In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results i...In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.展开更多
It is well known that aluminum and copper exhibit structural phase transformations in quasi-static and dynamic measurements,including shock wave loading.However,the dependence of phase transformations in a wide range ...It is well known that aluminum and copper exhibit structural phase transformations in quasi-static and dynamic measurements,including shock wave loading.However,the dependence of phase transformations in a wide range of crystallographic directions of shock loading has not been revealed.In this work,we calculated the shock Hugoniot for aluminum and copper in different crystallographic directions([100],[110],[111],[112],[102],[114],[123],[134],[221]and[401])of shock compression using molecular dynamics(MD)simulations.The results showed a high pressure(>160 GPa for Cu and>40 GPa for Al)of the FCC-to-BCC transition.In copper,different characteristics of the phase transition are observed depending on the loading direction with the[100]compression direction being the weakest.The FCC-to-BCC transition for copper is in the range of 150–220 GPa,which is consistent with the existing experimental data.Due to the high transition pressure,the BCC phase transition in copper competes with melting.In aluminum,the FCC-to-BCC transition is observed for all studied directions at pressures between 40 and 50 GPa far beyond the melting.In all considered cases we observe the coexistence of HCP and BCC phases during the FCC-to-BCC transition,which is consistent with the experimental data and atomistic calculations;this HCP phase forms in the course of accompanying plastic deformation with dislocation activity in the parent FCC phase.The plasticity incipience is also anisotropic in bothmetals,which is due to the difference in the projections of stress on the slip plane for different orientations of the FCC crystal.MD modeling results demonstrate a strong dependence of the FCC-to-BCC transition on the crystallographic direction,in which the material is loaded in the copper crystals.However,MD simulations data can only be obtained for specific points in the stereographic direction space;therefore,for more comprehensive understanding of the phase transition process,a feed-forward neural network was trained using MD modeling data.The trained machine learning model allowed us to construct continuous stereographic maps of phase transitions as a function of stress in the shock-compressed state of metal.Due to appearance and growth of multiple centers of new phase,the FCC-to-BCC transition leads to formation of a polycrystalline structure from the parent single crystal.展开更多
A three-dimensional multiphase particle-in-cell(MP-PIC)method was adopted to establish a liquid-solid two-phase flow model accounting for complex fracture networks.The model was validated using physical experimental d...A three-dimensional multiphase particle-in-cell(MP-PIC)method was adopted to establish a liquid-solid two-phase flow model accounting for complex fracture networks.The model was validated using physical experimental data.On this basis,the main factors influencing proppant transport in fracture network were analyzed.The study shows that proppant transport in fracture network can be divided into three stages:initial filling,dominant channel formation and fracture network extension.These correspond to three transport patterns:patch-like accumulation near the wellbore,preferential placement along main fractures,and improved the coverage of planar placement as fluid flows into branch fractures.Higher proppant density,lower fracturing fluid viscosity,lower injection rate,and larger proppant grain size result in shorter proppant transport distance and smaller planar placement coefficient.The use of low-density,small-diameter proppant combined with high-viscosity fracturing fluid and appropriately increased injection rate can effectively enlarge the stimulated volume.A smaller angle between the main fracture and branch fractures leads to longer proppant banks,broader coverage,more uniform distribution,and better stimulation performance in branch fractures.In contrast,a larger angle increases the likelihood of proppant accumulation near the branch fracture entrance and reduces the planar placement coefficient.展开更多
The 2025 M_(w)7.7 Myanmar earthquake highlighted the challenge of near-fault seismic intensity field reconstruction due to sparse seismic networks.To address this limitation,a framework was proposed integrating seismi...The 2025 M_(w)7.7 Myanmar earthquake highlighted the challenge of near-fault seismic intensity field reconstruction due to sparse seismic networks.To address this limitation,a framework was proposed integrating seismic wave simulation with a data-constrained finite-fault rupture model.The constraint is implemented by identifying the optimal ground motion models(GMMs)through a scoring system that selects the best-fit GMMs to mid-and far-field China Earthquake Networks Center(CENC)seismic network data;and applying the optimal GMMs to refine the rupture model parameters for near-fault intensity field simulation.The simulated near-fault seismic intensity field reproduces seismic intensities collected from Myanmar’s sparse seismic network and concentrated in≥Ⅷintensity zones within 50 km of the projected fault plane;and identifies abnormal intensity regions exhibiting≥Ⅹintensity along the Meiktila-Naypyidaw corridor and near Shwebo that are attributed to soft soil amplification effects and near-fault directivity.This framework can also be applied to post-earthquake assessments in other similar regions.展开更多
This study addresses the pressing challenge of generating realistic strong ground motion data for simulating earthquakes,a crucial component in pre-earthquake risk assessments and post-earthquake disaster evaluations,...This study addresses the pressing challenge of generating realistic strong ground motion data for simulating earthquakes,a crucial component in pre-earthquake risk assessments and post-earthquake disaster evaluations,particularly suited for regions with limited seismic data.Herein,we report a generative adversarial network(GAN)framework capable of simulating strong ground motions under various environmental conditions using only a small set of real earthquake records.The constructed GAN model generates ground motions based on continuous physical variables such as source distance,site conditions,and magnitude,effectively capturing the complexity and diversity of ground motions under different scenarios.This capability allows the proposed model to approximate real seismic data,making it applicable to a wide range of engineering purposes.Using the Shandong Pingyuan earthquake as an example,a specialized dataset was constructed based on regional real ground motion records.The response spectrum at target locations was obtained through inverse distance-weighted interpolation of actual response spectra,followed by continuous wavelet transform to derive the ground motion time histories at these locations.Through iterative parameter adjustments,the constructed GAN model learned the probability distribution of strong-motion data for this event.The trained model generated three-component ground-motion time histories with clear P-wave and S-wave characteristics,accurately reflecting the non-stationary nature of seismic records.Statistical comparisons between synthetic and real response spectra,waveform envelopes,and peak ground acceleration show a high degree of similarity,underscoring the effectiveness of the model in replicating both the statistical and physical characteristics of real ground motions.These findings validate the feasibility of GANs for generating realistic earthquake data in data-scarce regions,providing a reliable approach for enriching regional ground motion databases.Additionally,the results suggest that GAN-based networks are a powerful tool for building predictive models in seismic hazard analysis.展开更多
In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled ...In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled transfer,which has the limitations of high cost,a large amount of calculating data and high computing power requirements.A novel network based on physics-informed neural network(PINN)was designed to realize the fast and high-fidelity prediction of the distribution of electromagnetic field and temperature field in ESR process.The physical laws were combined with the deep learning network through PINN,and physical constraints were embedded to achieve effective solution of partial differential equations(PDEs).PINN was used to minimize the loss function consisting of data error,physical information error and boundary condition error.The physical laws and boundary condition constraints in the ESR process were considered to maintain high PDE solution accuracy under different spatial and temporal resolutions.Automatic differentiation(Autodiff)technique and gradient descent algorithm were used to optimize the network parameters.The experimental results show that compared with the mechanistic models,PINN can effectively replace thermal experiments to realize the physical field simulation of ESR process with only a few experimental data,which can avoid the disadvantages of pure data-driven network simulation that requires a large amount of training data.Moreover,the solution of PINN has good physical interpretability and reliability of simulation results.For simulating electromagnetic field and temperature field distribution,the training time of the network is only 140 and 203 s,and the regression indicators of root mean square error can reach 12.65 and 13.76,respectively.展开更多
Projective synchronization problems of a drive system and a particular response network were investigated,where the drive system is an arbitrary system with n+1 dimensions;it may be a linear or nonlinear system,and ev...Projective synchronization problems of a drive system and a particular response network were investigated,where the drive system is an arbitrary system with n+1 dimensions;it may be a linear or nonlinear system,and even a chaotic or hyperchaotic system,the response network is complex system coupled by N nodes,and every node is showed by the approximately linear part of the drive system.Only controlling any one node of the response network by designed controller can achieve the projective synchronization.Some numerical examples were employed to verify the effectiveness and correctness of the designed controller.展开更多
UHMWPE fibers exhibit impressive modulus and strength,but they have not reached their theoretical limits.Researchers focus on molecular weight,orientation,and crystallinity of UHMWPE,yet their contributions to mechani...UHMWPE fibers exhibit impressive modulus and strength,but they have not reached their theoretical limits.Researchers focus on molecular weight,orientation,and crystallinity of UHMWPE,yet their contributions to mechanical properties are unclear.Molecular dynamics simulations are valuable but often limited by computational constraints.Our aim is to simulate higher molecular weights to better represent real UHMWPE fibers.We used Packmol and Polyply methodologies to construct PE systems,with Polyply reproducing more reasonable properties of UHMWPE fibers.Additionally,tensile simulations showed that orientation and crystallinity greatly impact Young's modulus more than molecular weight.Energy decomposition indicated that higher molecular weights lead to covalent bonds that can withstand more energy during stretching,thus increasing breaking strength.Combining simulations with machine learning,we found that orientation has the most significant impact on Young's modulus,contributing 60%,and molecular weight plays the most crucial role in determining the breaking strength,accounting for 65%.This study provides a theoretical basis and guidelines for enhancing UHMWPE's modulus and strength.展开更多
Land use/cover change(LUCC)constitutes the spatial and temporal patterns of ecological security,and the construction of ecological networks is an effective way to ensure ecological security.Exploring the spatial and t...Land use/cover change(LUCC)constitutes the spatial and temporal patterns of ecological security,and the construction of ecological networks is an effective way to ensure ecological security.Exploring the spatial and temporal change characteristics of ecological network and analyzing the integrated relationship between LUCC and ecological security are crucial for ensuring regional ecological security.Gansu is one of the provinces with fragile ecological environment in China,and rapid changes in land use patterns in recent decades have threatened ecological security.Therefore,taking Gansu Province as the study area,this study simulated its land use pattern in 2050 using patch-generating land use simulation(PLUS)model based on the LUCC trend from 2000 to 2020 and integrated the LUCC into morphological spatial pattern analysis(MSPA)to identify ecological sources and extract the ecological corridors to construct ecological network using circuit theory.The results revealed that,according to the prediction results in 2050,the areas of cultivated land,forest land,grassland,water body,construction land,and unused land would be 63,447.52,39,510.80,148,115.18,4605.21,8368.89,and 161,752.40 km^(2),respectively.The number of ecological sources in Gansu Province would increase to 80,with a total area of 99,927.18 km^(2).The number of ecological corridors would increase to 191,with an estimated total length of 6120.66 km.Both ecological sources and ecological corridors showed a sparse distribution in the northwest and dense distribution in the southeast of the province at the spatial scale.The number of ecological pinch points would reach 312 and the total area would expect to increase to 842.84 km^(2),with the most pronounced increase in the Longdong region.Compared with 2020,the number and area of ecological barriers in 2050 would decrease significantly by 63 and 370.71 km^(2),respectively.In general,based on the prediction results,the connectivity of ecological network of Gansu Province would increase in 2050.To achieve the predicted ecological network in 2050,emphasis should be placed on the protection of cultivated land and ecological land,the establishment of ecological sources in desert areas,the reinforcement of the protection for existing ecological sources,and the construction of ecological corridors to enhance the stability of ecological network.This study provides valuable theoretical support and references for the future construction of ecological networks and regional land resource management decision-making.展开更多
The flight envelope of Air Turbo Rocket(ATR)engines is broader compared to conventional aero-engines,and designing a full-envelope controller using traditional methods poses significant challenges due to a burdensome ...The flight envelope of Air Turbo Rocket(ATR)engines is broader compared to conventional aero-engines,and designing a full-envelope controller using traditional methods poses significant challenges due to a burdensome design process.To address this issue,this paper proposes a self-learning neural network controller design method based on Reinforcement Learning(RL).Additionally,a method for predictive compensation and stability rewards is proposed to reduce the system oscillation caused by actuator delay.This approach simplifies the actuator to a firstorder inertial element exhibiting pure delay.A simulation environment for the ATR engineactuator system is first established.Based on this environment,a self-learning neural network controller using a predictive compensator and the Proximal Policy Optimization(PPO)algorithm is then developed.Furthermore,the temporal difference signals from the controller output are integrated into the reward function to enhance system stability.The proposed method is validated through numerical simulations and semi-physical experiments.The numerical simulation results demonstrate that the proposed method increases the system's tolerance to delays from 20 ms to 400 ms.Under an actuator delay of 400 ms,the average steady-state error remains less than0.1%,the overshoot is limited to 1%,and the settling time does not exceed 3 s.Moreover,compared to the traditional method,the proposed method exhibits higher adaptability to model errors and variations in flight conditions.In the conducted semi-physical simulation experiments,the proposed method achieves stable control of a real electric pump.展开更多
Urban spatial morphology(USM)optimization is critical to balancing biodiversity conservation and sustainable urbanization.However,previous studies predominantly focused on the socio-economic efficiency and static ecol...Urban spatial morphology(USM)optimization is critical to balancing biodiversity conservation and sustainable urbanization.However,previous studies predominantly focused on the socio-economic efficiency and static ecological metrics and rarely addressed the dynamic USM optimization across spatial scales.Here,we developed a multi-level ecological network(MEN)framework to resolve the tension between urban expansion and ecological integrity.By integrating the cost-weighted distance analysis with a hierarchical network transmission mechanism,we established a cross-scale spatial optimization system,which coordinated the regional ecological corridors and local habitat patches.Comparative experiments with conventional single-scale approaches and scenario simulations using the PLUS model show that the MEN framework had superior performance in three dimensions:(1)spatial governance:the primary-level network(peri-urban natural reserves)effectively contained urban sprawl,and the secondary-level network(intra-urban green corridors)mitigated habitat fragmentation and improved the built-environment;(2)scenario robustness:the model maintained an optimal compactness-loose balance in multiple development pathways;(3)landscape metrics:patch fragmentation decreased by 18.25%,and the internal landscape richness improved by 10.66%compared to the scenario without USM optimization.The findings provide new insight to establish a hierarchical ecological optimization framework as a nature-based spatial protocol to reconcile metropolitan growth with landscape sustainability.展开更多
Solar energy is a pivotal clean energy source in the transition to carbon neutrality from fossil fuels.However,the intermittent and stochastic characteristics of solar radiation pose challenges for accurate simulation...Solar energy is a pivotal clean energy source in the transition to carbon neutrality from fossil fuels.However,the intermittent and stochastic characteristics of solar radiation pose challenges for accurate simulation and prediction.Accurately simulating and predicting solar radiation and its variability are crucial for optimizing solar energy utilization.This study conducted simulation experiments using the WRF-Solar model from 25 June to 25 July 2022,to evaluate the accuracy and performance of the simulated solar radiation across China.The simulations covered the whole country with a grid spacing of 27 km and were compared with ground observation network data from the Chinese Ecosystem Research Network.The results indicated that WRF-Solar can accurately capture the spatiotemporal patterns of global horizontal irradiance over China,but there is still an overestimation of solar radiation,and the model underestimates the total cloud cover.The root-mean-square error ranged from 92.83 to 188.13 W m^(-2) and the mean bias(MB)ranged from 21.05 to 56.22 W m^(-2).The simulation showed the smallest MB at Lhasa on the Qinghai–Tibet Plateau,while the largest MB was observed in Southeast China.To enhance the accuracy of solar radiation simulation,the authors compared the Fast All-sky Radiation Model for Solar with the Rapid Radiative Transfer Model for General Circulation Models and found that the former provides better simulation.展开更多
Underground engineering in extreme environments necessitates understanding rock mechanical behavior under coupled high-temperature and dynamic loading conditions.This study presents an innovative multi-scale cross-pla...Underground engineering in extreme environments necessitates understanding rock mechanical behavior under coupled high-temperature and dynamic loading conditions.This study presents an innovative multi-scale cross-platform PFC-FDEM coupling methodology that bridges microscopic thermal damage mechanisms with macroscopic dynamic fracture responses.The breakthrough coupling framework introduces:(1)bidirectional information transfer protocols enabling seamless integration between PFC’s particle-scale thermal damage characterization and FDEM’s continuum-scale fracture propagation,(2)multi-physics mapping algorithms that preserve crack network geometric invariants during scale transitions,and(3)cross-platform cohesive zone implementations for accurate SHTB dynamic loading simulation.The coupled approach reveals distinct three-stage crack evolution characteristics with temperature-dependent density following an exponential model.High-temperature exposure significantly reduces dynamic strength ratio(60%at 800℃)and diminishes strain-rate sensitivity,with dynamic increase factor decreasing from 1.0 to 2.2(25℃)to 1.0-1.3(800℃).Critically,the coupling methodology captures fundamental energy redistribution mechanisms:thermal crack networks alter elastic energy proportion from 75%to 35%while increasing fracture energy from 5%to 30%.Numerical predictions demonstrate excellent experimental agreement(±8%peak stress-strain errors),validating the PFC-FDEM coupling accuracy.This integrated framework provides essential computational tools for predicting complex thermal-mechanical rock behavior in underground engineering applications.展开更多
The forthcoming sixth generation(6G)of mobile communication networks is envisioned to be AInative,supporting intelligent services and pervasive computing at unprecedented scale.Among the key paradigms enabling this vi...The forthcoming sixth generation(6G)of mobile communication networks is envisioned to be AInative,supporting intelligent services and pervasive computing at unprecedented scale.Among the key paradigms enabling this vision,Federated Learning(FL)has gained prominence as a distributed machine learning framework that allows multiple devices to collaboratively train models without sharing raw data,thereby preserving privacy and reducing the need for centralized storage.This capability is particularly attractive for vision-based applications,where image and video data are both sensitive and bandwidth-intensive.However,the integration of FL with 6G networks presents unique challenges,including communication bottlenecks,device heterogeneity,and trade-offs between model accuracy,latency,and energy consumption.In this paper,we developed a simulation-based framework to investigate the performance of FL in representative vision tasks under 6G-like environments.We formalize the system model,incorporating both the federated averaging(FedAvg)training process and a simplified communication costmodel that captures bandwidth constraints,packet loss,and variable latency across edge devices.Using standard image datasets(e.g.,MNIST,CIFAR-10)as benchmarks,we analyze how factors such as the number of participating clients,degree of data heterogeneity,and communication frequency influence convergence speed and model accuracy.Additionally,we evaluate the effectiveness of lightweight communication-efficient strategies,including local update tuning and gradient compression,in mitigating network overhead.The experimental results reveal several key insights:(i)communication limitations can significantly degrade FL convergence in vision tasks if not properly addressed;(ii)judicious tuning of local training epochs and client participation levels enables notable improvements in both efficiency and accuracy;and(iii)communication-efficient FL strategies provide a promising pathway to balance performance with the stringent latency and reliability requirements expected in 6G.These findings highlight the synergistic role of AI and nextgeneration networks in enabling privacy-preserving,real-time vision applications,and they provide concrete design guidelines for researchers and practitioners working at the intersection of FL and 6G.展开更多
Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin...Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.展开更多
Combining the phase-field method and the moving boundary method,a three-dimensional phase-field simulation was conducted for the growth and grain evolution of Ti films deposited by physical vapor deposition under diff...Combining the phase-field method and the moving boundary method,a three-dimensional phase-field simulation was conducted for the growth and grain evolution of Ti films deposited by physical vapor deposition under different deposition rates and grain orientations.The evolution of grain morphology and grain orientation was also taken into consideration.Simulation results show that at lower deposition rates,the surface of the formed Ti film exhibits a distinct oriented texture structure.The surface roughness of the Ti film is positively correlated with the grain misorientation.Moreover,the surface roughness obtained from the simulation is in good agreement with the experiment results.展开更多
To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review a...To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review and SwissADME platform.Genes related to the inflammation were collected using Genecards and OMIM databases,and the intersection genes were submitted on STRING and DAVID websites.Then,the protein interaction network(PPI),gene ontology(GO)and pathway(KEGG)were analyzed.Cytoscape 3.7.2 software was used to construct the“Hibiscus mutabilis L.-active ingredient-target-inflammation”network diagram,and AutoDockTools-1.5.6 software was used for the molecular docking verification.The antiinflammatory effect of Hibiscus mutabilis L.active ingredient was verified by the RAW264.7 inflammatory cell model.The results showed that 11 active components and 94 potential targets,1029 inflammatory targets and 24 intersection targets were obtained from Hibiscus mutabilis L..The key anti-inflammatory active ingredients of Hibiscus mutabilis L.are quercetin,apigenin and luteolin.Its action pathway is mainly related to NF-κB,cancer pathway and TNF signaling pathway.Cell experiments showed that total flavonoids of Hibiscus mutabilis L.could effectively inhibit the expression of tumor necrosis factor(TNF-α),interleukin 8(IL-8)and epidermal growth factor receptor(EGFR)in LPS-induced RAW 264.7 inflammatory cells.It also downregulates the phosphorylation of human nuclear factor ĸB inhibitory protein α(IĸBα)and NF-κB p65 subunit protein(p65).Overall,the anti-inflammatory effect of Hibiscus mutabilis L.is related to many active components,many signal pathways and targets,which provides a theoretical basis for its further development and application.展开更多
文摘In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.
基金founded by the Ministry of Science and Higher Education of the Russian Federation,State assignments for research,registration No.1024032600084-8-1.3.2Study of the grain growth and the formation of polycrystalline structure as a result of phase transition(Section 6)was founded by the Russian Science Foundation,Project No.24-71-00078+3 种基金https://rscf.ru/en/project/24-71-00078/(accessed on 01 December 2025).Study of the orientation dependence of the phase transition of aluminum in Section 3 was founded by the Russian Science Foundation,Project No.24-19-00684https://rscf.ru/en/project/24-19-00684/(accessed on 01 December 2025).
文摘It is well known that aluminum and copper exhibit structural phase transformations in quasi-static and dynamic measurements,including shock wave loading.However,the dependence of phase transformations in a wide range of crystallographic directions of shock loading has not been revealed.In this work,we calculated the shock Hugoniot for aluminum and copper in different crystallographic directions([100],[110],[111],[112],[102],[114],[123],[134],[221]and[401])of shock compression using molecular dynamics(MD)simulations.The results showed a high pressure(>160 GPa for Cu and>40 GPa for Al)of the FCC-to-BCC transition.In copper,different characteristics of the phase transition are observed depending on the loading direction with the[100]compression direction being the weakest.The FCC-to-BCC transition for copper is in the range of 150–220 GPa,which is consistent with the existing experimental data.Due to the high transition pressure,the BCC phase transition in copper competes with melting.In aluminum,the FCC-to-BCC transition is observed for all studied directions at pressures between 40 and 50 GPa far beyond the melting.In all considered cases we observe the coexistence of HCP and BCC phases during the FCC-to-BCC transition,which is consistent with the experimental data and atomistic calculations;this HCP phase forms in the course of accompanying plastic deformation with dislocation activity in the parent FCC phase.The plasticity incipience is also anisotropic in bothmetals,which is due to the difference in the projections of stress on the slip plane for different orientations of the FCC crystal.MD modeling results demonstrate a strong dependence of the FCC-to-BCC transition on the crystallographic direction,in which the material is loaded in the copper crystals.However,MD simulations data can only be obtained for specific points in the stereographic direction space;therefore,for more comprehensive understanding of the phase transition process,a feed-forward neural network was trained using MD modeling data.The trained machine learning model allowed us to construct continuous stereographic maps of phase transitions as a function of stress in the shock-compressed state of metal.Due to appearance and growth of multiple centers of new phase,the FCC-to-BCC transition leads to formation of a polycrystalline structure from the parent single crystal.
基金Supported by the National Natural Science Foundation of China(U21B2071,U23B20156)。
文摘A three-dimensional multiphase particle-in-cell(MP-PIC)method was adopted to establish a liquid-solid two-phase flow model accounting for complex fracture networks.The model was validated using physical experimental data.On this basis,the main factors influencing proppant transport in fracture network were analyzed.The study shows that proppant transport in fracture network can be divided into three stages:initial filling,dominant channel formation and fracture network extension.These correspond to three transport patterns:patch-like accumulation near the wellbore,preferential placement along main fractures,and improved the coverage of planar placement as fluid flows into branch fractures.Higher proppant density,lower fracturing fluid viscosity,lower injection rate,and larger proppant grain size result in shorter proppant transport distance and smaller planar placement coefficient.The use of low-density,small-diameter proppant combined with high-viscosity fracturing fluid and appropriately increased injection rate can effectively enlarge the stimulated volume.A smaller angle between the main fracture and branch fractures leads to longer proppant banks,broader coverage,more uniform distribution,and better stimulation performance in branch fractures.In contrast,a larger angle increases the likelihood of proppant accumulation near the branch fracture entrance and reduces the planar placement coefficient.
基金Scientific Research Fund of Institute of Engineering Mechanics,China Earthquake Administration under Grant No.2023C01National Natural Science Foundation of China under Grant No.52478570Distinguished Young Scholars Program of the Natural Science Foundation of Heilongjiang Province,China under Grant No.JQ2024E002。
文摘The 2025 M_(w)7.7 Myanmar earthquake highlighted the challenge of near-fault seismic intensity field reconstruction due to sparse seismic networks.To address this limitation,a framework was proposed integrating seismic wave simulation with a data-constrained finite-fault rupture model.The constraint is implemented by identifying the optimal ground motion models(GMMs)through a scoring system that selects the best-fit GMMs to mid-and far-field China Earthquake Networks Center(CENC)seismic network data;and applying the optimal GMMs to refine the rupture model parameters for near-fault intensity field simulation.The simulated near-fault seismic intensity field reproduces seismic intensities collected from Myanmar’s sparse seismic network and concentrated in≥Ⅷintensity zones within 50 km of the projected fault plane;and identifies abnormal intensity regions exhibiting≥Ⅹintensity along the Meiktila-Naypyidaw corridor and near Shwebo that are attributed to soft soil amplification effects and near-fault directivity.This framework can also be applied to post-earthquake assessments in other similar regions.
基金Funded by the National Key Research and Development Program(2022YFC3003502).
文摘This study addresses the pressing challenge of generating realistic strong ground motion data for simulating earthquakes,a crucial component in pre-earthquake risk assessments and post-earthquake disaster evaluations,particularly suited for regions with limited seismic data.Herein,we report a generative adversarial network(GAN)framework capable of simulating strong ground motions under various environmental conditions using only a small set of real earthquake records.The constructed GAN model generates ground motions based on continuous physical variables such as source distance,site conditions,and magnitude,effectively capturing the complexity and diversity of ground motions under different scenarios.This capability allows the proposed model to approximate real seismic data,making it applicable to a wide range of engineering purposes.Using the Shandong Pingyuan earthquake as an example,a specialized dataset was constructed based on regional real ground motion records.The response spectrum at target locations was obtained through inverse distance-weighted interpolation of actual response spectra,followed by continuous wavelet transform to derive the ground motion time histories at these locations.Through iterative parameter adjustments,the constructed GAN model learned the probability distribution of strong-motion data for this event.The trained model generated three-component ground-motion time histories with clear P-wave and S-wave characteristics,accurately reflecting the non-stationary nature of seismic records.Statistical comparisons between synthetic and real response spectra,waveform envelopes,and peak ground acceleration show a high degree of similarity,underscoring the effectiveness of the model in replicating both the statistical and physical characteristics of real ground motions.These findings validate the feasibility of GANs for generating realistic earthquake data in data-scarce regions,providing a reliable approach for enriching regional ground motion databases.Additionally,the results suggest that GAN-based networks are a powerful tool for building predictive models in seismic hazard analysis.
基金supported by National Natural Science Foundation of China(52274323 and 524743495)the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20240231.
文摘In the electroslag remelting(ESR)process,it mainly relies on thermal experiments or analysis via mechanistic models to realize the physical fields simulation of the electromagnetic field and temperature field coupled transfer,which has the limitations of high cost,a large amount of calculating data and high computing power requirements.A novel network based on physics-informed neural network(PINN)was designed to realize the fast and high-fidelity prediction of the distribution of electromagnetic field and temperature field in ESR process.The physical laws were combined with the deep learning network through PINN,and physical constraints were embedded to achieve effective solution of partial differential equations(PDEs).PINN was used to minimize the loss function consisting of data error,physical information error and boundary condition error.The physical laws and boundary condition constraints in the ESR process were considered to maintain high PDE solution accuracy under different spatial and temporal resolutions.Automatic differentiation(Autodiff)technique and gradient descent algorithm were used to optimize the network parameters.The experimental results show that compared with the mechanistic models,PINN can effectively replace thermal experiments to realize the physical field simulation of ESR process with only a few experimental data,which can avoid the disadvantages of pure data-driven network simulation that requires a large amount of training data.Moreover,the solution of PINN has good physical interpretability and reliability of simulation results.For simulating electromagnetic field and temperature field distribution,the training time of the network is only 140 and 203 s,and the regression indicators of root mean square error can reach 12.65 and 13.76,respectively.
基金Supported by the National Natural Science Foundation of China (11161027)。
文摘Projective synchronization problems of a drive system and a particular response network were investigated,where the drive system is an arbitrary system with n+1 dimensions;it may be a linear or nonlinear system,and even a chaotic or hyperchaotic system,the response network is complex system coupled by N nodes,and every node is showed by the approximately linear part of the drive system.Only controlling any one node of the response network by designed controller can achieve the projective synchronization.Some numerical examples were employed to verify the effectiveness and correctness of the designed controller.
基金financially supported by the National Natural Science Foundation of China(Nos.52303298 and 52233002)。
文摘UHMWPE fibers exhibit impressive modulus and strength,but they have not reached their theoretical limits.Researchers focus on molecular weight,orientation,and crystallinity of UHMWPE,yet their contributions to mechanical properties are unclear.Molecular dynamics simulations are valuable but often limited by computational constraints.Our aim is to simulate higher molecular weights to better represent real UHMWPE fibers.We used Packmol and Polyply methodologies to construct PE systems,with Polyply reproducing more reasonable properties of UHMWPE fibers.Additionally,tensile simulations showed that orientation and crystallinity greatly impact Young's modulus more than molecular weight.Energy decomposition indicated that higher molecular weights lead to covalent bonds that can withstand more energy during stretching,thus increasing breaking strength.Combining simulations with machine learning,we found that orientation has the most significant impact on Young's modulus,contributing 60%,and molecular weight plays the most crucial role in determining the breaking strength,accounting for 65%.This study provides a theoretical basis and guidelines for enhancing UHMWPE's modulus and strength.
基金supported by the Science Fund for the Gansu Provincial Natural Science Foundation Project(22JR5RA339).
文摘Land use/cover change(LUCC)constitutes the spatial and temporal patterns of ecological security,and the construction of ecological networks is an effective way to ensure ecological security.Exploring the spatial and temporal change characteristics of ecological network and analyzing the integrated relationship between LUCC and ecological security are crucial for ensuring regional ecological security.Gansu is one of the provinces with fragile ecological environment in China,and rapid changes in land use patterns in recent decades have threatened ecological security.Therefore,taking Gansu Province as the study area,this study simulated its land use pattern in 2050 using patch-generating land use simulation(PLUS)model based on the LUCC trend from 2000 to 2020 and integrated the LUCC into morphological spatial pattern analysis(MSPA)to identify ecological sources and extract the ecological corridors to construct ecological network using circuit theory.The results revealed that,according to the prediction results in 2050,the areas of cultivated land,forest land,grassland,water body,construction land,and unused land would be 63,447.52,39,510.80,148,115.18,4605.21,8368.89,and 161,752.40 km^(2),respectively.The number of ecological sources in Gansu Province would increase to 80,with a total area of 99,927.18 km^(2).The number of ecological corridors would increase to 191,with an estimated total length of 6120.66 km.Both ecological sources and ecological corridors showed a sparse distribution in the northwest and dense distribution in the southeast of the province at the spatial scale.The number of ecological pinch points would reach 312 and the total area would expect to increase to 842.84 km^(2),with the most pronounced increase in the Longdong region.Compared with 2020,the number and area of ecological barriers in 2050 would decrease significantly by 63 and 370.71 km^(2),respectively.In general,based on the prediction results,the connectivity of ecological network of Gansu Province would increase in 2050.To achieve the predicted ecological network in 2050,emphasis should be placed on the protection of cultivated land and ecological land,the establishment of ecological sources in desert areas,the reinforcement of the protection for existing ecological sources,and the construction of ecological corridors to enhance the stability of ecological network.This study provides valuable theoretical support and references for the future construction of ecological networks and regional land resource management decision-making.
基金co-supported by the National Science and Technology Major Project(No.J2019-Ⅲ-0010-0054)the National Natural Science Foundation of China(No.52336002)。
文摘The flight envelope of Air Turbo Rocket(ATR)engines is broader compared to conventional aero-engines,and designing a full-envelope controller using traditional methods poses significant challenges due to a burdensome design process.To address this issue,this paper proposes a self-learning neural network controller design method based on Reinforcement Learning(RL).Additionally,a method for predictive compensation and stability rewards is proposed to reduce the system oscillation caused by actuator delay.This approach simplifies the actuator to a firstorder inertial element exhibiting pure delay.A simulation environment for the ATR engineactuator system is first established.Based on this environment,a self-learning neural network controller using a predictive compensator and the Proximal Policy Optimization(PPO)algorithm is then developed.Furthermore,the temporal difference signals from the controller output are integrated into the reward function to enhance system stability.The proposed method is validated through numerical simulations and semi-physical experiments.The numerical simulation results demonstrate that the proposed method increases the system's tolerance to delays from 20 ms to 400 ms.Under an actuator delay of 400 ms,the average steady-state error remains less than0.1%,the overshoot is limited to 1%,and the settling time does not exceed 3 s.Moreover,compared to the traditional method,the proposed method exhibits higher adaptability to model errors and variations in flight conditions.In the conducted semi-physical simulation experiments,the proposed method achieves stable control of a real electric pump.
基金National Key Research and Development Program of China,No.2019YFD1101304National Natural Science Foundation of China,No.52278059+1 种基金Natural Science Foundation of Hunan Province of China,No.2024JJ8316Hunan Provincial Innovation Foundation For Postgraduate,No.CX20250634。
文摘Urban spatial morphology(USM)optimization is critical to balancing biodiversity conservation and sustainable urbanization.However,previous studies predominantly focused on the socio-economic efficiency and static ecological metrics and rarely addressed the dynamic USM optimization across spatial scales.Here,we developed a multi-level ecological network(MEN)framework to resolve the tension between urban expansion and ecological integrity.By integrating the cost-weighted distance analysis with a hierarchical network transmission mechanism,we established a cross-scale spatial optimization system,which coordinated the regional ecological corridors and local habitat patches.Comparative experiments with conventional single-scale approaches and scenario simulations using the PLUS model show that the MEN framework had superior performance in three dimensions:(1)spatial governance:the primary-level network(peri-urban natural reserves)effectively contained urban sprawl,and the secondary-level network(intra-urban green corridors)mitigated habitat fragmentation and improved the built-environment;(2)scenario robustness:the model maintained an optimal compactness-loose balance in multiple development pathways;(3)landscape metrics:patch fragmentation decreased by 18.25%,and the internal landscape richness improved by 10.66%compared to the scenario without USM optimization.The findings provide new insight to establish a hierarchical ecological optimization framework as a nature-based spatial protocol to reconcile metropolitan growth with landscape sustainability.
基金supported by the National Natural Science Foundation of China[grant number 42175132]the National Key R&D Program[grant number 2020YFA0607802]the CAS Information Technology Program[grant number CAS-WX2021SF-0107-02]。
文摘Solar energy is a pivotal clean energy source in the transition to carbon neutrality from fossil fuels.However,the intermittent and stochastic characteristics of solar radiation pose challenges for accurate simulation and prediction.Accurately simulating and predicting solar radiation and its variability are crucial for optimizing solar energy utilization.This study conducted simulation experiments using the WRF-Solar model from 25 June to 25 July 2022,to evaluate the accuracy and performance of the simulated solar radiation across China.The simulations covered the whole country with a grid spacing of 27 km and were compared with ground observation network data from the Chinese Ecosystem Research Network.The results indicated that WRF-Solar can accurately capture the spatiotemporal patterns of global horizontal irradiance over China,but there is still an overestimation of solar radiation,and the model underestimates the total cloud cover.The root-mean-square error ranged from 92.83 to 188.13 W m^(-2) and the mean bias(MB)ranged from 21.05 to 56.22 W m^(-2).The simulation showed the smallest MB at Lhasa on the Qinghai–Tibet Plateau,while the largest MB was observed in Southeast China.To enhance the accuracy of solar radiation simulation,the authors compared the Fast All-sky Radiation Model for Solar with the Rapid Radiative Transfer Model for General Circulation Models and found that the former provides better simulation.
基金supported by the National Natural Science Foundations of China(Nos.12272411 and 42007259)the State Key Laboratory for GeoMechanics and Deep Underground Engineering,the China University of Mining&Technology(No.SKLGDUEK2207)the Department of Science and Technology of Shaanxi Province(Nos.2022KXJ-107 and 2022JC-LHJJ-16).
文摘Underground engineering in extreme environments necessitates understanding rock mechanical behavior under coupled high-temperature and dynamic loading conditions.This study presents an innovative multi-scale cross-platform PFC-FDEM coupling methodology that bridges microscopic thermal damage mechanisms with macroscopic dynamic fracture responses.The breakthrough coupling framework introduces:(1)bidirectional information transfer protocols enabling seamless integration between PFC’s particle-scale thermal damage characterization and FDEM’s continuum-scale fracture propagation,(2)multi-physics mapping algorithms that preserve crack network geometric invariants during scale transitions,and(3)cross-platform cohesive zone implementations for accurate SHTB dynamic loading simulation.The coupled approach reveals distinct three-stage crack evolution characteristics with temperature-dependent density following an exponential model.High-temperature exposure significantly reduces dynamic strength ratio(60%at 800℃)and diminishes strain-rate sensitivity,with dynamic increase factor decreasing from 1.0 to 2.2(25℃)to 1.0-1.3(800℃).Critically,the coupling methodology captures fundamental energy redistribution mechanisms:thermal crack networks alter elastic energy proportion from 75%to 35%while increasing fracture energy from 5%to 30%.Numerical predictions demonstrate excellent experimental agreement(±8%peak stress-strain errors),validating the PFC-FDEM coupling accuracy.This integrated framework provides essential computational tools for predicting complex thermal-mechanical rock behavior in underground engineering applications.
文摘The forthcoming sixth generation(6G)of mobile communication networks is envisioned to be AInative,supporting intelligent services and pervasive computing at unprecedented scale.Among the key paradigms enabling this vision,Federated Learning(FL)has gained prominence as a distributed machine learning framework that allows multiple devices to collaboratively train models without sharing raw data,thereby preserving privacy and reducing the need for centralized storage.This capability is particularly attractive for vision-based applications,where image and video data are both sensitive and bandwidth-intensive.However,the integration of FL with 6G networks presents unique challenges,including communication bottlenecks,device heterogeneity,and trade-offs between model accuracy,latency,and energy consumption.In this paper,we developed a simulation-based framework to investigate the performance of FL in representative vision tasks under 6G-like environments.We formalize the system model,incorporating both the federated averaging(FedAvg)training process and a simplified communication costmodel that captures bandwidth constraints,packet loss,and variable latency across edge devices.Using standard image datasets(e.g.,MNIST,CIFAR-10)as benchmarks,we analyze how factors such as the number of participating clients,degree of data heterogeneity,and communication frequency influence convergence speed and model accuracy.Additionally,we evaluate the effectiveness of lightweight communication-efficient strategies,including local update tuning and gradient compression,in mitigating network overhead.The experimental results reveal several key insights:(i)communication limitations can significantly degrade FL convergence in vision tasks if not properly addressed;(ii)judicious tuning of local training epochs and client participation levels enables notable improvements in both efficiency and accuracy;and(iii)communication-efficient FL strategies provide a promising pathway to balance performance with the stringent latency and reliability requirements expected in 6G.These findings highlight the synergistic role of AI and nextgeneration networks in enabling privacy-preserving,real-time vision applications,and they provide concrete design guidelines for researchers and practitioners working at the intersection of FL and 6G.
文摘Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
基金National MCF Energy R&D Program of China(2018YFE0306100)Natural Science Foundation of Hunan Province for Distinguished Young Scholars(2021JJ10062)+1 种基金National Natural Science Foundation of China(52101028)China Postdoctoral Science Foundation(2021M703628)。
文摘Combining the phase-field method and the moving boundary method,a three-dimensional phase-field simulation was conducted for the growth and grain evolution of Ti films deposited by physical vapor deposition under different deposition rates and grain orientations.The evolution of grain morphology and grain orientation was also taken into consideration.Simulation results show that at lower deposition rates,the surface of the formed Ti film exhibits a distinct oriented texture structure.The surface roughness of the Ti film is positively correlated with the grain misorientation.Moreover,the surface roughness obtained from the simulation is in good agreement with the experiment results.
文摘To explore the material basis and mechanisms of the anti-inflammatory effects of Hibiscus mutabilis L..The active ingredients and potential targets of Hibiscus mutabilis L.were obtained through the literature review and SwissADME platform.Genes related to the inflammation were collected using Genecards and OMIM databases,and the intersection genes were submitted on STRING and DAVID websites.Then,the protein interaction network(PPI),gene ontology(GO)and pathway(KEGG)were analyzed.Cytoscape 3.7.2 software was used to construct the“Hibiscus mutabilis L.-active ingredient-target-inflammation”network diagram,and AutoDockTools-1.5.6 software was used for the molecular docking verification.The antiinflammatory effect of Hibiscus mutabilis L.active ingredient was verified by the RAW264.7 inflammatory cell model.The results showed that 11 active components and 94 potential targets,1029 inflammatory targets and 24 intersection targets were obtained from Hibiscus mutabilis L..The key anti-inflammatory active ingredients of Hibiscus mutabilis L.are quercetin,apigenin and luteolin.Its action pathway is mainly related to NF-κB,cancer pathway and TNF signaling pathway.Cell experiments showed that total flavonoids of Hibiscus mutabilis L.could effectively inhibit the expression of tumor necrosis factor(TNF-α),interleukin 8(IL-8)and epidermal growth factor receptor(EGFR)in LPS-induced RAW 264.7 inflammatory cells.It also downregulates the phosphorylation of human nuclear factor ĸB inhibitory protein α(IĸBα)and NF-κB p65 subunit protein(p65).Overall,the anti-inflammatory effect of Hibiscus mutabilis L.is related to many active components,many signal pathways and targets,which provides a theoretical basis for its further development and application.
