Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.

Genome-Wide Identification of the GST Gene Family in Loquat (Eriobotrya japonica Lindl.) and Their Expression under Cold Stress with ALA Pretreatment

Loquat(Eriobotrya japonica Lindl.),a rare fruit native to China,has a long history of cultivation in China.Low temperature is the key factor restricting loquat growth and severely affects yield.Low temperature induces the regeneration and metabolism of reduced glutathione(GSH)to alleviate stress damage via the participation of glu-tathione S-transferases(GSTs)in plants.In this study,16 GSTs were identified from the loquat genome according to their protein sequence similarity with Arabidopsis GSTs.On the basis of domain characteristics and phyloge-netic analysis of AtGSTs,these EjGSTs can be divided into 4 subclasses:Phi,Theta,Tau and Zeta.The basic prop-erties,subcellular localization,structures,motifs,chromosomal distribution and collinearity of the EjGST proteins or genes were further analyzed.Tandem and segmental gene duplications play pivotal roles in EjGST expansion.Cis-elements that respond to various hormones and stresses,especially those associated with low-temperature responsiveness,were predicted to be present in the promoters of EjGSTs.Moreover,analysis of gene expression profiles revealed that 9 of 16 EjGSTs may be involved in the low-temperature responsiveness of loquat leaves.In agriculture,5-aminolevulinic acid(ALA),a potential multifunctional plant growth regulator,can improve the stress response of plants.Among the 9 low-temperature-responsive EjGSTs,the expression of EjGSTU1 and EjGSTF1 significantly differed under cold stress in response to exogenous 5-aminolevulinic acid(ALA)pretreat-ment.The remarkable increase in GST activity and GSH/GSSG ratio reflected the increase in the cold response ability of loquat plants caused by exogenous ALA,thereby alleviating H2O2 accumulation and membrane lipid preoxidation.Overall,this study provides an initial exploration of the cold tolerance function of GSTs in loquat,offering a theoretical foundation for the development of cold-resistant loquat cultivars and new antifreeze agents.

Identification of Mulberry Bacterial Blight Caused by Klebsiella oxytoca in Bazhong,Sichuan,China

To provide a scientific basis for controlling mulberry bacterial blight in Bazhong,Sichuan,China(BSC),this study aimed to isolate and purify pathogenic bacteria from diseased branches of mulberry trees in the region and to clarify their taxonomic status using morphological observation,physiological and biochemical detection,molecular-level identification,and the construction of a phylogenetic tree.A total of 218 bacterial strains were isolated from samples of diseased mulberry branches.Of these,7 strains were identified as pathogenic bacteria based on pathogenicity tests conducted in accordance with Koch’s postulates.Preliminary findings from the analysis of the 16S rRNA sequence indicated that the 7 pathogenic bacteria are members of Klebsiella spp.Morphological observation revealed that the pathogenic bacteria were oval-shaped and had capsules but no spores.They could secrete pectinase,cellulase,and protease and were able to utilize D-glucose,D-mannose,D-maltose,and D-Cellobiose.The 7 strains of pathogenic bacteria exhibited the highest homology with Klebsiella oxytoca.This study identifies Klebsiella oxytoca as the causative agent of mulberry bacterial blight in BSC,laying the foundation for the prevention and control of this pathogen and further investigation into its pathogenic mechanism.

Application of Transgenic Technology in Identification for Gene Function on Grasses

Perennial grasses have developed intricate mechanisms to adapt to diverse environments,enabling their resistance to various biotic and abiotic stressors.These mechanisms arise from strong natural selection that contributes to enhancing the adaptation of forage plants to various stress conditions.Methods such as antisense RNA technology,CRISPR/Cas9 screening,virus-induced gene silencing,and transgenic technology,are commonly utilized for investigating the stress response functionalities of grass genes in both warm-season and cool-season varieties.This review focuses on the functional identification of stress-resistance genes and regulatory elements in grasses.It synthesizes recent studies on mining functional genes,regulatory genes,and protein kinase-like signaling factors involved in stress responses in grasses.Additionally,the review outlines future research directions,providing theoretical support and references for further exploration of(i)molecular mechanisms underlying grass stress responses,(ii)cultivation and domestication of herbage,(iii)development of high-yield varieties resistant to stress,and(iv)mechanisms and breeding strategies for stress resistance in grasses.

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

The global pandemic of novel coronavirus that started in 2019 has ser-iously affected daily lives and placed everyone in a panic condition.Widespread coronavirus led to the adoption of social distancing and people avoiding unneces-sary physical contact with each other.The present situation advocates the require-ment of a contactless biometric system that could be used in future authentication systems which makesfingerprint-based person identification ineffective.Periocu-lar biometric is the solution because it does not require physical contact and is able to identify people wearing face masks.However,the periocular biometric region is a small area,and extraction of the required feature is the point of con-cern.This paper has proposed adopted multiple features and emphasis on the periocular region.In the proposed approach,combination of local binary pattern(LBP),color histogram and features in frequency domain have been used with deep learning algorithms for classification.Hence,we extract three types of fea-tures for the classification of periocular regions for biometric.The LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB channel.In order to extract the frequency domain fea-tures,the wavelet transformation is obtained.By learning from these features,a convolutional neural network(CNN)becomes able to discriminate the features and can provide better recognition results.The proposed approach achieved the highest accuracy rates with the lowest false person identification.

Intelligent Risk-Identification Algorithm with Vision and 3D LiDAR Patterns at Damaged Buildings

Existingfirefighting robots are focused on simple storage orfire sup-pression outside buildings rather than detection or recognition.Utilizing a large number of robots using expensive equipment is challenging.This study aims to increase the efficiency of search and rescue operations and the safety offirefigh-ters by detecting and identifying the disaster site by recognizing collapsed areas,obstacles,and rescuers on-site.A fusion algorithm combining a camera and three-dimension light detection and ranging(3D LiDAR)is proposed to detect and loca-lize the interiors of disaster sites.The algorithm detects obstacles by analyzingfloor segmentation and edge patterns using a mask regional convolutional neural network(mask R-CNN)features model based on the visual data collected from a parallelly connected camera and 3D LiDAR.People as objects are detected using you only look once version 4(YOLOv4)in the image data to localize persons requiring rescue.The point cloud data based on 3D LiDAR cluster the objects using the density-based spatial clustering of applications with noise(DBSCAN)clustering algorithm and estimate the distance to the actual object using the center point of the clustering result.The proposed artificial intelligence(AI)algorithm was verified based on individual sensors using a sensor-mounted robot in an actual building to detectfloor surfaces,atypical obstacles,and persons requiring rescue.Accordingly,the fused AI algorithm was comparatively verified.

Radio frequency fingerprint identification for Internet of Things:A survey

Radio frequency fingerprint(RFF)identification is a promising technique for identifying Internet of Things(IoT)devices.This paper presents a comprehensive survey on RFF identification,which covers various aspects ranging from related definitions to details of each stage in the identification process,namely signal preprocessing,RFF feature extraction,further processing,and RFF identification.Specifically,three main steps of preprocessing are summarized,including carrier frequency offset estimation,noise elimination,and channel cancellation.Besides,three kinds of RFFs are categorized,comprising I/Q signal-based,parameter-based,and transformation-based features.Meanwhile,feature fusion and feature dimension reduction are elaborated as two main further processing methods.Furthermore,a novel framework is established from the perspective of closed set and open set problems,and the related state-of-the-art methodologies are investigated,including approaches based on traditional machine learning,deep learning,and generative models.Additionally,we highlight the challenges faced by RFF identification and point out future research trends in this field.

Genome-Wide Exploration of the Grape GLR Gene Family and Differential Responses of VvGLR3.1 and VvGLR3.2 to Low Temperature and Salt Stress

Grapes,one of the oldest tree species globally,are rich in vitamins.However,environmental conditions such as low temperature and soil salinization significantly affect grape yield and quality.The glutamate receptor(GLR)family,comprising highly conserved ligand-gated ion channels,regulates plant growth and development in response to stress.In this study,11 members of the VvGLR gene family in grapes were identified using whole-genome sequence analysis.Bioinformatic methods were employed to analyze the basic physical and chemical properties,phylogenetic trees,conserved domains,motifs,expression patterns,and evolutionary relationships.Phylogenetic and collinear analyses revealed that the VvGLRs were divided into three subgroups,showing the high conservation of the grape GLR family.These members exhibited 2 glutamate receptor binding regions(GABAb and GluR)and 3-4 transmembrane regions(M1,M2,M3,and M4).Real-time quantitative PCR analysis demonstrated the sensitivity of all VvGLRs to low temperature and salt stress.Subsequent localization studies in Nicotiana tabacum verified that VvGLR3.1 and VvGLR3.2 proteins were located on the cell membrane and cell nucleus.Additionally,yeast transformation experiments confirmed the functionality of VvGLR3.1 and VvGLR3.2 in response to low temperature and salt stress.Thesefindings highlight the significant role of the GLR family,a highly conserved group of ion channels,in enhancing grape stress resistance.This study offers new insights into the grape GLR gene family,providing fundamental knowledge for further functional analysis and breeding of stress-resistant grapevines.

Exploration of transferable deep learning-aided radio frequency fingerprint identification systems

Radio frequency fingerprint identification(RFFI)shows great potential as a means for authenticating wireless devices.As RFFI can be addressed as a classification problem,deep learning techniques are widely utilized in modern RFFI systems for their outstanding performance.RFFI is suitable for securing the legacy existing Internet of Things(IoT)networks since it does not require any modifications to the existing end-node hardware and communication protocols.However,most deep learning-based RFFI systems require the collection of a great number of labelled signals for training,which is time-consuming and not ideal,especially for the Io T end nodes that are already deployed and configured with long transmission intervals.Moreover,the long time required to train a neural network from scratch also limits rapid deployment on legacy Io T networks.To address the above issues,two transferable RFFI protocols are proposed in this paper leveraging the concept of transfer learning.More specifically,they rely on fine-tuning and distance metric learning,respectively,and only require only a small amount of signals from the legacy IoT network.As the dataset used for transfer is small,we propose to apply augmentation in the transfer process to generate more training signals to improve performance.A Lo Ra-RFFI testbed consisting of 40 commercial-off-the-shelf(COTS)Lo Ra IoT devices and a software-defined radio(SDR)receiver is built to experimentally evaluate the proposed approaches.The experimental results demonstrate that both the fine-tuning and distance metric learning-based RFFI approaches can be rapidly transferred to another Io T network with less than ten signals from each Lo Ra device.The classification accuracy is over 90%,and the augmentation technique can improve the accuracy by up to 20%.

The technology of radio frequency fingerprint identification based on deep learning for 5G application

User Equipment(UE)authentication holds paramount importance in upholding the security of wireless networks.A nascent technology,Radio Frequency Fingerprint Identification(RFFI),is gaining prominence as a means to bolster network security authentication.To expedite the integration of RFFI within fifth-generation(5G)networks,this research undertakes the creation of a comprehensive link-level simulation platform tailored for 5G scenarios.The devised platform emulates various device impairments,including an oscillator,IQ modulator,and power amplifier(PA)nonlinearities,alongside simulating channel distortions.Consequent to this,a plausibility analysis is executed,intertwining transmitter device impairments with 3rd Generation Partnership Project(3GPP)new radio(NR)protocols.Subsequently,an exhaustive exploration is conducted to assess the impact of transmitter impairments,deep neural networks(DNNs),and channel effects on RF fingerprinting performance.Notably,under a signal-to-noise ratio(SNR)of 15 d B,the deep learning approach demonstrates the capability to accurately classify 100 UEs with a commendable 91%accuracy rate.Through a multifaceted evaluation,it is ascertained that the Attention-based network architecture emerges as the optimal choice for the RFFI task,serving as the new benchmark model for RFFI applications.

Genome-Wide Discovery and Expression Profiling of the SWEET Sugar Transporter Gene Family in Woodland Strawberry (Fragaria vesca) under Developmental and Stress Conditions: Structural and Evolutionary Analysis

The SWEET(sugar will eventually be exported transporter)family proteins are a recently identified class of sugar transporters that are essential for various physiological processes.Although the functions of the SWEET proteins have been identified in a number of species,to date,there have been no reports of the functions of the SWEET genes in woodland strawberries(Fragaria vesca).In this study,we identified 15 genes that were highly homolo-gous to the A.thaliana AtSWEET genes and designated them as FvSWEET1–FvSWEET15.We then conducted a structural and evolutionary analysis of these 15 FvSWEET genes.The phylogenetic analysis enabled us to categor-ize the predicted 15 SWEET proteins into four distinct groups.We observed slight variations in the exon‒intron structures of these genes,while the motifs and domain structures remained highly conserved.Additionally,the developmental and biological stress expression profiles of the 15 FvSWEET genes were extracted and analyzed.Finally,WGCNA coexpression network analysis was run to search for possible interacting genes of FvSWEET genes.The results showed that the FvSWEET10 genes interacted with 20 other genes,playing roles in response to bacterial and fungal infections.The outcomes of this study provide insights into the further study of FvSWEET genes and may also aid in the functional characterization of the FvSWEET genes in woodland strawberries.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

一株猪细小病毒7群的鉴定和分离

猪细小病毒7群(Porcine parvovirus 7,PPV-7)是2016年首次从美国猪群发现和鉴定的新的猪细小病毒。为弄清我国猪群中是否存在PPV-7,本文用PPV-7特异性的实时荧光定量PCR和常规PCR方法对采集的10份猪流产胎儿和10份保育仔猪病料分别进行检测,结果发现其中1份保育仔猪样品的常规PCR和实时荧光定量PCR检测结果均为阳性,常规PCR扩增出的246 bp特异性条带测序和分子遗传演化时发现,该序列与PPV-7参考毒株KU5637332和KY996757的同源性分别为99.6%和98.0%,表明该样品中含有PPV-7,进一步用PK-15细胞分离病毒,连续传代5次后PK-15细胞都没有出现典型的细胞病变,但其上清液用实时荧光定量PCR方法都可以检测到该病毒。本研究结果证实我国猪群中存在PPV-7的感染,且检测到的PPV-7毒株能在PK15细胞中增殖。

^(18)F标记的新型鏻正阳离子PET心肌灌注显像剂的制备及生物性能评价

为研制新型的PET心肌灌注显像剂,设计合成了18 F标记的鏻正阳离子:3-氟-18 F-甲基苄基-三-(2,6-二甲氧基苯基)鏻盐(18F-2),并进行了小鼠生物分布研究。18F-2采用一锅法制备得到,总的标记时间小于60min,校正后的产率为(31±3)%,放化纯度>95%;小鼠生物分布结果表明,18 F-2在小鼠心肌中有很高的初始摄取和良好的滞留,给药后2min和60min的心肌摄取分别为(53.88±7.45)%ID/g和(23.93±3.28)%ID/g;其在肝、肺、血等非靶组织中的摄取低,且清除快,给药后60min心与肝、心与肺、心与血放射性摄取比分别为3.99、3.80和9.17。值得进一步深入研究。

参数识别问题混合有限元解的最大模误差估计

研究了参数识别问题混合有限元解的最大模误差估计.利用1阶Raviart-Thomas混合有限元离散状态和对偶状态变量,利用分片线性函数逼近控制变量,获得了状态变量和控制变量的最大模误差估计,这里控制变量的收敛阶是h^2,状态变量的收敛阶是h3/2|lnh|1/2.最后利用数值算例验证了理论结果.

荧光标记技术在井内流体离子检测中的应用

将广泛应用于生物医学领域的荧光标记技术移植到井内流体离子分析中,从荧光标记技术的原理出发,综述了荧光标记检测的主要井内流体离子,对荧光标记技术在井内流体离子检测中的应用进行了探索和分析,主要包括储层流体性质识别、钻井安全保障、泥浆配方调整、注水方案设计、外来污染及井下设备结垢的判断等。该技术可检测井内流体中油气勘探领域所关注的主要阴离子、阳离子,也可进行离子检测环境的pH值测量。要实现井内流体离子荧光标记地面在线检测,还需解决泥浆循环过程中的固液分离技术、荧光标记离子的光谱检测分析技术和荧光标记离子检测过程中的各种影响因素消除技术。

阳离子胶体金标记活细胞阴离子位点的双光子荧光成像及其应用

使用阳离子胶体金标记中国仓鼠卵巢细胞(CHO-K1)的阴离子位点,并采用双光子荧光显微成像和荧光寿命成像技术记录活细胞的阴离子场分布.阳离子胶体金是纳米量级金微粒与多聚L-赖氨酸的结合物,金纳米微粒在超短激光脉冲的照射下可以产生高度局域化的光热效应.当飞秒激光脉冲聚焦在细胞膜上标记的金纳米微粒时会产生这种纳米尺度的微光热效应,并在不影响细胞活性的前提下暂时提高细胞膜的通透性.基于这种效应,使用聚焦的飞秒激光脉冲三维扫描照射CHO-K1细胞,将分子质量为10ku的荧光探针大分子异硫氰酸荧光素葡聚糖(fluorescein isothiocyanate-dextran,FITC-D)递送到CHO-K1细胞的内部,并用双光子荧光图像记录其递送的过程.使用流式细胞仪分析不同实验条件下FITC-D的转导率和细胞死亡率的关系.

茶叶挥发性成分中关键呈香成分研究进展

香气是决定茶叶品质的重要因子之一。研究表明,茶叶挥发性成分中存在少量关键呈香成分,对茶叶的香气品质具有重要影响,具有重要的研究价值和研究意义。本文综述了近年来茶叶挥发性成分中这些关键呈香成分的研究进展,包括关键呈香成分的分析鉴定方法和主要茶类(绿茶、红茶、乌龙茶、黑茶)以及其他再加工茶(主要为花茶)中已经鉴定出的关键呈香成分的汇总分析,并探讨茶叶中关键呈香成分未来的研究方向。这些研究结果丰富了茶叶风味品质化学基础理论,可为提升现代茶叶加工技术和开展茶叶香气品质定向调控等提供科学依据。

禽偏肺病毒研究进展

禽偏肺病毒(avian metapneumovirus,aMPV)可感染火鸡、鸡、鸭以及一些野生禽类,传染性强、传播迅速,易造成继发感染,导致严重的上呼吸道感染和产蛋率下降等症状。该病在世界范围内广泛流行,并呈地方性流行,给养禽业造成严重经济损失。本文以国内外对aMPV在禽类中的流行病学调查和基因分析研究报道为基础,从病原学、流行病学、病毒分离鉴定和防治的角度,对其在禽类中的感染情况和引起的相关疾病进行简要概述;比较多种分子生物学检测方法的优缺点和实用性,为建立快速、简便、实用的aMPV检测方法提供参考。

对虾急性肝胰腺坏死综合征不同检测方法结果分析

急性肝胰腺坏死综合征(acute hepatopancreas necrosis disease,AHPND)是一种危害大、新出现的对虾疫病。目前认为该病由一种副溶血性弧菌(Vibrio parahaemolyticus,VP)引起。本试验通过生化鉴定、血清学分型、MALDI-TOF-MS鉴定以及文献建立的PCR方法,在福建省南美白对虾养殖场,对分离于AHPND病虾肝胰腺的VP进行了检测。通过VITEK-2鉴定出81株VP分离株。从中选取9株分别进行MALDI-TOF-MS鉴定、PCR检测和血清学分型,发现这9株分离菌株均与MALDI-TOF-MS数据库中的VP匹配。其中:4株PCR检测为阳性,被鉴定为AHPND VP,其血清型包括O1:KUT和O1:K68;另5株PCR检测为阴性,血清型包括O1:KUT、O3:K6和O1:K68。试验表明:AHPND VP分离株存在不同血清型,可通过MALDI-TOF-MS进行菌种鉴定;MALDI-TOF-MS与PCR结合使用,可以准确、快速鉴定AHPND VP,这有利于开展AHPND的病原学分析、流行病学调查以及诊断和监测。