Potential of glucose measurement in soil and food sample using low molecular weight O-(2-hydroxyl)propyl-3-trimethylammonium chitosan chloride nanoparticle-glucose oxidase immobilised on a natural fibre membrane

Low, medium and high molecular weights O-(2-hydroxyl)propyl-3-trimethylammonium chitosan chloride nanoparticles (L-, M- and H-O-HTCC NP) have been synthesised and used to immobilise glucose oxidase on eggshell membranes for glucose biosensing. Among these O-HTCC NP-based biosensors, L-O-HTCC NP provides the highest sensitivity to glucose with a linear response of 0.012–0.60 mM and a detection limit of 12 μM (S/N = 3). The effect of L-O-HTCC NP and enzyme loading ratio, pH, temperature and phosphate buffer concentration on the sensitivity of the biosensor was studied in detail. The biosensor exhibits fast response time (40 s), good repeatability (3.0%, n = 10) and storage stability (95% of initial sensitivity after 1-year storage). Common interferents including acetic acid, DL-α-alanine, L-ascorbic acid, butyric acid, citric acid, DL-cysteine, ethanol, folic acid, glycine, lactic acid, lactose, propionic acid, sodium benzoate and sucrose do not cause significant interference on the L-O-HTCC NP biosensor. The recoveries 88.3%–102% and 102%–116% and relative standard deviations (RSDs) 3.90%–5.56% and 1.25%–3.00% are respectively, for the soil and food sample analyses. The proposed biosensor method has been applied to determine glucose in soil and food samples with good accuracy and recovery, inferring that it has potential for detection and quantification of diversified samples of different matrices. Finally, it has successfully monitored the changes of glucose contents in soil samples at various incubation times, demonstrating its potential use in environmental and geochemical analysis.     

一种递归神经网络空间分集均衡器

水声信道中的衰变多途特性常引起难以克服的码间干扰(ISI)。为了降低误码率、改善系统性能,本文提出了一种递归神经网络空间分集均衡器。它在传统的空间分集结构中融入了多层感知器,该结构能够充分利用训练信号的信息,在可调权数较少的情况下,能够得到较好的均衡效果;针对该结构,采取了初始设定权值的办法,从而使该均衡器的收敛速度得到大大提高。仿真与湖试试验结果表明,该均衡器结构合理,对空间分集均衡性能有一定的改善。     

Evaluation of laser induced breakdown spectroscopy for multielemental determination in soils under sewage sludge application

Laser induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy technique for simple, direct and clean analysis, with great application potential in environmental sustainability studies. In a single LIBS spectrum it is possible to obtain qualitative information on the sample composition. However, quantitative analysis requires a reliable model for analytical calibration. Multilayer perceptron (MLP), an artificial neural network, is a multivariate technique that is capable of learning to recognize features from examples. Therefore MLP can be used as a calibration model for analytical determinations. Accordingly, the present study proposes to evaluate the traditional linear fit and MLP models for LIBS calibration, in order to attain a quantitative multielemental method for contaminant determination in soil under sewage sludge application. Two sets of samples, both composed of two kinds of soils were used for calibration and validation, respectively. The analyte concentrations in these samples, used as reference, were determined by a reference analytical method using inductively coupled plasma optical emission spectrometry (ICP OES). The LIBS-MLP was compared to a LIBS-linear fit method. The values determined by LIBS-MLP showed lower prediction errors, correlation above 98% with values determined by ICP OES, higher accuracy and precision, lower limits of detection and great application potential in the analysis of different kinds of soils.     

Solvation effect on the vertical ionization energy of adenine-thymine base pair: From microhydration to bulk

In the present work, we investigate the effect of aqueous environment on the vertical ionization potential (VIP) of adenine-thymine (AT) base pair using a multilayer equation of the motion-coupled cluster method. The microsolvation can cause both blue-shift and red-shit of the IP values. However, the bulk water environment always results in the red-shift of the vertical ionization potential. Our study shows that the correct treatment of the short-range interaction plays an essential role in determining the magnitude of the red-shift. We have developed a biased sampling scheme based on Koopmans' energy, which can significantly speed up the convergence with respect to the number of solvent-solute configurations.     

基于LBP和多层次结构的异质手背静脉身份识别

针对多源异质的手背静脉异质图像的识别研究,提出了基于LBP和多层次结构的识别算法;首先对图像做适当的预处理,然后将LBP特征提取算法编码的手背静脉纹理特征图像作为多层次结构的输入,通过多层次结构的逐层由具体到抽象的特征提取,得到的特征具有更大的鲁棒性;最后该算法在多源异质的手背静脉图像库得到的识别率比传统的算法识别率高,达到96.57%;进一步表明该算法能够较好地解决由于多源异质问题对手背静脉识别所造成的识别率低的影响。     

基于SCFNN之PAM非线性信道均衡器成效研究

自组织型模糊类神经网络(SCFNN)可依据一定的法则自我构建神经网络的组织结构,从而适用于当前控制对象;多层神经元是传统的类神经网络,广泛应用于各个领域;倒传递学习法与最陡坡降法相结合,可使以上两种类神经网络进行有效的融合;目前,信道均衡器上的系统架构种类非常多,各种类神经网络应用于信道均衡器也颇为普遍;在研究SCFNN的基础上,将其应用于通道均衡器确实可行,效果良好;比较了SCFNN与MLP在通道均衡器的成效;仿真表明,在相同通道环境下,SCFNN的训练收敛速度、位错误率与系统敏感度优于MLP,完成结构学习后SCFNN的结构也颇为精简。     

Synthesis of carbon nanotubes directly over TEM grids aiming the study of nucleation and growth mechanisms

A novel approach to produce electron-transparent multi-layer membranes over TEM grids for transmission electron microscopy analysis is presented. The membranes have been used to grow and analyze carbon nanostructured materials in a thermal-chemical vapor deposition process using Ni and Cu as catalysts and silicon oxide thin films as support layers, at temperatures as high as 900 °C. It is important that conditions of the synthesis using the electron-transparent multi-layer membrane system are similar to those for a conventional chemical vapor deposition process, where oxidized silicon wafers are employed. In particular, the thickness of the silicon oxide and the catalyst layers are the same, and similar carbon tubular structures were grown using both substrates. The use of membranes was crucial especially for the study of the nucleation mechanism for carbon nanotubes. These electron-transparent multi-layer membranes are relatively easy to obtain and they can be used for transmission electron microscopy studies of high-temperature synthesis of different nanostructured materials.     

Backfitting neural networks

Summary  Regression and classification problems can be viewed as special cases of the problem of function estimation. It is rather well known that a two-layer perceptron with sigmoidal transformation functions can approximate any continuous function on the compact subsets ofRP if there are sufficient number of hidden nodes. In this paper, we present an algorithm for fitting perceptron models, which is quite different from the usual backpropagation or Levenberg-Marquardt algorithm. This new algorithm based on backfitting ensures a better convergence than backpropagation. We have also used resampling techniques to select an ideal number of hidden nodes automatically using the training data itself. This resampling technique helps to avoid the problem of overfitting that one faces for the usual perceptron learning algorithms without any model selection scheme. Case studies and simulation results are presented to illustrate the performance of this proposed algorithm.     

激光入射角度对薄膜热场分布影响的数值分析

在多层介质薄膜中，激光的入射方式是影响薄膜抗损伤能力的关键因素之一.提出了一种模拟锥角高斯光入射多层介质薄膜后电场和热场分布的方法.该方法能够分析薄膜中高斯光各个角谱分量叠加形成的电场分布，进而得到由于薄膜本征吸收产生的热量沉积以及薄膜内部的温度场分布.针对中心波长为4.3 μm的中红外高反膜进行了分析，给出了高反膜系的温升峰值随激光入射角度和偏振态的变化.结果表明：对于s偏振光，斜入射时膜系的最高温升峰值高于垂直入射峰值，而p光的结果则相反.此种模拟方法克服了原有方法对激光入射角度的限制，较好地反映出斜入射情况下激光偏振态对薄膜损伤的影响. 关键词： 多层介质薄膜 高斯光 热过程 数值分析     

复杂微力－电系统的细微尺度力学

现代高新技术的崛起，提出了大量新的经典力学所不能完全包容的力学问题。这将是现代应用力学发展的巨大动力。微电子技术中微电子材料、器件、系统和微电子－机械系统（ｍｉｃｒｏｅｌｅｃｔｒｏ－ｍｅｃｈａｎｉｃａｌｓｙｓｔｅｍ，ＭＥＭＳ）所组成的复杂微力－电系统，是跨世纪发展的新科技方向，本文简要介绍了复杂微力，电系统的工业技术背景和发展；综述了这一领域存在的力学问题，主要讨论细微尺度下的结构力学与破坏力学。并评介与展望了这一新的力学领域的研究状况和发展趋势。