Desalination of sea water projects are critical for addressing water scarcity in regions like Egypt,but they face numerous risks that can hinder their success.This study identiies and analyzes 53 risk factors affectin...Desalination of sea water projects are critical for addressing water scarcity in regions like Egypt,but they face numerous risks that can hinder their success.This study identiies and analyzes 53 risk factors affecting renewable energy desalination projects through expert interviews,literature review,and a questionnaire survey completed by 47 experts.Statistical methods,including descriptive statistics(mean,mode,standard error,and standard deviation),Pearson correlation,and Cronbach’s alpha,were employed to validate the reliability and signiicance of these factors.The overall questionnaire showed excellent reliability(α=0.815 for probability of occurrence;α=0.921 for degree of impact).The results indicate a strong consensus among industry experts.Inlation and priceluctuations was ranked as the highest‑probability risk(mean=4.32/5),while faulty design of plant components(intake,outfall,mechanical systems)was ranked as the highest‑impact risk(mean=4.51/5).Conversely,environmental disasters(earthquakes,loods)showed the lowest probability of occurrence(mean=1.91/5),and social pressures from entities not directly invested in the project’s success showed the lowest degree of impact(mean=2.70/5).These statistically validatedindings provide project stakeholders with critical insights into the most signiicant threats to desalination initiatives in Egypt’s unique operational context.Theseindings provide a robust basis for understanding and managing risks in desalination projects,contributing to grow the knowledge on desalination project sustainability and offers actionable insights for stakeholders in Egypt and similar arid regions.展开更多
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 accordi...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.展开更多
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 avo...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.展开更多
In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a...In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.展开更多
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...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.展开更多
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 scatt...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.展开更多
Cultivable bacteria were isolated from seawater-based retting treatment of hemp, in which three of purified strains (SW- 1, SW- 2, and S-SW1) produced relatively high levels of pectinase activities, and also produced ...Cultivable bacteria were isolated from seawater-based retting treatment of hemp, in which three of purified strains (SW- 1, SW- 2, and S-SW1) produced relatively high levels of pectinase activities, and also produced mannanases and xylanases. PCR-based entebacterial repetitive intergenic consensus primers (ERIC-PCR) were employed for fingerprinting DNA of the bacterial strains. The ERIC-PCR fingerprints of stains SW- 1, SW- 2, and S-SW1 were found to be different, and should be further identified for each isolate. Strains SW- 1 and SW- 2 were identified as Stenotrophomnas maltophilia, while strain S-SW1 was assigned to Ochrobactrum anthropi by BIOLOG system. These two species represented rhizosphere bacterial genera, and possibly were introduced by the hemp plants. These organisms seemed potentially capable of producing pectinase and hemicellulase, and thus effectively degrading the gum substances in the seawater retting. This research could be helpful for improving a novel seawater-based retting treatment of hemp.展开更多
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...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.展开更多
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 p...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.展开更多
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 a...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.展开更多
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 ...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.展开更多
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 contri...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.展开更多
Dear Editor,Childhood Disintegrative Disorder(CDD),also known as Heller’s syndrome and disintegrative psychosis,is a rare progressive neurological disorder,characterized by a late onset([2 years of age)and regression...Dear Editor,Childhood Disintegrative Disorder(CDD),also known as Heller’s syndrome and disintegrative psychosis,is a rare progressive neurological disorder,characterized by a late onset([2 years of age)and regression of language,social.展开更多
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 rang...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.展开更多
The development of facial recognition technology has become an increasingly powerful tool in wild animal indi-vidual recognition.In this paper,we develop an automatic detection and recognition method with the combinat...The development of facial recognition technology has become an increasingly powerful tool in wild animal indi-vidual recognition.In this paper,we develop an automatic detection and recognition method with the combinations of body features of big cats based on the deep convolutional neural network(CNN).We collected dataset including 12244 images from 47 individual Amur tigers(Panthera tigris altaica)at the Siberian Tiger Park by mobile phones and digital camera and 1940 images and videos of 12 individual wild Amur leopard(Panthera pardus orientalis)by infrared cameras.First,the single shot multibox detector algorithm is used to perform the automatic detection process of feature regions in each image.For the different feature regions of the image,like face stripe or spots,CNNs and multi-layer perceptron models were applied to automatically identify tiger and leopard individuals,in-dependently.Our results show that the identification accuracy of Amur tiger can reach up to 93.27%for face front,93.33%for right body stripe,and 93.46%for left body stripe.Furthermore,the combination of right face,left body stripe,and right body stripe achieves the highest accuracy rate,up to 95.55%.Consequently,the combination of different body parts can improve the individual identification accuracy.However,it is not the higher the number of body parts,the higher the accuracy rate.The combination model with 3 body parts has the highest accuracy.The identification accuracy of Amur leopard can reach up to 86.90%for face front,89.13%for left body spots,and 88.33%for right body spots.The accuracy of different body parts combination is lower than the independent part.For wild Amur leopard,the combination of face with body spot part is not helpful for the improvement of identification accuracy.The most effective identification part is still the independent left or right body spot part.It can be applied in long-term monitoring of big cats,including big data analysis for animal behavior,and be helpful for the individual identification of other wildlife species.展开更多
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...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%.展开更多
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 equ...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.展开更多
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 bols...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.展开更多
One hundred andfifty-three isolates from the environment and 36 reference strains of the Legionella were studied with regards to their composition of cellular fatty acids as determined by gas chromatography,and then we...One hundred andfifty-three isolates from the environment and 36 reference strains of the Legionella were studied with regards to their composition of cellular fatty acids as determined by gas chromatography,and then were classified into 41 groups by numerical analysis.Most reference strains formed only a single group,except L.micdadei,L.jamestowniensis,L.parisiensis,L.jorda-nis,L.feeleii and L.longbeachae,which were clustered into two or three groups.Even serological types of L.pneumophila could be clearly identified.Therefore,in this study,numerical analysis of cellular fatty acid composition is an effective method for identifying Legionella species.展开更多
This article focuses on the problem of how to accurately calculate the joint control torques when the explosion-proof robot performs collision detection without sensors and gives a complete solution.Nonlinear joint fr...This article focuses on the problem of how to accurately calculate the joint control torques when the explosion-proof robot performs collision detection without sensors and gives a complete solution.Nonlinear joint frictions are incorporated into the dynamic model of a robotic manip-ulator to improve calculation accuracy.A genetic algorithm is used to optimise the excitation trajectories to fully stimulate the robot dynamic characteristics.Effective and applicable data filtering and smoothing methods are proposed and the Iteratively Reweighted Least-Squares method based on the error term is applied to identify the robot dynamic parameters.Compared with Ordinary Least-Squares method,the proposed algorithm improves the accuracy of joint control torques estimation.展开更多
文摘Desalination of sea water projects are critical for addressing water scarcity in regions like Egypt,but they face numerous risks that can hinder their success.This study identiies and analyzes 53 risk factors affecting renewable energy desalination projects through expert interviews,literature review,and a questionnaire survey completed by 47 experts.Statistical methods,including descriptive statistics(mean,mode,standard error,and standard deviation),Pearson correlation,and Cronbach’s alpha,were employed to validate the reliability and signiicance of these factors.The overall questionnaire showed excellent reliability(α=0.815 for probability of occurrence;α=0.921 for degree of impact).The results indicate a strong consensus among industry experts.Inlation and priceluctuations was ranked as the highest‑probability risk(mean=4.32/5),while faulty design of plant components(intake,outfall,mechanical systems)was ranked as the highest‑impact risk(mean=4.51/5).Conversely,environmental disasters(earthquakes,loods)showed the lowest probability of occurrence(mean=1.91/5),and social pressures from entities not directly invested in the project’s success showed the lowest degree of impact(mean=2.70/5).These statistically validatedindings provide project stakeholders with critical insights into the most signiicant threats to desalination initiatives in Egypt’s unique operational context.Theseindings provide a robust basis for understanding and managing risks in desalination projects,contributing to grow the knowledge on desalination project sustainability and offers actionable insights for stakeholders in Egypt and similar arid regions.
基金supported by the Innovation Foundation of Provincial Education Department of Gansu(2024B-005)the Gansu Province National Science Foundation(22YF7GA182)the Fundamental Research Funds for the Central Universities(No.lzujbky2022-kb01)。
文摘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.
文摘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.
基金supported by the project of the National Social Science Fundation(21BJL052,20BJY020,20BJL127,19BJY090)the 2018 Fujian Social Science Planning Project(FJ2018B067)The Planning Fund Project of Humanities and Social Sciences Research of the Ministry of Education in 2019(19YJA790102),The grant has been received by Aoqi Xu.
文摘In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.
基金This research was funded by the Natural Science Foundation of Shandong Province of China(ZR2022MC144).
文摘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.
基金supported by the National Key Research and Development Program of China(No.2018YFA0702800)the National Natural Science Foundation of China(No.12072056)supported by National Defense Fundamental Scientific Research Project(XXXX2018204BXXX).
文摘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.
基金Programfor Changjiang Scholars and Innovative Research Teamin University,China( No .IRT0654)Major State Basic Research Development Program,China ( No .2008CB617506)Analysis and Test Foundation of Zhejiang Province,China ( No .2007F70040)
文摘Cultivable bacteria were isolated from seawater-based retting treatment of hemp, in which three of purified strains (SW- 1, SW- 2, and S-SW1) produced relatively high levels of pectinase activities, and also produced mannanases and xylanases. PCR-based entebacterial repetitive intergenic consensus primers (ERIC-PCR) were employed for fingerprinting DNA of the bacterial strains. The ERIC-PCR fingerprints of stains SW- 1, SW- 2, and S-SW1 were found to be different, and should be further identified for each isolate. Strains SW- 1 and SW- 2 were identified as Stenotrophomnas maltophilia, while strain S-SW1 was assigned to Ochrobactrum anthropi by BIOLOG system. These two species represented rhizosphere bacterial genera, and possibly were introduced by the hemp plants. These organisms seemed potentially capable of producing pectinase and hemicellulase, and thus effectively degrading the gum substances in the seawater retting. This research could be helpful for improving a novel seawater-based retting treatment of hemp.
基金This research was funded by grants from the Fujian Provincial Science and Technology Project(2021N5014,2022N5006)the Science and Technology Plan Project of Putian(2023GJGZ001).
文摘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.
基金funded by the Fujian Provincial Science and Technology Project(2021N5014,2022N5006)the Key Research Project of the Putian Science and Technology Bureau(2021ZP08,2021ZP09,2021ZP10,2021ZP11,2023GJGZ001).
文摘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.
基金supported by Key Scientific and Technological Grant of Zhejiang for Breeding New Agricultural Varieties(2021C02072-6)the Natural Science Foundation of Anhui Provincial Education Department(KJ2019A0574).
文摘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.
基金supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education(No.2020R1I1A3068274),Received by Junho Ahn.https://www.nrf.re.kr/supported by the Korea Agency for Infrastructure Technology Advancement(KAIA)by the Ministry of Land,Infrastructure and Transport under Grant(No.22QPWO-C152223-04),Received by Chulsu Kim.https://www.kaia.re.kr/.
文摘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.
基金supported by the Chief Scientist Program of Qinghai Province(2024-SF-101).
文摘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.
基金supported by the Chinese Academy of Sciences Strategic Priority Research Program,China(XDB02050400)the National Natural Science Foundation of China(91432111)the Shanghai Second Medical University-Institute of Neuroscience Research Center for Brain Disorders,China(2015NKX005)
文摘Dear Editor,Childhood Disintegrative Disorder(CDD),also known as Heller’s syndrome and disintegrative psychosis,is a rare progressive neurological disorder,characterized by a late onset([2 years of age)and regression of language,social.
基金supported in part by the National Natural Science Foundation of China under Grant 62171120 and 62001106National Key Research and Development Program of China(2020YFE0200600)+2 种基金Jiangsu Provincial Key Laboratory of Network and Information Security No.BM2003201Guangdong Key Research and Development Program under Grant2020B0303010001Purple Mountain Laboratories for Network and Communication Security
文摘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.
基金funded by the Fundamental Research Funds for the Central Universities(2572020BC05)the Heilongjiang postdoctoral fund project(LBH-Z18003)+3 种基金the Biodiversity Survey,Monitoring and Assessment Project of Ministry of Ecology and Environment,China(2019HB2096001006)the National Natural Science Foundation of China(NSFC 31872241)the Individual Identification Technological Research on Cameratrapping images of Amur tigers(NFGA 2017)National Innovation and Entrepreneurship Training Program for College Student(S202010225022).
文摘The development of facial recognition technology has become an increasingly powerful tool in wild animal indi-vidual recognition.In this paper,we develop an automatic detection and recognition method with the combinations of body features of big cats based on the deep convolutional neural network(CNN).We collected dataset including 12244 images from 47 individual Amur tigers(Panthera tigris altaica)at the Siberian Tiger Park by mobile phones and digital camera and 1940 images and videos of 12 individual wild Amur leopard(Panthera pardus orientalis)by infrared cameras.First,the single shot multibox detector algorithm is used to perform the automatic detection process of feature regions in each image.For the different feature regions of the image,like face stripe or spots,CNNs and multi-layer perceptron models were applied to automatically identify tiger and leopard individuals,in-dependently.Our results show that the identification accuracy of Amur tiger can reach up to 93.27%for face front,93.33%for right body stripe,and 93.46%for left body stripe.Furthermore,the combination of right face,left body stripe,and right body stripe achieves the highest accuracy rate,up to 95.55%.Consequently,the combination of different body parts can improve the individual identification accuracy.However,it is not the higher the number of body parts,the higher the accuracy rate.The combination model with 3 body parts has the highest accuracy.The identification accuracy of Amur leopard can reach up to 86.90%for face front,89.13%for left body spots,and 88.33%for right body spots.The accuracy of different body parts combination is lower than the independent part.For wild Amur leopard,the combination of face with body spot part is not helpful for the improvement of identification accuracy.The most effective identification part is still the independent left or right body spot part.It can be applied in long-term monitoring of big cats,including big data analysis for animal behavior,and be helpful for the individual identification of other wildlife species.
基金in part supported by UK Engineering and Physical Sciences Research Council under grant ID EP/V027697/1in part by the National Key Research and Development Program of China under grant ID 2020YFE0200600
文摘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%.
基金This researchwas supported by a grant(14CTAP-C077285-01-000000)from Infrastructure and transportation technology promotion research Program funded by MOLIT(Min-istry of Land,Infrastructure and Transport)of Korean government and a grant(2013-R1A12058208)from NRF(National Research Foundation of Korea)funded by MEST(Ministry of Education and Science Technology)of Korean government.
文摘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.
基金supported by the National Natural Science Foundation of China(No:62201172)the National Key Research and Development Program of China(2022YFE0136800)
文摘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.
基金supported by the National Standardization Committee of China(No.20081021-T-361)the Ministry of Science and Technology of the People’s Republic of Chinathe National High Technology Grant(No.2008ZX10004-006).
文摘One hundred andfifty-three isolates from the environment and 36 reference strains of the Legionella were studied with regards to their composition of cellular fatty acids as determined by gas chromatography,and then were classified into 41 groups by numerical analysis.Most reference strains formed only a single group,except L.micdadei,L.jamestowniensis,L.parisiensis,L.jorda-nis,L.feeleii and L.longbeachae,which were clustered into two or three groups.Even serological types of L.pneumophila could be clearly identified.Therefore,in this study,numerical analysis of cellular fatty acid composition is an effective method for identifying Legionella species.
基金supported by the National Key Research and Development Program:[Grant Number 2018YFB1305700].
文摘This article focuses on the problem of how to accurately calculate the joint control torques when the explosion-proof robot performs collision detection without sensors and gives a complete solution.Nonlinear joint frictions are incorporated into the dynamic model of a robotic manip-ulator to improve calculation accuracy.A genetic algorithm is used to optimise the excitation trajectories to fully stimulate the robot dynamic characteristics.Effective and applicable data filtering and smoothing methods are proposed and the Iteratively Reweighted Least-Squares method based on the error term is applied to identify the robot dynamic parameters.Compared with Ordinary Least-Squares method,the proposed algorithm improves the accuracy of joint control torques estimation.
