2018年中国4省脱粒小麦中9种真菌毒素污染情况调查

目的 调查2018年湖北、安徽、江苏和河北4省脱粒小麦中5种镰刀菌毒素和4种黄曲霉毒素（aflatoxins，AFs）的污染情况。方法 从4省采集2018年5~6月收获的脱粒小麦371份，经乙腈-水（84:16，V:V）提取、Mycosep 226多功能净化柱净化后，采用高效液相色谱串联质谱同时测定样品中脱氧雪腐镰刀菌烯醇（deoxynivalenol，DON）、3-乙酰基-DON（3-acetyl-DON，3-Ac-DON）、15-乙酰基-DON（15-acetyl-DON，15-Ac-DON）、雪腐镰刀菌烯醇（nivalenol，NIV）、玉米赤霉烯酮（zearalenone，ZEN）和4种AF, 共9种毒素的含量。 结果 4省脱粒小麦受多种镰刀菌毒素, 尤其是DON的污染较为严重，而AF污染相对较轻。DON的检出率最高,为90.3%，阳性样品中DON平均污染水平为2706.3 μg/kg，45.3%样品DON的含量超过国家限量标准1000 μg/kg；4种AF的检出率和平均污染水平均较低，其中AFB1和AFB2的检出率最高，均为3.2%，2份样品AFB1含量超过国家限量标准5 μg/kg，仅1份样品检出AFG2，所有样品均未检出AFG1。从地区来看，湖北省脱粒小麦中各毒素污染最严重，尤其是DON、其检出率和平均污染水平分别为100%和6314.9 μg/kg；江苏省样品中ZEN的平均污染水平为1971.2 μg/kg，远高于其它省份。Spearman相关性分析表明，5种镰刀菌毒素的污染具有显著相关性，DON与已检出的3种AF及3种AF间的污染相关性均较弱或无显著相关性。 结论 4省脱粒小麦受5种镰刀菌毒素的污染较为严重且具有显著相关性。污染程度为湖北>安徽>江苏>河北。因此，有必要对4省脱粒小麦中多种毒素的协同污染情况进行持续监测。     

云南17种核桃仁氨基酸组成分析及营养价值评价

采集云南17个产地的核桃仁并测定其蛋白质及氨基酸含量,通过氨基酸分类、氨基酸比值系数法、聚类分析法综合考察氨基酸模式及营养价值。结果表明,各地的核桃仁都富含17种氨基酸(色氨酸未检测),氨基酸总量为3.35%～5.14%,必需氨基酸含量为1.13%～1.76%,药效氨基酸含量占氨基酸总量的67.47%～68.63%,呈味氨基酸含量占总氨基酸含量的36.86%～40.63%。所测核桃仁氨基酸模式中必需氨基酸搭配不均衡,第一限制性氨基酸为赖氨酸。核桃仁的比值系数分为61.48～69.54,其营养价值相对较好。经聚类分析得出,云南17种核桃仁中娘青(漾濞)、鲁甸大麻1号(昭通)、宁香(昌宁)、寻倘1号(寻甸)、庆丰3号(昭仁5)产地的核桃仁品质最好。     

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.     

Preparation and characterization of starch-based composite films reinforced by apricot and walnut shells

Starch-based biodegradable films were prepared by using solution-casting method and reinforced by agricultural residues [apricot and walnut shell (APS and WNS) powder]. The powder of both shells was added in different ratios (0, 2.5, 5, 7.5, and 10%) to investigate the microstructures and performances (mechanical and thermal properties) of the starch-based film. Different techniques such as impact, tensile testing, scanning electron microscope, optical microscope (OM), X-ray diffraction (XRD), water vapor transmission rate (WVTR), and dynamic mechanical analysis were applied to study the thermomechanical and barrier properties of the composite films. Results showed that the incorporation of both shells significantly improved the WVTR and mechanical properties of starch-based films. The shells powder was significantly increased the Young's modulus and tensile strength of the starch-based films. Both OM and SEM results showed reasonably good compatibility between starch and reinforced shells. OM and XRD indicated that the APS and WNS not only retained their crystalline structure in the film but they also strengthened the peak intensity of the film. This phenomenon can be used to explain the mechanism of mechanical reinforcement. Since all the components used in the preparation of the films are food grade ingredients, it is expected that the films developed in this work will be used for food packaging applications. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47978.     

基于SPSO优化Multiple Kernel-TWSVM的滚动轴承故障诊断

双子支持向量机(twin support vector machine,简称TWSVM)的核函数选择对其分类性能有着重要影响,TWSVM其核函数一般是局部核函数或者全局核函数,这两种核函数的泛化能力和分类性能不能兼顾。笔者利用综合加权的高斯局部核函数和多项式全局核函数方法组成双核函数来改进TWSVM以提高其泛化能力和分类性能,并采用简化粒子群优化(simple particle swarm optimization,简称SPSO)方法来对权值和参数进行优化,提出了SPSO优化Multiple Kernel-TWSVM模型,将该模型应用到滚动轴承故障诊断模式识别中。实验结果表明,双核TWSVM比单核TWSVM和反向传播(back propagation,简称BP)神经网络具有更高的分类准确率。     

Hybridizing adaptive large neighborhood search with kernel search: a new solution approach for the nurse routing problem with incompatible services and minimum demand

The average age of the population has grown steadily in recent decades along with the number of people suffering from chronic diseases and asking for treatments. Hospital care is expensive and often unsafe, especially for older individuals. This is particularly true during pandemics as the recent SARS-CoV-2. Hospitalization at home has become a valuable alternative to face efficiently a huge increase in treatment requests while guaranteeing a high quality of service and lower risk to fragile patients. This new model of care requires the redefinition of health services organization and the optimization of scarce resources (e.g., available nurses). In this paper, we study a Nurse Routing Problem that tries to find a good balance between hospital costs reduction and the well-being of patients, also considering realistic operational restrictions like maximum working times for the nurses and possible incompatibilities between services jointly provided to the same patient. We first propose a Mixed Integer Linear Programming formulation for the problem and use some valid inequalities to strengthen it. A simple branch-and-cut algorithm is proposed and validated to derive ground benchmarks. In addition, to efficiently solve the problem, we develop an Adaptive Large Neighborhood Search hybridized with a Kernel Search and validate its performance over a large set of different realistic working scenarios. Computational tests show how our matheuristic approach manages to find good solutions in a reasonable amount of time even in the most difficult settings. Finally, some interesting managerial insights are discussed through an economic analysis of the operating context.     

基于加权统计局部核主元分析的非线性化工过程微小故障诊断方法

传统统计局部核主元分析（statistical local kernel principal component analysis, SLKPCA）在构造改进残差时未考虑样本的差异性，使得故障样本信息易于被其他样本所掩盖，针对该问题，提出一种基于加权统计局部核主元分析（weighted statistical local kernel principal component analysis, WSLKPCA）的非线性化工过程微小故障诊断方法。该方法首先利用KPCA获取过程的得分向量和特征值并构建初始残差。然后设计了一种基于测试样本与训练样本之间距离的加权策略构建加权改进残差，对含有较强微小故障信息的样本赋予较大权值，以增强故障样本的影响。最后，采用基于测量变量与监控统计量之间的加权互信息构建贡献图以识别故障源变量。在连续搅拌反应釜和田纳西伊斯曼（Tennessee Eastman, TE）化工过程上的仿真结果表明,所提方法具有良好的微小故障检测与识别性能。     

高维空间可分性指标在转子诊断系统优化中的应用

二叉树相关向量机系统中正负类样本的选取往往通过方差进行可分性度量。常用的高斯核函数是在高维空间中完成分类，由于高维空间中数据点存在度量集中现象，欧氏距离往往并不能较好地度量样本点的可分性。分数范数计算出的高维空间距离差异性更大，故构造了一种基于分数范数的样本点距离度量指标。基于该指标优化各层分类器样本选取，通过实验1验证了基于高维可分性指标优化后的系统相较于欧氏距离优化后的系统在分类精度上有了较为显著的提高；实验2表明优化后的系统与智能诊断算法相比，在分类精度和耗时方面也具有优势。     

番茄红素在核桃油中的抗氧化动力学研究

为了探究不同温度条件下番茄红素不同添加量在核桃油中的抗氧化规律。本文采用Schaal烘箱加速氧化法,定期测定番茄红素不同添加量在核桃油中的过氧化值和茴香胺值,并计算总氧化值。运用动力学模型拟合过氧化值、茴香胺值和总氧化值与氧化时间的方程式,符合线性规律,并通过Arrnenus方程进行认证。结果表明:核桃油的氧化速率不仅和番茄红素添加量有关,也和加速氧化的温度有关,温度越高,其对应的氧化速率就越大。当温度为40℃、60℃和80℃,番茄红素在核桃油中的添加剂量分别为0.005 0%,0.005 0%和0.015 0%时,其核桃油的Ln K与1/T之间线性关系(R20.987),且与阿伦尼乌斯(Arrhenius)定律一致。