基于荧光光谱分析的农药残留检测研究进展

在农业生产中施用农药可提高农产品的产量,然而农药的过量使用也威胁着我国农产品的质量安全,因此对农产品的农药残留进行快速有效的检测成为农业生产大环境下的迫切要求。荧光光谱分析技术具有突出的高灵敏度以及有利的时间标度,对多组分农药残留检测具有良好的分辨能力,与气相色谱法、液相色谱法、气-质联用法等农药残留检测方法相比具备前处理简单、检测速度快等优点,在复杂的农药残留检测环境中有较大的优势。介绍了基于荧光光谱分析技术的农药残留快速检测方法,概述了传统的荧光光谱分析方法在农药残留检测上的应用,以及荧光光谱分析结合同步-导数法、三维荧光光谱、人工神经网络,生物传感器,金属纳米材料等方法与技术用于农药残留检测的研究发展现状,分析了基于荧光光谱分析的农药残留检测现阶段仍存在的局限与挑战,以及未来发展趋势。荧光光谱分析技术在农药残留检测上的普遍推广及应用需通过荧光检测仪器不断朝集成化、模块化发展来实现,使得检测更快捷高效。     

基于光谱技术的芒果糖度酸度无损检测方法研究

提出了一种用近红外光谱技术结合遗传算法和人工神经网络模型的芒果糖度酸度快速无损检测的新方法。首先用偏最小二乘法计算芒果糖度酸度光谱数据的主成分得分值,以此获取芒果的近红外指纹图谱,再结合遗传算法优化人工神经网络技术(GA-BP)进行检测。PLS分析表明,主因子选取18时对糖度具有较好的聚类作用,而主因子数17个时对酸度的聚类效果好。选取最佳主因子作为芒果糖度酸度的神经网络的输入,建立三层GA-BP人工神经网络模型。用135个芒果样本的糖度酸度用来建立遗传算法优化神经网络的芒果糖度酸度检测模型,对未知的45个芒果样本进行糖度酸度的预测。结果表明,提出的遗传算法和人工神经网络模型相结合的光谱分析方法具有很好的预测能力,为芒果糖度酸度检测方法提供了一种新方法。     

人工神经网络在超声无损检测中的应用

人工神经网络对于超声无损检测的发展具有重要的意义，本文对国内外人工神经网络在超声无损检测中应用研究做了较为详细的介绍和分析，指出人工神经网络是实现超声无损检测测定量化的有效途径。     

原子荧光光谱分析的进展

荧光光谱分析技术,近年来有较快发展。关于原子荧光光谱分析,有不少作者作过发光机理的论述以及实验技术的介绍和述评。但在我国,原子荧光光谱分析的研究和应用,远不及原子吸收和发射光谱分析普及。因此本文将着重介绍现代原子荧光光谱分析的有关技术,并就它们应用情况和发展前景作一扼要讨论。     

中草药大黄的近红外光谱和人工神经网络鉴别研究

大黄是我国最常用的中草药之一。对正品和非正品大黄的快速、准确鉴别对于大黄及其中草药产品的质量控制具有重要的意义。将近红外漫反射光谱分析技术与人工神经网络方法相结合,对52种大黄样品进行了测定和鉴别,正确率可达96%。并对神经网络的隐含层个数和动量因子的影响做了讨论。由于近红外光谱法具有样品前处理少,测定快速和非破坏性等特点,因而特别适合于中草药的鉴别。     

三维光谱学--二维红外光谱和时间分辨光谱

本文介绍了一类重要的光谱学分析方法-三维光谱学方法,对其中的时间分辨光谱和二维红外光谱作了详细的介绍,包括它们的基本原理、获取谱图的方法、谱图的性质等.介绍了二维红外光谱方法在光谱学分析中的一些应用领域.三维光谱分析方法已经成为光谱分析中重要的研究方法之一.     

光谱类分析仪器的主要特点及其发展现状

阐述了光谱类分析仪器的两大分类,即原子光谱分析法和分子光谱分析法,并对不同类型光谱分析仪器的物理和化学原理、仪器的特点和应用范围作了较详细的介绍,对光谱类分析仪器在科学研究领域的应用和发展趋势进行了归纳.     

双飞秒激光频率梳光谱测量技术研究进展

双光梳光谱分析技术近年来凭借高分辨率、高灵敏度、宽光谱覆盖和快速测量的优势,在分子和原子光谱分析领域得到了快速发展。从双光梳光谱分析的原理出发,以使用泵浦光源数量为分类依据,对近年来国内外基于不同实现方案的多种双光梳光谱分析技术的原理进行了简要介绍,叙述了在不同数量泵浦光源条件下,不同实验小组提出的多种实验方案,介绍了他们进行的光谱实验结果。随着研究的不断深入,减少使用泵浦光源数量是双光梳光谱仪实现设备小型化的发展方向之一,增多光源则是从一维走向多维的重要发展思路,经典的双光源配置在测量精度和二维化方面起到了重要作用。     

血清自体荧光光谱联合肿瘤标志物群在肺癌诊断中的价值

血清自体荧光光谱可以反映血清中癌细胞在代谢过程中发生的异常改变而导致的血清中荧光物质的成分、含量及微环境的变化,可作为癌症辅助诊断的一种新方法。利用荧光光谱分析技术,探讨了肺癌、肺良性疾病以及正常人血清的荧光光谱的异同,建立了血清荧光光谱检测的方法。同时联合肿瘤标志物群CEA, NSE, SCC-Ag, CYFRA21-1和p16甲基化,并运用人工神经网络技术和Fisher线性判别分析法分别建立了肺癌的诊断预测模型,并用ROC判别法对其预测结果进行比较。结果表明,荧光光谱联合肿瘤标志物建立的人工神经网络模型的预测效果优于单纯的荧光光谱神经网络模型,判别效果优于Fisher线性判别分析。     

激光微区光谱分析

本文介绍了微区光谱分析的装置和仪器，并对其特点和应用作了简要说明。     

Co-evolution of the brand effect and competitiveness in evolving networks

The principle that ‘the brand effect is attractive’ underlies the preferential attachment. Here we show that the brand effect is just one dimension of attractiveness. Another dimension is competitiveness. We firstly introduce a general framework that allows us to investigate the competitive aspect of real networks, instead of simply preferring popular nodes. Our model accurately describes the evolution of social and technological networks. The phenomenon that more competitive nodes become richer can help us to understand the evolution of many competitive systems in nature and society. In general,the paper provides an explicit analytical expression of degree distributions of the network. In particular, the model yields a nontrivial time evolution of nodes’ properties and the scale-free behavior with exponents depending on the microscopic parameters characterizing the competition rules. Secondly, through theoretical analyses and numerical simulations, we reveal that our model has not only the universality for the homogeneous weighted network, but also the character for the heterogeneous weighted network. Thirdly, we also develop a model based on the profit-driven mechanism. It can better describe the observed phenomenon in enterprise cooperation networks. We show that the standard preferential attachment,the growing random graph, the initial attractiveness model, the fitness model, and weighted networks can all be seen as degenerate cases of our model.     

A packet routing strategy using neural networks on scale-free networks

We propose a dynamic packet routing strategy by using neural networks on scale-free networks. In this strategy, in order to determine the nodes to which the packets should be transmitted, we use path lengths to the destinations of the packets, and adjust the connection weights of the neural networks attached to the nodes from local information and the path lengths. The performances of this strategy on scale-free networks which have the same degree distribution and different degree correlations are compared to one another. Our numerical simulations confirm that this routing strategy is more effective than the shortest path based strategy on scale-free networks with any degree correlations and that the performance of our strategy on assortative scale-free networks is better than that on disassortative and uncorrelated scale-free networks.     

二元随机网

在二分网的基础上明确提出了二元网的概念：同样包含两类节点,二分网同类节点之间没有连接,而二元网同类节点之间可以有连接.构建了一个二元随机网模型,并对二元随机网的结构性质进行研究.二元随机网可以具有小世界效应,并且其平均最短路径长度比单元随机网还要小.另外,两类节点的规模比是一个重要参量,会对网络的结构性质产生重大影响. 关键词： 复杂网络 二分网 二元网 小世界     

权重分布对加权网络效率的影响

加权网络可以对复杂系统的相互作用结构提供更加细致的刻画,而改变边权也成为调整和改善网络性质与功能的新途径.基于已有无权网络的效率概念,文中给出了相似权和相异权网络的网络效率定义,并研究了权重分布对于网络效率的影响.从平权的规则网络出发,通过改变权重的分布形式考察权重分布对网络效率的影响,结果发现,在规则网络上,权重分布随机性的增加提高了网络效率,而在几种常见的权重分布形式中,指数分布对网络效率的改进最为显著.同时,权重随机化之后网络最小生成树的总权重减小,意味着网络的运输成本随着权重异质性的增加而降低.以上结果为深入理解权重对网络结构与功能的影响提供了基础. 关键词： 复杂网络 加权网络 权重 网络效率     

新节点的边对网络无标度性影响

分析新节点边对网络无标度性的影响.虽然亚线性增长网络瞬态平均度分布尾部表现出了幂律分布性质,但是,这个网络的稳态度分布并不是幂律分布,由此可见,计算机模拟预测不出网络稳态度分布,它只能预测网络的瞬态度分布.进而建立随机增长网络模型,利用随机过程理论得到了这个模型的度分布的解析表达式,结果表明这个网络是无标度网络. 关键词： 复杂网络 无标度网络 小世界网络 度分布     

新节点的边对网络无标度性影响

分析新节点边对网络无标度性的影响.虽然亚线性增长网络瞬态平均度分布尾部表现出了幂律分布性质，但是，这个网络的稳态度分布并不是幂律分布，由此可见，计算机模拟预测不出网络稳态度分布，它只能预测网络的瞬态度分布.进而建立随机增长网络模型，利用随机过程理论得到了这个模型的度分布的解析表达式，结果表明这个网络是无标度网络.     

多种确定性广义Farey组织的网络金字塔

提出复杂网络的另一类家族的确定性金字塔,它是由确定性的不同连接方式构造的广义Farey组织的网络金字塔(GFONP),理论推导了该类金字塔网络的拓扑性质及其转变特点,发现三种GFONP网络的度分布都服从指数分布形式,随着时间t的不断演化网络金字塔群聚系数不断减小并趋于一个常数,同时网络金字塔从异配向同配转变,度-度相关系数也趋于一个正的常数.结果显示这类型网络的新特性具有应用潜力.     

A Path-Based Distribution Measure for Network Comparison

As network data increases, it is more common than ever for researchers to analyze a set of networks rather than a single network and measure the difference between networks by developing a number of network comparison methods. Network comparison is able to quantify dissimilarity between networks by comparing the structural topological difference of networks. Here, we propose a kind of measures for network comparison based on the shortest path distribution combined with node centrality, capturing the global topological difference with local features. Based on the characterized path distributions, we define and compare network distance between networks to measure how dissimilar the two networks are, and the network entropy to characterize a typical network system. We find that the network distance is able to discriminate networks generated by different models. Combining more information on end nodes along a path can further amplify the dissimilarity of networks. The network entropy is able to detect tipping points in the evolution of synthetic networks. Extensive numerical simulations reveal the effectivity of the proposed measure in network reduction of multilayer networks, and identification of typical system states in temporal networks as well.     

Topology of the Erasmus student mobility network

The collaboration network generated by the Erasmus student mobilities in the year 2003 is analyzed and modeled. Nodes of this bipartite network are European universities and links are the Erasmus mobilities between these universities. This network is a complex directed and weighted graph. The non-directed and non-weighted projection of this network does not exhibit a scale-free nature, but proves to be a small-word type random network with a giant component. The connectivity data indicates an exponential degree distribution, a relatively high clustering coefficient and a small radius. It can be easily modeled by using a simple configuration model and arguing the exponential degree distribution. The weighted and directed version of the network can also be described by means of simple random network models.     

The structural evolution of an online discussion network

A discussion network is an important kind of social network, and it has been researched by many scholars in recent years. In this paper, we mainly studied its structural evolution based on an empirical study of a famous online discussion that happened in China in 2008. We found that the scale growth of the network shows an S shape, the degree distribution represents the power law in the first halfway, and the network shows a degree of disassortativity characteristic. We also classified the most active participants into different groups by their opinions and studied the structural evolution of opposite groups. It was observed that the evolution of nodes in each group were very similar, but the evolution of densities were obviously different. Specifically, we found that the most active participants preferred to converse with those belonging to the opposite groups at the beginning and tended to converse with anybody regardless of group as the discussion network grew. In the paper, these evolution patterns are revealed, and future lines of research are also considered.