矿物组分快速定量提取模型及其应用

矿物成分快速鉴定是提高遥感矿产勘探、遥感矿物填图以及诸多地学研究等工作效率的关键。由于技术等各方面的限制,国内外针对矿物快速分析的模型和软件较少。20世纪90年代以来近红外光谱仪在技术上的突破和计算机的发展使得近红外光谱技术在矿物快速识别领域的应用变得可行,先后出现了基于吸收位置的反演模型(模型一)和基于波形匹配的反演模型(模型二)。文章提出了特征光谱线性反演模型。经美国地质调查局矿物光谱库(USGS)端元混合实验数据验证,该模型精度接近100%,远优于模型一和二。对新疆包古图地区地表随机所采23个样本分析,该模型平均精度为64.6%,另外两模型分别为：33.8%和8.1%,优于模型一和二。虽精度尚低于传统镜下鉴定方法,该模型具有高效、方便、工作量小、人为误差小等优点,已初步应用于新疆包古图地区遥感矿产勘探工作,有较好的推广前景。     

平面团簇稳定结构的蒙特卡罗树搜索

以平面团簇为例提出了一种结合结构识别和蒙特卡罗树技术搜索稳定结构的新方法.体系原子之间的相互作用由两类模型势能函数来描述:Lennard-Jones二体势函数与基于Lennard-Jones势的三体势函数.考虑可能的三角晶格碎片作为候选结构,引入编号策略对结构进行快速识别,并运用蒙特卡罗树搜索研究稳定结构随着原子数增大的演化过程;对于能量较低的候选结构,进一步采取局域优化来获得对应体系的稳定结构.计算表明,Lennard-Jones二体势函数对应的三角晶格团簇更稳定;在特定的参数下,三体势函数对应的六角晶格团簇更稳定.结合结构识别和蒙特卡罗树搜索可以对候选结构空间进行高效扫描,在较短时间内更容易搜索到稳定的团簇结构,并可以与第一原理计算结合实现材料的结构预测.     

Carbon K‐edge spectra of carbonate minerals

Carbon K‐edge X‐ray spectroscopy has been applied to the study of a wide range of organic samples, from polymers and coals to interstellar dust particles. Identification of carbonaceous materials within these samples is accomplished by the pattern of resonances in the 280–320 eV energy region. Carbonate minerals are often encountered in the study of natural samples, and have been identified by a distinctive resonance at 290.3 eV. Here C K‐edge and Ca L‐edge spectra from a range of carbonate minerals are presented. Although all carbonates exhibit a sharp 290 eV resonance, both the precise position of this resonance and the positions of other resonances vary among minerals. The relative strengths of the different carbonate resonances also vary with crystal orientation to the linearly polarized X‐ray beam. Intriguingly, several carbonate minerals also exhibit a strong 288.6 eV resonance, consistent with the position of a carbonyl resonance rather than carbonate. Calcite and aragonite, although indistinguishable spectrally at the C K‐edge, exhibited significantly different spectra at the Ca L‐edge. The distinctive spectral fingerprints of carbonates provide an identification tool, allowing for the examination of such processes as carbon sequestration in minerals, Mn substitution in marine calcium carbonates (dolomitization) and serpentinization of basalts.     

应用BP神经网络识别内层表面细小缺陷的研究

内层表面细小凹坑的识别是超声无损评估的一个难点。本文利用人工神经网络对于信号的分类功能,建构和训练了一个BP神经网络,并用它对尺寸为1mm的圆锥形和半球形两种凹坑成功地进行了识别。研究表明,应用凹坑回波的DCT谱作为缺陷特片输入,可使BP神经网络的训练和缺陷识别既快捷又有效。     

A method for automatic identification of water and fat images from a symmetrically sampled dual-echo Dixon technique

Sampling water and fat signals symmetrically (i.e., at 0° and 180° relative phase angles) in a dual-echo Dixon technique offers high intrinsic tolerance to phase fluctuations in postprocessing and maximum signal-to-noise performance for the separated water and fat images. However, identification of which image is water and which image is fat after their separation is not possible based on the phase information alone. In this work, we proposed a semiempirical automatic image identification method that is based on the intrinsic asymmetry between the water and fat chemical shift spectra. Specifically, the approximately bimodal feature of the fat spectra and the observation that most in vivo tissues are either predominantly water or predominantly fat are used to construct a spectrum-based algorithm. Additional refinement is accomplished by considering the spatial distribution of the tissues that may have a coexistence of water and fat. The final improved algorithm was tested on a total of 131 three-dimensional patient datasets collected from different scanners and found to yield correct water and fat identification in all datasets.     

Traffic flow monitoring in software-defined network using modified recursive learning

The recursive network design used in this paper to monitor traffic flow ensures accurate anomaly identification. The suggested method enhances the effectiveness of cyber attacks in SDN. The suggested model achieves a remarkable attack detection performance in the case of distributed denial-of-service (DDoS) attacks by preventing network forwarding performance degradation. The suggested methodology is designed to teach users how to match traffic flows in ways that increase granularity while proactively protecting the SDN data plane from overload. The application of a learnt traffic flow matching control policy makes it possible to obtain the best traffic data for detecting abnormalities obtained during runtime, improving the performance of cyber-attack detection. The accuracy of the suggested model is superior to the MMOS, FMS, DATA, Q-DATA, and DEEP-MC by 19.23%, 16.25%, 47.61%, 16.25%, and 12.04%.     

Unsupervised LoS/NLoS identification in mmWave communication using two-stage machine learning framework

Identification of line-of-sight (LoS)/ non-LoS (NLoS) condition in millimeter wave (mmWave) communication is important for localization and unobstructed transmission between a base station (BS) and a user. A sudden obstruction in a link between a BS and a user can result in poorly received signal strength or termination of communication. Channel features obtained by the estimation of channel state information (CSI) of a user at the BS can be used for identifying LoS/NLoS condition. With the assumption of labeled CSI, existing machine learning (ML) methods have achieved satisfactory performance for LoS/NLoS identification. However, in a real communication environment, labeled CSI is not available. In this paper, we propose a two-stage unsupervised ML based LoS/NLoS identification framework to address the lack of labeled data. We conduct experiments for the outdoor scenario by generating data from the NYUSIM simulator. We compare the performance of our method with the supervised deep neural network (SDNN) in terms of accuracy and receiver characteristic curves. The proposed framework can achieve an accuracy of 87.4% and it outperforms SDNN. Further, we compare the performance of our method with other state-of-the-art LoS/NLoS identification schemes in terms of accuracy, recall, precision, and F1-score.     

Identification of wild collected mosquito vectors of diseases using gas chromatography–mass spectrometry in Jazan Province,Saudi Arabia

Thirty‐three species of mosquitoes have been reported from the Kingdom of Saudi Arabia. Several of these mosquitoes, Anopheles gambiae Giles s.l., Anopheles stephensi Liston, Culex pipiens Linnaeus, Culex quinquefasciatus Say, Culex tritaeniorhynchus Giles, Stegomyia aegypti (Linnaeus) and Aedimorphus vexans arabiensis (Patton) are known vectors of human and animal diseases. In this study, the cuticular hydrocarbon profiles of eight mosquito species using gas chromatography–mass spectrometry were analyzed. Wild collected fourth‐instar larvae were reared, and single, newly emerged, unfed adult females were used for the analysis. A total of 146–160 peaks were detected from the cuticular extracts by gas chromatography. Repeated analysis of variance (ANOVA) and Tukey HSD Post Hoc test was used to test for quantitative differences in relative hydrocarbon quantity. In addition, a linear regression model was applied using Enter method to determine the diagnostic peaks for the eight mosquito specimens. The ANOVA test indicated that relative peaks were significant (P < 0.05) when selected pairs of peaks were compared. Also, seven compounds showed qualitative differences among the five mosquito vectors tested. The classes of constituents present were n‐alkanes, monomethylalkanes, dimethylalkanes, trimethylalkanes, alkenes, branched aromatic hydrocarbons, aldehydes and esters. These compounds have a carbon chain length ranging from 8 to 18 carbons. The most abundant compound in all adult mosquito specimens was n‐hexylacrylate [retention time (RT) 6.73 min], which was not detected in Cx. pipiens. In Cx. pipiens, the most abundant peak was benzaldehyde (RT 2.98 min). Gas chromatography–mass spectrometry is a suitable method to identify adult mosquitoes, especially from focal areas of public health concern such as Jazan Province, Saudi Arabia. This method allows a wide range of adult collected material to be identified with high accuracy. Copyright © 2013 John Wiley & Sons, Ltd.     

The Analysis of Phenolic Compounds in Walnut Husk and Pellicle by UPLC-Q-Orbitrap HRMS and HPLC

Husk and pellicle as the agri-food waste in the walnut-product industry are in soaring demand because of their rich polyphenol content. This study investigated the differential compounds related to walnut polyphenol between husk and pellicle during fruit development stage. By using ultra-high performance liquid chromatography-quadrupole-orbitrap (UHPLC-Q-Orbitrap), a total of 110 bioactive components, including hydrolysable tannins, flavonoids, phenolic acids and quinones, were tentatively identified, 33 of which were different between husk and pellicle. The trend of dynamic content of 16 polyphenols was clarified during walnut development stage by high-performance liquid chromatography (HPLC). This is the first time to comprehensive identification of phenolic compounds in walnut husk and pellicle, and our results indicated that the pellicle is a rich resource of polyphenols. The dynamic trend of some polyphenols was consistent with total phenols. The comprehensive characterization of walnut polyphenol and quantification of main phenolic compounds will be beneficial for understanding the potential application value of walnut and for exploiting its metabolism pathway.     

Compounds with inhibitory activity on peristalsis from the seeds of Holarrhena antidysenterica

Eight compounds were isolated from the seeds of Holarrhena antidysenterica Wall.ex A.DC. On the basis of physico-chemical properties and spectroscopic data, holarrhenanan (1) was identified as a new compound, compounds 2–3 were isolated from H. antidysenterica for the first time, and five known compounds were also obtained. Inhibitory effects of some compounds and extracts to the intestinal peristalsis were evaluated. Results showed that the extracts and compounds 4, 6 exhibited remarkable inhibitory effects with tension inhibition rate of 32.77, 32.77% and amplitude inhibition rate of 59.51, 55.98%, respectively on the vitro rabbit intestinal peristalsis.