Application of a multivariate approach for analyte focusing by micelle collapse‐micellar electrokinetic chromatography for analyzing sunscreen agents in cosmetics

The operating parameters that affect the performance of the online preconcentration technique “analyte focusing by micelle collapse‐MEKC (AFMC‐MEKC)” were examined using a multivariate approach involving experimental design to determine the sunscreen agents in cosmetics. Compared to the single‐variable approach, the advantage of the multivariate approach was that many factors could be investigated simultaneously to obtain the best separation condition. A fractional factorial design was used to identify the fewest significant factors in the central composite design (cCD). The cCD was adopted for evaluating the location of the minimum or maximum response in this study. The influences of the experimental variables on the response were investigated by applying a chromatographic exponential function. The optimized condition and the relationship between the experimental variables were acquired using the JMP software. The ANOVA analysis indicated that the Tris pH value, SDS concentration, and ethanol percentage influenced the separation quality and significantly contributed to the model. The optimized condition of the running buffer was 10 mM Tris buffer (pH 9.5) containing 60 mM SDS, 7 mM γ‐CD, and 20% v/v ethanol. The sample was prepared in 100 mM Tris buffer (pH 9.0) containing 7.5 mM SDS and 20% v/v ethanol. The SDS concentration in the sample matrix was slightly greater than the CMC value that makes the micelle be easily collapsed and the analytes be accumulated in the capillary. In addition, sunscreen agents in cosmetics after 1000‐fold dilution were successfully determined by AFMC‐MEKC.     

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.     

Calibration transfer employing univariate correction and robust regression

This paper proposes a new method for calibration transfer, which was specifically designed to work with isolated variables, rather than the full spectrum or spectral windows. For this purpose, a univariate procedure is initially employed to correct the spectral measurements of the secondary instrument, given a set of transfer samples. A robust regression technique is then used to obtain a model with low sensitivity with respect to the univariate correction residuals. The proposed method is employed in two case studies involving near infrared spectrometric determination of specific mass, research octane number and naphthenes in gasoline, and moisture and oil in corn. In both cases, better calibration transfer results were obtained in comparison with piecewise direct standardization (PDS). The proposed method should be of a particular value for use with application-targeted instruments that monitor only a small set of spectral variables.     

大颗粒磷钼酸铵:制备与结晶动力学(英文)

通过缓慢滴加焦磷酸钾的硝酸溶液到钼酸铵溶液中制得了大颗粒磷钼酸铵(AMP)。研究了AMP的成核速率(G)与晶体生长速率。与晶体生长速率相比成核速率的反应级数更高。最初,大颗粒磷钼酸铵的结晶过程处于相变反应控制的动力学区域,此时溶液的过饱和生成速率比过饱和消除速率高。晶体线生长速率与溶液的过饱和度先增加后降低。在滴加中期,过饱和消除速率增长到与其生成速率相当。在滴加后期,晶体成核速率快速增高,而晶体的线生长速率下降。晶体的成核速率成为过饱和消除的唯一控制步骤。因此,AMP成核大部分是在首先接触到滴加液的局部溶液中完成的。     

Enhanced light‐vapor interactions and all optical switching in a chip scale micro‐ring resonator coupled with atomic vapor

The coupling of atomic and photonic resonances serves as an important tool for enhancing light‐matter interactions and enables the observation of multitude of fascinating and fundamental phenomena. Here, by exploiting the platform of atomic‐cladding wave guides, the resonant coupling of rubidium vapor and an atomic cladding micro ring resonator is experimentally demonstrated. Specifically, cavity‐atom coupling in the form of Fano resonances having a distinct dependency on the relative frequency detuning between the photonic and the atomic resonances is observed. Moreover, significant enhancement of the efficiency of all optical switching in the V‐type pump‐probe scheme is demonstrated. The coupled system of micro‐ring resonator and atomic vapor is a promising building block for a variety of light vapor experiments, as it offers a very small footprint, high degree of integration and extremely strong confinement of light and vapor. As such it may be used for important applications, such as all optical switching, dispersion engineering (e.g. slow and fast light) and metrology, as well as for the observation of important effects such as strong coupling, and Purcell enhancement.

     

伽罗瓦环上的一类No序列的构造

近年来,伽罗瓦环上的序列理论成为人们研究的热点问题.有限域上的No序列是一类伪随机序列,它在序列密码中占具十分重要的角色.本文利用伽罗瓦环上的置换,构造了伽罗瓦环Z_(p~e)上的一类新的No序列,并且研究了其线性复杂度.研究的结果表明此类No序列具有相当大的线性复杂度.     

粮食最低收购价政策问题研究

利用聚类分析、逐步回归分析确定小麦与稻谷种植面积模型和指标体系.建立综合指标评价模型和ARMA模型,可知:最低收购政策具有托市效应.结合时间趋势剔除法、蛛网模型,可知:小麦和稻谷价格波动呈现5、3年的周期性,分别满足发散型、收敛型蛛网形态.建立基于补偿法的最低收购价定价模型并用BP神经网络模型进行检验.基于反证法,推知调控最低收购价提高5%小麦种植面积的做法是不可行的.最后提出了调控粮食种植的建议·     

小样本定数截尾场合下威布尔和对数正态分布的拟合检验

本文给出了小样本定数截尾场合下两参数威布尔分布和对数正态分布的拟合检验方法,该方法还适用于一般的位置-刻度参数族分布,论文还通过实际算例说明方法的可行性。     

白骨化尸体死亡时间的推断

本文分析了15具白骨化尸体标本的股骨汞(Hg),铅(Pb),镉(Cd)元素含量数据,在三年的时间内采集了3次,一共收集到45个数据。首先将这组数据看着纵向数据,利用线性随机效应混合模型、Cox随机混合效应模型进行分析,结果显示,如果对每个白骨化尸体标本建立线性模型,可以精确预测出死亡时间,而且不需要采集铅元素含量数据。混合效应模型的预测效果也很好,最大误差不会超过1个月。其次我们对数据不作任何假设,利用机器学习中随机森林方法分析数据,并利用5折交叉验证方法来判断结果的可靠性,训练集和测试集的NMSE分别为0.1205944,0.5604286,因此可以用训练出的模型来预测死亡时间。