DNA extraction method selection for agricultural soil using TOPSIS multiple criteria decision-making model

There is an increased interest in the extraction of nucleic acids from various environmental samples since culture-independent molecular techniques contribute to deepen and broaden the understanding of a greater portion of uncultivable microorganisms. Due to difficulties to select the optimum DNA extraction method in view of downstream molecular analyses, this article presents a straightforward mathematical framework for comparing some of the most commonly used methods. Four commercial DNA extraction kits and two physical-chemical methods (bead-beating and freeze-thaw) were compared for the extraction of DNA under several quantitative DNA analysis criteria: yield of extraction, purity of extracted DNA (A260/280 and A260/230 ratios), degradation degree of DNA, easiness of PCR amplification, duration of extraction, and cost per extraction. From a practical point of view, it is unlikely that a single DNA extraction strategy can be optimum for all selected criteria. Hence, a systematic Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to compare the methods. The PowerSoil? DNA Isolation Kit was systematically defined as the best performing method for extracting DNA from soil samples. More specifically, for soil:manure and soil:manure:biochar mixtures, the PowerSoil?DNA Isolation Kit method performed best, while for neat soil samples its alternative version gained the first rank.

A theoretical extraction scheme of transport information based on exclusion models

In order to explore how to extract more transport information from current fluctuation, a theoretical extraction scheme is presented in a single barrier structure based on exclusion models, which include counter-flows model and tunnel model. The first four cumulants of these two exclusion models are computed in a single barrier structure, and their characteristics are obtained. A scheme with the help of the first three cumulants is devised to check a transport process to follow the counter-flows model, the tunnel model or neither of them. Time series generated by Monte Carlo techniques is adopted to validate the abstraction procedure, and the result is reasonable.

谱峭度和Vold-kalman阶比跟踪在风电机组齿轮箱故障诊断中的应用

针对风电机组齿轮箱在时变工况下的振动信号具有非平稳特性,提出一种谱峭度和Vold-kalman阶比跟踪(Vold-Kalman Filter Based Order Tracking,VKF-OT)相结合的故障特征提取方法。以转频和啮合频率作为VKF-OT的提取频率,获得随转速变化的阶比信号,通过阶比信号复包络直接求两种频率分量的幅值、相位,经实验分析这种方法能保留齿轮箱的瞬变信息。而后计算两种频率分量的谱峭度,以最大谱峭度对应的频率带能量与原阶比信号总能量之比作为故障特征,最后采用高斯混合模型对风电机组齿轮箱在不同工况下的150组振动信号进行特征描述,运用最大贝叶斯分类器实现故障识别。故障识别率表明该方法可有效地识别任意时变工况下的齿轮早期局部微弱故障。

基于同步提取变换与Vold-Kalman滤波的燃机阶次跟踪方法研究

针对管道、船舶、航空等行业使用的燃气轮机转速频繁变化,难以提取振动信号中目标阶次成分的问题,对燃机振动信号阶次跟踪方法进行了研究,提出了一种基于同步提取变换与Vold-Kalman滤波的燃气轮机振动阶次跟踪方法。首先,对振动加速度信号进行了积分,得到了转子工频成分明显的振动速度信号;然后,参考同步性较差的转速数据在振动速度信号中使用同步提取变换的方法,提取了瞬时频率,即同步精确的转子工频;将振动加速度或速度信号与转子工频作为输入,利用Vold-Kalman滤波阶次跟踪方法,提取了目标阶次成分的时域波形;最后,将该方法应用于实际燃机的振动数据分析中。研究结果表明:该方法能够准确、有效地提取出燃机目标阶次时域波形,可为振动状态监测及预警提供数据支撑。

Gabor Order Tracking Filtering Technology in Rotary Machinery

Order analysis is one of the most important technique means of condition monitoring and fault diagnosis for rotary machinery.The traditional order analyses usually employ the Vold-Kalman filtering,however this method is confined to the expensive hardware equipments.This paper starts from Gabor transform and applies the Gabor time-frequency filtering to vibration signal.The order component＇s time-frequency coefficients are extracted by mask operation.The order component is reconstructed from the obtained coefficients.The following four key technologies,such as smoothing rotary speed curve,defining filtering band width,constructing the mask operation matrix and reconstructing signal component,are also deeply discussed.Moreover,the technique to smooth the rotary speed curve based on polynomial approximation,the method to determine filtering band width,the arithmetic to constitute mask array and the iterative algorithm to reconstruct signal based on minimum mean square error are specifically analyzed.The 4th order component is successfully gained by using the methods that Gabor time-frequency filter,and the validity and feasibility of this method are approved.This method can solve the problem of order tracking filter technologies which used to depend on hardware and efficiently improve the accuracy of order analysis.

Comparison of correcting myopia and astigmatism with SMILE or FS-LASIK and postoperative higher-order aberrations

AIM:To compare the effect of myopia and astigmatism correction and postoperative change in higher-order aberration as results of receiving small-incision lenticule extraction(SMILE)and femtosecond laser-assisted in situ keratomileusis(FS-LASIK).METHODS:A prospective and non-randomized controlled study was conducted.The subjects are divided into two groups according to different operations received:229 eyes of 116 patients in the SMILE group and 168 eyes of 86 patients in the FS-LASIK group.All subjects were followed up for 3 mo by monitoring their uncorrected visual acuity(UCVA),best-corrected visual acuity(BCVA),spherical equivalent,higher-order aberrations,and the preoperative and postoperative complications.RESULTS:At 1 wk,1,and 3 mo post-surgery,224 eyes(97.8%),227 eyes(99.1%)and 229 eyes(100%)had UCVA≥20/20 in the SMILE group,while 165 eyes(98.2%),167 eyes(99.4%)and 167 eyes(99.4%)had UCVA≥20/20 in the FS-LASIK group,respectively(χ^(2)=0.146,2.135,and 1.124;all P>0.05).BCVA reduction was not observed in both groups at 1 and 3 mo of post-surgery(χ^(2)=0.734 and 1.898,P>0.05).There was no statistically significant difference in the spherical equivalent between the two groups at 1 and 3 mo post-surgery,though the percentage of the spherical equivalent within±0.50 D at 3 mo postsurgery was 98%in the SMILE group,which was higherthan that of the FS-LASIK group(92%,χ^(2)=1.872,P>0.05).The root mean square(RMS)values of total high-order aberration,coma,and spherical aberration of the two groups increased significantly in the early postoperative period and decreased after 3 mo,but the values were still higher than the preoperative levels(P<0.05);there was no significant difference between the two groups in the RMS values of total higher-order aberrations and specific higherorder aberrations(P>0.05).The incidence of complications in the SMILE group was lower than that in the FS-LASIK group(χ^(2)=14.52,P<0.05).CONCLUSION:SMILE and FS-LASIK can effectively treat myopia,significantly improve visual acuity,and increase the total high-order aberration,spherical aberration,and coma.The incidence of complications after SMILE is relatively low.

基于Vold-Kalman阶次提取的非平稳声源定位方法

非平稳声源识别与定位是旋转机械状态监测和故障诊断的重要环节。利用非平稳旋转机械的转速特征,将Vold-Knalman阶次追踪方法与传统波束形成方法结合,提出一种新的非平稳声场信号波束成形声源定位方法。首先对旋转机械转速进行追踪,结合时频分析方法用Vold-Kalman分解提取出非平稳声信号的特定阶次成分,然后利用分离出的特定阶次信号重建声场,从而定位声源。该方法利用对特征阶次声信号的提取,避免多个阶次声信号成像时的混叠现象,通过多重线性调频信号叠加声场声源识别仿真实验与电机升速实验进行声源识别验证,证明所述方法能够准确有效定位声源。这种方法在非平稳声源识别和噪声控制中有良好工程应用潜力。

Quality oriented multimode processes monitoring based on a novel hierarchical common and specific structure with different order information

Due to higher demands on product diversity,flexible shift between productions of different products in one equipment becomes a popular solution,resulting in existence of multiple operation modes in a single process.In order to handle such multi-mode process,a novel double-layer structure is proposed and the original data are decomposed into common and specific characteristics according to the relationship between variables among each mode.In addition,both low and high order information are considered in each layer.The common and specific information within each mode can be captured and separated into several subspaces according to the different order information.The performance of the proposed method is further validated through a numerical example and the Tennessee Eastman(TE)benchmark.Compared with previous methods,superiority of the proposed method is validated by the better monitoring results.

AUTOMATIC TARGET RECOGNITION OF RADAR HRRP BASED ON HIGH ORDER CENTRAL MOMENTS FEATURES

The paper addresses the problem of target recognition using High-resolution Radar Range Profiles(HRRP).A novel approach of feature extraction and dimension reduction based on extended high order central moments is proposed in order to reduce the dimension of range profiles.Features extracted from radar HRRPs are normalized and smoothed,and then comparative analysis of the similar approaches is done.The range profiles are obtained by step frequency technique using the two-dimensional backscatters distribution data of four different aircraft models.The template matching method by nearest neighbor rules,which is based on the theory of kernel methods for pattern analysis,is used to classify and identify the range profiles from four different aircrafts.Numerical simulation results show that the proposed approach can achieve good performance of stability,shift independence and higher recognition rate.It is helpful for real-time identification and the engineering implements of automatic target recognition using HRRP.The number of required templates could be reduced con-siderably while maintaining an equivalent recognition rate.

First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images

In literature, features based on First and Second Order Statistics that characterizes textures are used for classification of images. Features based on statistics of texture provide far less number of relevant and distinguishable features in comparison to existing methods based on wavelet transformation. In this paper, we investigated performance of texture-based features in comparison to wavelet-based features with commonly used classifiers for the classification of Alzheimer’s disease based on T2-weighted MRI brain image. The performance is evaluated in terms of sensitivity, specificity, accuracy, training and testing time. Experiments are performed on publicly available medical brain images. Experimental results show that the performance with First and Second Order Statistics based features is significantly better in comparison to existing methods based on wavelet transformation in terms of all performance measures for all classifiers.

Considerations for Application of SOBI to Order Tracking

Current methods of order tracking, such as synchronous resampling, Gabor filtering, and Vold-Kalman filtering have undesirable traits. Each method has two or more of the following deficiencies: requires measurement or estimate of rotational speed over time, failure to isolate the contribution of crossing orders in the vicinity of the crossing time, large computational expense, end effects. In this work a new approach to the order tracking problem is taken. The Second Order Blind Identification (SOBI) algorithm is applied to synthesized data. The technique is shown to be very successful at isolating crossing orders and circumvents all of the above deficiencies. The method has its own restric-tions: multiple sensors are required and sensors must be mounted on a structure that responds quasi-statically to exci-tation of the rotational system.

粉煤灰提铝残渣制备4A分子筛及Cu(Ⅱ)吸附研究

4A分子筛由于其优异的阳离子交换能力,成为处理重金属废水优良的吸附材料。为了达到以废治废的目的,以粉煤灰酸法提铝残渣为原料制备地质聚合物,再经水热原位转化合成体型化4A分子筛。采用XRD、FT-IR、SEM、BET、TGA等手段对4A沸石进行表征,探讨吸附时间、Cu(Ⅱ)初始浓度、吸附剂用量及溶液p H对其吸附性能的影响,并结合动力学方程和吸附等温线模型对其吸附过程进行研究。结果表明,所合成的4A分子筛孔隙发达且微孔含量较多,总比表面积为71.85 m^(2)/g,其中微孔比表面积为53.91 m^(2)/g,微孔孔径主要集中在1.3 nm左右。体型化4A分子筛吸附Cu(Ⅱ)的最佳条件为吸附时间为240 min、Cu(Ⅱ)初始质量浓度为100 mg/L、吸附剂用量为2.0 g/L、溶液p H为6~9,此时吸附量为32.97 mg/g,Cu(Ⅱ)的去除率可达65.93%。吸附过程符合准二级动力学模型,以化学吸附为主。Langmuir模型拟合效果较好,表明Cu(Ⅱ)在合成4A沸石上的吸附过程为单分子层吸附,且拟合得到的Cu(Ⅱ)的最大吸附量为37.05 mg/g。

增强组合差分乘积形态学滤波的轴承故障特征提取方法

针对滚动轴承故障信号的非线性、非平稳、强噪声特性导致的常规时频域特征提取方法受限问题,提出一种增强组合差分乘积形态学滤波的轴承故障特征提取方法。在分析数学形态学4种基本运算的正、负冲击脉冲提取特性的基础上,运用级联、差分、乘积构造的一种新的组合差分乘积算子(combination difference multiply operator,CDMO)具备了同时提取正、负冲击脉冲的能力,并发挥梯度乘积运算对脉冲提取更敏感的优势,实现故障信息的充分提取。引入故障特征频率比指标优化CDMO结构元素参数,修正待处理信号的几何特征,提取与结构元素相匹配的信号特征信息。在CDMO滤波的基础上,借助三阶累积量切片谱技术能够抑制高斯噪声、突出二次耦合分量的优势,准确提取故障特征频率及其倍频,增强轴承故障特征提取能力并抑制噪声干扰。依托2种不同来源的工程实际信号并与经典故障特征提取方法对比分析,验证了所提方法的有效性。

语义增强与高阶强交互的SAR图像舰船检测

合成孔径雷达(synthetic aperture radar,SAR)图像背景信息复杂、舰船目标边缘模糊,且多为容易丢失的小尺度舰船目标。针对上述问题,提出语义增强与高阶强交互的SAR图像舰船检测。该方法利用部分卷积与非对称卷积构建部分非对称卷积聚合网络,在减少计算复杂度、轻量化主干网络的同时,更好地捕捉多尺度舰船特征,同时在上采样部分引入双层路由注意力,增强对图像上下文信息的利用。另外,通过递归的方式进行特征提取,可以较好解决区域内信息交互的问题,实现不同级别特征之间的高阶交互建模,提升模型检测能力。在公开的HRSID遥感数据集上进行实验的结果表明,该方法的检测精度达到91.23%,相比原模型提升5.13%,准确率与召回率分别提升2.41%和7.16%,与主流算法相比具有较好的检测效果。

基于路径重积分特征提取的轴承故障诊断

滚动轴承振动信号呈现非线性和非平稳特征,为充分挖掘滚动轴承振动信号的有效信息,提高故障诊断准确率,提出基于振动信号路径重积分(path signature, PS)的滚动轴承故障信号特征提取方法。首先,对一维故障振动信号进行时延重构,构成有限维路径空间;其次,对路径进行多重迭代积分得到高阶路径积分特征作为故障振动信号初始特征,利用主成分分析(principal component analysis, PCA)对其进行降维得到能充分表征故障信号本征信息的特征;最后,将不同故障信号基于路径重积分的故障特征构成故障特征集,输入到支持向量机(support vector machine, SVM)完成故障的识别和分类。实验结果表明,该方法在公开数据集上的10种故障类型诊断准确率达99.33%,对比其他几种方法,所提方法能快速准确地识别滚动轴承不同故障类型。

改进的两步动态慢特征分析算法及其故障检测模型

针对两步动态慢特征分析(two-steps dynamic slow feature analysis,TS-DSFA)未充分考虑动态工业过程数据的高阶时序相关特性的问题,提出改进的两步动态慢特征分析算法(modified dynamic slow feature analysis,MDSFA).首先,在TS-DSFA算法基础之上,重新描述慢特征关于时间的导数估计公式,设计了同时满足所提取的特征变化最缓、高阶时序自相关性最大的优化目标函数;然后,以动态潜变量能捕获一些变化信息并相互正交为约束,提取出一组随时间慢速变化和具有显式动态自回归表示的潜在特征;最后,基于所提出的算法构建针对线性动态过程的故障检测模型,并计算统计量及其相应控制限,实现实时动态过程的稳态故障和动态故障检测.通过对数值系统和田纳西–伊斯曼过程进行仿真验证,证明了所提算法的故障检测效果优于动态内部主成分分析和TS-DSFA算法等已有的故障检测算法.

散光精准定位标记工具用于飞秒激光小切口角膜基质透镜取出术矫正近视散光的效果

目的探讨散光精准定位标记工具用于飞秒激光小切口角膜基质透镜取出术(SMILE)矫正近视散光的效果。方法选取2022年1月至12月在我院行SMILE且散光在-5.00~-1.25 D的患者120例120眼(均选右眼)作为研究对象,按随机数表将患者分为标记组(60例)和不标记组(60例)。标记组患者由手术医生使用我科自主研发的散光精准定位标记工具进行标记。所有患者分别于术前及术后1 d、术后1周、术后3个月进行检查,收集术前及术后3个月的检查数据(裸眼视力、最佳矫正视力、电脑验光结果)。利用矢量分析工具计算相关散光评价指标:|平均差异矢量|(|DV|)、矫正指数(CI)、误差角度绝对值(|AofE|)。采用尼德克屈光分析仪测量患者高阶像差,包括球差、彗差、三叶草差。应用SPSS 26.0软件对两组患者相关数据进行统计学分析。结果术前两组患者最佳矫正视力、球镜度和柱镜度差异均无统计学意义(均为P>0.05)。标记组患者术后3个月裸眼视力(logMAR)优于不标记组,差异有统计学意义(P<0.05);术后3个月两组患者球镜度、柱镜度差异均无统计学意义(均为P>0.05)。术后3个月两组患者|DV|、CI差异均无统计学意义(均为P>0.05);术后3个月标记组患者|AofE|小于不标记组,差异有统计学意义(P<0.05)。术前两组患者彗差、三叶草差、球差差异均无统计学意义(均为P>0.05);术后3个月两组患者彗差、三叶草差、球差均较术前明显增加(均为P<0.05)。术后3个月标记组患者彗差小于不标记组,差异有统计学意义(P<0.05)。结论散光精准定位标记工具标记后行SMILE较不标记具有更佳的散光矫正效果,能明显优化SMILE所带来的彗差,提高患者视觉质量。

SMILE术后患者早期角膜生物力学变化与角膜体积变化、角膜高阶像差变化之间的关系

目的探讨飞秒激光小切口角膜基质透镜取出术(SMILE)术后患者早期角膜生物力学的变化,分析角膜生物力学变化与角膜体积变化、角膜高阶像差变化之间的关系。方法选取2021年1月至8月在佛山市第二人民医院视光学中心拟行SMILE的近视及近视散光患者72例(72眼)作为研究对象,取患者的右眼作为观测眼。术前和术后3个月分别应用新一代Corvis ST测量患者角膜生物力学新型参数[非接触生物力学校正眼压(bIOP)、2 mm处变形幅度的比值(DA ratio)、综合半径(IR)、最薄点厚度/厚度变化率(ARTh;用来评估角膜的形态和质量)、第一次压平时角膜硬度参数(SP-A1)、Corvis生物力学指数(CBI)和应力和应变参数(SSI)];应用Pentacam测量患者角膜生物力学参数,包括:角膜体积(CV_(3 mm)、CV_(5 mm)、CV_(7 mm)和CV_(10 mm));全角膜、角膜前表面、角膜后表面总均方根[RMS(Cornea、CF、CB)];全角膜、角膜前表面、角膜后表面高阶像差均方根[RMS HOA(Cornea、CF、CB)]。采用配对t检验或Wilcoxon符号秩检验分析各项参数的变化;采用偏最小二乘回归分析和Spearman相关性分析研究SMILE术后角膜生物力学新型参数变化量与角膜体积变化量、角膜高阶像差参数变化量之间的相关性。结果术后3个月,患者角膜bIOP、ARTh、SP-A1和SSI较术前明显下降;DA^(ratio)、IR和CBI较术前明显升高,差异均有统计学意义(均为P<0.05)。术后3个月,患者角膜CV_(3 mm)、CV_(5 mm)、CV_(7 mm)和CV_(10 mm)均较术前显著降低,差异均有统计学意义(均为P<0.05)。角膜体积变化量从小到大依次为CV_(3 mm)、CV_(5 mm)、CV_(7 mm)、CV_(10 mm),越往周边减少量增加。术后3个月,患者RMS(Cornea、CF、CB)、RMS HOA(Cornea、CF、CB)均较术前显著增加,差异均有统计学意义(均为P<0.05);相对于角膜前表面高阶像差的变化,角膜后表面高阶像差的变化相对稳定。相关分析结果显示,△ARTh与△CV_(3 mm)、△CV_(5 mm)均呈正相关,与△CV_(10 mm)呈负相关(均为P<0.05);△bIOP与△CV_(3 mm)、△CV_(5 mm)、△RMS HOA(CF)、△RMS HOA(Cornea)均呈负相关,与△CV_(10 mm)呈正相关(均为P<0.05);△IR与△CV_(10 mm)呈负相关(P<0.05);△SP-A1与△CV_(10 mm)呈正相关(P<0.05);△CBI与△CV_(3 mm)、△CV_(5 mm)均呈负相关,与△CV_(10 mm)、△RMS(CF)均呈正相关(均为P<0.05)。结论SMILE术后患者早期角膜生物力学发生了一些变化,这些角膜生物力学变化与不同区域角膜体积变化、角膜高阶像差参数变化存在一定的相关性。

不同程度近视患者行SMILE术后的有效光学区及角膜高阶像差比较

目的:比较低、中、高度近视患者行飞秒激光小切口基质透镜取出术(SMILE)后的有效光学区和角膜高阶像差。方法:收集2019-02/2021-02在我院行SMILE手术的患者134例,均取右眼入组,按等效球镜度(SE)分为低度近视组SE>-3.00 D,中度近视组-6.00 D<SE≤-3.00 D,高度近视组SE≤-6.00 D三组,分别于术前和术后1 mo行Pentacam检查,分析各组内手术前后角膜总高阶像差(tHOA)、球差、彗差有无差异,及术后三组间角膜总高阶像差、球差、彗差及有效光学区大小(EOZ)有无差异。结果:SMILE术后1 mo,三组术后有效光学区均小于预设光学区,随着屈光度数增加,有效光学区越小(P<0.05);术后1 mo角膜总高阶像差、球差、彗差均高于术前,除低近视度组球差术前与术后1 mo无差异(P>0.05),其余组别均有差异(P<0.05);角膜总高阶像差、球差和彗差随着屈光度数增加而增加,三组角膜总高阶像差和球差有差异(均P<0.05),高度近视组与中度近视组、低度近视组彗差结果均有差异(P<0.05),中度近视组与低度近视组彗差结果无差异(P>0.05)。结论:随着手术矫正的屈光度数增加,术后有效光学区减少越多,角膜高阶像差增加越明显;SMILE术后1 mo角膜高阶像差较术前增加。

基于FWECS-CYCBD的轴承故障特征提取研究

针对最大二阶循环平稳盲解卷积(maximum second-order cyclostationary blind deconvolution,简称CYCBD)特征提取中循环频率和滤波带宽难确定的问题,引入频率加权能量相关谱(frequency weighted energy correlation spectrum,简称FWECS)来改进CYCBD,实现了低信噪比条件下的滚动轴承故障特征提取。首先,通过FWECS获取周期冲击频率,构造循环频率集;其次,利用最大加权谐波显著性指标设计了一种等步长搜索策略,自适应选取滤波器长度;最后,基于优选的循环频率和滤波带宽进行CYCBD解卷积。轴承仿真和实验数据表明:在循环频率等先验信息未知的情况下,FWECS-CYCBD对故障信号中的微弱冲击特征更敏感;与最小熵解卷积、改进最大相关峭度解卷积和自适应最大二阶循环平稳盲解卷积等方法相比,所提方法在低信噪比条件下能较好地提取轴承故障特征频率信息。