基于XML的信息物理融合系统组件建模与仿真

信息物理融合系统（CPS）涉及多种计算模型的集成和协同工作，针对CPS设计方法不统一、重塑性差、复杂度高、难以协同建模验证等问题，提出一种结构化、可描述行为的异元组件模型。首先，用统一组件建模方法进行建模，解决模型不开放问题；然后，用可扩展标记语言（XML）规范描述各类组件，解决不同计算模型描述语言不一致和不可扩展问题；最后，用多级开放组件模型的协同仿真验证方式进行仿真验证，解决验证的不可协同问题。通过通用组件建模方法、XML组件规范描述语言以及验证工具平台XModel对医用恒温箱进行了建模、描述和仿真。医用恒温箱的案例表明，这种模型驱动建立可重塑异元组件并确认其设计正确性的过程，支持信息物理协同设计和边构建边纠正，可避免在系统实现过程中发现问题时再进行反复修改。     

Field‐based robotic phenotyping of sorghum plant architecture using stereo vision

Sorghum (Sorghum bicolor) is known as a major feedstock for biofuel production. To improve its biomass yield through genetic research, manually measuring yield component traits (e.g. plant height, stem diameter, leaf angle, leaf area, leaf number, and panicle size) in the field is the current best practice. However, such laborious and time‐consuming tasks have become a bottleneck limiting experiment scale and data acquisition frequency. This paper presents a high‐throughput field‐based robotic phenotyping system which performed side‐view stereo imaging for dense sorghum plants with a wide range of plant heights throughout the growing season. Our study demonstrated the suitability of stereo vision for field‐based three‐dimensional plant phenotyping when recent advances in stereo matching algorithms were incorporated. A robust data processing pipeline was developed to quantify the variations or morphological traits in plant architecture, which included plot‐based plant height, plot‐based plant width, convex hull volume, plant surface area, and stem diameter (semiautomated). These image‐derived measurements were highly repeatable and showed high correlations with the in‐field manual measurements. Meanwhile, manually collecting the same traits required a large amount of manpower and time compared to the robotic system. The results demonstrated that the proposed system could be a promising tool for large‐scale field‐based high‐throughput plant phenotyping of bioenergy crops.     

高熵合金的抗辐照性能研究进展

高熵合金作为一种新型多主元固溶体合金，成分复杂、全局无序，且具有多主元效应，表现出较为优异的综合性能，有望作为新型抗辐照结构材料应用于先进核能反应堆系统。本文介绍了目前高熵合金抗辐照性能的研究现状，主要涉及高熵合金辐照缺陷演化、微观结构变化和性能退化等辐照损伤演化过程，梳理了多主元效应对辐照损伤演化过程的影响规律。针对高熵合金的抗辐照性能研究，总结了目前高熵合金的几种抗辐照损伤机制，归纳了高熵合金抗辐照性能研究存在的问题，以及对高熵合金后续的研究方向进行了展望。     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

A dimensional reduction approach to modulate the core ruminal microbiome associated with methane emissions via selective breeding

The rumen is a complex microbial system of substantial importance in terms of greenhouse gas emissions and feed efficiency. This study proposes combining metagenomic and host genomic data for selective breeding of the cow hologenome toward reduced methane emissions. We analyzed nanopore long reads from the rumen metagenome of 437 Holstein cows from 14 commercial herds in 4 northern regions in Spain. After filtering, data were treated as compositional. The large complexity of the rumen microbiota was aggregated, through principal component analysis (PCA), into few principal components (PC) that were used as proxies of the core metagenome. The PCA allowed us to condense the huge and fuzzy taxonomical and functional information from the metagenome into a few PC. Bivariate animal models were applied using these PC and methane production as phenotypes. The variability condensed in these PC is controlled by the cow genome, with heritability estimates for the first PC of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being >0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (≥0.70), with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. Enteric methane production was positively associated with relative abundance of eukaryotes (protozoa and fungi) through the first component of the PCA at phylum, class, order, family, and genus. Nanopore long reads allowed the characterization of the core rumen metagenome using whole-metagenome sequencing, and the purposed aggregated variables could be used in animal breeding programs to reduce methane emissions in future generations.     

Study of aroma compound formations and transformations during Jinxuan and Qingxin oolong tea processing

We characterised the formation and dynamic changes in the main aroma compounds produced during the manufacturing process of Jinxuan and Qingxin oolong tea. Thirty-five aroma compounds were investigated. Subsequent principal component and cluster analyses showed that the fresh leaves, spread leaves and rocked leaves of the two varieties of tea were distinguished from each other. Particularly, the aldehyde and ‘other’ compounds showed the highest correlation coefficients (0.71 and −0.70) among the principal. The heat map showed that the proportions of acetic acid, 3-methylbutanal and 2-methylbutanal significantly changed during the manufacturing process of the two tea varieties. The key amino acids (isoleucine, leucine, valine and aspartic acid) and enzymes (branched-chain amino acid aminotransferase, aspartate aminotransferase and acetaldehyde dehydrogenase) involved in the synthesis of 3-methylbutanal and 2-methylbutanal differed between the two varieties during manufacture. Our study reveals the characteristics of different varieties of oolong tea and their aroma formation after manufacture using the same process.     

熔融制样-能量色散X射线荧光光谱仪分析硅酸盐中32 种组分

为充分发挥小型实验室设备的功能,实验采用熔融制样,使用台式能量色散X射线荧光光谱仪(10W, Rh靶)定量分析了硅酸盐样品中的Na₂O、MgO、Al₂O₃、SiO₂、P₂O₅、SO₃、Cl、K₂O、CaO、TiO₂、V、Cr、MnO、Fe₂O₃、Co、Ni、Cu、Zn、Ga、Rb、Sr、Y、Zr、Nb、As、Ba、La、Ce、Th、U、Hf、Pb等32种主、次、痕量组分。对3种不同的解谱方法进行比较,确定Na、Mg、Al、Si、P、As、Ce、Cr、Cu、Ga、La、Nb、Ni、Pb、Th、U、V、Y选择感兴趣区解谱,其余元素用高斯或经改进的高斯函数的最小二乘法拟合解谱。选用有代表性的多个硅酸盐类样品,比较了理论检出限及10次重复测定计算3倍标准偏差得出的检出限,发现以3倍标准偏差作为检出限再取它们的平均值则更具代表性和使用意义。各组分的检出限为0.2~1740μg/g。精密度试验表明,各组分测定结果的相对标准偏差(RSD)为0.12%~10.5%(Na为轻元素,由于含量低,小能谱测定的精密度稍差);对两个土壤标准样品进行正确度验证,测定值与标准值一致。     

不同硫组分在菱锌矿表面吸附的计算研究

采用量子化学计算的第一性原理模拟了不同硫组分在菱锌矿(001)解离面上的吸附,研究了硫组分在菱锌矿表面的吸附机理。吸附能计算结果显示:相比HS-,S2-在菱锌矿(001)解离面的吸附更加稳定,吸附能的绝对值更大,同时发现硫组分主要吸附在Zn位上;吸附前后的态密度对比分析表明,硫组分在菱锌矿表面的吸附形成了新的Zn—S化学键;布居分析发现:硫组分吸附在菱锌矿表面新形成Zn—S键的过程中,Zn、S之间发生了明显的电荷转移。研究结果阐释了菱锌矿的硫化机理,为菱锌矿硫化浮选研究提供了理论依据。     

一种灰度化混合法在集装箱箱号识别中的运用

研究一种基于机器视觉的集装箱箱号识别方法。对于集装箱彩色图像预处理过程中的灰度化方法，传统的灰度化算法不能有效弥补图像中污损或其他信息缺失的部分，因此，本文提出使用主成分分析法（PCA）结合贝叶斯阈值估计灰度变化率的混合法对图像的灰度化进行优化，可以在判断图像中某一点灰度值与周围相邻像素点的灰度值的变化率后，弥补缺失信息，有效确定边缘特征，从而使后续的字符识别准确率大大提高。最后使用该算法模型设计实现一套用于港口集装箱的智能检测系统。经过Matlab实验验证，在对50幅港口集装箱箱号图像的识别中，通过使用本文提出的混合灰度化方法，与普通的均值法和加权平均法的灰度化方法相比，准确率更高，其中单一字符准确率可达96%，箱号准确率可达92%。     

Predicting pollutant removal in constructed wetlands using artificial neural networks(ANNs)

Growth in urban population,urbanisation,and economic development has increased the demand for water,especially in water-scarce regions.Therefore,sustainable approaches to water management are needed to cope with the effects of the urbanisation on the water environment.This study aimed to design novel configurations of tidal-flow vertical subsurface flow constructed wetlands(VFCWs) for treating urban stormwater.A series of laboratory experiments were conducted with semi-synthetic influent stormwater to examine the effects of the design and operation variables on the performance of the VFCWs and to identify optimal design and operational strategies,as well as maintenance requirements.The results show that the VFCWs can significantly reduce pollutants in urban stormwater,and that pollutant removal was related to specific VFCW designs.Models based on the artificial neural network(ANN) method were built using inputs derived from data exploratory techniques,such as analysis of variance(ANOVA) and principal component analysis(PCA).It was found that PCA reduced the dimensionality of input variables obtained from different experimental design conditions.The results show a satisfactory generalisation for predicting nitrogen and phosphorus removal with fewer variable inputs,indicating that monitoring costs and time can be reduced.