Geo-interfaces refer to the contact surfaces between multiple media within geological strata,as well as the transition zones that regulate the migration of three-phase matter,changes in physical states,and the deforma...Geo-interfaces refer to the contact surfaces between multiple media within geological strata,as well as the transition zones that regulate the migration of three-phase matter,changes in physical states,and the deformation and stability of rock and soil masses.Owing to the combined effects of natural factors and human activities,geo-interfaces play crucial roles in the emergence,propagation,and triggering of geological disasters.Over the past three decades,the material point method(MPM)has emerged as a preferred approach for addressing large deformation problems and simulating soil-water-structure interactions,making it an ideal tool for analyzing geo-interface behaviors.In this review,we offer a systematic summary of the basic concepts,classifications,and main characteristics of the geo-interface,and provide a comprehensive overview of recent advances and developments in simulating geo-interface using the MPM.We further present a brief description of various MPMs for modeling different types of geo-interfaces in geotechnical engineering applications and highlight the existing limitations and future research directions.This study aims to facilitate innovative applications of the MPM in modeling complex geo-interface problems,providing a reference for geotechnical practitioners and researchers.展开更多
The equivalent source(ES)method in the spherical coordinate system has been widely applied to processing,reduction,field modeling,and geophysical and geological interpretation of satellite magnetic anomaly data.Howeve...The equivalent source(ES)method in the spherical coordinate system has been widely applied to processing,reduction,field modeling,and geophysical and geological interpretation of satellite magnetic anomaly data.However,the inversion for the ES model suffers from nonuniqueness and instability,which remain unresolved.To mitigate these issues,we introduce both the minimum and flattest models into the model objective function as an alternative regularization approach in the spherical ES method.We first present the methods,then analyze the accuracy of forward calculation and test the proposed ES method in this study by using synthetic data.The experimental results from simulation data indicate that our proposed regularization effectively suppresses the Backus effect and mitigates inversion instability in the low-latitude region.Finally,we apply the proposed method to magnetic anomaly data from China Seismo-Electromagnetic Satellite-1(CSES-1)and Macao Science Satellite-1(MSS-1)magnetic measurements over Africa by constructing an ES model of the large-scale lithospheric magnetic field.Compared with existing global lithospheric magnetic field models,our ES model demonstrates good consistency at high altitudes and predicts more stable fields at low altitudes.Furthermore,we derive the reduction to the pole(RTP)magnetic anomaly fields and the apparent susceptibility contrast distribution based on the ES model.The latter correlates well with the regional tectonic framework in Africa and surroundings.展开更多
As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and s...As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and smart maintenance.While promising,both methods have issues that need to be addressed.For example,model-based methods are limited by low computational accuracy and a high computational burden,and data-driven methods always suffer from poor interpretability and redundant features.To address these issues,the concept of data-model fusion(DMF)emerges as a promising solution.DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods.Despite growing efforts in the field of DMF,a unanimous definition of DMF remains elusive,and a general framework of DMF has been rarely discussed.This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods.Subsequently,this paper also presents the definition and categorization of DMF and discusses the general framework of DMF.Moreover,the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering.Finally,this paper directs the future directions of DMF.展开更多
In this paper,we propose a multiphysics finite element method for a nonlinear poroelasticity model with nonlinear stress-strain relation.Firstly,we reformulate the original problem into a new coupled fluid system-a ge...In this paper,we propose a multiphysics finite element method for a nonlinear poroelasticity model with nonlinear stress-strain relation.Firstly,we reformulate the original problem into a new coupled fluid system-a generalized nonlinear Stokes problem of displacement vector field related to pseudo pressure and a diffusion problem of other pseudo pressure fields.Secondly,a fully discrete multiphysics finite element method is performed to solve the reformulated system numerically.Thirdly,existence and uniqueness of the weak solution of the reformulated model and stability analysis and optimal convergence order for the multiphysics finite element method are proven theoretically.Lastly,numerical tests are given to verify the theoretical results.展开更多
Rolling bearings are important parts of industrial equipment,and their fault diagnosis is crucial to maintaining these equipment’s regular operations.With the goal of improving the fault diagnosis accuracy of rolling...Rolling bearings are important parts of industrial equipment,and their fault diagnosis is crucial to maintaining these equipment’s regular operations.With the goal of improving the fault diagnosis accuracy of rolling bearings under complex working conditions and noise,this study proposes a multiscale information fusion method for fault diagnosis of rolling bearings based on fast Fourier transform(FFT)and variational mode decomposition(VMD),as well as the Senet(SE)-TCNnet(TCN)model.FFT is used to transform the original one-dimensional time domain vibration signal into a frequency domain signal,while VMD is used to decompose the original signal into several inherent mode functions(IMFs)of different scales.The center frequency method also determines the number of mode decompositions.Then,the data obtained by the two methods are fused into data containing the bearing fault information of different scales.Finally,the fused data are sent to the SE-TCN model for training.Experimental tests are conducted to verify the performance of this method.The findings reveal that an average accuracy of 98.39%can be achieved when noise is added and can even reach 100%when the signal-to-noise ratio is 6 dB.When the load changes,the accuracy of the model can reach 97.45%.The proposed method has the characteristics of high accuracy and strong generalization ability in bearing fault diagnosis.Furthermore,it can effectively overcome the effects of noise and variable working conditions in actual industrial environments,thus providing some ideas for future practical applications of bearing fault diagnosis.展开更多
We propose a robust earthquake clustering method:the Bayesian Gaussian mixture model with nearest-neighbor distance(BGMM-NND)algorithm.Unlike the conventional nearest neighbor distance method,the BGMM-NND algorithm el...We propose a robust earthquake clustering method:the Bayesian Gaussian mixture model with nearest-neighbor distance(BGMM-NND)algorithm.Unlike the conventional nearest neighbor distance method,the BGMM-NND algorithm eliminates the need for hyperparameter tuning or reliance on fixed thresholds,offering enhanced flexibility for clustering across varied seismic scales.By integrating cumulative probability and BGMM with principal component analysis(PCA),the BGMM-NND algorithm effectively distinguishes between background and triggered earthquakes while maintaining the magnitude component and resolving the issue of excessively large spatial cluster domains.We apply the BGMM-NND algorithm to the Sichuan–Yunnan seismic catalog from 1971 to 2024,revealing notable variations in earthquake frequency,triggering characteristics,and recurrence patterns across different fault zones.Distinct clustering and triggering behaviors are identified along different segments of the Longmenshan Fault.Multiple seismic modes,namely,the short-distance mode,the medium-distance mode,the repeating-like mode,the uniform background mode,and the Wenchuan mode,are uncovered.The algorithm's flexibility and robust performance in earthquake clustering makes it a valuable tool for exploring seismicity characteristics,offering new insights into earthquake clustering and the spatiotemporal patterns of seismic activity.展开更多
Shale gas reservoirs have large burial depths,thin thickness,and low resistance,which lead to problems with weak surface observation,abnormal information,and multiple inversion solutions.The traditional electromagneti...Shale gas reservoirs have large burial depths,thin thickness,and low resistance,which lead to problems with weak surface observation,abnormal information,and multiple inversion solutions.The traditional electromagnetic method cannot effectively identify information from deep,low-resistance thin layers in terms of detection depth and accuracy.Wide field electromagnetic method(WFEM)with large depth and high precision has become the main method for deep earth exploration.This method has been widely used in the exploration of deep oil and gas energy,as well as mineral resources.However,an in-depth analysis of the various factors that affect the deep detection ability of WFEM is lacking.Therefore,the analysis of system parameters has significant theoretical importance and practical value for studying the effectiveness of WFEM in deep-layer identification.In this study,a multilayer geoelectric model is established in this study using the measured well data.The influence characteristics of different observation system parameters on the resolution of specific deep-seated targets under the WFEM_E-Ex mode are analyzed in detail through forward modeling and inversion.Results show that the resolution ability of WFEM for deep,low-resistance thin layers is affected by factors such as transceiver distance,target layer thickness,and resistivity difference between the target body and the surrounding rock,but the influence range differs.This study analyzes the influence characteristics of various system parameters.It provides targeted work scheme design and feasibility analysis for deep shale gas exploration.It also offers an important theoretical basis for optimizing construction schemes and improving the recognition ability of WFEM for deep,low-resistance targets.展开更多
In this study,a powerful thermo-hydro-mechanical(THM)coupling solution scheme for saturated poroelastic media involving brittle fracturing is developed.Under the local thermal non-equilibrium(LTNE)assumption,this sche...In this study,a powerful thermo-hydro-mechanical(THM)coupling solution scheme for saturated poroelastic media involving brittle fracturing is developed.Under the local thermal non-equilibrium(LTNE)assumption,this scheme seamlessly combines the material point method(MPM)for accurately tracking solid-phase deformation and heat transport,and the Eulerian finite element method(FEM)for effectively capturing fluid flow and heat advection-diffusion behavior.The proposed approach circumvents the substantial challenges posed by large nonlinear equation systems with the monolithic solution scheme.The staggered solution process strategically separates each physical field through explicit or implicit integration.The characteristic-based method is used to stabilize advection-dominated heat flows for efficient numerical implementation.Furthermore,a fractional step approach is employed to decompose fluid velocity and pressure,thereby suppressing pore pressure oscillation on the linear background grid.The fracturing initiation and propagation are simulated by a rate-dependent phase field model.Through a series of quasi-static and transient simulations,the exceptional performance and promising potential of the proposed model in addressing THM fracturing problems in poro-elastic media is demonstrated.展开更多
Objective:To explore the application effect of scenario simulation teaching method based on Debriefing-GAS mode in the teaching of clinical nursing students in neurosurgery and its influence on critical thinking,clini...Objective:To explore the application effect of scenario simulation teaching method based on Debriefing-GAS mode in the teaching of clinical nursing students in neurosurgery and its influence on critical thinking,clinical practice ability and teaching satisfaction.Methods:A total of 100 nursing students who were doing their internship in the Department of Neurosurgery at a tertiary hospital in Sichuan Province from July 2024 to June 2025 were selected and divided into a control group and an experimental group,with 50 students in each group,using the historical control grouping method.The control group used the traditional teaching method for scenario simulation,while the experimental group used the Debriefing-GAS model for scenario simulation teaching.The teaching effect was evaluated using the DASH scale,the CIDI-CV scale,the clinical practice ability scale and the self-made teaching satisfaction questionnaire,and statistical analysis was conducted using SPSS 25.0.Results:The total DASH score of the experimental group(6.45±0.41)was significantly higher than that of the control group(5.12±0.47,t=14.01,p<0.001);The total score of critical thinking ability(338.6±22.5)was higher than that of the control group(307.8±24.1,t=6.55,p<0.001);The score of clinical practice ability(189.4±15.2)was significantly higher than that of the control group(168.7±14.9,t=6.49,p<0).Teaching satisfaction was 96%in the experimental group and 82%in the control group,statistically significant(χ^(2)=4.32,p=0.038).Conclusion:The scenario simulation teaching method based on the Debriefing-GAS model can significantly improve the learning initiative,reflective ability,critical thinking level and clinical practice ability of clinical nursing students,and enhance teaching satisfaction.展开更多
Evaluation of water richness in sandstone is an important research topic in the prevention and control of mine water disasters,and the water richness in sandstone is closely related to its porosity.The refl ection sei...Evaluation of water richness in sandstone is an important research topic in the prevention and control of mine water disasters,and the water richness in sandstone is closely related to its porosity.The refl ection seismic exploration data have high-density spatial sampling information,which provides an important data basis for the prediction of sandstone porosity in coal seam roofs by using refl ection seismic data.First,the basic principles of the variational mode decomposition(VMD)method and the random forest method are introduced.Then,the geological model of coal seam roof sandstone is constructed,seismic forward modeling is conducted,and random noise is added.The decomposition eff ects of the empirical mode decomposition(EMD)method and VMD method on noisy signals are compared and analyzed.The test results show that the firstorder intrinsic mode functions(IMF1)and IMF2 decomposed by the VMD method contain the main eff ective components of seismic signals.A prediction process of sandstone porosity in coal seam roofs based on the combination of VMD and random forest method is proposed.The feasibility and eff ectiveness of the method are verified by trial calculation in the porosity prediction of model data.Taking the actual coalfield refl ection seismic data as an example,the sandstone porosity of the 8 coal seam roof is predicted.The application results show the potential application value of the new porosity prediction method proposed in this study.This method has important theoretical guiding significance for evaluating water richness in coal seam roof sandstone and the prevention and control of mine water disasters.展开更多
Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implemen...Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implementation,or configuration.To guarantee the durability&robustness of the software,vulnerability identification and fixation have become crucial areas of focus for developers,cybersecurity experts and industries.This paper presents a thorough multi-phase mathematical model for efficient patch management and vulnerability detection.To uniquely model these processes,the model incorporated the notion of the learning phenomenon in describing vulnerability fixation using a logistic learning function.Furthermore,the authors have used numerical methods to approximate the solution of the proposed framework where an analytical solution is difficult to attain.The suggested systematic architecture has been demonstrated through statistical analysis using patch datasets,which offers a solid basis for the research conclusions.According to computational research,learning dynamics improves security response and results in more effective vulnerability management.The suggested model offers a systematic approach to proactive vulnerability mitigation and has important uses in risk assessment,software maintenance,and cybersecurity.This study helps create more robust software systems by increasing patch management effectiveness,which benefits developers,cybersecurity experts,and sectors looking to reduce security threats in a growing digital world.展开更多
Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types o...Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.展开更多
The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system...The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system,the connection point of each network is regarded as a neuron in the nervous system,and each connection point has input and output functions.Only when the input of nodes reaches a certain threshold can the output function of nodes be stimulated.Using the networking mode of the artificial neural network model,the mobile node can output in multiple directions.If the input direction of a certain path is the same as that of other nodes,it can choose to avoid and choose another path.The weighted value of each path between nodes is different,which means that the influence of the front node on the current node varies.The control method based on the artificial neural network model can be applied to vehicle control,household sweeping robots,and other fields,and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.展开更多
With the increasing accuracy requirements of satellite magnetic detection missions,reducing low-frequency noise has become a key focus of satellite magnetic cleanliness technology.Traditional satellite magnetic simula...With the increasing accuracy requirements of satellite magnetic detection missions,reducing low-frequency noise has become a key focus of satellite magnetic cleanliness technology.Traditional satellite magnetic simulation methods have matured in static magnetic dipole simulations,but there is still significant room for optimization in the simulation and computation of low-frequency magnetic dipole models.This study employs the Gauss-Newton method and Fourier transform techniques for modeling and simulating low-frequency magnetic dipoles.Compared to the traditional particle swarm optimization(PSO)algorithm,this method achieves significant improvements,with errors reaching the order of10^(-13)%under noise-free conditions and maintaining an error level of less than 0.5%under 10%noise.Additionally,the use of Fourier transform and the Gauss-Newton method enables high-precision magnetic field frequency identification and rapid computation of the dipole position and magnetic moment,greatly enhancing the computational efficiency and accuracy of the model.展开更多
This study introduces a comprehensive theoretical framework for accurately calculating the electronic band-structure of strained long-wavelength InAs/GaSb type-Ⅱsuperlattices.Utilizing an eight-band k·p Hamilto⁃...This study introduces a comprehensive theoretical framework for accurately calculating the electronic band-structure of strained long-wavelength InAs/GaSb type-Ⅱsuperlattices.Utilizing an eight-band k·p Hamilto⁃nian in conjunction with a scattering matrix method,the model effectively incorporates quantum confinement,strain effects,and interface states.This robust and numerically stable approach achieves exceptional agreement with experimental data,offering a reliable tool for analyzing and engineering the band structure of complex multi⁃layer systems.展开更多
A parallel finite element scheme for 3D resistivity method forward modeling is introduced in this article.The domain decomposition algorithm,along with a message passing interface,is used to implement parallelism.The ...A parallel finite element scheme for 3D resistivity method forward modeling is introduced in this article.The domain decomposition algorithm,along with a message passing interface,is used to implement parallelism.The computational domain is divided into subdomains,and mesh partitioning is combined with load balancing.Unstructured meshes and local mesh refinement strategies are used to realize high precision for complex topography models.Furthermore,an improved linear solver for multi-electrode resistivity method modeling is adopted.Recycling preconditioned conjugate gradient,which is a linear solver,is based on the similarity of linear systems between point sources.The multiple right-hand-side linear systems corresponding to different point source positions are constructed,and the accelerated convergence is obtained through recycling subspace using the linear solver.The computational accuracy and efficiency of the forward scheme for complex topography models are verified using the numerical test results.展开更多
To achieve the manufacturing of Thin-Wall and High-Rib Components(TWHRC)with high precision,a novel heavy load Multi-DOF Envelope Forming Press(MEFP)with Parallel Kinematic Mechanism(PKM),driven by six Permanent Magne...To achieve the manufacturing of Thin-Wall and High-Rib Components(TWHRC)with high precision,a novel heavy load Multi-DOF Envelope Forming Press(MEFP)with Parallel Kinematic Mechanism(PKM),driven by six Permanent Magnet Synchronous Motors(PMSMs),is developed.However,on account of the heavy forming load,the PMSM parameters are in great variation.Meanwhile,the PMSM is always in a transient state caused by fast time-varying forming load,resulting in low identification precision of varied PMSM parameters and control precision of PMSM under traditional parameter identification methods.To solve this problem,a novel Sliding Mode Control Method with Enhanced PMSM Parameter Identification(SMCMEPPI)for heavy load MEFP is proposed.Firstly,the kinematic model of MEFP is established.Secondly,the variation law of PMSM parameters under heavy load is revealed.Thirdly,an enhanced PMSM parameter identification method is proposed,in which the q axis current of PMSM is used to represent the changing rate of forming load and the adjustment factor is first proposed to remove improper input of PMSM parameter identification online.Fourthly,the Electromechanical Coupling Dynamic Model(ECDM)of MEFP,which includes identified PMSM parameters,is developed.Finally,based on the developed ECDM,a novel SMCMEPPI is proposed to realize the high-precision control of heavy load MEFP.The experimental results indicate that the proposed SMCMEPPI can significantly improve the control precision of heavy load MEFP.展开更多
The traditional detailed model of the dual active bridge(DAB)power electronic transformer is characterized by the high dimensionality of its nodal admittance matrix and the need for a small simulation step size,which ...The traditional detailed model of the dual active bridge(DAB)power electronic transformer is characterized by the high dimensionality of its nodal admittance matrix and the need for a small simulation step size,which limits the speed of electromagnetic transient(EMT)simulations.To overcome these limitations,a novel EMT equivalent model based on a generalized branch-cutting method is proposed to improve the simulation efficiency of the DAB model.The DAB topology is first decomposed into two subnetworks through branch-cutting and node-tearing methods without the introduction of a one-time-step delay.Sub-sequently,the internal nodes of each sub-network are eliminated through network simplification,and the equivalent circuit for the port cascade module is derived.The model is then validated through simulations across various operating conditions.The results demonstrate that the model avoids the loss of accuracy associated with one-time-step delay,the relative error across different conditions remains below 1%,and the simulation acceleration ratios improve as the number of modules increases.展开更多
Most reliability studies assume large samples or independence among components,but these assump-tions often fail in practice,leading to imprecise inference.We address this issue by constructing confidence intervals(CI...Most reliability studies assume large samples or independence among components,but these assump-tions often fail in practice,leading to imprecise inference.We address this issue by constructing confidence intervals(CIs)for the reliability of two-component systems with Weibull distributed failure times under a copula-frailty framework.Our construction integrates gamma-distributed frailties to capture unobserved heterogeneity and a copula-based dependence structure for correlated failures.The main contribution of this work is to derive adjusted CIs that explicitly incorporate the copula parameter in the variance-covariance matrix,achieving near-nominal coverage probabilities even in small samples or highly dependent settings.Through simulation studies,we show that,although traditional methods may suffice with moderate dependence and large samples,the proposed CIs offer notable benefits when dependence is strong or data are sparse.We further illustrate our construction with a synthetic example illustrating how penalized estimation can mitigate the issue of a degenerate Hessian matrix under high dependence and limited observations,so enabling uncertainty quantification despite deviations from nominal assumptions.Overall,our results fill a gap in reliability modeling for systems prone to correlated failures,and contribute to more robust inference in engineering,industrial,and biomedical applications.展开更多
Objective:To explore the application value of a new empowerment teaching method based on Kirkpatrick’s evaluation model in teaching Chinese medicine nursing in otorhinolaryngology.Methods:60 nurses who practiced in t...Objective:To explore the application value of a new empowerment teaching method based on Kirkpatrick’s evaluation model in teaching Chinese medicine nursing in otorhinolaryngology.Methods:60 nurses who practiced in the otolaryngology department of our hospital from June 2022 to October 2024 were included in the study and equally divided into two groups using a convenient sampling method.30 nurses who chose traditional Chinese medicine skill teaching management were included in the control group,and 30 nurses who chose the new empowerment teaching method based on Kirkpatrick’s evaluation model were included in the observation group.Relevant indicators such as clinical teaching environment perception,theoretical knowledge scores of Chinese medicine nursing,and excellent rate of practical operation assessment were compared.Results:The nurses in the observation group had higher scores for clinical teaching environment perception than the control group(P<0.05).However,the midterm and final exam scores for theoretical knowledge of Chinese medicine nursing were higher in the observation group than in the control group(P<0.05).Compared with the control group,the observation group had a higher excellent rate of practical operation assessment(93.33%>73.33%)and a higher Chinese medicine nursing ability score[(215.69±19.73)points>(184.87±15.66)points](P<0.05).Conclusion:Applying the new empowerment teaching method based on Kirkpatrick’s evaluation model to Chinese medicine nursing teaching in otolaryngology can help nurses understand the theoretical knowledge of Chinese medicine nursing and optimize the clinical teaching environment,thereby promoting their practical skills and Chinese medicine nursing abilities.展开更多
基金supported by the National Science Fund for Distinguished Young Scholars of China(Grant No.42225702)the National Natural Science Foundation of China(Grant Nos.42461160266 and 52379106).
文摘Geo-interfaces refer to the contact surfaces between multiple media within geological strata,as well as the transition zones that regulate the migration of three-phase matter,changes in physical states,and the deformation and stability of rock and soil masses.Owing to the combined effects of natural factors and human activities,geo-interfaces play crucial roles in the emergence,propagation,and triggering of geological disasters.Over the past three decades,the material point method(MPM)has emerged as a preferred approach for addressing large deformation problems and simulating soil-water-structure interactions,making it an ideal tool for analyzing geo-interface behaviors.In this review,we offer a systematic summary of the basic concepts,classifications,and main characteristics of the geo-interface,and provide a comprehensive overview of recent advances and developments in simulating geo-interface using the MPM.We further present a brief description of various MPMs for modeling different types of geo-interfaces in geotechnical engineering applications and highlight the existing limitations and future research directions.This study aims to facilitate innovative applications of the MPM in modeling complex geo-interface problems,providing a reference for geotechnical practitioners and researchers.
基金supported by the National Natural Science Foundation of China(Grant Nos.42250103 and 42174090)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB2023ZR02)the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources(Grant No.MSFGPMR2022-4).
文摘The equivalent source(ES)method in the spherical coordinate system has been widely applied to processing,reduction,field modeling,and geophysical and geological interpretation of satellite magnetic anomaly data.However,the inversion for the ES model suffers from nonuniqueness and instability,which remain unresolved.To mitigate these issues,we introduce both the minimum and flattest models into the model objective function as an alternative regularization approach in the spherical ES method.We first present the methods,then analyze the accuracy of forward calculation and test the proposed ES method in this study by using synthetic data.The experimental results from simulation data indicate that our proposed regularization effectively suppresses the Backus effect and mitigates inversion instability in the low-latitude region.Finally,we apply the proposed method to magnetic anomaly data from China Seismo-Electromagnetic Satellite-1(CSES-1)and Macao Science Satellite-1(MSS-1)magnetic measurements over Africa by constructing an ES model of the large-scale lithospheric magnetic field.Compared with existing global lithospheric magnetic field models,our ES model demonstrates good consistency at high altitudes and predicts more stable fields at low altitudes.Furthermore,we derive the reduction to the pole(RTP)magnetic anomaly fields and the apparent susceptibility contrast distribution based on the ES model.The latter correlates well with the regional tectonic framework in Africa and surroundings.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grants(52275471 and 52120105008)the Beijing Outstanding Young Scientist Program,and the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and smart maintenance.While promising,both methods have issues that need to be addressed.For example,model-based methods are limited by low computational accuracy and a high computational burden,and data-driven methods always suffer from poor interpretability and redundant features.To address these issues,the concept of data-model fusion(DMF)emerges as a promising solution.DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods.Despite growing efforts in the field of DMF,a unanimous definition of DMF remains elusive,and a general framework of DMF has been rarely discussed.This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods.Subsequently,this paper also presents the definition and categorization of DMF and discusses the general framework of DMF.Moreover,the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering.Finally,this paper directs the future directions of DMF.
基金Supported by the National Natural Science Foundation of China(Grant Nos.12371393,11971150 and 11801143)Natural Science Foundation of Henan Province(Grant No.242300421047).
文摘In this paper,we propose a multiphysics finite element method for a nonlinear poroelasticity model with nonlinear stress-strain relation.Firstly,we reformulate the original problem into a new coupled fluid system-a generalized nonlinear Stokes problem of displacement vector field related to pseudo pressure and a diffusion problem of other pseudo pressure fields.Secondly,a fully discrete multiphysics finite element method is performed to solve the reformulated system numerically.Thirdly,existence and uniqueness of the weak solution of the reformulated model and stability analysis and optimal convergence order for the multiphysics finite element method are proven theoretically.Lastly,numerical tests are given to verify the theoretical results.
基金supported by Handan Science and Technology Research and Development Plan Project under Grant no.23422901031Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province(Hebei University of Engineering)under Grant no.202206.
文摘Rolling bearings are important parts of industrial equipment,and their fault diagnosis is crucial to maintaining these equipment’s regular operations.With the goal of improving the fault diagnosis accuracy of rolling bearings under complex working conditions and noise,this study proposes a multiscale information fusion method for fault diagnosis of rolling bearings based on fast Fourier transform(FFT)and variational mode decomposition(VMD),as well as the Senet(SE)-TCNnet(TCN)model.FFT is used to transform the original one-dimensional time domain vibration signal into a frequency domain signal,while VMD is used to decompose the original signal into several inherent mode functions(IMFs)of different scales.The center frequency method also determines the number of mode decompositions.Then,the data obtained by the two methods are fused into data containing the bearing fault information of different scales.Finally,the fused data are sent to the SE-TCN model for training.Experimental tests are conducted to verify the performance of this method.The findings reveal that an average accuracy of 98.39%can be achieved when noise is added and can even reach 100%when the signal-to-noise ratio is 6 dB.When the load changes,the accuracy of the model can reach 97.45%.The proposed method has the characteristics of high accuracy and strong generalization ability in bearing fault diagnosis.Furthermore,it can effectively overcome the effects of noise and variable working conditions in actual industrial environments,thus providing some ideas for future practical applications of bearing fault diagnosis.
基金supported by the National Key Research and Development Program of China(Grant Nos.2021YFC3000705 and 2021YFC3000705-05)the National Natural Science Foundation of China(Grant No.42074049)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.2023471).
文摘We propose a robust earthquake clustering method:the Bayesian Gaussian mixture model with nearest-neighbor distance(BGMM-NND)algorithm.Unlike the conventional nearest neighbor distance method,the BGMM-NND algorithm eliminates the need for hyperparameter tuning or reliance on fixed thresholds,offering enhanced flexibility for clustering across varied seismic scales.By integrating cumulative probability and BGMM with principal component analysis(PCA),the BGMM-NND algorithm effectively distinguishes between background and triggered earthquakes while maintaining the magnitude component and resolving the issue of excessively large spatial cluster domains.We apply the BGMM-NND algorithm to the Sichuan–Yunnan seismic catalog from 1971 to 2024,revealing notable variations in earthquake frequency,triggering characteristics,and recurrence patterns across different fault zones.Distinct clustering and triggering behaviors are identified along different segments of the Longmenshan Fault.Multiple seismic modes,namely,the short-distance mode,the medium-distance mode,the repeating-like mode,the uniform background mode,and the Wenchuan mode,are uncovered.The algorithm's flexibility and robust performance in earthquake clustering makes it a valuable tool for exploring seismicity characteristics,offering new insights into earthquake clustering and the spatiotemporal patterns of seismic activity.
基金supported by the Jingdezhen Science and Technology Plan Project(No.20234SF005)the Jingdezhen University Science and Technology Project(No.2023xjkt-02).
文摘Shale gas reservoirs have large burial depths,thin thickness,and low resistance,which lead to problems with weak surface observation,abnormal information,and multiple inversion solutions.The traditional electromagnetic method cannot effectively identify information from deep,low-resistance thin layers in terms of detection depth and accuracy.Wide field electromagnetic method(WFEM)with large depth and high precision has become the main method for deep earth exploration.This method has been widely used in the exploration of deep oil and gas energy,as well as mineral resources.However,an in-depth analysis of the various factors that affect the deep detection ability of WFEM is lacking.Therefore,the analysis of system parameters has significant theoretical importance and practical value for studying the effectiveness of WFEM in deep-layer identification.In this study,a multilayer geoelectric model is established in this study using the measured well data.The influence characteristics of different observation system parameters on the resolution of specific deep-seated targets under the WFEM_E-Ex mode are analyzed in detail through forward modeling and inversion.Results show that the resolution ability of WFEM for deep,low-resistance thin layers is affected by factors such as transceiver distance,target layer thickness,and resistivity difference between the target body and the surrounding rock,but the influence range differs.This study analyzes the influence characteristics of various system parameters.It provides targeted work scheme design and feasibility analysis for deep shale gas exploration.It also offers an important theoretical basis for optimizing construction schemes and improving the recognition ability of WFEM for deep,low-resistance targets.
基金supported by National Natural Science Foundation of China(Grant No.42377149)the Research Grants Council of Hong Kong(General Research Fund Project No.17202423).
文摘In this study,a powerful thermo-hydro-mechanical(THM)coupling solution scheme for saturated poroelastic media involving brittle fracturing is developed.Under the local thermal non-equilibrium(LTNE)assumption,this scheme seamlessly combines the material point method(MPM)for accurately tracking solid-phase deformation and heat transport,and the Eulerian finite element method(FEM)for effectively capturing fluid flow and heat advection-diffusion behavior.The proposed approach circumvents the substantial challenges posed by large nonlinear equation systems with the monolithic solution scheme.The staggered solution process strategically separates each physical field through explicit or implicit integration.The characteristic-based method is used to stabilize advection-dominated heat flows for efficient numerical implementation.Furthermore,a fractional step approach is employed to decompose fluid velocity and pressure,thereby suppressing pore pressure oscillation on the linear background grid.The fracturing initiation and propagation are simulated by a rate-dependent phase field model.Through a series of quasi-static and transient simulations,the exceptional performance and promising potential of the proposed model in addressing THM fracturing problems in poro-elastic media is demonstrated.
基金2023 Teaching Reform Project of Chengdu University of Traditional Chinese Medicine(Project No.:JGJD202322)。
文摘Objective:To explore the application effect of scenario simulation teaching method based on Debriefing-GAS mode in the teaching of clinical nursing students in neurosurgery and its influence on critical thinking,clinical practice ability and teaching satisfaction.Methods:A total of 100 nursing students who were doing their internship in the Department of Neurosurgery at a tertiary hospital in Sichuan Province from July 2024 to June 2025 were selected and divided into a control group and an experimental group,with 50 students in each group,using the historical control grouping method.The control group used the traditional teaching method for scenario simulation,while the experimental group used the Debriefing-GAS model for scenario simulation teaching.The teaching effect was evaluated using the DASH scale,the CIDI-CV scale,the clinical practice ability scale and the self-made teaching satisfaction questionnaire,and statistical analysis was conducted using SPSS 25.0.Results:The total DASH score of the experimental group(6.45±0.41)was significantly higher than that of the control group(5.12±0.47,t=14.01,p<0.001);The total score of critical thinking ability(338.6±22.5)was higher than that of the control group(307.8±24.1,t=6.55,p<0.001);The score of clinical practice ability(189.4±15.2)was significantly higher than that of the control group(168.7±14.9,t=6.49,p<0).Teaching satisfaction was 96%in the experimental group and 82%in the control group,statistically significant(χ^(2)=4.32,p=0.038).Conclusion:The scenario simulation teaching method based on the Debriefing-GAS model can significantly improve the learning initiative,reflective ability,critical thinking level and clinical practice ability of clinical nursing students,and enhance teaching satisfaction.
基金National Natural Science Foundation of China(Grant No.42274180)National Key Research and Development Program of China(2021YFC2902003).
文摘Evaluation of water richness in sandstone is an important research topic in the prevention and control of mine water disasters,and the water richness in sandstone is closely related to its porosity.The refl ection seismic exploration data have high-density spatial sampling information,which provides an important data basis for the prediction of sandstone porosity in coal seam roofs by using refl ection seismic data.First,the basic principles of the variational mode decomposition(VMD)method and the random forest method are introduced.Then,the geological model of coal seam roof sandstone is constructed,seismic forward modeling is conducted,and random noise is added.The decomposition eff ects of the empirical mode decomposition(EMD)method and VMD method on noisy signals are compared and analyzed.The test results show that the firstorder intrinsic mode functions(IMF1)and IMF2 decomposed by the VMD method contain the main eff ective components of seismic signals.A prediction process of sandstone porosity in coal seam roofs based on the combination of VMD and random forest method is proposed.The feasibility and eff ectiveness of the method are verified by trial calculation in the porosity prediction of model data.Taking the actual coalfield refl ection seismic data as an example,the sandstone porosity of the 8 coal seam roof is predicted.The application results show the potential application value of the new porosity prediction method proposed in this study.This method has important theoretical guiding significance for evaluating water richness in coal seam roof sandstone and the prevention and control of mine water disasters.
基金supported by grants received by the first author and third author from the Institute of Eminence,Delhi University,Delhi,India,as part of the Faculty Research Program via Ref.No./IoE/2024-25/12/FRP.
文摘Software systems are vulnerable to security breaches as they expand in complexity and functionality.The confidentiality,integrity,and availability of data are gravely threatened by flaws in a system’s design,implementation,or configuration.To guarantee the durability&robustness of the software,vulnerability identification and fixation have become crucial areas of focus for developers,cybersecurity experts and industries.This paper presents a thorough multi-phase mathematical model for efficient patch management and vulnerability detection.To uniquely model these processes,the model incorporated the notion of the learning phenomenon in describing vulnerability fixation using a logistic learning function.Furthermore,the authors have used numerical methods to approximate the solution of the proposed framework where an analytical solution is difficult to attain.The suggested systematic architecture has been demonstrated through statistical analysis using patch datasets,which offers a solid basis for the research conclusions.According to computational research,learning dynamics improves security response and results in more effective vulnerability management.The suggested model offers a systematic approach to proactive vulnerability mitigation and has important uses in risk assessment,software maintenance,and cybersecurity.This study helps create more robust software systems by increasing patch management effectiveness,which benefits developers,cybersecurity experts,and sectors looking to reduce security threats in a growing digital world.
文摘Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.
文摘The topology structure of the artificial neural network is an intelligent control model,which is used for the intelligent vehicle control system and household sweeping robot.When setting the intelligent control system,the connection point of each network is regarded as a neuron in the nervous system,and each connection point has input and output functions.Only when the input of nodes reaches a certain threshold can the output function of nodes be stimulated.Using the networking mode of the artificial neural network model,the mobile node can output in multiple directions.If the input direction of a certain path is the same as that of other nodes,it can choose to avoid and choose another path.The weighted value of each path between nodes is different,which means that the influence of the front node on the current node varies.The control method based on the artificial neural network model can be applied to vehicle control,household sweeping robots,and other fields,and a relatively optimized scheme can be obtained from the aspect of time and energy consumption.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFC2206003)。
文摘With the increasing accuracy requirements of satellite magnetic detection missions,reducing low-frequency noise has become a key focus of satellite magnetic cleanliness technology.Traditional satellite magnetic simulation methods have matured in static magnetic dipole simulations,but there is still significant room for optimization in the simulation and computation of low-frequency magnetic dipole models.This study employs the Gauss-Newton method and Fourier transform techniques for modeling and simulating low-frequency magnetic dipoles.Compared to the traditional particle swarm optimization(PSO)algorithm,this method achieves significant improvements,with errors reaching the order of10^(-13)%under noise-free conditions and maintaining an error level of less than 0.5%under 10%noise.Additionally,the use of Fourier transform and the Gauss-Newton method enables high-precision magnetic field frequency identification and rapid computation of the dipole position and magnetic moment,greatly enhancing the computational efficiency and accuracy of the model.
文摘This study introduces a comprehensive theoretical framework for accurately calculating the electronic band-structure of strained long-wavelength InAs/GaSb type-Ⅱsuperlattices.Utilizing an eight-band k·p Hamilto⁃nian in conjunction with a scattering matrix method,the model effectively incorporates quantum confinement,strain effects,and interface states.This robust and numerically stable approach achieves exceptional agreement with experimental data,offering a reliable tool for analyzing and engineering the band structure of complex multi⁃layer systems.
基金supported by the National Natural Science Foundation of China(No:42274182)Guangxi Natural Science Foundation(No:2020 GXNSFAA297079)。
文摘A parallel finite element scheme for 3D resistivity method forward modeling is introduced in this article.The domain decomposition algorithm,along with a message passing interface,is used to implement parallelism.The computational domain is divided into subdomains,and mesh partitioning is combined with load balancing.Unstructured meshes and local mesh refinement strategies are used to realize high precision for complex topography models.Furthermore,an improved linear solver for multi-electrode resistivity method modeling is adopted.Recycling preconditioned conjugate gradient,which is a linear solver,is based on the similarity of linear systems between point sources.The multiple right-hand-side linear systems corresponding to different point source positions are constructed,and the accelerated convergence is obtained through recycling subspace using the linear solver.The computational accuracy and efficiency of the forward scheme for complex topography models are verified using the numerical test results.
基金the National Science and Technology Major Project of China(No.2019-Ⅶ-0017-0158)the National Natural Science Foundation of China(Nos.U2037204,U21A20131)the Innovative Research Team Development Program of Ministry of Education of China(No.IRT17R83)for the support given to this research。
文摘To achieve the manufacturing of Thin-Wall and High-Rib Components(TWHRC)with high precision,a novel heavy load Multi-DOF Envelope Forming Press(MEFP)with Parallel Kinematic Mechanism(PKM),driven by six Permanent Magnet Synchronous Motors(PMSMs),is developed.However,on account of the heavy forming load,the PMSM parameters are in great variation.Meanwhile,the PMSM is always in a transient state caused by fast time-varying forming load,resulting in low identification precision of varied PMSM parameters and control precision of PMSM under traditional parameter identification methods.To solve this problem,a novel Sliding Mode Control Method with Enhanced PMSM Parameter Identification(SMCMEPPI)for heavy load MEFP is proposed.Firstly,the kinematic model of MEFP is established.Secondly,the variation law of PMSM parameters under heavy load is revealed.Thirdly,an enhanced PMSM parameter identification method is proposed,in which the q axis current of PMSM is used to represent the changing rate of forming load and the adjustment factor is first proposed to remove improper input of PMSM parameter identification online.Fourthly,the Electromechanical Coupling Dynamic Model(ECDM)of MEFP,which includes identified PMSM parameters,is developed.Finally,based on the developed ECDM,a novel SMCMEPPI is proposed to realize the high-precision control of heavy load MEFP.The experimental results indicate that the proposed SMCMEPPI can significantly improve the control precision of heavy load MEFP.
基金The Technology Project of State Grid Corporation of China Headquarters(No.5400-202318547A-3-2-ZN).
文摘The traditional detailed model of the dual active bridge(DAB)power electronic transformer is characterized by the high dimensionality of its nodal admittance matrix and the need for a small simulation step size,which limits the speed of electromagnetic transient(EMT)simulations.To overcome these limitations,a novel EMT equivalent model based on a generalized branch-cutting method is proposed to improve the simulation efficiency of the DAB model.The DAB topology is first decomposed into two subnetworks through branch-cutting and node-tearing methods without the introduction of a one-time-step delay.Sub-sequently,the internal nodes of each sub-network are eliminated through network simplification,and the equivalent circuit for the port cascade module is derived.The model is then validated through simulations across various operating conditions.The results demonstrate that the model avoids the loss of accuracy associated with one-time-step delay,the relative error across different conditions remains below 1%,and the simulation acceleration ratios improve as the number of modules increases.
基金supported by the Colombian government through COLCIENCIA scholarships,National Doctoral Program,Call 727 of 2015C.Castro gratefully acknowledges partial financial support from the Centro de Matematica da Universidade do Minho(CMAT/UM),through UID/00013V.Leiva acknowledges funding from the Agencia Nacional de Investigacion y Desarrollo(ANID)of the Chilean Ministry of Science,Technology,Knowledge and Innovation,through FONDECYT project grant 1200525.
文摘Most reliability studies assume large samples or independence among components,but these assump-tions often fail in practice,leading to imprecise inference.We address this issue by constructing confidence intervals(CIs)for the reliability of two-component systems with Weibull distributed failure times under a copula-frailty framework.Our construction integrates gamma-distributed frailties to capture unobserved heterogeneity and a copula-based dependence structure for correlated failures.The main contribution of this work is to derive adjusted CIs that explicitly incorporate the copula parameter in the variance-covariance matrix,achieving near-nominal coverage probabilities even in small samples or highly dependent settings.Through simulation studies,we show that,although traditional methods may suffice with moderate dependence and large samples,the proposed CIs offer notable benefits when dependence is strong or data are sparse.We further illustrate our construction with a synthetic example illustrating how penalized estimation can mitigate the issue of a degenerate Hessian matrix under high dependence and limited observations,so enabling uncertainty quantification despite deviations from nominal assumptions.Overall,our results fill a gap in reliability modeling for systems prone to correlated failures,and contribute to more robust inference in engineering,industrial,and biomedical applications.
文摘Objective:To explore the application value of a new empowerment teaching method based on Kirkpatrick’s evaluation model in teaching Chinese medicine nursing in otorhinolaryngology.Methods:60 nurses who practiced in the otolaryngology department of our hospital from June 2022 to October 2024 were included in the study and equally divided into two groups using a convenient sampling method.30 nurses who chose traditional Chinese medicine skill teaching management were included in the control group,and 30 nurses who chose the new empowerment teaching method based on Kirkpatrick’s evaluation model were included in the observation group.Relevant indicators such as clinical teaching environment perception,theoretical knowledge scores of Chinese medicine nursing,and excellent rate of practical operation assessment were compared.Results:The nurses in the observation group had higher scores for clinical teaching environment perception than the control group(P<0.05).However,the midterm and final exam scores for theoretical knowledge of Chinese medicine nursing were higher in the observation group than in the control group(P<0.05).Compared with the control group,the observation group had a higher excellent rate of practical operation assessment(93.33%>73.33%)and a higher Chinese medicine nursing ability score[(215.69±19.73)points>(184.87±15.66)points](P<0.05).Conclusion:Applying the new empowerment teaching method based on Kirkpatrick’s evaluation model to Chinese medicine nursing teaching in otolaryngology can help nurses understand the theoretical knowledge of Chinese medicine nursing and optimize the clinical teaching environment,thereby promoting their practical skills and Chinese medicine nursing abilities.
