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
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
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
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
Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworth...Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.展开更多
Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In exist...Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.展开更多
In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a...In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.展开更多
Topography can strongly affect ground motion,and studies of the quantification of hill surfaces’topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method bas...Topography can strongly affect ground motion,and studies of the quantification of hill surfaces’topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method based upon the BP neural network algorithm and three-dimensional finite element method(FEM)was developed.The FEM simulation results were compared with seismic records and the results show that the PGA and response spectra have a tendency to increase with increasing elevation,but the correlation between PGA amplification factors and slope is not obvious for low hills.New BP neural network models were established for the prediction of amplification factors of PGA and response spectra.Two kinds of input variables’combinations which are convenient to achieve are proposed in this paper for the prediction of amplification factors of PGA and response spectra,respectively.The absolute values of prediction errors can be mostly within 0.1 for PGA amplification factors,and they can be mostly within 0.2 for response spectra’s amplification factors.One input variables’combination can achieve better prediction performance while the other one has better expandability of the predictive region.Particularly,the BP models only employ one hidden layer with about a hundred nodes,which makes it efficient for training.展开更多
The integrated simulation and optimization technology of reservoir-wellbore-pipe network is developed to reflect the mutual influence and restriction among reservoir engineering,oil production engineering and surface ...The integrated simulation and optimization technology of reservoir-wellbore-pipe network is developed to reflect the mutual influence and restriction among reservoir engineering,oil production engineering and surface engineering,and to obtain the scheme with minimum conflict and optimal benefit in each step.This technology is based on the concept of global optimization to maximize production and profit,reduce costs and increase benefit.This paper elaborates the current situation of integrated simulation technology of reservoir-wellbore-pipe network both at home and abroad,discusses its correlation with the primary business of Sinopec and its development from three aspects of modeling,cloud platform and intellectualization.Suggestions on its future development are put forward from underlying data,software platform,popularization and application,and cross-border integration to provide means and guidance for the construction of intelligent oil and gas fields.The results show that the integrated simulation of reservoir-wellbore-pipe network can better reflect the optimization requirements of each step,avoid the ineffective operation of field equipment,and effectively improve the efficiency of research and management.Coupling solution,global optimization method and pressure fitting,which can make the simulation results reflect the real situation,are the key technologies for the network.The theoretical technology and main function research of integrated simulation technology have been mature,but the large-scale application and local function improvement of oil and gas fields are yet to be promoted.In the future,the integrated simulation of reservoir-wellbore-pipe network will develop from digitalization to modeling and intellectualization,from local simulation to cloud computing,and from manual intervention to intelligent decision-making.We suggest speeding up the construction of the unified database and model base of the whole underlying platform,strengthening the construction of software integration and integration platform with independent intellectual property rights,speeding up the popularization and application of intelligent oil and gas field demonstration projects,and strengthening the integration of oil and gas industry with artificial intelligence(AI),big data and block chain for its development.展开更多
Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the u...Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.展开更多
Recently,numerous studies have demonstrated that the physics-informed neural network(PINN)can effectively and accurately resolve hyperelastic finite deformation problems.In this paper,a PINN framework for tackling hyp...Recently,numerous studies have demonstrated that the physics-informed neural network(PINN)can effectively and accurately resolve hyperelastic finite deformation problems.In this paper,a PINN framework for tackling hyperelastic-magnetic coupling problems is proposed.Since the solution space consists of two-phase domains,two separate networks are constructed to independently predict the solution for each phase region.In addition,a conscious point allocation strategy is incorporated to enhance the prediction precision of the PINN in regions characterized by sharp gradients.With the developed framework,the magnetic fields and deformation fields of magnetorheological elastomers(MREs)are solved under the control of hyperelastic-magnetic coupling equations.Illustrative examples are provided and contrasted with the reference results to validate the predictive accuracy of the proposed framework.Moreover,the advantages of the proposed framework in solving hyperelastic-magnetic coupling problems are validated,particularly in handling small data sets,as well as its ability in swiftly and precisely forecasting magnetostrictive motion.展开更多
Land use and land cover change(LUCC)process exhibits spatial correlation and temporal dependency.Accurate extraction of spatiotemporal features is important in enhancing the modeling capabilities of LUCC.Cellular auto...Land use and land cover change(LUCC)process exhibits spatial correlation and temporal dependency.Accurate extraction of spatiotemporal features is important in enhancing the modeling capabilities of LUCC.Cellular automaton(CA)models,recognized as powerful tools for simulating dynamic LUCC processes,are traditionally applied in LUCC,focusing on time-slice driving factor data,often neglecting the temporal dimension.However,the transformer architecture,a highly acclaimed model in machine learning,has been rarely integrated into CA models for the simulation of dynamic LUCC processes.To fill this gap,we proposed a novel spatiotemporal urban LUCC simulation model,namely,transformer-convolutional neural network(TC)-CA.Based on CA models that involve the utilization of a convolutional neural network(CNN)for extracting latent spatial features,TC-CA extends this paradigm by incorporating a transformer architecture to extract spatiotemporal information from temporal driving factor data and temporal spatial features.The evaluation results with Wuxi city as a study area indicated the advantage of our proposed TC-CA against random forest-CA,conventional CNN-CA,artificial neural network-CA,and transformer-CA.Compared with the three non-transformer-based CAs,the TC-CA improved the figure of merit by up to 2.85%-8.14%.This study contributes a fresh spatiotemporal perspective and transformer approach to the field of LUCC modeling.展开更多
Background:Based on network pharmacology and molecular docking,the present study investigated the mechanism of curcumin(CUR)in diabetic retinopathy treatment.Methods:Based on the DisGeNET,Swiss TargetPrediction,GeneCa...Background:Based on network pharmacology and molecular docking,the present study investigated the mechanism of curcumin(CUR)in diabetic retinopathy treatment.Methods:Based on the DisGeNET,Swiss TargetPrediction,GeneCards,Online Mendelian Inheritance in Man,Gene Expression Omnibus,and Comparative Toxicogenomics Database,the intersection core targets of CUR and diabetic retinopathy were identified.The intersection target was imported into the STRING database to obtain the protein-protein interaction map.According to the Database for Annotation,Visualization and Integrated Discovery database,the intersected targets were enriched in Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes pathways.Then Cytoscape 3.9.1 is used to make the drug-target-disease-pathway network.The mechanism of CUR and diabetic retinopathy was further verified by molecular docking and molecular dynamics simulation.Results:There were 203 intersecting targets of CUR and diabetic retinopathy identified.1320 GO entries were enriched for GO functions,which were primarily involved in the composition of cells such as identical protein binding,protein binding,enzyme binding,etc.It was found that 175 pathways were enriched using Kyoto Encyclopedia of Genes and Genomes pathway enrichment methods,which were mainly included in the lipid and atherosclerosis,AGE-RAGE signaling pathway in diabetic complications,pathways in cancer,etc.In the molecular docking analysis,CUR was found to have a good ability to bind to the core targets of albumin,IL-1B,and IL-6.The binding of albumin to CUR was further verified by molecular dynamics simulation.Conclusion:As a result of this study,CUR may exert a role in the treatment of diabetic retinopathy through multi-target and multi-pathway regulation,which indicates a possible direction of future research.展开更多
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 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.
基金supported by the National Natural Science Foundation of China(Grant Nos.42141019 and 42261144687)the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(Grant No.2019QZKK0102)+4 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB42010404)the National Natural Science Foundation of China(Grant No.42175049)the Guangdong Meteorological Service Science and Technology Research Project(Grant No.GRMC2021M01)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab)for computational support and Prof.Shiming XIANG for many useful discussionsNiklas BOERS acknowledges funding from the Volkswagen foundation.
文摘Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.
基金supported by National Natural Sciences Foundation of China(No.62271165,62027802,62201307)the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515030297)+2 种基金the Shenzhen Science and Technology Program ZDSYS20210623091808025Stable Support Plan Program GXWD20231129102638002the Major Key Project of PCL(No.PCL2024A01)。
文摘Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.
文摘In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.
基金supported by the National Natural Science Foundation of China(No.51878625)the Collaboratory for the Study of Earthquake Predictability in China Seismic Experimental Site(No.2018YFE0109700)the General Scientific Research Foundation of Shandong Earthquake Agency(No.YB2208).
文摘Topography can strongly affect ground motion,and studies of the quantification of hill surfaces’topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method based upon the BP neural network algorithm and three-dimensional finite element method(FEM)was developed.The FEM simulation results were compared with seismic records and the results show that the PGA and response spectra have a tendency to increase with increasing elevation,but the correlation between PGA amplification factors and slope is not obvious for low hills.New BP neural network models were established for the prediction of amplification factors of PGA and response spectra.Two kinds of input variables’combinations which are convenient to achieve are proposed in this paper for the prediction of amplification factors of PGA and response spectra,respectively.The absolute values of prediction errors can be mostly within 0.1 for PGA amplification factors,and they can be mostly within 0.2 for response spectra’s amplification factors.One input variables’combination can achieve better prediction performance while the other one has better expandability of the predictive region.Particularly,the BP models only employ one hidden layer with about a hundred nodes,which makes it efficient for training.
基金funded by the SINOPEC Science and Technology Project(No.P18080).
文摘The integrated simulation and optimization technology of reservoir-wellbore-pipe network is developed to reflect the mutual influence and restriction among reservoir engineering,oil production engineering and surface engineering,and to obtain the scheme with minimum conflict and optimal benefit in each step.This technology is based on the concept of global optimization to maximize production and profit,reduce costs and increase benefit.This paper elaborates the current situation of integrated simulation technology of reservoir-wellbore-pipe network both at home and abroad,discusses its correlation with the primary business of Sinopec and its development from three aspects of modeling,cloud platform and intellectualization.Suggestions on its future development are put forward from underlying data,software platform,popularization and application,and cross-border integration to provide means and guidance for the construction of intelligent oil and gas fields.The results show that the integrated simulation of reservoir-wellbore-pipe network can better reflect the optimization requirements of each step,avoid the ineffective operation of field equipment,and effectively improve the efficiency of research and management.Coupling solution,global optimization method and pressure fitting,which can make the simulation results reflect the real situation,are the key technologies for the network.The theoretical technology and main function research of integrated simulation technology have been mature,but the large-scale application and local function improvement of oil and gas fields are yet to be promoted.In the future,the integrated simulation of reservoir-wellbore-pipe network will develop from digitalization to modeling and intellectualization,from local simulation to cloud computing,and from manual intervention to intelligent decision-making.We suggest speeding up the construction of the unified database and model base of the whole underlying platform,strengthening the construction of software integration and integration platform with independent intellectual property rights,speeding up the popularization and application of intelligent oil and gas field demonstration projects,and strengthening the integration of oil and gas industry with artificial intelligence(AI),big data and block chain for its development.
文摘Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.
基金supported by the National Natural Science Foundation of China(Nos.12072105 and 11932006)。
文摘Recently,numerous studies have demonstrated that the physics-informed neural network(PINN)can effectively and accurately resolve hyperelastic finite deformation problems.In this paper,a PINN framework for tackling hyperelastic-magnetic coupling problems is proposed.Since the solution space consists of two-phase domains,two separate networks are constructed to independently predict the solution for each phase region.In addition,a conscious point allocation strategy is incorporated to enhance the prediction precision of the PINN in regions characterized by sharp gradients.With the developed framework,the magnetic fields and deformation fields of magnetorheological elastomers(MREs)are solved under the control of hyperelastic-magnetic coupling equations.Illustrative examples are provided and contrasted with the reference results to validate the predictive accuracy of the proposed framework.Moreover,the advantages of the proposed framework in solving hyperelastic-magnetic coupling problems are validated,particularly in handling small data sets,as well as its ability in swiftly and precisely forecasting magnetostrictive motion.
基金National Natural Science Foundation of China,No.42271418,No.42171088State Key Laboratory of Earth Surface Processes and Resource Ecology,No.2022-ZD-04,No.2023-WT-02。
文摘Land use and land cover change(LUCC)process exhibits spatial correlation and temporal dependency.Accurate extraction of spatiotemporal features is important in enhancing the modeling capabilities of LUCC.Cellular automaton(CA)models,recognized as powerful tools for simulating dynamic LUCC processes,are traditionally applied in LUCC,focusing on time-slice driving factor data,often neglecting the temporal dimension.However,the transformer architecture,a highly acclaimed model in machine learning,has been rarely integrated into CA models for the simulation of dynamic LUCC processes.To fill this gap,we proposed a novel spatiotemporal urban LUCC simulation model,namely,transformer-convolutional neural network(TC)-CA.Based on CA models that involve the utilization of a convolutional neural network(CNN)for extracting latent spatial features,TC-CA extends this paradigm by incorporating a transformer architecture to extract spatiotemporal information from temporal driving factor data and temporal spatial features.The evaluation results with Wuxi city as a study area indicated the advantage of our proposed TC-CA against random forest-CA,conventional CNN-CA,artificial neural network-CA,and transformer-CA.Compared with the three non-transformer-based CAs,the TC-CA improved the figure of merit by up to 2.85%-8.14%.This study contributes a fresh spatiotemporal perspective and transformer approach to the field of LUCC modeling.
基金supported by the Hubei Province Research Innovation Team Project(T2021022)Scientific Research Projects of Hubei Health Commission(WJ2023M119).
文摘Background:Based on network pharmacology and molecular docking,the present study investigated the mechanism of curcumin(CUR)in diabetic retinopathy treatment.Methods:Based on the DisGeNET,Swiss TargetPrediction,GeneCards,Online Mendelian Inheritance in Man,Gene Expression Omnibus,and Comparative Toxicogenomics Database,the intersection core targets of CUR and diabetic retinopathy were identified.The intersection target was imported into the STRING database to obtain the protein-protein interaction map.According to the Database for Annotation,Visualization and Integrated Discovery database,the intersected targets were enriched in Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes pathways.Then Cytoscape 3.9.1 is used to make the drug-target-disease-pathway network.The mechanism of CUR and diabetic retinopathy was further verified by molecular docking and molecular dynamics simulation.Results:There were 203 intersecting targets of CUR and diabetic retinopathy identified.1320 GO entries were enriched for GO functions,which were primarily involved in the composition of cells such as identical protein binding,protein binding,enzyme binding,etc.It was found that 175 pathways were enriched using Kyoto Encyclopedia of Genes and Genomes pathway enrichment methods,which were mainly included in the lipid and atherosclerosis,AGE-RAGE signaling pathway in diabetic complications,pathways in cancer,etc.In the molecular docking analysis,CUR was found to have a good ability to bind to the core targets of albumin,IL-1B,and IL-6.The binding of albumin to CUR was further verified by molecular dynamics simulation.Conclusion:As a result of this study,CUR may exert a role in the treatment of diabetic retinopathy through multi-target and multi-pathway regulation,which indicates a possible direction of future research.
