Aerodynamic performances of axial compressors are significantly affected by variation of Reynolds number in aero-engines.In the design and analysis of compressors,previous correction methods for cascades and stages ha...Aerodynamic performances of axial compressors are significantly affected by variation of Reynolds number in aero-engines.In the design and analysis of compressors,previous correction methods for cascades and stages have difficulties in predicting comprehensively Reynolds number effects on airfoils,matching and characteristics curves.This study proposes Re-correction models for loss,deviation angle and endwall blockage based on classical theories and cascade tests,and loss and deviation models show good agreement in test data of NACA65 and C4 cascades.Throughflow method considering Reynolds number effects is developed by integrating the correction models into a verified Streamline Curvature(SLC)tool.A three-stage axial compressor is investigated through SLC and CFD methods from design Reynolds number(Red=2106)to low Re=4104,and the numerical methods are validated with test data of characteristic curves and spanwise distributions at Red.With Re reduction,SLC method with correction models well predicts variation in overall performances compared with CFD calculations and Wassell's model.Streamwise and spanwise matching such as total pressure and loss distributions in SLC predictions are basically consistent with those in CFD results at near-stall points under design and low Reynolds numbers.SLC and CFD methods share similar detections of stall risks in the third stage(Stg3),and their analyses of diffusion processes deviate to some extent due to different predictions in separated endwall flow.The correction models can be adopted to consider Reynolds number effects in through-flow design and analysis of axial compressors.展开更多
The flow of a tetra-hybrid Casson nanofluid(Al_(2)O_(3)-CuO-TiO_(2)-Ag/H_(2)O)over a nonlinear stretching sheet is investigated.The Buongiorno model is used to account for thermophoresis and Brownian motion,while ther...The flow of a tetra-hybrid Casson nanofluid(Al_(2)O_(3)-CuO-TiO_(2)-Ag/H_(2)O)over a nonlinear stretching sheet is investigated.The Buongiorno model is used to account for thermophoresis and Brownian motion,while thermal radiation is incorporated to examine its influence on the thermal boundary layer.The governing partial differential equations(PDEs)are reduced to a system of nonlinear ordinary differential equations(ODEs)with fully non-dimensional similarity transformations involving all independent variables.To solve the obtained highly nonlinear system of differential equations,a novel Clique polynomial collocation method is applied.The analysis focuses on the effects of the Casson parameter,power index,radiation parameter,thermophoresis parameter,Brownian motion parameter,and Lewis number.The key findings show that thermal radiation intensifies the thermal boundary layer,the Casson parameter reduces the velocity,and the Lewis number suppresses the concentration with direct relevance to polymer processing,coating flows,electronic cooling,and biomedical applications.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
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
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
Objective:To investigate the effects of“Three Methods and Three Acupoints”(TMTP)Tuina therapy on spinal microcirculation in sciatic nerve injury(SNI).Methods:Thirty-six SpragueeDawley rats were randomly assigned to ...Objective:To investigate the effects of“Three Methods and Three Acupoints”(TMTP)Tuina therapy on spinal microcirculation in sciatic nerve injury(SNI).Methods:Thirty-six SpragueeDawley rats were randomly assigned to four groups:normal,sham operation,model,and TMTP Tuina.Successful model induction was confirmed by observable hind limb lameness.After 20 sessions,hind limb grip strength and motor nerve conduction velocity(MNCV)were measured at baseline and following the 10th and 20th intervention.CD31 and a-SMA in the ventral horn of SNI model rats were detected using immunofluorescence.Motor neurons in the ventral horn were detected by Nissl staining.PTEN levels in the ventral horn were measured by ELISA,and PI3K,Akt,BDNF,VEGF,and HIF-1a expression was determined by RT-PCR.Spinal cord microcirculation was evaluated by western blotting analysis of the levels of Akt,p-Akt,BDNF,and VEGF.Results:Hind limb grip strength and MNCV significantly improved in the TMTP Tuina group compared to the model group(both P<.001).Morphology of ventral horn motor neurons in the TMTP Tuina group improved compared to the model group,with increased expressions of a-SMA(P=.002)and CD31(P=.006).Western blot analysis indicated increased expression of VEGF(P=.005),p-Akt(P<.001),and BDNF(P=.008)in the ventral horn following Tuina treatment.RT-PCR analysis revealed increased expression of PI3K,Akt,BDNF,VEGF and HIF-1a(all P<.05).In contrast,expression of PTEN decreased compared to the model group(P<.001).Conclusion:TMTP Tuina therapy may restore motor function in rats,enhance ventral horn motor neuron morphology,and promote angiogenesis and vascular smooth muscle proliferation.The mechanism may involve the activation of the PI3K/Akt signaling pathway.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
Deep learning(DL)has become a crucial technique for predicting the El Niño-Southern Oscillation(ENSO)and evaluating its predictability.While various DL-based models have been developed for ENSO predictions,many f...Deep learning(DL)has become a crucial technique for predicting the El Niño-Southern Oscillation(ENSO)and evaluating its predictability.While various DL-based models have been developed for ENSO predictions,many fail to capture the coherent multivariate evolution within the coupled ocean-atmosphere system of the tropical Pacific.To address this three-dimensional(3D)limitation and represent ENSO-related ocean-atmosphere interactions more accurately,a novel this 3D multivariate prediction model was proposed based on a Transformer architecture,which incorporates a spatiotemporal self-attention mechanism.This model,named 3D-Geoformer,offers several advantages,enabling accurate ENSO predictions up to one and a half years in advance.Furthermore,an integrated gradient method was introduced into the model to identify the sources of predictability for sea surface temperature(SST)variability in the eastern equatorial Pacific.Results reveal that the 3D-Geoformer effectively captures ENSO-related precursors during the evolution of ENSO events,particularly the thermocline feedback processes and ocean temperature anomaly pathways on and off the equator.By extending DL-based ENSO predictions from one-dimensional Niño time series to 3D multivariate fields,the 3D-Geoformer represents a significant advancement in ENSO prediction.This study provides details in the model formulation,analysis procedures,sensitivity experiments,and illustrative examples,offering practical guidance for the application of the model in ENSO research.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
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
The measurement of the pairing gap is crucial for investigating the physical properties of superconductors or superfluids.We propose a strategy to measure the pairing gap through the dynamical excitations.With the ran...The measurement of the pairing gap is crucial for investigating the physical properties of superconductors or superfluids.We propose a strategy to measure the pairing gap through the dynamical excitations.With the random phase approximation(RPA),we study the dynamical excitations of a two-dimensional attractive Fermi-Hubbard model by calculating its dynamical structure factor.Two distinct collective modes emerge:a Goldstone phonon mode at transferred momentum q=[0,0]and a roton mode at q=[p,p].The roton mode exhibits a sharp molecular peak in the low-energy regime.Notably,the area under the roton molecular peak scales with the square of the pairing gap,which holds even in three-dimensional and spin-orbit coupled(SOC)optical lattices.This finding suggests an experimental approach to measure the pairing gap in lattice systems by analyzing the dynamical structure factor at q=[p,p].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
This paper introduces a framework for modeling random fields,with a particular emphasis on analyzing anisotropic spatial variability.It establishes a clear connection between the correlation function and the Kriging v...This paper introduces a framework for modeling random fields,with a particular emphasis on analyzing anisotropic spatial variability.It establishes a clear connection between the correlation function and the Kriging variogram across various anisotropic modes,providing mathematical models to enhance our understanding of random fields.A new anisotropy index,called LSAI,is introduced to quantify anisotropy based on the autocorrelation length and the orientation of the principal axes within the variogram.An LSAI value closer to one indicates a lower degree of anisotropy.The present study examines how the degree of anisotropy varies with different autocorrelation lengths and angles between the principal axes,providing valuable insights into these relationships.To improve the accuracy of parameter probability distribution estimations,this study integrates limited field test data using a Bayesian inference approach.Additionally,the Markov chain Monte Carlo simulation method is employed to develop a conditional random field(CRF)for the deformation modulus.By incorporating data from field bearing plate tests,the posterior variance data for the deformation modulus are derived.This process facilitates the construction of a detailed and reliable CRF for the deformation modulus.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
基金supported by the National Science and Tech-nology Major Project of China(Nos.2017-II-0007-0021 and J2019-II-0017-0038)。
文摘Aerodynamic performances of axial compressors are significantly affected by variation of Reynolds number in aero-engines.In the design and analysis of compressors,previous correction methods for cascades and stages have difficulties in predicting comprehensively Reynolds number effects on airfoils,matching and characteristics curves.This study proposes Re-correction models for loss,deviation angle and endwall blockage based on classical theories and cascade tests,and loss and deviation models show good agreement in test data of NACA65 and C4 cascades.Throughflow method considering Reynolds number effects is developed by integrating the correction models into a verified Streamline Curvature(SLC)tool.A three-stage axial compressor is investigated through SLC and CFD methods from design Reynolds number(Red=2106)to low Re=4104,and the numerical methods are validated with test data of characteristic curves and spanwise distributions at Red.With Re reduction,SLC method with correction models well predicts variation in overall performances compared with CFD calculations and Wassell's model.Streamwise and spanwise matching such as total pressure and loss distributions in SLC predictions are basically consistent with those in CFD results at near-stall points under design and low Reynolds numbers.SLC and CFD methods share similar detections of stall risks in the third stage(Stg3),and their analyses of diffusion processes deviate to some extent due to different predictions in separated endwall flow.The correction models can be adopted to consider Reynolds number effects in through-flow design and analysis of axial compressors.
基金the UGC,New Delhi,India for financial assistance via the UGC-Junior Research Fellowship(CSIR-UGC NET JULY 2024)(Student ID:241610090610)。
文摘The flow of a tetra-hybrid Casson nanofluid(Al_(2)O_(3)-CuO-TiO_(2)-Ag/H_(2)O)over a nonlinear stretching sheet is investigated.The Buongiorno model is used to account for thermophoresis and Brownian motion,while thermal radiation is incorporated to examine its influence on the thermal boundary layer.The governing partial differential equations(PDEs)are reduced to a system of nonlinear ordinary differential equations(ODEs)with fully non-dimensional similarity transformations involving all independent variables.To solve the obtained highly nonlinear system of differential equations,a novel Clique polynomial collocation method is applied.The analysis focuses on the effects of the Casson parameter,power index,radiation parameter,thermophoresis parameter,Brownian motion parameter,and Lewis number.The key findings show that thermal radiation intensifies the thermal boundary layer,the Casson parameter reduces the velocity,and the Lewis number suppresses the concentration with direct relevance to polymer processing,coating flows,electronic cooling,and biomedical applications.
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 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 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(82274675&82074573)the Beijing Natural Science Foundation(7232278).
文摘Objective:To investigate the effects of“Three Methods and Three Acupoints”(TMTP)Tuina therapy on spinal microcirculation in sciatic nerve injury(SNI).Methods:Thirty-six SpragueeDawley rats were randomly assigned to four groups:normal,sham operation,model,and TMTP Tuina.Successful model induction was confirmed by observable hind limb lameness.After 20 sessions,hind limb grip strength and motor nerve conduction velocity(MNCV)were measured at baseline and following the 10th and 20th intervention.CD31 and a-SMA in the ventral horn of SNI model rats were detected using immunofluorescence.Motor neurons in the ventral horn were detected by Nissl staining.PTEN levels in the ventral horn were measured by ELISA,and PI3K,Akt,BDNF,VEGF,and HIF-1a expression was determined by RT-PCR.Spinal cord microcirculation was evaluated by western blotting analysis of the levels of Akt,p-Akt,BDNF,and VEGF.Results:Hind limb grip strength and MNCV significantly improved in the TMTP Tuina group compared to the model group(both P<.001).Morphology of ventral horn motor neurons in the TMTP Tuina group improved compared to the model group,with increased expressions of a-SMA(P=.002)and CD31(P=.006).Western blot analysis indicated increased expression of VEGF(P=.005),p-Akt(P<.001),and BDNF(P=.008)in the ventral horn following Tuina treatment.RT-PCR analysis revealed increased expression of PI3K,Akt,BDNF,VEGF and HIF-1a(all P<.05).In contrast,expression of PTEN decreased compared to the model group(P<.001).Conclusion:TMTP Tuina therapy may restore motor function in rats,enhance ventral horn motor neuron morphology,and promote angiogenesis and vascular smooth muscle proliferation.The mechanism may involve the activation of the PI3K/Akt signaling pathway.
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 by the Laoshan Laboratory(No.LSKJ202202402)the National Natural Science Foundation of China(No.42030410)+2 种基金the Startup Foundation for Introducing Talent of Nanjing University of Information Science&Technology,and Jiangsu Innovation Research Group(No.JSSCTD 202346)supported by the China National Postdoctoral Program for Innovative Talents(No.BX20240169)the China Postdoctoral Science Foundation(No.2141062400101)。
文摘Deep learning(DL)has become a crucial technique for predicting the El Niño-Southern Oscillation(ENSO)and evaluating its predictability.While various DL-based models have been developed for ENSO predictions,many fail to capture the coherent multivariate evolution within the coupled ocean-atmosphere system of the tropical Pacific.To address this three-dimensional(3D)limitation and represent ENSO-related ocean-atmosphere interactions more accurately,a novel this 3D multivariate prediction model was proposed based on a Transformer architecture,which incorporates a spatiotemporal self-attention mechanism.This model,named 3D-Geoformer,offers several advantages,enabling accurate ENSO predictions up to one and a half years in advance.Furthermore,an integrated gradient method was introduced into the model to identify the sources of predictability for sea surface temperature(SST)variability in the eastern equatorial Pacific.Results reveal that the 3D-Geoformer effectively captures ENSO-related precursors during the evolution of ENSO events,particularly the thermocline feedback processes and ocean temperature anomaly pathways on and off the equator.By extending DL-based ENSO predictions from one-dimensional Niño time series to 3D multivariate fields,the 3D-Geoformer represents a significant advancement in ENSO prediction.This study provides details in the model formulation,analysis procedures,sensitivity experiments,and illustrative examples,offering practical guidance for the application of the model in ENSO research.
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 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 National Natural Science Foundation of China[Grant Nos.U23A2073(P.Z.)and 11547034(H.Z.)].
文摘The measurement of the pairing gap is crucial for investigating the physical properties of superconductors or superfluids.We propose a strategy to measure the pairing gap through the dynamical excitations.With the random phase approximation(RPA),we study the dynamical excitations of a two-dimensional attractive Fermi-Hubbard model by calculating its dynamical structure factor.Two distinct collective modes emerge:a Goldstone phonon mode at transferred momentum q=[0,0]and a roton mode at q=[p,p].The roton mode exhibits a sharp molecular peak in the low-energy regime.Notably,the area under the roton molecular peak scales with the square of the pairing gap,which holds even in three-dimensional and spin-orbit coupled(SOC)optical lattices.This finding suggests an experimental approach to measure the pairing gap in lattice systems by analyzing the dynamical structure factor at q=[p,p].
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 the Doctoral Research Funds for Nanchang HangKong University,China(Grant No.EA202411211)support is gratefully acknowledged.
文摘This paper introduces a framework for modeling random fields,with a particular emphasis on analyzing anisotropic spatial variability.It establishes a clear connection between the correlation function and the Kriging variogram across various anisotropic modes,providing mathematical models to enhance our understanding of random fields.A new anisotropy index,called LSAI,is introduced to quantify anisotropy based on the autocorrelation length and the orientation of the principal axes within the variogram.An LSAI value closer to one indicates a lower degree of anisotropy.The present study examines how the degree of anisotropy varies with different autocorrelation lengths and angles between the principal axes,providing valuable insights into these relationships.To improve the accuracy of parameter probability distribution estimations,this study integrates limited field test data using a Bayesian inference approach.Additionally,the Markov chain Monte Carlo simulation method is employed to develop a conditional random field(CRF)for the deformation modulus.By incorporating data from field bearing plate tests,the posterior variance data for the deformation modulus are derived.This process facilitates the construction of a detailed and reliable CRF for the deformation modulus.
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.
