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5,025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：
Research on Automatic Vectorization Mapping Technology of Topographic Map Elements Based on Deep Learning 认领 引用
1
作者 YING Huafang 《外文科技期刊数据库（文摘版）自然科学》 2026年第2期112-116,共5页
Automatic vectorization of topographic map elements is a core task for the rapid update and intelligent upgrade of map data in the surveying, mapping and geographic information industry. Traditional vectorization meth... Automatic vectorization of topographic map elements is a core task for the rapid update and intelligent upgrade of map data in the surveying, mapping and geographic information industry. Traditional vectorization methods, when dealing with complex terrain features, generally have problems of insufficient accuracy and low degree of automation, highly rely on manual post-processing, and the production efficiency is difficult to meet the demand for large-scale data update. With the technological breakthroughs of deep learning in image recognition and segmentation, a new path has been provided for the intelligent extraction and vectorization of topographic map features. This paper presents a deep learning-based framework for automatic vectorization of topographic map features. It first analyzes the morphology and distribution patterns of point, line, and surface topographic features, standardizes the construction process of the training sample set, and focuses on improving the recognition effect of fine and dense linear features by improving the network core structure and introducing multi-scale feature fusion and context information utilization strategies. Build a full-process post-processing system from pixel recognition results to complete vectorization results, covering key links such as geometric vectorization reconstruction, topological relationship construction, and automatic assignment of semantic attributes. Verified by multiple sets of comparative experiments, this solution has significant advantages over traditional methods in terms of element recognition integrity and vectorization geometry accuracy, greatly enhancing the level of job automation and providing practical and feasible technical support for large-scale high-precision geographic information data production. 展开更多
关键词 Deep learning Topographic map elements Auto vectorization Image segmentation Geographic Information System
暂未订购 下载PDF
Efficient rock joint detection from large-scale 3D point clouds using vectorization and parallel computing approaches 认领 引用
2
作者 Yunfeng Ge Zihao Li +2 位作者 Huiming Tang Qian Chen Zhongxu Wen 《Geoscience Frontiers》 SCIE CAS CSCD 2025年第5期1-15,共15页
The application of three-dimensional(3D)point cloud parametric analyses on exposed rock surfaces,enabled by Light Detection and Ranging(LiDAR)technology,has gained significant popularity due to its efficiency and the ... The application of three-dimensional(3D)point cloud parametric analyses on exposed rock surfaces,enabled by Light Detection and Ranging(LiDAR)technology,has gained significant popularity due to its efficiency and the high quality of data it provides.However,as research extends to address more regional and complex geological challenges,the demand for algorithms that are both robust and highly efficient in processing large datasets continues to grow.This study proposes an advanced rock joint identification algorithm leveraging artificial neural networks(ANNs),incorporating parallel computing and vectorization of high-performance computing.The algorithm utilizes point cloud attributes—specifically point normal and point curvatures-as input parameters for ANNs,which classify data into rock joints and non-rock joints.Subsequently,individual rock joints are extracted using the density-based spatial clustering of applications with noise(DBSCAN)technique.Principal component analysis(PCA)is subsequently employed to calculate their orientations.By fully utilizing the computational power of parallel computing and vectorization,the algorithm increases the running speed by 3–4 times,enabling the processing of large-scale datasets within seconds.This breakthrough maximizes computational efficiency while maintaining high accuracy(compared with manual measurement,the deviation of the automatic measurement is within 2°),making it an effective solution for large-scale rock joint detection challenges.©2025 China University of Geosciences(Beijing)and Peking University. 展开更多
关键词 Rock joints Pointclouds Artificialneuralnetwork High-performance computing Parallel computing Vectorization
A New Selective Neural Network Ensemble Method Based on Error Vectorization and Its Application in High-density Polyethylene (HDPE) Cascade Reaction Process 认领 引用 被引量：1
3
作者 朱群雄 赵乃伟 徐圆 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2012年第6期1142-1147,共6页
Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy o... Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability. 展开更多
关键词 high-density polyethylene modeling selective neural network ensemble diversity definition error vectorization
暂未订购 下载PDF
Vectorization and Parallel Computation of a CFD Code on YH-2 Parallel Supercomputer 认领 引用
4
作者 Wang Zhenghua and Li Xiaomei Xiaomei 《Wuhan University Journal of Natural Sciences》 CAS 1996年第Z1期547-552,共6页
MacCormack explicit scheme and Baldwin-Lomax algebraic turbulent model are employed to solve the axisymmetric compressible Navier-Stokes equations for the numerical simulation of the supersonic mustanl floats interact... MacCormack explicit scheme and Baldwin-Lomax algebraic turbulent model are employed to solve the axisymmetric compressible Navier-Stokes equations for the numerical simulation of the supersonic mustanl floats interacted with transverse injection at the base of a cone. A temperature switch function must be added to the artificial viscous model suggested by jameson etc to enhance the scheme's ability to eliminate oscillation for some injection case.The typical code optimization techniques about vectorization and some useful concepts and terminology about multiprocessing of YH-2 parallel supercmputer is given and explatined with some examples After reconstruction and optimization the code gets a spedup 5 .973 on pipeline computer YH- 1 and gets a speedup 1 886 for 2 processors and 3.545 for 4 processors on YH-2 parallel supeercomputer by using domain decomposition method.. 展开更多
关键词 Navier-Stokes equations Vectorization Patallelism Speedup.Domain decomposition
暂未订购 下载PDF
Using mixed kernel support vector machine to improve the predictive accuracy of genome selection 认领 引用 被引量：2
5
作者 Jinbu Wang Wencheng Zong +6 位作者 Liangyu Shi Mianyan Li Jia Li Deming Ren Fuping Zhao Lixian Wang Ligang Wang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第2期775-787,共13页
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc... The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS. 展开更多
关键词 genome selection machine learning support vector machine kernel function mixed kernel function
暂未订购 下载PDF
Adaptive regional segmentation and fitting-based building LOD reconstruction method 认领 引用
6
作者 Bowen LI Xukun SHEN Yong HU 《虚拟现实与智能硬件（中英文）》 EI CSCD 2026年第2期202-218,共17页
Reconstructing accurate urban building models remains challenging because of large-scale variability and complex topologies.We introduce a practical multi-step framework that reconstructs a single building from a dens... Reconstructing accurate urban building models remains challenging because of large-scale variability and complex topologies.We introduce a practical multi-step framework that reconstructs a single building from a dense triangular mesh by explicitly exploiting its block-wise composition.The pipeline first partitioned the mesh into spatially coherent regions,then extracted height-aware contours,and classified the geometry into facades,roofs,and appurtenances.For each part,we performed contour-guided vectorized modeling with profile fitting and consistency constraints,producing watertight,semantically structured models at the level of detail(LOD)2.3.The interactive refinement module further supported user adjustments to resolve rare failures and enforced design regularity.The proposed decomposition and layered fitting yielded compact outputs while preserving the salient geometry and made the reconstruction robust across scales and moderate noise.We also analyzed the modeling assumptions and implementation details to ensure reproducibility.Overall,this study offers a component-aware,engineering-ready solution for converting unstructured meshes into structured building models that are amenable to downstream urban modeling and visualization. 展开更多
关键词 Building reconstruction Block-wise segmentation Contour-guided vectorization LOD Interactive refinement Watertight modeling
暂未订购 下载PDF
Enhancing Intrusion Detection Systems Using Hybrid AI-Based Approaches 认领 引用 被引量：1
7
作者 Mohammad Alshinwan Radwan M.Batyha +5 位作者 Walaa Alayed Saad Said Alqahtany Suhaila Abuowaida Hamza A.Mashagba Azlan B.Abd Aziz Samir Salem Al-Bawri 《Computers, Materials & Continua》 SCIE EI 2026年第5期2208-2245,共38页
Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have... Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have been developed,their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency.This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization(PDO)with the exploitation behavior of Ant Colony Optimization(ACO).The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSLKDD dataset and evaluates them using a Support Vector Machine(SVM)classifier.Experimental analyses reveal that the PDO–ACO model achieves superior detection accuracy of 98%while significantly lowering false alarms and computational overhead.Further validation on the CEC2017 benchmark suite confirms the robustness and adaptability of the hybrid model across diverse optimization landscapes,positioning PDO–ACO as an efficient and scalable approach for intelligent intrusion detection. 展开更多
关键词 Intrusion detection system prairie dog optimization artificial bee colony support vector machine
暂未订购 下载PDF
Support Vector Clustering Uncovered:Insights,Challenges,and Future Outlook 认领 引用
8
作者 M.Tanveer Mohammad Tabish +2 位作者 Anuradha Kumari Ashwani Kumar Malik Weiping Ding 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期749-775,共27页
Support vector clustering(SVC)has emerged as a powerful unsupervised learning technique,derived from support vector machines(SVMs),offering a robust solution to a wide range of complex clustering challenges.Its unique... Support vector clustering(SVC)has emerged as a powerful unsupervised learning technique,derived from support vector machines(SVMs),offering a robust solution to a wide range of complex clustering challenges.Its unique ability to handle noise,outliers,and clusters of diverse,irregular shapes sets it apart from traditional clustering methods.SVC's distinct advantage lies in its capacity to autonomously determine the optimal number of clusters without prior topological knowledge of the data.SVC maps data to a higher-dimensional space,encloses it in a minimal sphere,and identifies clusters when mapped back,supporting complex shapes and ensuring optimality through kernel functions.This review paper provides a comprehensive analysis of the SVC algorithms,exploring their variants such as robust,sparse,and fuzzy-based models and adaptations for large-scale data.Moreover,we analyze the potential of twin support vector clustering(TWSVC),with an emphasis on the use of various loss functions.Finally,the paper explores emerging trends and outlines promising future research directions for both SVC and twin SVC.These include advancements in feature engineering,extension to semi-supervised and weakly supervised learning,and the integration of multi-view and multi-modal data.Our work aims to deepen the understanding of SVC,fostering advancements that address the evolving needs of clustering in real-world scenarios. 展开更多
关键词 Clustering kernel methods support vector machine(SVM) twin support vector clustering (TWSVC) unsupervised learning
暂未订购 下载PDF
Real-time safety control of shield attitude considering tunneling efficiency 认领 引用
9
作者 Tugen FENG Jinjian HU +3 位作者 Jian ZHANG Guoping REN Yongbo LI Xiaopeng ZHAO 《ENGINEERING Structure and Civil Engineering》 SCIE EI CAS CSCD 2026年第1期80-95,共16页
Shield attitude control is a critical aspect that must be continuously monitored during shield tunneling.To achieve scientifically rational settings for shield tunneling parameters,this study constructed multiple mach... Shield attitude control is a critical aspect that must be continuously monitored during shield tunneling.To achieve scientifically rational settings for shield tunneling parameters,this study constructed multiple machine learning prediction models,including shield attitude deviations and tunneling speed,and optimized the hyperparameters of these models using Bayesian algorithms.Subsequently,a constrained grey wolf optimization(GWO)algorithm was employed to establish a real-time safety control method for attitude that considers tunneling efficiency,by dynamically updating the upper and lower bounds for adjustable parameters.The results indicate that the k-nearest neighbors(KNN)model achieved the highest prediction accuracy;however,due to its specific algorithmic principles,KNN is unsuitable for optimization tasks.Embedding the extreme gradient boosting model into the GWO algorithm yielded the best attitude control performance:the absolute attitude deviations were reduced by an average of 45.1%compared to actual values,while the rate of change for adjustable parameters did not exceed 30%.This approach ensures safety and tunneling efficiency during attitude correction and exhibits universal applicability.Compared with other optimization algorithms,GWO demonstrated significant advantages in both optimization effectiveness and computational time. 展开更多
关键词 shield attitude tunneling speed K-nearest neighbors support vector regression extreme gradient boosting grey wolf optimization
暂未订购 下载PDF
Robust 3D object tracking with X-Triplet markers:A machine learning-based approach 认领 引用
10
作者 Cai Meng Yue Zeng +2 位作者 Hongbin Shen Xinliang Deng Diansheng Chen 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期145-154,共10页
Object tracking in 3D space is a classical problem in computer vision.In this paper,an efficient and robust X-Triplet detection method is proposed based on the support vector machine(SVM) and an adjacent matrix for lo... Object tracking in 3D space is a classical problem in computer vision.In this paper,an efficient and robust X-Triplet detection method is proposed based on the support vector machine(SVM) and an adjacent matrix for locating and tracking objects through stereo vision with minimal feature points.The X-Triplet,denoted as Tri-X,is a composite marker consisting of three sequential X-corners.The definition and types of Tri-X markers are introduced at first.Then a fast and robust X-corner detector based on the block search strategy and SVM is proposed to extract X-corner candidates with sub-pixel locations and orientations.Thereafter the X-corner adjacent matrix(XAM) is constructed using the orientation angle error to describe the possibility that any X-corner pair form a valid edge vector.The Tri-X candidates are then extracted efficiently from the XAM.Finally once the Tri-X markers are detected in binocular images,their 6D pose information can be recovered through stereo matching and triangulation technique.When multiple targets are involved simultaneously,different Tri-X markers can be utilized to identify different objects.Experimental results show that the proposed method outperformed the state-of-the-art in terms of both accuracy and efficiency for Tri-X marker detection.In localization precision test,it achieved 0.1 mm error for the position and 1° error for the orientation.Our method exhibits great potential for utilization in user-defined specific tracking tasks,offering flexibility and adaptability to various tracking requirements,especially multi-tool tracking in medical robotics. 展开更多
关键词 Object tracking Localization Tri-X composite marker Support vector machine X-corner adjacent matrix
暂未订购 下载PDF
Programmable mixed-kernel based on MoTe2/MoS2heterojunction for support vector machine learning 认领 引用
11
作者 Xinyu Huang Jiapeng Du +3 位作者 Langlang Xu Lei Tong Xiangxiang Yu Lei Ye 《Journal of Semiconductors》 EI CAS CSCD 2026年第3期110-116,共7页
The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware... The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware with on-chip parameter tunability,which directly accelerates machine learning functions.This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure.We designed and fabricated a MoTe2/MoS2type-Ⅱvertical heterojunction phototransistor,which exhibits a non-monotonic,Gaussian-like optoelectronic response owing to its unique inter-layer charge transfer mechanism.This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics.Furthermore,the hardware kernel can be continuously modulated by in-situ tuning of external opti-cal stimuli.The mixed-kernel exhibited exceptional performance,achieving precision,accuracy,and area under the curve(AUC)values of 95.8%,96%,and 0.9986,respectively,significantly outperforming conventional kernels.By successfully embedding a complex,adaptable mathematical function into the intrinsic physical properties of a single device,this work pioneers a novel pathway toward next-generation,energy-efficient intelligent systems with hardware-level adaptability. 展开更多
关键词 programmable mixed-kernel heterojunction support vector machine
暂未订购 下载PDF
A semi-replicating VSV(srVSV)-based platform for developing broad-spectrum mucosal vaccines against influenza A viruses 认领 引用
12
作者 Xianmiao Ye Junnan Lu +10 位作者 Zirong Han Zhishan Fan Xinru Hu Lichuang Jiao Lisha Deng Wenming Liu Junqi Liu Weiqi Pan Ling Chen Liqiang Feng Caijun Sun 《Virologica Sinica》 SCIE CAS CSCD 2026年第2期423-438,共16页
Influenza A viruses(IAVs)are significant respiratory pathogens characterized by high mutation rates and frequent genetic reassortments,underscoring the need for vaccines that can induce robust and broadly protective m... Influenza A viruses(IAVs)are significant respiratory pathogens characterized by high mutation rates and frequent genetic reassortments,underscoring the need for vaccines that can induce robust and broadly protective mucosal immunity.While replication-competent vesicular stomatitis virus(VSV)vectors have the potential to elicit mucosal immunity,their neurovirulence raises significant safety concerns.Herein,we report that a semi-replicating VSV(srVSV)vector,composed of one VSV with the glycoprotein(G)gene deleted(rVSVΔG)and another with the L gene deleted(rVSVΔL),has improved safety.Using srVSV,we constructed a monovalent vaccine(srVSV-N1),expressing the neuraminidase 1(N1)of IAV.A single intranasal dose of srVSV-N1 elicited both systemic and mucosal immune responses against N1,and provided sterilizing immunity against homologous influenza virus.We further generated a bivalent IAV vaccine(srVSV-N1/N2),co-expressing N1 and N2.A single intranasal dose of srVSV-N1/N2 conferred 80%protection against heterologous IAVs(H1N1 and H3N2).Notably,low-dose priming immunization followed by a high-dose boost with srVSV-N1/N2 fully protected mice against lethal heterologous IAV challenges.These findings demonstrate the potential of the srVSV platform for developing mucosal vaccines against IAVs and other respiratory viruses. 展开更多
关键词 Vesicular stomatitis virus(VSV) Semi-replicating vector Influenza A virus(IAV) Mucosal vaccine Cross-protection
暂未订购 下载PDF
Probability prediction of true-triaxial compressive strength of intact rocks based on the improved PSO-RVM model 认领 引用
13
作者 Qi Zhang Maohui Wang +2 位作者 Ning Wang Yixin Shen Xuzhen He 《Deep Underground Science and Engineering》 EI CAS CSCD 2026年第2期396-409,共14页
In deep underground engineering design,the true-triaxial compressive strength of intact rocks is a critical evaluation index.Traditional methods for acquiring true-triaxial strength data are hampered by labor-intensiv... In deep underground engineering design,the true-triaxial compressive strength of intact rocks is a critical evaluation index.Traditional methods for acquiring true-triaxial strength data are hampered by labor-intensive manual operations.To mitigate the time-consuming nature of true-triaxial experiments,this study leverages the unique capabilities of the relevance vector machine(RVM)to develop machine learning prediction models.These models aim to streamline the process and enhance predictive accuracy,thereby offering a more efficient alternative to conventional experimental approaches.The proposed models establish a correlation between the major principal stress(σ1)and the material constants,alongside other Hoek–Brown(H–B)strength parameters.A comprehensive data set,encompassing 408 sets of true-triaxial experimental data from 12 different rock types,was collated from previous studies.This true-triaxial strength data set was systematically divided into three groups based on the intact rock material content(mi),facilitating subsequent validation efforts.To enhance prediction accuracy and generalization capability,particle swarm optimization(PSO)is employed to optimize the hybrid kernel function parameters of the RVM.This study introduces a dynamic inertia weight decreasing method,demonstrating superior prediction accuracy compared to conventional PSO improvement techniques.In comparison with five three-dimensional H–B type criteria and two other machine learning models,the improved PSO-RVM model demonstrated superior performance across three distinct mi groups.Additionally,the proposed model is capable of generating probabilistic predictions,thereby effectively capturing the inherent uncertainty associated with rock strength.The probability distribution of model prediction errors closely aligns with that indicated by the generalized Zhang–Zhu criterion,underscoring the improved PSO-RVM model's ability to capture the uncertainty in true-triaxial compressive strength.Furthermore,this study explores sample selection for combined tests integrating true-triaxial experiments and the proposed improved PSO-RVM model,providing a tentative optimal ratio for predicting the true-triaxial compressive strength of intact rocks. 展开更多
关键词 intact rock particle swarm optimization probabilistic prediction relevance vector machine true-triaxial compressive strength
暂未订购 下载PDF
Pixel-level landslide stability mapping based on three-dimensional time-series InSAR monitoring and dynamics factors extraction 认领 引用
14
作者 Jichao Lv Rui Zhang +4 位作者 Oriol Monserrat Wenfei Mao Xin Bao Renzhe Wu Guoxiang Liu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5194-5217,共24页
Conventional methods for evaluating landslide stability based on physical-mechanical properties and deformation often fail to accurately capture engineering geological conditions and spatiotemporal kinematic evolution... Conventional methods for evaluating landslide stability based on physical-mechanical properties and deformation often fail to accurately capture engineering geological conditions and spatiotemporal kinematic evolution across entire slopes.In this study,we propose a pixel-level landslide stability mapping method that uses three-dimensional time-series interferometric synthetic aperture radar(InSAR)monitoring and dynamic factor extraction.The method was applied to two reactivated landslides,Xiongba and Sela,in Gongjue County,Xizang.Using 212 Sentinel-1 ascending and descending orbit images(acquired between 2017 and 2021),we derived line-of-sight(LOS)deformation velocities using the Small Baseline Subset(SBAS)InSAR technique.Subsequently,these velocities were decomposed into full three-dimensional displacements based on a surface-parallel flow assumption and Helmert variance component estimation.A dual-parameter stability criterion combining the displacement vector angle and rate was established and compared with the traditional tangential angle method by integrating kinematics with elastoplastic principles.The results show that the new criterion effectively reduces sensitivity to deformation fluctuationsand reveals dominant elastic deformation in both landslides,controlled by Poisson’s ratio and stress proportionality.The Xiongba source area,platform,and front edges correspond to the plastic,initial,and elastic stages,respectively,while the Sela landslide exhibits elastic,plastic,and sliding stages,both of which contain secondary deformation zones.Meteorological data analysis identifiedthe Jinsha River’s scouring erosion and seasonal heavy rainfall as the primary external factors triggering landslide reactivation.Additionally,the 2018 Baige landslide-dam burst disaster chain further worsened the Xiongba landslide deformation.These findingsoffer valuable insights for future disaster prevention efforts. 展开更多
关键词 Landslide stability Interferometric synthetic aperture radar(InSAR) Displacement vector angle Xiongba landslide Sela landslide
暂未订购 下载PDF
Predicting gastrointestinal bleeding and audio biomarkers based on machine learning analysis of bowel sounds 认领 引用
15
作者 Lei Zhang Meng-Yue Wang +6 位作者 Shi-Yu Wei Cheng Su Sen-Yi Hu Xiao-Yang Ren Ya-Ping Liu Chang Liu Yong Wan 《World Journal of Gastroenterology》 SCIE CAS 2026年第14期81-95,共15页
BACKGROUND Early diagnosis of upper gastrointestinal bleeding(UGIB)relies on invasive endoscopy and laboratory tests,which carry procedural risks and diagnostic delays.The pathophysiological relationship between bowel... BACKGROUND Early diagnosis of upper gastrointestinal bleeding(UGIB)relies on invasive endoscopy and laboratory tests,which carry procedural risks and diagnostic delays.The pathophysiological relationship between bowel sounds(BSs)as a noninvasive monitoring metric and UGIB remains to be elucidated.AIM To investigate the feasibility of BS acoustic signatures as UGIB screening biomarkers,analyze their pathological correlations with hematological indices,and construct a machine learning-assisted diagnostic model.METHODS A prospective study enrolled 40 UGIB patients(endoscopy-confirmed within 24 hours)and 40 age-/sex-matched healthy controls.BS signals were recorded at the right lower umbilical quadrant using a G-200 device(60 seconds/subject,4 kHz sampling).After denoising via variational mode decomposition,78-dimensional features were extracted across four domains:Time-domain,frequency-domain,time-frequency domain,and nonlinear dynamics.Weighted feature importance was calculated using an integrated strategy and gradient-optimized feature subsets were used to train four classifiers:Support vector machine,random forest,logistic regression,and K-nearest neighbor.SHapley Additive exPlanations analysis was conducted on the features of the optimal model.Model performance was evaluated by fivefold cross-validation.Spearman’s correlation analysis was performed to assess key BS features against red blood cell count,hemoglobin,hematocrit,C-reactive protein(CRP),and high-sensitivity CRP.RESULTS The support vector machine classifier with 25-feature subsets achieved optimal performance(area under the curve>0.89),significantly outperforming other models.Acoustic feature importance analysis identified band_Energy and Mel-frequency cepstral coefficient variance as core biomarkers(cumulative contribution>60%).Key pathological correlations included:(1)Significant negative correlations between spectral centroid and red blood cell count/hemoglobin/hematocrit(P<0.01);(2)Positive correlation between wavelet entropy and these hematological parameters(P<0.05),suggesting multiscale microcirculatory flow fluctuations;and(3)Positive wavelet energy correlations with CRP/high-sensitivity-CRP(P<0.05).CONCLUSION Multidimensional BS features enable noninvasive UGIB screening.Their strong correlation with anemia/inflammation indicators reveals an acoustic-hemato-physiological coupling mechanism,providing a novel paradigm for early UGIB monitoring. 展开更多
关键词 Bowel sound analysis Upper gastrointestinal bleeding Support vector machine Machine learning Non-invasive diagnosis
暂未订购 下载PDF
P-and S-Wave Poynting Vectors Derived from the Divergence and Curl Mode Separation for Elastic Wave Reverse Time Migration 认领 引用
16
作者 Nan Hu Min-gui Cai +4 位作者 Yang-guang Yin Yun-sheng Zhao Hao Li Jun-wu Li Bing-feng Zhou 《Applied Geophysics》 SCIE CSCD 2026年第1期56-70,427,共15页
Reverse Time Migration(RTM)stands as one of the foremost advanced seismic wave imaging techniques.For elastic wave RTM,the separation of P-and S-waves prior to imaging is crucial to eectively prevent cross-talk interf... Reverse Time Migration(RTM)stands as one of the foremost advanced seismic wave imaging techniques.For elastic wave RTM,the separation of P-and S-waves prior to imaging is crucial to eectively prevent cross-talk interference between these wave modes.While more sophisticated P-and S-wave separation methods based on decoupled wave equations currently exist,the approach utilizing divergence and curl operators retains signicant practical value in elastic RTM due to its inherent simplicity in implementation and lower computational demand.However,existing P-and S-wave separation methods founded on divergence and curl operators lack a corresponding methodology for calculating decoupled P-and S-wave Poynting vectors.These decoupled Poynting vectors are vital as they can respectively indicate the propagation directions of P-and S-waves,and their application within elastic RTM can markedly improve imaging quality.This paper derives new formulas for calculating P-and S-wave Poynting vectors that correspond to the wave separation achieved through divergence and curl operators.This approach permits the accurate determination of P-and S-wave propagation directions without altering the original wave equations and has been applied to elastic RTM,ensuring higher computational efciency throughout the imaging process.Imaging test results from both the Graben and Marmousi models demonstrate that,compared to traditional coupled Poynting vectors,the decoupled P-and S-wave Poynting vectors proposed herein achieve superior suppression of migration noise and artifacts in elastic RTM.Furthermore,they facilitate accurate S-wave polarity correction,leading to clearer imaging interfaces in migration proles and more reliable overall results.The methodology presented in this paper broadens the application scenarios for elastic RTM under current computational resource constraints and is poised to stimulate further development of P-and S-wave separation methods based on divergence and curl operators within the eld of RTM. 展开更多
关键词 Reverse Time Migration Poynting Vectors Wavefield Decoupling Noise Suppression Polarity Correction
暂未订购 下载PDF
Laser photocoagulation improves the transduction efficiency of lentivirus vectors injected intravitreally 认领 引用
17
作者 Jian Jiao Yi-Jun Xu +2 位作者 Xue-Dong Li Wen Hua Yan-Rong Jiang 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第6期1057-1064,共8页
AIM:To evaluate the effect of laser photocoagulation on transfection efficiency of lentivirus(LV)vectors injected intravitreally.METHODS:The rat retina was photocoagulated prior to intravitreal injection of LV vectors... AIM:To evaluate the effect of laser photocoagulation on transfection efficiency of lentivirus(LV)vectors injected intravitreally.METHODS:The rat retina was photocoagulated prior to intravitreal injection of LV vectors encoding red fluorescent protein.Rats given intravitreal injection or photocoagulation alone served as controls.The transduction efficiency was analyzed by fundus angiography,histopathology,and immunofluorescence staining.Potential adverse effect of laser photocoagulation was evaluated in choroid flat mounts by observing the morphology of retinal pigment epithelium(RPE)that formed the outer blood-retina barrier.RESULTS:Laser photocoagulation enhanced the retinal area transduced by LV vectors in fundus images and increased the maximum transduction thickness in histopathological sections.Choroid flat mounts showed the RPE transduced by LV vectors maintained normal morphology.CONCLUSION:Laser photocoagulation improves the transduction efficiency of LV vectors injected intravitreally,identifying this as a promising gene therapy strategy for inherited retinal dystrophies caused by gene mutations in RPE and photoreceptor cells. 展开更多
关键词 gene therapy laser photocoagulation lentivirus vector transduction efficiency intravitreal injection
暂未订购 下载PDF
Genomic assembly,rescue,and characterization of a functional pseudorabies virus 认领 引用
18
作者 Hengrui Hu Xiquan Ke +7 位作者 Han Xiao Xianfei Shang Qiran Yin Jiang Li Xiangmin Li Zhihong Hu Ping Qian Manli Wang 《Virologica Sinica》 SCIE CAS CSCD 2026年第1期97-106,共10页
With recent advances in synthetic biology methods,the genomes of several large DNA viruses have been de novo synthesized and assembled,leading to the functional rescue of the respective viruses.Pseudorabies virus(PRV)... With recent advances in synthetic biology methods,the genomes of several large DNA viruses have been de novo synthesized and assembled,leading to the functional rescue of the respective viruses.Pseudorabies virus(PRV),a large DNA virus belonging to the family Herpesviridae,causes severe diseases in swine,resulting in significant economic losses to the global pig farming industry.Genome editing is crucial for attenuating virulence and developing safer vaccines for PRV.However,its complex repetitive sequences and extremely high GC-rich genome pose significant challenges for genetic manipulation.In this study,we developed a PRV genome assembly platform using yeast-based transformation-associated recombination(TAR)technology.The genome of a prevalent genotype Ⅱ variant strain,PRV-GX-2011(GenBank number PV405324.1),was divided into nine A-level fragments and cloned into vectors via TAR.Subsequently,three B-level fragments were generated by recombining three A-level fragments each.In vitro CRISPR/Cas9-mediated editing was introduced to insert an egfp gene into the non-coding intergenic region between UL23 and UL22 genes.Infectious viruses were rescued by co-transfection of linearized B-level fragments in Vero cells,and an isolated virus,PRV-GX-Syn1,was purified via plaque assay.While PRV-GX-Syn1 exhibited reduced viral titer and smaller plaque size compared to the parental strain,its morphological characteristics remained indistinguishable from the parental virus.In BALB/c mice,PRV-GX-Syn1 caused lethal infection,producing lung pathology comparable to the parental strain.This TAR-based platform offers faster and more flexible genomic modification of PRV,facilitating both basic research and PRV-based vaccine vectors. 展开更多
关键词 Pseudorabies virus(PRV) Genome assembly TAR CRISPR/Cas9 Attenuated vaccine vector Virus rescue
暂未订购 下载PDF
Geodesic Nature and Quantization of Shift Vector 认领 引用
19
作者 Hua Wang Kai Chang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第2期143-148,共6页
We present the geodesic nature and quantization of geometric shift vector in quantum systems,with the parameter space defined by the Bloch momentum,using the Wilson loop approach.Our analysis extends to include bosoni... We present the geodesic nature and quantization of geometric shift vector in quantum systems,with the parameter space defined by the Bloch momentum,using the Wilson loop approach.Our analysis extends to include bosonic phonon drag shift vectors with non-vertical transitions.We demonstrate that the gauge invariant shift vector can be quantized as integer values,analogous to the Euler characteristic based on the Gauss-Bonnet theorem for a manifold with a smooth boundary.We reveal intricate relationships among geometric quantities such as the shift vector,Berry curvature,and quantum metric.Our findings demonstrate that the loop integral of the shift vector in the quantized interband formula contributes to the non-quantized component of the trace of conductivity in the circular photogalvanic effect.The Wilson loop method facilitates first-principles calculations,providing insights in the geometric underpinnings of these interband gauge invariant quantities and shedding light on their nonlinear optical manifestations in real materials. 展开更多
关键词 geometric shift vector geodesic nature gauge invariant shift vector wilson loop approachour bosonic phonon drag shift vectors bloch momentumusing euler characteristic quantization
暂未订购 下载PDF
On the compressible Navier-Stokes equation with Coriolis force and density-dependent viscosity:the Cartesian vector solutions and their properties 认领 引用
20
作者 Hongli An Ziliang Li +1 位作者 Anthony Suen Manwai Yuen 《Communications in Theoretical Physics》 SCIE CAS CSCD 2026年第5期1-10,共10页
The Navier-Stokes(NS)equation with Coriolis force and density-dependent viscosity is an important physical model,which has been widely used to understand and analyze a wide array of phenomena,including behaviors of th... The Navier-Stokes(NS)equation with Coriolis force and density-dependent viscosity is an important physical model,which has been widely used to understand and analyze a wide array of phenomena,including behaviors of the Gulf stream,dynamics of hurricanes,operation of chemical reactors and functionality of rotating machines.In this paper,based on the matrix and curve integration techniques,we build a sufficient condition for the existence of Cartesian vector solutions u=b(t)+A(t)x for the N-dimensional NS equation,in which A satisfies appropriate matrix equations.Then,we discuss two special cases of A and thereby explicit analytical solutions are obtained.To shed light on these solutions,we give some illustrative examples.Among them,some examples form the generalization previously obtained by other authors and some examples are quite new.Finally,we analyze the properties of Cartesian vector solutions in a special case. 展开更多
关键词 the compressible Navier-Stokes equation Cartesian vector solutions matrix differential equations curve integration technique
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