELECT algorithm was used while a wavelet transform was applied for data compression.Correct classification rates obtained by cross-validation varied between 60% and 90% depending on the followed procedure. Most accurate results were obtained using the fused NIR and MIR data, with either feature selection or data compression.Chemometrical strategies applied to fused NIR and MIR spectra represent an effective method for classification of extra virgin olive oils on the basis of the olive cultivar.     

Variable-weighted least-squares support vector machine for multivariate spectral analysis

Multivariate spectral analysis has been widely applied in chemistry and other fields. Spectral data consisting of measurements at hundreds and even thousands of analytical channels can now be obtained in a few seconds. It is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In this paper, the concept of traditional wavelength variable selection has been extended and the idea of variable weighting is incorporated into least-squares support vector machine (LS-SVM). A recently proposed global optimization method, particle swarm optimization (PSO) algorithm is used to search for the weights of variables and the hyper-parameters involved in LS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. All the computation process of this method is automatic. Two real data sets are investigated and the results are compared those of PLS, uninformative variable elimination-PLS (UVE-PLS) and LS-SVM models to demonstrate the advantages of the proposed method.