The theoretical basis of column chromatography in multicomponent separations. Part 2: Peak capacity. Column systems with good selectivity

The selectivity of a column system, S, defined by equation 28, includes a variation coefficient (β) of plate number with capacity ratio which has a large influence on the peak capacity, as shown in Fig. 18. Some typical chromatograms are given. In order to predict S for a column system from Kovat's Index, equations 40 and 41 are given to calculate the constant of the carbon number rule for squalane at different temperatures or for different stationary phases. The specific retention value of heptane on squalane at different temperatures can be calculated from equation 42. The nonpolarity index, defined by equation 43, was used to calculate the retention value of heptane on various stationary phases. In liquid chromatography, the order of elution may be reversed by changing the composition of the eluent on the same chemically bonded silica (manufactured in China). The linear relations between the log retention values of different kinds of solutes or of a single solute on the silicas of different surface areas when using the same eluent are given.     

用人工神经网络预测催化精馏塔开工过程的研究

催化精馏是使化学反应过程和精馏分离过程结合在一起,是伴有化学反应的新型特殊精馏过程.“反应精馏”概念自20年代提出以来,从30年代到60年代初,研究都是对特定体系的工艺探索;70年代后,研究扩展到非均相催化反应体系,出现了非均相催化精馏过程,成为了反应精馏的一个重要分支;     

基于荧光多光谱融合的水质化学需氧量的检测

为了对水中的有机污染物进行绿色、快速、准确的检测,提出了一种基于荧光多光谱融合的水质化学需氧量(Chemical Oxygen Demand, COD)的检测方法。实验样本为包含近岸海水和地表水在内的实际水样53份,采用标准化学方法获取样本的化学需氧量的理化值,利用荧光分光光度计采集样本的三维荧光光谱并对光谱数据进行处理和建模。在200～300 nm(间隔5 nm)的激发波长范围内将三维光谱展开成二维的发射光谱(发射波长范围250～500 nm,间隔2 nm)。采用ACO-iPLS(蚁群-区间偏最小二乘)算法提取发射光谱特征,PSO-LSSVM(粒子群优化的最小二乘支持向量机)算法建立预测模型,分别建立了单激发波长下的荧光发射光谱数据预测模型、多激发波长下发射光谱的数据级融合(LLDF)预测模型以及多激发波长下发射光谱的特征级融合(MLDF)预测模型,通过对预测效果的对比,得出结论。实验结果表明,对于不同激发波长下荧光发射光谱数据而言,265 nm激发光作用下的发射谱数据的预测模型最优,其检验集决定系数R2_P和外部检验均方根误差RMSEP分别为0.990 1和1.198 6 mg·L-1;对于荧光多光谱数据级融合模型(简写为：LLDF-PSO-LSSVM)而言,在235,265和290 nm激发光作用下的发射光谱的LLDF模型效果最优,其检验集的R2_ｐ和RMSEP分别为0.992 2和1.055 1 mg·L-1;对于荧光多光谱特征级融合模型(MLDF-PSO-LSSVM)而言,在265,290和305 nm激发光作用下的荧光发射光谱的MLDF模型效果最优,其R2_p=0.998 2,RMSEP=0.534 2 mg·L-1。综合比较各类建模结果可知,MLDF-PSO-LSSVM的模型效果最优,说明基于荧光发射光谱数据,采用多光谱特征级融合模型检测水质COD时,检测的精度更高,预测效果更好。     

不同含水量尾砂的光谱特征与遥感模型

我国尾矿库数量众多,分布广泛,在低含水量条件下,风力作用引起的尾砂扬尘会对周边环境造成污染。而尾矿库表面积大,含水量变化快,传统的含水量监测方法效率低、安全性差、成本高,难以实现尾矿库含水量的大面积、实时、快速的监测。目前,基于光谱特征的遥感模型虽可以较为准确地预测土壤含水量,但矿区尾砂与常规土壤在成分上存在差异性,使得土壤含水量的光谱预测遥感模型可能无法适用于尾矿库含水量的预测。为此,选择辽宁省风水沟尾矿库作为研究区,采集尾砂配置成不同含水量的样品,测试其可见光-近红外光谱,分析不同含水量样品的光谱特征以及含水量与光谱特征之间的关系,建立针对尾砂的含水量遥感预测模型,并应用于辽宁省风水沟尾矿库表面含水量的预测。结果表明：（1）含水量对尾砂的光谱特征有显著影响,二者存在高度的相关性,光谱反射率随含水量增加而下降,且波长越长,含水量对光谱的影响越显著;（2）构建了基于尾砂光谱特征的含水量遥感预测模型,选择Landsat8-OLI传感器的B6和B7波段,定义了比值指数（RTI）、归一化差异指数（NDTI）和差值指数（DTI）3种尾砂光谱指数,并将这3种指数作为输入自变量,使用随机森林方法进行训练以及含水量的建模预测,并与B7波段建立的对数反射率预测模型进行比较。结果表明,光谱指数+随机森林的预测模型效果优于基于B7波段建立的对数反射率模型。（3）使用光谱指数+随机森林的预测模型,通过Landsat8-OLI数据对实地尾矿库提取了含水量的空间分布图,结果表明模型预测的含水量与实测结果之间的决定系数R2达0.798,均方根误差RMSE为0.077,相对分析误差RPD为1.970,平均相对精度ARE为20.1%,在现有技术条件下,达到了较好的预测效果。该研究为变质型铁矿尾矿库含水量的预测提供一种大面积、实时、快速的实用方法。     

Is Prediction Possible in General Relativity?

Here we briefly review the concept of “prediction” within the context of classical relativity theory. We prove a theorem asserting that one may predict one’s own future only in a closed universe. We then question whether prediction is possible at all (even in closed universes). We note that interest in prediction has stemmed from considering the epistemological predicament of the observer. We argue that the definitions of prediction found thus far in the literature do not fully appreciate this predicament. We propose a more adequate alternative and show that, under this definition, prediction is essentially impossible in general relativity.     

喷墨打印系统多光谱特征化正向模型的建立

随着光谱技术的发展,打印系统的光谱特征化模型成为研究热点。基于光谱匹配的特征化模型通过直接预测设备基色的光谱反射比数据,可以有效的减少同色异谱现象的发生,为实现高保真印刷提供条件。主要基于Yule-Nielsen修正的Neugebauer光谱模型,开展了关于12色打印系统光谱特征化模型如何提高模型精度的研究。首先通过对颜色测量仪器及测量条件、喷墨打印机打印系统进行稳定性及精确度验证,为后续样本设计和测量提供可行性依据。然后,建立该研究设备适用的正向YNSN模型。依据CIELAB颜色空间中明度值L*均匀分布的原则,设计并输出了1 331个测试样本,抽取部分样本做训练样本,对所建立的光谱的特征化正向模型进行验证。结果表明,基于光谱的特征化模型预测精度较高,具有明显的优势。经验证,通过引入Yule-Nielsen修正参数n值,可进一步改善光谱预测精度。     

A new formulation to estimate the variance of model prediction. Application to near infrared spectroscopy calibration

Evaluation of uncertainty affecting predictions is a major trend in analytical chemistry and chemometrics. Several approximate expressions and resampling methods have been proposed for the estimation of prediction uncertainty when using multivariate calibration. This article proposes a new expression for the variance of prediction, adapted to near infrared spectroscopy specificities and particularly to the spectral error structure, induced by the high colinearity of the variables. The proposed analytical expression enables a detailed evaluation of the different contributions and components of uncertainty affecting the model. An application to real data of feedstuff near infrared spectra related to protein content has shown its advantages.     

Book review

Probability in Social Science by Samuel Goldberg     

Far Casting Cross-Validation

Cross-validation has long been used for choosing tuning parameters and other model selection tasks. It generally performs well provided the data are independent, or nearly so. Improvements have been suggested which address ordinary cross-validation’s (OCV) shortcomings in correlated data. Whereas these techniques have merit, they can still lead to poor model selection in correlated data or are not readily generalizable to high-dimensional data.

The proposed solution, far casting cross-validation (FCCV), addresses these problems. FCCV withholds correlated neighbors in every aspect of the cross-validation procedure. The result is a technique that stresses a fitted model’s ability to extrapolate rather than interpolate. This generally leads to better model selection in correlated datasets.

Whereas FCCV is less than optimal in the independence case, our improvement of OCV applies more generally to higher dimensional error processes and to both parametric and nonparametric model selection problems. To facilitate introduction, we consider only one application, namely estimating global bandwidths for curve estimation with local linear regression. We provide theoretical motivation and report some comparative results from a simulation experiment and on a time series of annual global temperature deviations. For such data, FCCV generally has lower average squared error when disturbances are correlated.

Supplementary materials are available online.     

橡胶贮存寿命预测方法研究进展与思考建议

概述了用数学模型法预测橡胶贮存寿命的方法,包括阿伦尼斯模型,用ASTM D412评估橡胶拉伸性能,应力应变老化模型,压缩永久变形的预测方法,橡胶疲劳寿命损伤模型,用有限元法考核橡胶的裂纹长度与抗裂能之间的关系,基于叠加原理的寿命预测模型等,针对上述模型预测研究结果提出了相关思考建议。认为以老化动力学为基础预测材料寿命的数学模型法发展非常迅速,建议深入研究并拓宽应用;在透彻了解和掌握必需的分子结构参数的基础上,如果结合计算机技术模拟长期贮存或使用条件,对橡胶老化反应机理的研究可能是一个有前景的发展方向。