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.

Damage Identification of Truss Structures Based on Force Method

An computationally efficient damage identification technique for the planar and space truss structures is presented based on the force method and the micro ge-netic algorithm.For this purpose,the general equilibrium equations and the kinematic relations in which the reaction forces and the displacements at nodes are take into ac-count,respectively,are formulated.The compatibility equations in terms of forces are explicitly presented using the singular value decomposition(SVD)technique.Then governing equations with unknown reaction forces and initial elongations are derived.Next,the micro genetic algorithm(MGA)is used to properly identify the site and ex-tent of multiple damage cases in truss structures.In order to verify the accuracy and the superiority of the proposed damage detection technique,the numerical solutions are presented for the planar and space truss models.The numerical results indicate that the combination of the force method and the MGA can provide a reliable tool to accurately and efficiently identify the multiple damages of the truss structures.

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.

Structural modal parameter identifi cation and damage diagnosis based on Hilbert-Huang transform

Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.

User Behaviors Analysis in Website Identification Registration

Nowadays, an increasing number of web applications require identification registration. However, the behavior of website registration has not ever been thoroughly studied. We use the database provided by the Chinese Software Develop Net (CSDN) to provide a complete perspective on this research point. We concentrate on the following three aspects: complexity, correlation, and preference. From these analyses, we draw the following conclusions: firstly, a considerable number of users have not realized the importance of identification and are using very simple identifications that can be attacked very easily. Secondly, there is a strong complexity correlation among the three parts of identification. Thirdly, the top three passwords that users like are 123456789, 12345678 and 11111111, and the top three email providers that they prefer are NETEASE, qq and sina. Further, we provide some suggestions to improve the quality of user passwords.

Identification of TRPM7 channels in human intestinal interstitial cells of Cajal

AIM： To investigate the characteristics of slow electrical waves and the presence of transient receptor potential melastatin-type 7 （TRPM7） in the human gastrointestinal （GI） tract. METHODS： Conventional microelectrode techniques were used to record intracellular electrical responses from human GI smooth muscle tissue. Immunohistochemistry was used to identify TRPM7 channels in interstitial cells of Cajat （ICCs）. RESULTS： The human GI tract generated slow electrical waves and had ICCs which functioned as pacemak er cells. Flufenamic acid, a nonselective cation channel blocker, and 2-APB （2-aminoethoxydiphenyl borate） and La3＋, TRPM7 channel blockers, inhibited the slowwaves. Also, TRPM7 channels were expressed in ICCs in human tissue. CONCLUSION： These results suggest that the human GI tract generates slow waves and that TRPM7 channels expressed in the ICCs may be involved in the gen- eration of the slow waves.

Identifi cation of paralytic shellfi sh toxin-producing microalgae using machine learning and deep learning methods

Paralytic shellfi sh poisoning(PSP)microalgae,as one of the harmful algal blooms,causes great damage to the of fshore fi shery,marine culture,and marine ecological environment.At present,there is no technique for real-time accurate identifi cation of toxic microalgae,by combining three-dimensional fluorescence with machine learning(ML)and deep learning(DL),we developed methods to classify the PSP and non-PSP microalgae.The average classifi cation accuracies of these two methods for microalgae are above 90%,and the accuracies for discriminating 12 microalgae species in PSP and non-PSP microalgae are above 94%.When the emission wavelength is 650-690 nm,the fl uorescence characteristics bands(excitation wavelength)occur dif ferently at 410-480 nm and 500-560 nm for PSP and non-PSP microalgae,respectively.The identification accuracies of ML models(support vector machine(SVM),and k-nearest neighbor rule(k-NN)),and DL model(convolutional neural network(CNN))to PSP microalgae are 96.25%,96.36%,and 95.88%respectively,indicating that ML and DL are suitable for the classifi cation of toxic microalgae.

The Theory and Practice of the Interpretation Identification System Designing of Geoparks

In this paper,the author defined the interpretation system of geoparks,studied the designing princeples and methods of the interpretation identification system,including the information,content and appearance. Furthermore,the designing of the interpretation identification system designing of the Hanas National Geopark was conducted as an empirical study,

Enabling an Inorganic-Rich Interface via Cationic Surfactant for High-Performance Lithium Metal Batteries

An anion-rich electric double layer(EDL)region is favorable for fabricating an inorganic-rich solid-electrolyte interphase(SEI)towards stable lithium metal anode in ester electrolyte.Herein,cetyltrimethylammonium bromide(CTAB),a cationic surfactant,is adopted to draw more anions into EDL by ionic interactions that shield the repelling force on anions during lithium plating.In situ electrochemical surface-enhanced Raman spectroscopy results combined with molecular dynamics simulations validate the enrichment of NO_(3)^(−)/FSI−anions in the EDL region due to the positively charged CTA^(+).In-depth analysis of SEI structure by X-ray photoelectron spectroscopy and time-of-flight secondary ion mass spectrometry results confirmed the formation of the inorganic-rich SEI,which helps improve the kinetics of Li^(+)transfer,lower the charge transfer activation energy,and homogenize Li deposition.As a result,the Li||Li symmetric cell in the designed electrolyte displays a prolongated cycling time from 500 to 1300 h compared to that in the blank electrolyte at 0.5 mA cm^(-2) with a capacity of 1 mAh cm^(-2).Moreover,Li||LiFePO_(4) and Li||LiCoO_(2) with a high cathode mass loading of>10 mg cm^(-2) can be stably cycled over 180 cycles.

Metal cation exchanged silica gel for identification of cationic and nonionic surfactants with preliminary separation by thin layer chromatography

Silica gel impregnated with 1% aqueous solutions of different metal cations （Li^＋, Mg^2＋, Zn^2＋, Cu^2＋, Co^2＋, Ni^2＋, Ba^2＋and Th^4＋） has been used for the analysis of nonionic and cationic surfaetants using simple aqueous acetone as mobile phase system. Co^2＋ was found the most suitable impregnant for the mutual separation of nonionic surfactants （Brij-35 and Brij-57） and cationic from nonionic surfactants （tetmdecyltrimethylammonium bromide and Cween-20）. Zinc sulphate impregnation （Zn^2＋-silica gel） shows identical chromatographic behavior and these layers are useful to separate nonionic surfactant （Brij-35） from cationic surfaetant （cetylpyridinium chloride）. The mutual separation of B J-35 and B J-57 is not influenced by the presence of optical brightener in the sample.

Point-of-care ultrasound identifi cation of yolk stalk sign in a case of failed fi rst trimester pregnancy

Emergency physicians are often the first providersto encounter patients with complications in earlypregnancy. Point-of-care （POC） pelvic ultrasound isbeing increasingly used in the evaluation of emergencydepartment （ED） patients with first trimester symptoms.[1]While the initial aim of POC ultrasound in this settingis to confirm an intrauterine pregnancy, a secondarygoal is to differentiate between a normal and abnormalpregnancy. There exist a number of sonographic featuresto suggest a pregnancy is non-viable.

Identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

In today’s growing modern world environment,as human food activities are changing,it is affecting human health,thus leading to diseases like cancer.Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death.So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observa-tion,which has become necessary to classify the type in cancer research.The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment.This paper introduces a Maximal Region-Based Candidate Feature Selection(MRCFS)for early risk diagnosing using Soft-Max Feed Forward Neural Classification(SMF2NC)to solve the above pro-blem.The predictive model is based on a different relational feature learning model,which is possessed to candidate selection to reduce the dimensionality.The redundant features are processed marginal weight rates for observing similar features’variants and the absolute value.Softmax neural hidden layers are trained using the Sigmoid Activation Function(SAF)to create the logical condition for feed-forward layers.Further,the maximal features are introduced to invite a deep neural network con-structed on the Feed Forward Recurrent Neural Network(FFRNN).The classifier produces higher classification accuracy than the previous methods and observes the cancer detection,which is recommended for early diagnosis.

Cation effects in electrocatalytic reduction reactions:Recent advances

Electrocatalytic reduction reactions,powered by clean energy sources such as solar energy and wind,offer a sustainable method for converting inexpensive feedstocks(e.g.,CO_(2),N_(2)/NO_(x),organics,and O_(2))into high-value-added chemicals or fuels.The design and modification of electrocatalysts have been widely implemented to improve their performance in these reactions.However,bottle-necks are encountered,making it challenging to further improve performance through catalyst development alone.Recently,cations in the electrolyte have emerged as critical factors for tuning both the activity and product selectivity of reduction reactions.This review summarizes recent advances in understanding the role of cation effects in electrocatalytic reduction reactions.First,we introduce the mechanisms underlying cation effects.We then provide a comprehensive overview of their application in electroreduction reactions.Characterization techniques and theoretical calcula-tion methods for studying cation effects are also discussed.Finally,we address remaining challeng-es and future perspectives in this field.We hope that this review offers fundamental insights and design guidance for utilizing cation effects,thereby advancing their development.

Exploring the mechanism of a novel cationic surfactant in bastnaesite flotation via the integration of DFT calculations,in-situ AFM and electrochemistry

Effectively separating bastnaesite from calcium-bearing gangue minerals(particularly calcite)presents a formidable challenge,making the development of efficient collectors crucial.To achieve this,we have designed and synthesized a novel,highly efficient,water-soluble cationic collector,N-dodecylisopropanolamine(NDIA),for use in the bastnaesite-calcite flotation process.Density functional theory(DFT)calculations identified the amine nitrogen atom in NDIA as the site most susceptible to electrophilic attack and electron loss.By introducing an OH group into the traditional collector dodecylamine(DDA)structure,NDIA provided additional adsorption sites,enabling synergistic adsorption on the surface of bastnaesite,thereby significantly enhancing both the floatability and selectivity of these minerals.The recovery of bastnaesite was 76.02%,while the calcite was 1.26%.The NDIA markedly affected the zeta potential of bastnaesite,while its impact on calcite was relatively minor.Detailed Fourier-transform infrared spectroscopy(FTIR)and X-ray photoelectron spectroscopy(XPS)results elucidated that the―NH―and―OH groups in NDIA anchored onto the bastnaesite surface through robust electrostatic and hydrogen bonding interactions,thereby enhancing bastnaesite's affinity for NDIA.Furthermore,in situ atomic force microscopy(AFM)provided conclusive evidence of NDIA aggregation on the bastnaesite surface,improving contact angle and hydrophobicity,and significantly boosting the flotation recovery of bastnaesite.

Waste acid recovery utilizing monovalent cation permselective membranes through selective electrodialysis

Selective electrodialysis(SED)has surfaced as a highly promising membrane separation technique in the realm of acid recovery owing to its ability to effectively separate monovalent ions through the utilization of a potential difference.However,the current SED process is limited by conventional commercial monovalent cation permselective membranes(MCPMs).This study systematically investigates the use of an independently developed MCPM in the SED process for acid recovery.Various factors such as current density,volume ratio,initial ion concentration,and waste acid systems are considered.The independently developed MCPM offers several advantages over the commercial monovalent selective cation-exchange membrane(CIMS),including higher recovered acid concentration,better ion flux ratio,improved acid recovery efficiency,increased recovered acid purity,and higher current efficiency.The SED process with the MCPM achieves a recovered acid of 95.9%and a concentration of 2.3 mol·L^(–1) in the HCl/FeCl_(2) system,when a current density of 20 mA·cm^(-2) and a volume ratio of 1:2 are applied.Similarly,in the H_(2)SO_(4)/FeSO_(4) system,a purity of over 99%and a concentration of 2.1 mol·L^(–1) can be achieved in the recovered acid.This study thoroughly examines the impact of operation conditions on acid recovery performance in the SED process.The independently developed MCPM demonstrates outstanding acid recovery performance,highlighting its potential for future commercial utilization.

Fabrication and Properties of a New Reactive Diluent for Cationic UV Curing

The reactive diluent prepared by siloxane modified Trimethylene oxide can improve the performance of the UV curing system.Therefore,1,7-bis[(3-ethyl-3-methoxyoxacylobutane)propyl]octadecylosiloxane(BEMOPOMTS)was synthesized from diethyl carbonate,trimethylopropanes,allyl bromide,and 1,1,3,3,5,5,7,7-octadecylosiloxane as the main raw materials.BEMOPOMTS can be used as reactive diluents in the field of cationic UV curing.It has good thermal stability,and the addition of BEMOPOMTS significantly improves the tensile strength and elongation at break of epoxy resin.Compared with the pure epoxy resin,adding 20%BEMOPOMTS increased the elastic modulus by 25%to 677 MPa.