An integrated calculated approach based on weakly coupled finite element(FEM)-viscoplastic selfconsistent(VPSC)model was established to simulate the texture evolution during the variable strain path extrusion process ...An integrated calculated approach based on weakly coupled finite element(FEM)-viscoplastic selfconsistent(VPSC)model was established to simulate the texture evolution during the variable strain path extrusion process of magnesium alloys.The spiral die extrusion(SDE)process with additional circumferential shear deformation was applied to investigate the effect of path control on texture adjustment and verify the accuracy of the model.The results indicated that the additional spiral shear resulting from the overall inclined flow path effectively reduced the intensity of the{0002}//ED fiber texture by suppressing basal slip activation in the core area,while the local shear deformation along the spiral equal channel strain path led to the formation of an inclined{0002}//ND plane texture on the side.Using the modified Hall-Petch relationship,the correlation between texture and yield strength was quantified.Specifically,the weakening of the texture effectively suppressed{10-12}tensile twinning,which compensated for the deficiency of compressive yield strength without significantly sacrificing tensile yield strength,and thus improved the tension-compression asymmetry.Furthermore,the strongly inclined{0002}//ND plane texture inhibited the widespread activation of basal slip during tensile yielding,thereby enhancing the yield strength.展开更多
A self-consistent analysis of a pulsed direct-current (DC) N2 glow discharge is presented. The model is based on a numerical solution of the continuity equations for electron and ions coupled with Poisson's equati...A self-consistent analysis of a pulsed direct-current (DC) N2 glow discharge is presented. The model is based on a numerical solution of the continuity equations for electron and ions coupled with Poisson's equation. The spatial-temporal variations of ionic and electronic densities and electric field are obtained. The electric field structure exhibits all the characteristic regions of a typical glow discharge (the cathode fall, the negative glow, and the positive column). Current-voltage characteristics of the discharge can be obtained from the model. The calculated current-voltage results using a constant secondary electron emission coefficient for the gas pressure 133.32 Pa are in reasonable agreement with experiment.展开更多
The plastic deformation behavior of new Mg-Gd-Y-Zn-Mn magnesium alloys gains great necessity to clarify and understand the mechanism deeply. In the present work,the tensile mechanical property test and visco-plastic s...The plastic deformation behavior of new Mg-Gd-Y-Zn-Mn magnesium alloys gains great necessity to clarify and understand the mechanism deeply. In the present work,the tensile mechanical property test and visco-plastic self-consistent (VPSC) model are used to investigate the activities of deformation modes of VW84M and VW94M magnesium alloys during the tensile deformation. The results show that the mechanical properties of the above extruded alloys are similar but VW94M has higher strength than VW84M after the same aging process. Compared with the extruded alloys,the as-aged alloys have significantly higher activation of pyramidal slip at the later stage of plastic deformation. In addition,the as-aged VW94M alloy with higher strength has the largest activity of pyramidal slip. In summary,the addition of Gd increases the critical resolved shear stress (CRSS)in each slip system of VW94M,while the increase in the strength and the decrease in the elongation of as-aged alloys are associated with the significant activation of pyramidal slip.展开更多
The paper analyzes the motion of electron in plasma antenna and the distribution of electromagnetic field power around the plasma antenna, and proposes a self-consistent model according to the structure of cylindrical...The paper analyzes the motion of electron in plasma antenna and the distribution of electromagnetic field power around the plasma antenna, and proposes a self-consistent model according to the structure of cylindrical monopole plasma antenna excited by surface wave;calculation of the model is based on Maxwell-Boltzmann equation and gas molecular dynamics theory. The calculation results show that this method can reflect the relationships between the external excitation power, gas pressure, discharge current and the characteristic of plasma. It is an accurate method to predicate and calculate the parameters of plasma antenna.展开更多
In this work,the evolutions of stresses in both phases of the Al/SiCp composite subjected to thermal cycling during in situ compression test were measured using Time of Flight neutron diffraction.It was confirmed that...In this work,the evolutions of stresses in both phases of the Al/SiCp composite subjected to thermal cycling during in situ compression test were measured using Time of Flight neutron diffraction.It was confirmed that inter-phase stresses in the studied composite can be caused by differences in the coefficient of thermal expansion for the reinforcement and matrix,leading to a different variation of phase volumes during sample heating or cooling.The results of the diffraction experiment during thermal cycling were well predicted by the Thermo-Mechanical Self-Consistent model.The experimental study of elastic-plastic deformation was carried out in situ on a unique diffractometer EPSILON-MDS(JINR in Dubna,Russia)with nine detector banks measuring interplanar spacings simultaneously in 9 orientations of scattering vector.For the first time,the performed analysis of experimental data allowed to study the evolution of full stress tensor in both phases of the composite and to consider the decomposition of this tensor into deviatoric and hydrostatic components.It was found that the novel Developed Thermo-Mechanical SelfConsistent model correctly predicted stress evolution during compressive loading,taking into account the relaxation of thermal origin hydrostatic stresses.The comparison of this model with experimental data at the macroscopic level and the level of phases showed that strengthening of the Al/SiCp composite is caused by stress transfer from the plastically deformed A12124 matrix to the elastic SiCp reinforcement,while thermal stresses relaxation does not significantly affect the overall composite properties.展开更多
A 1D radially self-consistent model in helicon plasmas has been established to investigate the influence of radial heat conduction on plasma transport and wave propagation.Two kinds of 1D radial fluid models,with and ...A 1D radially self-consistent model in helicon plasmas has been established to investigate the influence of radial heat conduction on plasma transport and wave propagation.Two kinds of 1D radial fluid models,with and without considering heat conduction,have been developed to couple the 1D plasma-wave interaction model,and self-consistent solutions have been obtained.It is concluded that in the low magnetic field range the radial heat conduction plays a moderate role in the transport of helicon plasmas and the importance depends on the application of the helicon source.It influences the local energy balance leading to enhancement of the electron temperature in the bulk region and a decrease in plasma density.The power deposition in the plasma is mainly balanced by collisional processes and axial diffusion,whereas it is compensated by heat conduction in the bulk region and consumed near the boundary.The role of radial heat conduction in the large magnetic field regime becomes negligible and the two fluid models show consistency.The local power balance,especially near the wall,is improved when conductive heat is taken into account.展开更多
In this study,we investigate the ef-ficacy of a hybrid parallel algo-rithm aiming at enhancing the speed of evaluation of two-electron repulsion integrals(ERI)and Fock matrix generation on the Hygon C86/DCU(deep compu...In this study,we investigate the ef-ficacy of a hybrid parallel algo-rithm aiming at enhancing the speed of evaluation of two-electron repulsion integrals(ERI)and Fock matrix generation on the Hygon C86/DCU(deep computing unit)heterogeneous computing platform.Multiple hybrid parallel schemes are assessed using a range of model systems,including those with up to 1200 atoms and 10000 basis func-tions.The findings of our research reveal that,during Hartree-Fock(HF)calculations,a single DCU ex-hibits 33.6 speedups over 32 C86 CPU cores.Compared with the efficiency of Wuhan Electronic Structure Package on Intel X86 and NVIDIA A100 computing platform,the Hygon platform exhibits good cost-effective-ness,showing great potential in quantum chemistry calculation and other high-performance scientific computations.展开更多
The self-consistent field theory(SCFT)was employed to numerically study the interaction and interpenetration between two opposing weak polyelectrolyte(PE)brushes formed by grafting weak PE chains onto the surfaces of ...The self-consistent field theory(SCFT)was employed to numerically study the interaction and interpenetration between two opposing weak polyelectrolyte(PE)brushes formed by grafting weak PE chains onto the surfaces of two long and parallel columns with rectangularshaped cross-section immersed in a salty aqueous solution.The dependences of the brush heights and the average degree of ionization on various system parameters were also investigated.When the brush separation is relatively large compared with the unperturbed brush height,the degree of interpenetration between the two opposing PE brushes was found to increase with increasing grafting density and bulk degree of ionization.The degree of interpenetration also increases with the bulk salt concentration in the osmotic brush regime.Numerical results further revealed that,at a brush separation comparable to the unperturbed brush height,the degree of interpenetration does not increase further with increasing bulk degree of ionization,bulk salt concentration in the osmotic regime and grafting density.The saturation of the degree of interpenetration with these system parameters indicates that the grafted PE chains in the gap between the two columns retract and tilt in order to reduce the unfavorable electrostatic and steric repulsions between the two opposing PE brushes.Based on salt ion concentrations at the midpoint between the two opposing brushes,a quantitative criterion in terms of the unperturbed brush height and Debye screening length was established to determine the threshold value of the brush separation beyond which they are truly independent from each other.展开更多
A self-consistent and three-dimensional （3D） model of argon discharge in a large-scale rectangular surface-wave plasma （SWP） source is presented in this paper, which is based on the finite-difference time-domain ...A self-consistent and three-dimensional （3D） model of argon discharge in a large-scale rectangular surface-wave plasma （SWP） source is presented in this paper, which is based on the finite-difference time-domain （FDTD） approximation to Maxwell＇s equations self-consistently coupled with a fluid model for plasma evolution. The discharge characteristics at an input microwave power of 1200 W and a filling gas pressure of 50 Pa in the SWP source are analyzed. The simulation shows the time evolution of deposited power density at different stages, and the 3D distributions of electron density and temperature in the chamber at steady state. In addition, the results show that there is a peak of plasma density approximately at a vertical distance of 3 cm from the quartz window.展开更多
The fully self-consistent model of modern semiconductor lasers used to design their advanced structures and to understand more deeply their properties is given in the present paper. Operation of semiconductor lasers d...The fully self-consistent model of modern semiconductor lasers used to design their advanced structures and to understand more deeply their properties is given in the present paper. Operation of semiconductor lasers de- pends not only on many optical, electrical, thermal, recombination, and sometimes mechanical phenomena taking place within their volumes but also on numerous mutual interactions between these phenomena. Their experimental investigation is quite complex, mostly because of miniature device sizes. Therefore, the most convenient and exact method to analyze expected laser operation and to determine laser optimal structures for various applications is to examine the details of their performance with the aid of a simulation of laser operation in various considered condi- tions. Such a simulation of an operation of semiconductor lasers is presented in this paper in a full complexity of all mutual interactions between the above individual physical processes. In particular, the hole-buming effect has been discussed. The impacts on laser performance introduced by oxide apertures （their sizes and localization） have been analyzed in detail. Also, some important details concerning the operation of various types of semiconductor lasers are discussed. The results of some applications of semiconductor lasers are shown for successive laser structures.展开更多
BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized p...BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper...Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and langua...Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.51975146,52205344)Shandong Province Natural Science Foundation(Grant No.ZR2020QE171)supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIP)(Nos.NRF-2021R1A2C3006662,NRF-2022R1A5A1030054).
文摘An integrated calculated approach based on weakly coupled finite element(FEM)-viscoplastic selfconsistent(VPSC)model was established to simulate the texture evolution during the variable strain path extrusion process of magnesium alloys.The spiral die extrusion(SDE)process with additional circumferential shear deformation was applied to investigate the effect of path control on texture adjustment and verify the accuracy of the model.The results indicated that the additional spiral shear resulting from the overall inclined flow path effectively reduced the intensity of the{0002}//ED fiber texture by suppressing basal slip activation in the core area,while the local shear deformation along the spiral equal channel strain path led to the formation of an inclined{0002}//ND plane texture on the side.Using the modified Hall-Petch relationship,the correlation between texture and yield strength was quantified.Specifically,the weakening of the texture effectively suppressed{10-12}tensile twinning,which compensated for the deficiency of compressive yield strength without significantly sacrificing tensile yield strength,and thus improved the tension-compression asymmetry.Furthermore,the strongly inclined{0002}//ND plane texture inhibited the widespread activation of basal slip during tensile yielding,thereby enhancing the yield strength.
基金The project supported by the National Nature Science Foundation of China (No. 10275010)
文摘A self-consistent analysis of a pulsed direct-current (DC) N2 glow discharge is presented. The model is based on a numerical solution of the continuity equations for electron and ions coupled with Poisson's equation. The spatial-temporal variations of ionic and electronic densities and electric field are obtained. The electric field structure exhibits all the characteristic regions of a typical glow discharge (the cathode fall, the negative glow, and the positive column). Current-voltage characteristics of the discharge can be obtained from the model. The calculated current-voltage results using a constant secondary electron emission coefficient for the gas pressure 133.32 Pa are in reasonable agreement with experiment.
文摘The plastic deformation behavior of new Mg-Gd-Y-Zn-Mn magnesium alloys gains great necessity to clarify and understand the mechanism deeply. In the present work,the tensile mechanical property test and visco-plastic self-consistent (VPSC) model are used to investigate the activities of deformation modes of VW84M and VW94M magnesium alloys during the tensile deformation. The results show that the mechanical properties of the above extruded alloys are similar but VW94M has higher strength than VW84M after the same aging process. Compared with the extruded alloys,the as-aged alloys have significantly higher activation of pyramidal slip at the later stage of plastic deformation. In addition,the as-aged VW94M alloy with higher strength has the largest activity of pyramidal slip. In summary,the addition of Gd increases the critical resolved shear stress (CRSS)in each slip system of VW94M,while the increase in the strength and the decrease in the elongation of as-aged alloys are associated with the significant activation of pyramidal slip.
文摘The paper analyzes the motion of electron in plasma antenna and the distribution of electromagnetic field power around the plasma antenna, and proposes a self-consistent model according to the structure of cylindrical monopole plasma antenna excited by surface wave;calculation of the model is based on Maxwell-Boltzmann equation and gas molecular dynamics theory. The calculation results show that this method can reflect the relationships between the external excitation power, gas pressure, discharge current and the characteristic of plasma. It is an accurate method to predicate and calculate the parameters of plasma antenna.
基金supported by grants from the National Science Centre,Poland(NCN)No.UMO-2017/25/B/ST8/00134 and UMO2015/19/D/ST8/00818supported by the Polish-JINR Programme 2017(item 24)supported by the Federal Ministry for Education and Research in Germany。
文摘In this work,the evolutions of stresses in both phases of the Al/SiCp composite subjected to thermal cycling during in situ compression test were measured using Time of Flight neutron diffraction.It was confirmed that inter-phase stresses in the studied composite can be caused by differences in the coefficient of thermal expansion for the reinforcement and matrix,leading to a different variation of phase volumes during sample heating or cooling.The results of the diffraction experiment during thermal cycling were well predicted by the Thermo-Mechanical Self-Consistent model.The experimental study of elastic-plastic deformation was carried out in situ on a unique diffractometer EPSILON-MDS(JINR in Dubna,Russia)with nine detector banks measuring interplanar spacings simultaneously in 9 orientations of scattering vector.For the first time,the performed analysis of experimental data allowed to study the evolution of full stress tensor in both phases of the composite and to consider the decomposition of this tensor into deviatoric and hydrostatic components.It was found that the novel Developed Thermo-Mechanical SelfConsistent model correctly predicted stress evolution during compressive loading,taking into account the relaxation of thermal origin hydrostatic stresses.The comparison of this model with experimental data at the macroscopic level and the level of phases showed that strengthening of the Al/SiCp composite is caused by stress transfer from the plastically deformed A12124 matrix to the elastic SiCp reinforcement,while thermal stresses relaxation does not significantly affect the overall composite properties.
基金National Natural Science Foundation of China(No.51907039)Shenzhen Technology Project(Nos.JCYJ20190806142603534 and ZDSYS201707280904031)+1 种基金ESPEOS project(No.PID2019108034RB-I00/AEI/10.13039/501100011033)funded by the Agencia Estatal de Investigacion(Spanish National Research Agency)。
文摘A 1D radially self-consistent model in helicon plasmas has been established to investigate the influence of radial heat conduction on plasma transport and wave propagation.Two kinds of 1D radial fluid models,with and without considering heat conduction,have been developed to couple the 1D plasma-wave interaction model,and self-consistent solutions have been obtained.It is concluded that in the low magnetic field range the radial heat conduction plays a moderate role in the transport of helicon plasmas and the importance depends on the application of the helicon source.It influences the local energy balance leading to enhancement of the electron temperature in the bulk region and a decrease in plasma density.The power deposition in the plasma is mainly balanced by collisional processes and axial diffusion,whereas it is compensated by heat conduction in the bulk region and consumed near the boundary.The role of radial heat conduction in the large magnetic field regime becomes negligible and the two fluid models show consistency.The local power balance,especially near the wall,is improved when conductive heat is taken into account.
基金supported by the National Natural Science Foundation of China(No.22373112 to Ji Qi,No.22373111 and 21921004 to Minghui Yang)GH-fund A(No.202107011790)。
文摘In this study,we investigate the ef-ficacy of a hybrid parallel algo-rithm aiming at enhancing the speed of evaluation of two-electron repulsion integrals(ERI)and Fock matrix generation on the Hygon C86/DCU(deep computing unit)heterogeneous computing platform.Multiple hybrid parallel schemes are assessed using a range of model systems,including those with up to 1200 atoms and 10000 basis func-tions.The findings of our research reveal that,during Hartree-Fock(HF)calculations,a single DCU ex-hibits 33.6 speedups over 32 C86 CPU cores.Compared with the efficiency of Wuhan Electronic Structure Package on Intel X86 and NVIDIA A100 computing platform,the Hygon platform exhibits good cost-effective-ness,showing great potential in quantum chemistry calculation and other high-performance scientific computations.
基金supported by the National Natural Science Foundation of China(No.21774067)The Foundation of Key Laboratory of Flexible Electronics of Zhejiang Province(No.2023FE004)C.T.acknowledges the support from K.C.Wong Magna at Ningbo University。
文摘The self-consistent field theory(SCFT)was employed to numerically study the interaction and interpenetration between two opposing weak polyelectrolyte(PE)brushes formed by grafting weak PE chains onto the surfaces of two long and parallel columns with rectangularshaped cross-section immersed in a salty aqueous solution.The dependences of the brush heights and the average degree of ionization on various system parameters were also investigated.When the brush separation is relatively large compared with the unperturbed brush height,the degree of interpenetration between the two opposing PE brushes was found to increase with increasing grafting density and bulk degree of ionization.The degree of interpenetration also increases with the bulk salt concentration in the osmotic brush regime.Numerical results further revealed that,at a brush separation comparable to the unperturbed brush height,the degree of interpenetration does not increase further with increasing bulk degree of ionization,bulk salt concentration in the osmotic regime and grafting density.The saturation of the degree of interpenetration with these system parameters indicates that the grafted PE chains in the gap between the two columns retract and tilt in order to reduce the unfavorable electrostatic and steric repulsions between the two opposing PE brushes.Based on salt ion concentrations at the midpoint between the two opposing brushes,a quantitative criterion in terms of the unperturbed brush height and Debye screening length was established to determine the threshold value of the brush separation beyond which they are truly independent from each other.
基金Project supported by the Special Fund of National High-Tech Development and Research Plan (Grant No 2008AA12A214)
文摘A self-consistent and three-dimensional （3D） model of argon discharge in a large-scale rectangular surface-wave plasma （SWP） source is presented in this paper, which is based on the finite-difference time-domain （FDTD） approximation to Maxwell＇s equations self-consistently coupled with a fluid model for plasma evolution. The discharge characteristics at an input microwave power of 1200 W and a filling gas pressure of 50 Pa in the SWP source are analyzed. The simulation shows the time evolution of deposited power density at different stages, and the 3D distributions of electron density and temperature in the chamber at steady state. In addition, the results show that there is a peak of plasma density approximately at a vertical distance of 3 cm from the quartz window.
基金the partial support from the Polish National Science Centre (2014/13/B/ST7/00633)
文摘The fully self-consistent model of modern semiconductor lasers used to design their advanced structures and to understand more deeply their properties is given in the present paper. Operation of semiconductor lasers de- pends not only on many optical, electrical, thermal, recombination, and sometimes mechanical phenomena taking place within their volumes but also on numerous mutual interactions between these phenomena. Their experimental investigation is quite complex, mostly because of miniature device sizes. Therefore, the most convenient and exact method to analyze expected laser operation and to determine laser optimal structures for various applications is to examine the details of their performance with the aid of a simulation of laser operation in various considered condi- tions. Such a simulation of an operation of semiconductor lasers is presented in this paper in a full complexity of all mutual interactions between the above individual physical processes. In particular, the hole-buming effect has been discussed. The impacts on laser performance introduced by oxide apertures （their sizes and localization） have been analyzed in detail. Also, some important details concerning the operation of various types of semiconductor lasers are discussed. The results of some applications of semiconductor lasers are shown for successive laser structures.
基金Supported by National Natural Science Foundation of China,No.81874390 and No.81573948Shanghai Natural Science Foundation,No.21ZR1464100+1 种基金Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission,No.22S11901700the Shanghai Key Specialty of Traditional Chinese Clinical Medicine,No.shslczdzk01201.
文摘BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金Fund supported this work for Excellent Youth Scholars of China(Grant No.52222708)the National Natural Science Foundation of China(Grant No.51977007)+1 种基金Part of this work is supported by the research project“SPEED”(03XP0585)at RWTH Aachen Universityfunded by the German Federal Ministry of Education and Research(BMBF)。
文摘Developing sensorless techniques for estimating battery expansion is essential for effective mechanical state monitoring,improving the accuracy of digital twin simulation and abnormality detection.Therefore,this paper presents a data-driven approach to expansion estimation using electromechanical coupled models with machine learning.The proposed method integrates reduced-order impedance models with data-driven mechanical models,coupling the electrochemical and mechanical states through the state of charge(SOC)and mechanical pressure within a state estimation framework.The coupling relationship was established through experimental insights into pressure-related impedance parameters and the nonlinear mechanical behavior with SOC and pressure.The data-driven model was interpreted by introducing a novel swelling coefficient defined by component stiffnesses to capture the nonlinear mechanical behavior across various mechanical constraints.Sensitivity analysis of the impedance model shows that updating model parameters with pressure can reduce the mean absolute error of simulated voltage by 20 mV and SOC estimation error by 2%.The results demonstrate the model's estimation capabilities,achieving a root mean square error of less than 1 kPa when the maximum expansion force is from 30 kPa to 120 kPa,outperforming calibrated stiffness models and other machine learning techniques.The model's robustness and generalizability are further supported by its effective handling of SOC estimation and pressure measurement errors.This work highlights the importance of the proposed framework in enhancing state estimation and fault diagnosis for lithium-ion batteries.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金supported by National Natural Science Foundation of China(62376219 and 62006194)Foundational Research Project in Specialized Discipline(Grant No.G2024WD0146)Faculty Construction Project(Grant No.24GH0201148).
文摘Large language models(LLMs)have undergone significant expansion and have been increasingly integrated across various domains.Notably,in the realm of robot task planning,LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.However,for embodied tasks,where robots interact with complex environments,textonly LLMs often face challenges due to a lack of compatibility with robotic visual perception.This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks.Additionally,we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions.Our results,based on diverse datasets,indicate that GPT-4V effectively enhances robot performance in embodied tasks.This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights towards bridging the gap in Human-Robot-Environment interaction.
