基于等额本息按揭贷款的脉冲增加还款模型

利用一阶线性差分方程建立了每隔半年增加还款一次的按揭贷款数学模型,并通过实例说明了其在经济生活中的具体应用.     

中国城市竞争力评价量化模型研究

本文建立一种将主、客观指标赋权方法优点相结合的城市竞争力综合评价分层构权主成分分析模型,在构建中国城市竞争力综合评价指标体系的基础上,基于该模型方法给出31个中国主要城市的城市竞争力的评价实例。并以并以判别分析方法对相关评价结果进行校验,实证分析的结果表明:这是一种适合于城市竞争力定量评价、稳定性较好且切实有效的方法模型。     

Simultaneous quantification of five major constituents in stems of Dracaena plants and related medicinal preparations from China and Vietnam by HPLC–DAD

A high‐performance liquid chromatographic (HPLC) method was developed for the simultaneous quantification of five major bioactive constituents in the stems of resiniferous Dracaena plants from China and Vietnam, as well as those in the related traditional Chinese medicinal preparations. A diode array detector with the wavelength of 330 nm was used to monitor resveratrol, 7,4′‐dihydroxyflavone and pterostilbene, while loureirin A and loureirin B were monitored at 280 nm. The five constituents were separated on an Agela SB C₁₈ column by gradient elution using 0.008% (v/v) formic acid solution (A) and acetonitrile (B) as the mobile phase. The validation of the method included recovery, linearity, accuracy and precision (intra‐ and inter‐day variation). The range of recoveries of this method was 98.1–104.9%, with all the constituents showing good linearity (r² > 0.9999). The accuracy and precision were satisfactory, with the overall intra‐ and inter‐day variation being less than 4%. The present method has been successfully applied for the determination of all five constituents in 21 related herbal samples including 10 D. cochinchinensis stem samples, seven D. cambodiana stem samples and four purchased medicinal preparations. The contents of these constituents were analyzed using principal component analysis, which can efficiently identify raw herb of Dracaena from different sources. The study may be considered helpful to the quality control of Dracena plants and its medicinal preparations. Copyright © 2009 John Wiley & Sons, Ltd.     

Extending Sets of Idempotents to Ring Extensions

In this paper the idea of an intrinsic extension of a ring, first proposed by Faith and Utumi, is generalized and studied in its own right. For these types of ring extensions, it is shown that, with relatively mild conditions on the base ring, R, a complete set of primitive idempotents (a complete set of left triangulating idempotents, a complete set of centrally primitive idempotents) can be constructed for an intrinsic extension, T, from a corresponding set in the base ring R. Examples and applications are given for rings that occur in functional analysis and group ring theory.     

一类四元环上常循环码是自由码的充要条件

本文研究了环F2 uF2上的奇长度的循环码和(1 u)-循环码.运用代数方法,得到了F2 uF2上的循环码和(1 u)-循环码成为自由码的几个充要条件.推广了Bonnecaze(1999)和Aydin(2002)的关于自由码的结果.     

The multivariate N-Component resolution problem with minimum assumptions

The present state of the minimum assumption multivariate component resolution theory is outlined. Some new developments are presented: limiting function domains; the analytical expression for the limiting function; efficient algorithms for defining the FIRPOL and INNPOL hyperpolyhedrons. A very low resolution data set is analyzed.     

Integral formula of Minkowski type and new characterization of the Wulff shape

Given a positive function F on S^n which satisfies a convexity condition, we introduce the r-th anisotropic mean curvature Mr for hypersurfaces in R^n＋1 which is a generalization of the usual r-th mean curvature Hr. We get integral formulas of Minkowski type for compact hypersurfaces in R^n＋1. We give some new characterizations of the Wulff shape by the use of our integral formulas of Minkowski type, in case F=1 which reduces to some well-known results.     

Large deviations: An introduction to 2007 Abel prize

2007 Abel prize has been awarded to S R S Varadhan for creating a unified theory of large deviations. We attempt to give a flavour of this branch of probability theory, highlighting the role of Varadhan.     

Correlation‐assisted nearest shrunken centroid classifier with applications for high dimensional spectral data

High throughput data are frequently observed in contemporary chemical studies. Classification through spectral information is an important issue in chemometrics. Linear discriminant analysis (LDA) fails in the large‐p‐small‐n situation for two main reasons: (1) the sample covariance matrix is singular when p > n and (2) there is an accumulation of noise in the estimation of the class centroid in high dimensional feature space. The Independence Rule is a class of methods used to overcome these drawbacks by ignoring the correlation information between spectral variables. However, a strong correlation is an essential characteristic of spectral data. We proposed a new correlation‐assisted nearest shrunken centroid classifier (CA‐NSC) to incorporate correlation information into the classification. CA‐NSC combines two sources of information [class centroid (mean) and correlation structure (variance)] to generate the classification. We used two real data analyses and a simulation study to verify our CA‐NSC method. In addition to NSC, we also performed a comparison with the soft independent modeling of class analogy (SIMCA) approach, which uses only correlation structure information for classification. The results show that CA‐NSC consistently improves on NSC and SIMCA. The misclassification rate of CA‐NSC is reduced by almost half compared with NSC in one of the real data analyses. Generally, correlation among variables will worsen the performance of NSC, even though the discriminatory information contained in the class centroid remains unchanged. If only correlation structure information is used (as in the case of SIMCA), the result will be satisfactory only when the correlation structure alone can provide sufficient information for classification. Copyright © 2015 John Wiley & Sons, Ltd.     

Characterization of Chickpea Flour by Near Infrared Spectroscopy and Chemometrics

Near infrared (NIR) spectrometry was used for the rapid characterization of quality parameters in desi chickpea flour (besan). Partial least square regression, principal component regression (PCR), interval partial least squares (iPLS), and synergy interval partial least squares (siPLS) were used to determine the protein, carbohydrate, fat, and moisture concentrations of besan. Spectra were collected in reflectance mode using a lab-built predispersive filter-based instrument from 700 to 2500?nm. The quality parameters were also determined by standard methods. The root mean square error (RMSE) for the calibration and validation sets was used to evaluate the performance of the models. The correlation coefficients for moisture, fat, protein, and carbohydrates in chickpea flour exceeded 0.96 using PLS and PCR models using the full spectral range. Wavelengths from 2100 to 2345?nm had the lowest RMSE for quality parameters by iPLS. The error was further decreased by 0.41, 0.1, and 1.1% for carbohydrates, fats, and proteins by siPLS. The NIR spectral regions yielding the lowest RMSE of prediction were 1620–2345?nm for carbohydrates, 1180–1590?nm and 1860–2094?nm for fat, and 1700–2345?nm for proteins. The study shows that chickpea flour quality parameters were accurately determined using the optimized wavelengths.