期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到109,810篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
CRISPR screening redefines therapeutic target identification and drug discovery with precision and scalability 认领 引用 被引量：1
1
作者 Yao He Xiao Tu +6 位作者 Yuxin Xue Yuxuan Chen Bengui Ye Xiaojie Li Dapeng Li Zhihui Zhong Qixing Zhong 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2026年第2期359-376,共18页
Clustered regularly interspaced short palindromic repeats(CRISPR)-Cas9 screening technology is redefining the landscape of drug discovery and therapeutic target identification by providing a precise and scalable platf... Clustered regularly interspaced short palindromic repeats(CRISPR)-Cas9 screening technology is redefining the landscape of drug discovery and therapeutic target identification by providing a precise and scalable platform for functional genomics.The development of extensive single-guide RNA(sgRNA)libraries enables high-throughput screening(HTS)that systematically investigates gene-drug interactions across the genome.This powerful approach has found broad applications in identifying drug targets for various diseases,including cancer,infectious diseases,metabolic disorders,and neurodegenerative conditions,playing a crucial role in elucidating drug mechanisms and facilitating drug screening.Despite challenges like off-target effects,data complexity,and ethical or regulatory concerns,ongoing advancements in CRISPR technology and bioinformatics are steadily overcoming these limitations.Additionally,by integrating with organoid models,artificial intelligence(AI),and big data technologies,CRISPR screening expands the scale,intelligence,and automation of drug discovery.This integration boosts data analysis efficiency and offers robust support for uncovering new therapeutic targets and mechanisms.This review outlines the fundamental principles and applications of CRISPR screening technology,delves into specific case studies and technical challenges,and highlights its expanding role in drug discovery and target identification.It also examines the potential for clinical translation and addresses the associated ethical and regulatory considerations. 展开更多
关键词 CRISPR-Cas Gene editing Targets identification Drug discovery
暂未订购 下载PDF
Microseismic signal processing and rockburst disaster identification:A multi-task deep learning and machine learning approach 认领 引用 被引量：1
2
作者 Chunchi Ma Weihao Xu +3 位作者 Xuefeng Ran Tianbin Li Hang Zhang Dongwei Xing 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第1期441-456,共16页
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id... Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters. 展开更多
关键词 Underground engineering Microseismic signal processing Deep learning Multi-task Rockburst identification
暂未订购 下载PDF
Physics-informed neural network for material identification via distortion-robust polychromatic x-ray attenuation correction in photon-counting detectors 认领 引用
3
作者 Xin Yan Jie Zhang +5 位作者 Kai He Yiheng Liu Yuetong Zhao Gang Wang Xinlong Chang Youwei Zhang 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第5期396-405,共10页
Spectral distortions in photon-counting detectors(PCDs)fundamentally limit the quantitative accuracy of material identification.While machine learning is used for compensation,current data-driven methods often lack ph... Spectral distortions in photon-counting detectors(PCDs)fundamentally limit the quantitative accuracy of material identification.While machine learning is used for compensation,current data-driven methods often lack physical constraints,limiting their interpretability and reliability across varying conditions.To address this issue,we propose a physics-informed neural network(PINN)framework that explicitly embeds the Beer-Lambert law into the learning architecture.By integrating an explicit differential layer to extract high-order curvature features from distorted spectra,the model enables direct inference of the effective atomic number and areal density.This approach effectively leverages the Z-dependent non-linear profile of the photoelectric effect,even when explicit absorption edges are outside the primary detection window.Simulation results establish a high-precision benchmark for Zeffestimation in the target low-Z range(613),with an RMSE of 0.2111.Experimental validation on a CdZnTe-PCD further demonstrates that this accuracy improvement is preserved under realistic pulse pile-up and noise conditions,achieving an RMSE of 0.2457 and an R2of 0.9670.Compared with conventional physical correction methods(typically±0.5 error margin),the proposed framework provides improved precision,with 92.86%of Zeffestimation errors falling within±0.4,corresponding to an approximately 20%tighter error bound.These results confirm that the proposed framework effectively mitigates spectral distortion,providing a robust,calibration-free solution for precise material identification of low-Z materials in industrial non-destructive testing. 展开更多
关键词 physics-informed neural networks (PINNs) photon-counting detectors (PCDs) material identification x-ray imaging
暂未订购 下载PDF
One-step strategy for developing wheat haploid inducer lines with efficient markers for haploid identification 认领 引用
4
作者 Shuwei Guo Zihao Jiang +4 位作者 Na Zhang Shaojiang Chen Xiaolong Qi Zhongfu Ni Chenxu Liu 《The Crop Journal》 SCIE CSCD 2026年第4期1481-1486,共6页
Wheat(Triticum aestivum L.)is one of the most important staple crops globally.Doubled haploid technology enables rapid development of pure lines and has been extended from maize to several other crop species.A key ste... Wheat(Triticum aestivum L.)is one of the most important staple crops globally.Doubled haploid technology enables rapid development of pure lines and has been extended from maize to several other crop species.A key step in DH breeding is the identification of haploids from diploids,which requires accurate and convenient phenotypic markers.In this study,we generated two wheat haploid inducers carrying different markers by a one-step strategy.One harbored a dual fluorescent marker system consisting of eGFP and TagRFP,the other carried a RUBY reporter.Both markers enabled near 100% accuracy of haploid identification at the immature embryo,mature embryo,and germinating seedling stages.Moreover,both lines consistently exhibited a high and stable haploid induction rate(~20%).This study not only provides efficient wheat haploid inducers but also establishes a convenient pipeline for developing haploid induction systems in other crop species. 展开更多
关键词 Wheat Haploid identification RUBY eGFP TagRFP
暂未订购 下载PDF
Unified physics-informed subspace identification and transformer learning for lithium-ion battery state-of-health estimation 认领 引用
5
作者 Yong Li Hao Wang +3 位作者 Chenyang Wang Liye Wang Chenglin Liao Lifang Wang 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第1期350-369,I0009,共20页
The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches ... The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO4and public LiCoO2datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance. 展开更多
关键词 Lithium-ion battery Transformer learning Physics-informed modeling Subspace identification State-of-health estimation
暂未订购 下载PDF
Enhancing in-situ coal-gangue identification in LTCC mining via secondary surfactant intervention and infrared thermography 认领 引用
6
作者 Jinwang Zhang Geng He +3 位作者 Shengli Yang Hongwei Fu Jin Zhao Fengchen Wang 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第6期1249-1274,共26页
Accurate in-situ identification of coal and gangue is critical for intelligent mining,particularly in longwall top coal caving(LTCC)mining,where it enables precise control of the gangue mixed ratio and enhances resour... Accurate in-situ identification of coal and gangue is critical for intelligent mining,particularly in longwall top coal caving(LTCC)mining,where it enables precise control of the gangue mixed ratio and enhances resource recovery.This study introduces a Secondary Intervention strategy to augment the conventional“liquid intervention+infrared detection”approach.Results demonstrate that Secondary Intervention can consistently enhance the thermal contrast between coal and gangue,and the average accuracy of infrared image recognition for coal and gangue increased from 79.14%after the First Intervention to 93.75%after the Secondary Intervention,representing an improvement of 14.61%.Furthermore,the average contact angle difference between coal and gangue expanded from 16.64°after the First Intervention to 33.80°after the Secondary Intervention,an increase of 17.16°.Meanwhile,the area difference between coal and gangue increased by 4.94 times.Moreover,based on comprehensive analysis of the temperature difference,accuracy of morphological identification,as well as the contact angle and area of droplets,the eco-friendly compound surfactant(EFCS)of Soapnut Saponin(SS)+CTAB with a concentration of0.06 wt%demonstrated optimal performance.These findings advance liquid intervention techniques for infrared-based recognition and support the development of greener,more intelligent coal production systems. 展开更多
关键词 Coal-gangue identification Infrared thermography Secondary Intervention Longwall top coal caving(LTCC) Intelligent mining Heat transfer
暂未订购 下载PDF
HISNET-FF:Hierarchical identification of species using a network with fused cranial and dental features 认领 引用
7
作者 Zhong Cao Qiu-Le Tang +16 位作者 Wei-Qi Zeng Kun-Hui Wang Quentin Martinez Ze-Ling Zeng Si-Ning Xie Qiu-Qin Lu Shi-Yun Liu Xiao-Yun Zheng Wen-Hua Yu Jun-Jie Hu Zhong-Zheng Chen Shao-Ying Liu Song Li Fei-Yun Tu Zi-Wen Hong Ming Bai Kai He 《Zoological Research》 SCIE CSCD 2026年第2期404-413,共10页
Accurate taxonomic identification based on mammalian craniodental features remains critical for evolutionary,ecological,and paleontological research,yet conventional approaches are time-intensive and demand expert inp... Accurate taxonomic identification based on mammalian craniodental features remains critical for evolutionary,ecological,and paleontological research,yet conventional approaches are time-intensive and demand expert input.To overcome these limitations,a deep learning framework,HISNET-FF,was developed with a dual-stream architecture that integrates global cranial morphology with local diagnostic signals from teeth and auditory bullae.The model operates within a hierarchical classification pipeline,processing from genus-level discrimination to species-level resolution.Evaluation on an extensive image dataset encompassing 51 species across 18 genera of Talpidae achieved exceptional accuracy at both the genus(99.6%±0.4%)and species(96.5%±1.3%)levels.This species-level performance substantially exceeded that of single-stream models employing either flat(91.2%±2.3%)or hierarchical(93.9%±2.1%)strategies.To support endto-end automation,a YOLO-based annotation module was implemented to localize key morphological traits with 97.8%recall,97.9%precision,and 81.5%mean average precision(mAP@[.50:.95]).Incorporating this module incurred only a marginal reduction of 1.9%in identification accuracy.Thus,HISNET-FF offers a robust and accurate framework that accelerates morphology-based species identification and enables automated taxonomic classification,with strong potential for broader implementation across diverse biological research domains. 展开更多
关键词 Craniodental morphology Deep learning Feature fusion Hierarchical classification Species identification
暂未订购 下载PDF
Molecular Identification and Description of the Tadpoles of Three Species(Amphibia:Anura)from Medog County,Xizang Autonomous Region,China 认领 引用
8
作者 Cheng LI Shun MA +2 位作者 Lulu SUI Tianyu QIAN Stéphane GROSJEAN 《Asian Herpetological Research》 SCIE CAS CSCD 2026年第2期166-172,共7页
Dear Editor,Tadpoles play vital roles in many aquatic ecosystems and show considerable morphological diversity(Altig and Mc Diarmid,1999).Understanding the morphology and ecology of these larval stages is therefore es... Dear Editor,Tadpoles play vital roles in many aquatic ecosystems and show considerable morphological diversity(Altig and Mc Diarmid,1999).Understanding the morphology and ecology of these larval stages is therefore essential for comprehending the ecological requirements of specific anuran species(Inger,1985;Dubois,2010).The Xizang Autonomous Region is home to highly diverse amphibian communities,with more than 58 species identified to date(Che et al.,2020). 展开更多
关键词 Amphibia Anura tadpoles morphology ecology anuran species inger dubois morphological diversity molecular identification comprehending ecological requirements Medog County
暂未订购 下载PDF
Parameter identification method of multi-particle model for lithium-ion batteries 认领 引用
9
作者 Junfu Li Xiaolong Li +2 位作者 Xueli Hu Quanqing Yu Zhaowei Zhang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期440-452,共13页
Electrochemical models,characterized by high fidelity and physical interpretability,have been applied in var-ious fields such as fast charging,battery state estimation,and battery material design.Currently,widely util... Electrochemical models,characterized by high fidelity and physical interpretability,have been applied in var-ious fields such as fast charging,battery state estimation,and battery material design.Currently,widely utilized single particle-based model exhibits high computational efficiency but suffers from low simulation accuracy under high-rate charge/discharge conditions.In this work,an electrochemical model for lithium-ion batteries based on multi-particle hypothesis is developed.Two particles are employed to represent the electrode char-acteristics of the positive and negative electrodes,respectively.Through theoretical derivation,mathematical equations are established to describe various processes within the battery,including solid-phase diffusion,li-quidphase diffusion,reaction polarization,and ohmic polarization.In addition,a method for obtaining model parameters is proposed.Finally,the model is experimentally validated by using lithium iron phosphate and nickel-cobalt-manganese lithium-ion batteries under constant current conditions.The identified battery elec-trochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results. 展开更多
关键词 Lithium-ion battery Electrochemical model Multi-particle assumption Parameter identification
暂未订购 下载PDF
Online accelerating precursor identification and dynamic probabilistic prediction for rock slope failures using Bayesian inference 认领 引用
10
作者 Mingxi Chen Zihan Fu +2 位作者 Feng Xiong Jie Jiang Qinghui Jiang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第5期3779-3803,共25页
Timely identification of accelerating precursors and performing reliable time-to-failure analysis are the key components in the management of slope failure risks.This study focuses on rock slope failures and proposes ... Timely identification of accelerating precursors and performing reliable time-to-failure analysis are the key components in the management of slope failure risks.This study focuses on rock slope failures and proposes a framework for online identification of accelerating precursors and dynamic probabilistic prediction of failure time grounded in Bayesian inference.By integrating the Bayesian online changepoint detection(BOCD)method with a typical dimensionless trend(TDT)model,the BOCD-TDT algorithm is first developed for online identification of acceleration events and their corresponding onset of acceleration(OA).Subsequently,a Bayesian approach is employed to estimate the parameters of the inverse velocity(INV)method,enabling the dynamic probabilistic prediction of slope failure time while quantifying observational and model uncertainties across different accelerating deformation stages.Building on this,the influence of starting point(SP)selection,trend update(TU),and multi-data fusion on prediction reliability is evaluated,and a novel decision criterion for impending slope failure is proposed.The feasibility of the proposed methods is then validated using 73 rock slope failure cases.Results show that using INV data,the BOCD-TDT algorithm can reliably identify acceleration events and the corresponding OA.In time-to-failure analysis,the reliability of dynamic failure predictions can be enhanced by incorporating both observational and model uncertainties corresponding to the deformation stages into the Bayesian prediction model,along with TU detection and multi-data fusion.The proposed failure probability criterion provides valuable guidance for the identification of impending failure and the establishment of ultimate alert thresholds. 展开更多
关键词 Rock slopes Accelerating precursor identification Time-to-failure analysis Failure probability criterion Bayesian inference Inverse velocity(INV)method
暂未订购 下载PDF
CoPt graphitic nanozyme enabled naked-eye identification and colorimetric/fluorescent dual-mode detection of phenylenediamine isomers 认领 引用
11
作者 Luyao Guan Zhaoxin Wang +2 位作者 Shengkai Li Phouphien Keoingthong Zhuo Chen 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第2期407-414,共8页
Simultaneous identification and quantitative detection of phenylenediamine(PDA)isomers,including o-phenylenediamine(OPD),m-phenylenediamine(MPD),and p-phenylenediamine(PPD),are essential for environmental risk assessm... Simultaneous identification and quantitative detection of phenylenediamine(PDA)isomers,including o-phenylenediamine(OPD),m-phenylenediamine(MPD),and p-phenylenediamine(PPD),are essential for environmental risk assessment and human health protection.However,current visual detection methods can only distinguish individual PDA isomers and failed to identify binary or ternary mixtures.Herein,a highly active and ultrastable peroxidase(POD)-like CoPt graphitic nanozyme was used for naked-eye identification and colorimetric/fluorescent(FL)dual-mode quantitative detection of PDA isomers.The CoPt@G nanozyme effectively catalyzed the oxidation of OPD,MPD,PPD,OPD+PPD,OPD+MPD,MPD+PPD and OPD+MPD+PPD into yellow,colorless,lilac,yellow,yellow,wine red and reddish-brown products,respectively,in the presence of H2O2.Thus,the MPD,PPD,MPD+PPD and OPD+MPD+PPD were easily identified based on the distinct color of their oxidation products,and the OPD,OPD+PPD,OPD+MPD could be further identified by the additional addition of MPD or PPD.Subsequently,CoPt@G/H2O2-,a 3,3′,5,5′-tetramethylbenzidine(TMB)/CoPt@G/H2O2-,and MPD/CoPt@G/H2O2-enabled colorimetric/FL dual-mode platforms for the quantitative detection of OPD,MPD and PPD were proposed.The experimental results illustrated that the constructed sensing platforms exhibit satisfactory sensitivity,comparable to that reported in previous studies.Finally,the evaluation of PDAs in water samples was realized,yielding satisfactory recoveries.This work expanded the application prospects of nanozymes in assessing environmental risks and protection of human security. 展开更多
关键词 Copt graphitic nanozyme Phenylenediamine isomers Naked-eye identification Colorimetric detection Fluorescent detection
暂未订购 下载PDF
Geometry-based image modeling method for intelligent state identification and fault prediction of wind turbines 认领 引用
12
作者 Jinman Luo Xiaoxia Li +2 位作者 Yuqing Li Haiji Wang Pu Zhang 《Control Theory and Technology》 EI CSCD 2026年第3期514-525,共12页
This paper introduces a geometry-based method to model wind turbine states and predict faults using a deep convolutional neural network(DCNN).Initially,3D-point cloud models are constructed in spaces defined by wind s... This paper introduces a geometry-based method to model wind turbine states and predict faults using a deep convolutional neural network(DCNN).Initially,3D-point cloud models are constructed in spaces defined by wind speed,power,and various auxiliary variables.Subsequently,the geometry feature of the point cloud in the 3D space is extracted,forming a 3D surface represented in the R,G,or B channels.For identification purposes,this 3D surface is transformed into a 2D image,with grayscale used to depict height.Additionally,the grayscale representing external environmental information sets the background of the 2D image.Consequently,the resulting stacked RGB image model encapsulates the necessary dynamic behavior and operating states of the wind turbine system.Finally,the DCNN,trained using the stacked image model through Xception-based transfer learning,identifies operating states and predicts faults.This paper’s method,which relies on the geometric distribution characteristics of sampling points rather than horizon characteristics,offers a novel perspective on wind turbine state identification and fault prediction.Experimental results demonstrate that the proposed method achieves exceptionally high accuracy in identifying the wind turbine’s operating state and predicts faults with up to 93.875%accuracy. 展开更多
关键词 Wind turbine State identification Fault prediction Geometry feature Image model
Feature-based molecular networking and building blocks identification advance novel natural products characterization:Sesquiterpene-chromone hybrids in agarwood as an application 认领 引用
13
作者 Qian Wang Han Li +5 位作者 Huiting Liu Yujie Pei Pengfei Tu Huixia Huo Jun Li Yuelin Song 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2026年第3期886-888,共3页
As important sources of new drugs,natural products(NPs)are conceptually biosynthesized from simple structural pioneers(i.e.building blocks).The traditional non-targeted purification strategy extensively suffers from t... As important sources of new drugs,natural products(NPs)are conceptually biosynthesized from simple structural pioneers(i.e.building blocks).The traditional non-targeted purification strategy extensively suffers from the time-consuming and laborious bottlenecks.Fortunately,liquid chromatography–mass spectrometry/mass spectrometry(LC–MS/MS)-guided separations widely succeed in recent decades.However,it is still challenging for LC–MS/MS to precisely capture new NPs.Efficiently extracting information from the chaotic chemical composition and confident structural annotation are two primary technical barriers for pursuing the interesting structures,particularly those exhibiting trace distributions and high-level structural complexity.Here,to provide accurate guidance for the follow-up phytochemical purification,molecular defect filtering(MDF)and feature-based molecular networking(FBMN)[1]were incorporated to explore NPs and thereafter,bottom-up structural analysis was undertaken through identifying building blocks with full exciting energy ramp(FEER)-MS 3 matching.Sesquiterpene-chromone hybrids(SCHs)structurally configured by two building blocks such as units A(chromone)and B(sesquiterpene)[2]in agarwood were characterized as a proof-of-concept.Twenty-five SCHs were captured and identified.Thereof,seven new SCHs were purified with a LC–MS/MS-guided manner and annotated using nuclear magnetic resonance(NMR)spectroscopy to justify the proposed structures.Moreover,their cell-protective and anti-inflammatory activities were evaluated.Together,the incorporation of post-acquisition data processing strategies and FEER-MS 3 spectrum matchingassisted building blocks identification facilitated novel NPs exploration and purification. 展开更多
关键词 sesquiterpene chromone hybrids agarwood liquid chromatography mass spectrometry mass spectrometry extracting information feature based molecular networking molecular defect filtering nuclear magnetic resonance spectroscopy building blocks identification
暂未订购 下载PDF
Identification of coal-rock interface under uniaxial compression using electric potential method 认领 引用
14
作者 Tiancheng Shan Zhonghui Li +6 位作者 Enyuan Wang Haishan Jia Xin Zhang Qiming Zhang Xiaoran Wang Yue Niu Shishi Deng 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第5期3483-3498,共16页
The instability of composite coal-rock structures can easily trigger severe dynamic disasters,such as rockbursts.The application of electric potential(EP)method shows promise for disaster prediction and accurate ident... The instability of composite coal-rock structures can easily trigger severe dynamic disasters,such as rockbursts.The application of electric potential(EP)method shows promise for disaster prediction and accurate identification of coal-rock interfaces.In this study,uniaxial compression experiments were conducted to monitor the EP spatiotemporal response of fine sandstone-coal and coarse sandstone-coal combined samples.EP distribution contour maps and three-dimensional(3D)EP models were utilized to explore the failure mechanisms and identify the interface state.Then the relationship between EP response and force field was examined through numerical simulations.An EP-based multifractal method was utilized to predict rock failure.Results show that the intensity and polarity of EPs differ between coal and rock but are correlated with stress state.The progressive failure features of two types of combined samples differ,triggering distinct EP responses.In the EP contour maps,the EP level increases with increasing height,and a low-intensity signal band appears around the interface before failure.When failure occurs,the EP field changes,and the low-intensity signal band becomes distorted.The 3D EP models effectively visualize the progressive failure of combined samples and clearly identify the interface location,similar to acoustic emission(AE)location.The evolution of force chain field is closely related to EP generation,and sparse strong force chain fields leads to a significant increase in EP level.Furthermore,the EPs display multifractal features,with precursory information being reflected in Δα and Δf.This study provides new ideas for early-warning of composite coal-rock and coal-rock interface identification. 展开更多
关键词 Coal-rock samples Electric potential method Mechanical response feature Spatial distribution imaging Interface identification Uniaxial compressive failure
暂未订购 下载PDF
Modeling of bolted beam utilizing modified joint element and identification of contact parameters at the joint 认领 引用
15
作者 Xiang Li Zhousuo Zhang +1 位作者 Zhuofan Zhou Dian Chen 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期374-382,共9页
Research on the modeling of bolted connection structures primarily centers on the characterization of the connection interface.Accurate equivalent modeling of the connection interface is crucial for the effective mode... Research on the modeling of bolted connection structures primarily centers on the characterization of the connection interface.Accurate equivalent modeling of the connection interface is crucial for the effective modeling of bolted connection structures.This article considers the misalignment between the bolt plane and the beam plane,and establishes a modified joint element representing the bolted connection by using the seriesstiffness method.The contact stiffness in this modified joint element are identified using a genetic algorithm with an “emperor selection” strategy.The bolted connection beam model is achieved by refining the Euler-Bernoulli beam model through the incorporation of a modified joint element.The maximum error between the model calculation results and experimental results for each order of natural frequency is 2.39%.This demonstrates the feasibility of accurately characterizing the bolted connection beam through the utilization of the modified joint element for equivalent modeling.This research proposes a modeling method for bolted connection beams that accounts for the misalignment between the bolt plane and the beam plane,significantly enhancing modeling accuracy. 展开更多
关键词 Bolted joints Modified joint element Genetic algorithm Parameter identification
暂未订购 下载PDF
A Clustering-Based Localization Method for Multiple Magnetic Anomaly Targets with Omission Identification 认领 引用
16
作者 Ji-hao Liu Xi-hai Li +2 位作者 Chao Niu Xiao-niu Zeng Yun Zhang 《Applied Geophysics》 SCIE CSCD 2026年第1期45-55,427,共11页
The technology of locating magnetic anomaly targets via geomagnetic eld measurements has been increasingly widely applied,with multiple magnetic anomaly target localization emerging as a critical research direction.Ho... The technology of locating magnetic anomaly targets via geomagnetic eld measurements has been increasingly widely applied,with multiple magnetic anomaly target localization emerging as a critical research direction.However,when two magnetic anomaly targets are horizontally close but vertically separated,traditional clustering-based localization methods tend to omit the deeper target.To address this issue,we propose an improved clustering-based localization method for multiple magnetic anomaly targets,which integrates two core innovations:the introduction of a reference target to establish a benchmark for normal magnetic moment distribution,and the utilization of spatial distribution characteristics of magnetic moment estimates to judge the presence of omitted targets.Simulation results demonstrate that the proposed method not only achieves accurate localization of conventional targets but also eectively identies the omission of deeper targets,providing a reliable basis for determining whether supplementary localization steps are required. 展开更多
关键词 Multiple Magnetic Anomaly Targets Magnetic Gradient Tensor Localization Omitted Target Identification
暂未订购 下载PDF
Lithology identification using borehole images by contrast-limited adaptive histogram equalization and machine learning models 认领 引用
17
作者 Enming Li Pablo Segarra +4 位作者 José A.Sanchidrián Zahir Ahmed Ignacio Catalán Alberto Fernández Santiago Gómez 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第3期1698-1718,共21页
Agile lithology identification can assist mining by providing important information in the exploration and production of mineral resources.This study proposes a new lithology recognition procedure using video-logging ... Agile lithology identification can assist mining by providing important information in the exploration and production of mineral resources.This study proposes a new lithology recognition procedure using video-logging of boreholes with an endoscope,applied to six production blocks in a limestone quarry.Images are automatically extracted from the videos and the lithology is classified into three classes based on clay content,i.e.massive limestone,brecciated limestone,and high amount of clay.The image quality is evaluated with a gray pixel intensity threshold and three no-reference image quality metrics,i.e.perception-based image quality evaluator,natural image quality evaluator,and blindeferenceless image spatial quality evaluator.After removing low-quality images,7583 images are retained and used for developing lithology classification models using six optimized classification techniques.The contrast-limited adaptive histogram equalization(CLAHE)technique is used to improve image quality.Ten color characteristics involving three percentiles of red,green and blue pixel intensities,together with color counting and five texture characteristics-correlation,entropy,homogeneity,contrast and energy-are used as inputs.Bayesian optimized light gradient boosting machine model performs best,with an overall accuracy of 88.04%,and a precision on the classes of massive limestone,brecciated limestone and high amount of clay of 90.72%,83.52%and 85.29%,respectively,for the testing set.The feature importance scores show that the color counting is the most significant parameter for the development of the classification model.Compared with previous image-based methodologies,this study provides a more flexible and cheaper procedure to identify lithology. 展开更多
关键词 Lithology identification Borehole images Endoscope Light gradient boosting machine Contrast-limited adaptive histogram equalization(CLAHE)
暂未订购 下载PDF
Intelligent identification for discrete memristive neuron map:An adaptive chaos game optimization algorithm studied from the perspectives of different sample sizes and objective functions 认领 引用
18
作者 Yuexi Peng Xinyi Luo +2 位作者 Zhijun Li Mengjiao Wang Minglin Ma 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第6期276-291,共16页
Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applica... Discrete memristive neuron systems have attracted considerable attention due to their nonlinear dynamical properties,low computational overhead,and ease of hardware implementation.For the practical engineering applications of discrete memristive neuron systems,effective control remains a key issue.Parameter identification using intelligent optimization algorithms is an important approach for controlling complex nonlinear systems.However,classical algorithms are prone to falling into local optima and often exhibit high computational complexity,resulting in slow convergence.Therefore,a new algorithm named adaptive chaos game optimization(ACGO)is proposed to address these issues.By introducing a differential evolution mutation strategy and a Cauchy adaptive parameter mechanism,the ACGO algorithm can effectively balance global exploration and local exploitation capabilities.To verify the effectiveness of the proposed algorithm,it is applied to parameter identification in five discrete memristive neuron maps(DMNMs)and compared with seven intelligent optimization algorithms.Simulation results demonstrate that the ACGO algorithm achieves higher accuracy and faster convergence.In addition,an in-depth investigation is conducted into the effects of sample size and objective function on identification performance.The results indicate that setting the sample size to 4 and selecting the mean squared error(MSE)as the objective function can achieve better identification performance and a high level of robustness. 展开更多
关键词 discrete memristive neuron map parameter identification chaos game optimization algorithm sample size
暂未订购 下载PDF
Fast identification of γ‑emitting radionuclides based on sequential Bayesian approach 认领 引用
19
作者 Xuan Zhang Jian-Wei Huang +5 位作者 Lin-Jian Wan Jia-Cheng Liu Xiao-Le Zhang De-Hong Li Fei Tuo Zhi-Jun Yang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第2期1-15,共15页
The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring suffi... The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring sufficient count accumulation for evaluation,thereby limiting response time.The sequential Bayesian approach,which utilizes prior information and considers both photon energies and interarrival times,can significantly enhance the performance of radionuclides identification.This study proposes a theoretical optimization method for the traditional sequential Bayesian approach.Each photon is processed sequentially,and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory.By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval,the type of γ-rays can be identified(background characteristic γ-rays,Compton plateaus γ-rays,or radionuclide-specific characteristic γ-rays).By integrating the information from these multiple characteristic γ-rays,the presence and type of radionuclides were determined based on the final decision function and a set threshold.Based on theoretical research,verification experiments were conducted using a LaBr3(Ce)detector in both low-and natural background radiation environments with typical radionuclides(137Cs,60Co,and 133Ba).The results show that this approach can identify 137Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10−3μGy/h),60Co in 8.1 s and 9.8 s(approximately 4.8×10−2μGy/h),and 133Ba in 4.05 s and 5.99 s(approximately 3.4×10−2μGy/h)under low and natural background radiation,respectively,with a miss rate below 0.01%.This demonstrates the effectiveness of the proposed approach for fast radionuclides identification,even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments. 展开更多
关键词 Sequential Bayesian approach Fast radionuclides identification LaBr3(Ce)detector Low background radiation laboratory
暂未订购 下载PDF
PIF-Identifier:Accurate Low-Overhead Identification of Persistent Infrequent Flows in Network Traffic 认领 引用
20
作者 Bing Xiong Zhuoxiong Li +2 位作者 Yongqing Liu Yu Tang Jinyuan Zhao 《Computers, Materials & Continua》 SCIE EI 2026年第7期629-643,共15页
Persistent Infrequent Flows(PIFs)refer to the packet flows that last for a long time but always at low frequencies in network traffic.Accurate identification of the PIFs plays a vital role in intrusion detection,attac... Persistent Infrequent Flows(PIFs)refer to the packet flows that last for a long time but always at low frequencies in network traffic.Accurate identification of the PIFs plays a vital role in intrusion detection,attack prevention,traffic engineering,and other network fields.However,existing methods often require to save all flows for finding out the PIFs due to their infrequency feature,which brings about the problem of low identification accuracy and high memory overhead.To solve this problem,this paper proposes an accurate PIF identification method with low overhead called PIF-Identifier,composed of a new-flow discriminator and a PIF tracker.Specifically,we first design a compact new-flow discriminator by applying probabilistic data structures,to quickly determine whether a packet flow arrives for the first time within current time window.Then we design a PIF tracker to accurately identify and report persistent infrequent flows.In the PIF tracker,we configure a small-size frequency counter for each tracked flow in accordance with the frequency threshold of the PIF,without sacrificing the accuracy of PIF identification.Furthermore,we design a probabilistic replacement strategy based on the number of time windows of flow persistence,to accommodate newly arrived potential PIFs when there is no vacancy in their mapped buckets of the PIF tracker.Finally,we evaluate the performance of our proposed PIF-Identifier by theoretical analysis and experimental verification with real network traffic traces.Experimental results indicate that the PIF-Identifier achieves the precision of 100%,much higher recall rate and F1 score,as well as lower average relative error than the state-of-the-art methods,significantly promoting the identification performance of persistent infrequent flows. 展开更多
关键词 Network traffic measurement persistent infrequent flows low-overhead PIF identification new-flow discriminator probabilistic replacement strategy
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈