环境中污染源废气检测方法及影响因素分析

对污染源废气监测原理进行了简单分析,阐述了当前我国常用的环境中污染源废气检测方法,并对废气检测影响因素及质量控制进行了深入探究。     

Detection of 5-HMF in apple juice with artificial sensing systems

5-HMF (5-hydroxymethyl-furfural) is a product of thermal treatment and is increasingly considered a food contaminant. Here, different concentrations of 5-HMF were measured in apple juice to evaluate the performance of the electronic nose (EN) and electronic tongue (ET) as rapid detection techniques for 5-HMF when coupled with chemometric analysis. Principal component analysis (PCA) and linear discriminant analysis (LDA) evaluated the discrimination capacity of EN and ET for 5-HMF. Loading analysis examined the discrimination contribution of the EN sensors. Partial least square (PLS) regression analysis established a quantitative prediction model for different concentrations of 5-HMF based on EN and ET data set. The optimal models had a coefficient of determination (R²) of 0.926 and a root-mean-square error of prediction (RMSEP) of 0.4168 in EN; there were R² of 0.914 and RMSEP of 0.5836 in ET. These results demonstrate that EN and ET coupled with chemometric analysis are two promising approaches for the rapid and online detection of 5-HMF in apple juice.     

基于柠檬酸-铕纳米配位聚合物构建荧光探针快速检测汤煲中5’-肌苷酸含量

基于柠檬酸-铕金属有机纳米配体聚合物（citrate/europium lanthanide coordination polymer nanoparticles，Cit/Eu LCP NPs）构建快速检测肉品汤煲中5’-肌苷酸（inosine-5’-monophosphate，5’-IMP）的荧光探针。研究结果表明，5’-IMP对Cit/Eu?LCP?NPs有良好的荧光猝灭作用。在最佳条件下，该荧光探针在5’-IMP?2.5～200?μg/mL的质量浓度范围内呈现出良好的线性关系，检出限为0.17?μg/mL，且具备良好的抗干扰、稳定性和重复性。为了验证方法可行性，将该方法应用于实际鸡汤样品中的5’-IMP检测，测得加标回收率为97.85%～103.95%，可为快速检测肉品汤煲中5’-IMP提供新的思路和方法。     

一种5 重real-time PCR筛查转基因水稻方法的建立

以转基因水稻中最常用的CaMV35S启动子、NOS终止子、Cry1Ab/Ac基因、HPT基因及SPS水稻内标基因为研究对象，利用5 种不同的荧光信号（FAM、HEX、Taxas Red、Cy5、Cy5.5）进行多重实时聚合酶链式反应（real-time polymerase chain reaction，real-time PCR）检测方法的研究。通过引物组合筛选、反应体系优化、特异性测试、灵敏度测试、适用性测试等一系列实验，建立了5 重real-time PCR方法，灵敏度可达0.032%。此方法具有灵敏度高、结果准确、通量大等优点，可实现水稻中转基因成分的快速、高效检测。     

Copy Move Forgery Detection based on double matching

Copy Move is a technique widespreadly used in digital image tampering, meaning Copy Move Forgery Detection (CMFD) is still a significant research. In this paper, a novel CMFD method is proposed, including double matching process and region localizing process. In double matching process, the first matching is conducted on Delaunay triangles consisting of Local Intensity Order Pattern (LIOP) keypoints, to find the approximate location of suspicious regions. In order to find sufficient keypoint pairs, the existing set of matching triangles is expanded by adding their neighbors iteratively, covering the whole tampered regions, and the second matching with a looser threshold is conducted on the vertices. In the region localizing process, considering the case of multiple copies, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to classify the keypoint pairs described in a new model. Experimental results indicate that the proposed method, with good robustness, outperforms some state-of-the-art methods.     

混凝土结构的压电声波检测分析

本文介绍了混凝土结构的压电体波和表面波检测的主要进展,对两种压电声波检测的优缺点进行了总结。体波检测设备一般埋入混凝土内部,需要选择合理的检测部位,检测结果较为精确;声表面波检测无需选择特定的部位,但是检测深度有限。在实际检测工作过程中,可以联合两种方法相互验证。     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.     

A robust locating multi-optima approach for damage identification of plate-like structures

This paper proposes a robust optimization approach for multiple damage identification of plate-like structures. Different from traditional particle swarm optimizations (PSOs), a combined PSO and niche technique (NPSO) is proposed to solve multimodal optimization problems, with the full consideration of subswarm creation, merging and absorbing mechanism. As a hypersensitive parameter to damage, the curvature mode shape is adopted to construct the objective function. Case studies are conducted to investigate the effectiveness and robustness of the algorithm on multi-damage identification. Simulation results show that the proposed algorithm exhibits robust search performance on identifying damage locations accurately with good convergence behavior. It is hoped that this study can provide guidance on robust damage detection, especially when the structure is subject to multiple damages and external disturbances.