Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between...Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).展开更多
Milling force is key to the understanding of cutting mechanism and the control of machining process.Traditional milling force models have limited prediction accuracy due to their simplified conditions and incomplete k...Milling force is key to the understanding of cutting mechanism and the control of machining process.Traditional milling force models have limited prediction accuracy due to their simplified conditions and incomplete knowledge contained for model construction.On the other hand,due to the lack of guidance from physics,the data-driven models lack interpretability,making them challenging to generalize to practical applications.To meet these difficulties,a deep network model guided by milling dynamics is proposed in this study to predict the instantaneous milling force and spindle vibration under varying cutting conditions.The model uses a milling dynamics model to generate data sets to pre-train the deep network and then integrates the experimental data for fine-tuning to improve the model’s generalization and accuracy.Additionally,the vibration equation is incorporated into the loss function as the physical constraint,enhancing the model’s interpretability.A milling experiment is conducted to validate the effectiveness of the proposed model,and the results indicate that the physics incorporated could improve the network learning capability and interpretability.The predicted results are in good agreement with the measured values,with an average error as low as 2.6705%.The prediction accuracy is increased by 24.4367%compared to the pure data-driven model.展开更多
Accurate acquisition of the lithological composition of a tunnel face is crucial for efficient tunneling and hazard prevention in large-diameter slurry shield tunnels.While widely applied,current data-driven methods o...Accurate acquisition of the lithological composition of a tunnel face is crucial for efficient tunneling and hazard prevention in large-diameter slurry shield tunnels.While widely applied,current data-driven methods often face challenges such as indirect prediction,data sparsity,and data drift,which limit their accuracy and generalizability.This study develops an integrated method that combines a knowledge-driven method to directly compute distribution patterns of lithological components,which are used as a priori knowledge to guide the development of a data-driven method.Coupled Markov chain(CMC)and deep neural networks(DNNs)serve as the knowledge-driven and data-driven components,respectively.Additionally,a dynamic prediction strategy is proposed,where the model is continuously optimized as construction progresses and training samples accumulate,rather than being statically trained on post-construction data,as is common in data-driven methods.Finally,the proposed method is evaluated using a real-world project.The evaluation results show that the integrated method outperforms both individual data-and knowledge-driven methods,demonstrating higher predictive performance,greater stability,and greater robustness to data scarcity and data drift.Furthermore,the dynamic prediction strategy better captures the effects of gradual data accumulation and lithological spatial variability on prediction performance during construction,providing new insights for real-time prediction in practical tunneling applications.展开更多
Water inflow into mountain tunnels exhibits high variability and nonlinear seepage behavior,leading to significant prediction inaccuracies and poor pattern recognition when conventional analytical methods are applied....Water inflow into mountain tunnels exhibits high variability and nonlinear seepage behavior,leading to significant prediction inaccuracies and poor pattern recognition when conventional analytical methods are applied.This study proposes a dynamic water inflow prediction method specifically designed for mountain tunnels.The method is based on groundwater dynamics theory,employing nonDarcian law as the governing equation and deriving analytical solutions applicable to both confined and phreatic aquifer conditions.The method incorporates spatiotemporal variations along the tunnel alignment,enabling both short-term and long-term dynamic predictions of water inflow.The study examines the nonlinear characteristics of the seepage field during tunnel water inrush.The research findings indicate that the predictive results are consistent with the hypothesized two-stage water inflow pattern,with relative errors for key parameters,such as maximum water inflow,normal water inflow,and duration of water inflow,remaining within 10%.The magnitude of water inflow is positively correlated with the permeability coefficient,head height;it is negatively correlated with the axial distance to the tunnel face and the non-Darcian influence coefficient.Both water inflow and water pressure are subject to non-Darcian effects within a defined influence zone extending approximately 1.3 times the tunnel diameter.Comparisons with established predictive methods,numerical simulations,and data from existing tunnel projects confirm the effectiveness of the proposed method.Moreover,the method was successfully applied to a mountain tunnel in the Tibet Plateau region in southwestern China,where it achieved prediction errors within 3%to 8%,demonstrating high reliability.展开更多
A mathematical model of principal elements of the aircraft hydraulic system is presented based on the heat transfer theory. The dynamic heat transfer process of the hydraulic oil and the pump shells within an aircraft...A mathematical model of principal elements of the aircraft hydraulic system is presented based on the heat transfer theory. The dynamic heat transfer process of the hydraulic oil and the pump shells within an aircraft hydraulic system are analyzed by the difference method. A kind of means for the prediction to variational trends of the aircraft hydraulic system temperature is provided during operation. The numerical prediction and simulation under the operational conditions are presented for ground trial running and the decelerated operation in flight. Computational results show that there is a good coincidence between the experimental data and the numerical predictions.展开更多
Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal sca...Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.展开更多
The coal dynamic characteristic stress identification under dynamic load is important for guiding underground mineral mining and predicting underground dynamic disasters.In this article,the dynamic compression test of...The coal dynamic characteristic stress identification under dynamic load is important for guiding underground mineral mining and predicting underground dynamic disasters.In this article,the dynamic compression test of anthracite under five strain rates is carried out,the evolution law of three kinds of crack characteristic stress is analyzed,and a prediction model of the crack characteristic stress threshold considering the strain rate effect is established.Then,the rationality of crack characteristic stress under dynamic loading is discussed from the damage evolution standpoint,and the crack extension response mechanism during dynamic compression of anthracite is discussed.The result shows that the crack characteristic stress threshold is significantly influenced by the strain rate.The three characteristic stress thresholds are positively correlated with the strain rate,but the ratios to the crest stress gradually decrease.The increase in the strain rate strongly contributes to the crack extension behavior of anthracite.In the crack unstable extension phase,because of the increase of the strain rate,anthracite shows more energy dissipation under the same deformation in association with the stress concentration effect and the dynamic strength enhancement effect.The crack propagation rate is increased,the crack propagation path of the section is more complex,and more severe damage occurs before the dynamic failure of anthracite,which leads to even more severe damage.展开更多
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In...Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.展开更多
Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies ha...Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.展开更多
In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structu...In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.展开更多
Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable d...Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable dynamic gaps,resulting in conservative and suboptimal trajectories.To address these challenges,this paper proposes a hierarchical reinforcement learning(RL)framework that integrates global path guidance,local trajectory generation,predictive safety evaluation,and neural network-based decision-making.Specifically,the global planner provides long-term navigation guidance,and the local module then utilizes an improved 3D dynamic window approach(DWA)to generate dynamically feasible candidate trajectories.To enhance safety in dense dynamic scenarios,the algorithm introduces a predictive axis-aligned bounding box(AABB)strategy to model the future occupancy of obstacles,combined with convex hull verification for efficient trajectory safety assessment.Furthermore,a double deep Q-network(DDQN)is employed with structured feature encoding,enabling the neural network to reliably select the optimal trajectory from the candidate set,thereby improving robustness and generalization.Comparative experiments conducted in a high-fidelity simulation environment show that the algorithm outperforms existing algorithms,reducing the average number of collisions to 0.2 while shortening the average task completion time by approximately 15%,and achieving a success rate of 97%.展开更多
To study the prediction of the anomalous precipitation and general circulation for the summer（June–July–August） of1998, the Community Climate System Model Version 4.0（CCSM4.0） integrations were used to drive ver...To study the prediction of the anomalous precipitation and general circulation for the summer（June–July–August） of1998, the Community Climate System Model Version 4.0（CCSM4.0） integrations were used to drive version 3.2 of the Weather Research and Forecasting（WRF3.2） regional climate model to produce hindcasts at 60 km resolution. The results showed that the WRF model produced improved summer precipitation simulations. The systematic errors in the east of the Tibetan Plateau were removed, while in North China and Northeast China the systematic errors still existed. The improvements in summer precipitation interannual increment prediction also had regional characteristics. There was a marked improvement over the south of the Yangtze River basin and South China, but no obvious improvement over North China and Northeast China. Further analysis showed that the improvement was present not only for the seasonal mean precipitation, but also on a sub-seasonal timescale. The two occurrences of the Mei-yu rainfall agreed better with the observations in the WRF model,but were not resolved in CCSM. These improvements resulted from both the higher resolution and better topography of the WRF model.展开更多
Based on the hindcast results of summer rainfall anomalies over China for the period 1981-2000 by the Dynamical Climate Prediction System （IAP-DCP） developed by the Institute of Atmospheric Physics, a correction met...Based on the hindcast results of summer rainfall anomalies over China for the period 1981-2000 by the Dynamical Climate Prediction System （IAP-DCP） developed by the Institute of Atmospheric Physics, a correction method that can account for the dependence of model＇s systematic biases on SST anomalies is proposed. It is shown that this correction method can improve the hindcast skill of the IAP-DCP for summer rainfall anomalies over China, especially in western China and southeast China, which may imply its potential application to real-time seasonal prediction.展开更多
Currently, the modeling of cutting process mainly focuses on two aspects: one is the setup of the universal cutting force model that can be adapted to a broader cutting condition; the other is the setup of the exact c...Currently, the modeling of cutting process mainly focuses on two aspects: one is the setup of the universal cutting force model that can be adapted to a broader cutting condition; the other is the setup of the exact cutting force model that can accurately reflect a true cutting process. However, there is little research on the prediction of chatter stablity in milling. Based on the generalized mathematical model of inserted cutters introduced by ENGIN, an improved geometrical, mechanical and dynamic model for the vast variety of inserted cutters widely used in engineering applications is presented, in which the average directional cutting force coefficients are obtained by means of a numerical approach, thus leading to an analytical determination of stability lobes diagram (SLD) on the axial depth of cut. A new kind of SLD on the radial depth of cut is also created to satisfy the special requirement of inserted cutter milling. The corresponding algorithms used for predicting cutting forces, vibrations, dimensional surface finish and stability lobes in inserted cutter milling under different cutting conditions are put forward. Thereafter, a dynamic simulation module of inserted cutter milling is implemented by using hybrid program of Matlab with Visual Basic. Verification tests are conducted on a vertical machine center for Aluminum alloy LC4 by using two different types of inserted cutters, and the effectiveness of the model and the algorithm is verified by the good agreement of simulation result with that of cutting tests under different cutting conditions. The proposed model can predict the cutting process accurately under a variety of cutting conditions, and a high efficient and chatter-free milling operation can be achieved by a cutting condition optimization in industry applications.展开更多
This article presents a novel approach for predicting transition locations over airfoils,which are used to activate turbulence model in a Reynolds-averaged Navier-Stokes flow solver.This approach combines Dynamic Mode...This article presents a novel approach for predicting transition locations over airfoils,which are used to activate turbulence model in a Reynolds-averaged Navier-Stokes flow solver.This approach combines Dynamic Mode Decomposition(DMD)with e^Ncriterion.The core idea is to use a spatial DMD analysis to extract the modes of unstable perturbations from a steady flowfield and substitute the local Linear Stability Theory(LST)analysis to quantify the spatial growth of Tollmien–Schlichting(TS)waves.Transition is assumed to take place at the stream-wise location where the most amplified mode’s N-factor reaches a prescribed threshold and a turbulence model is activated thereafter.To improve robustness,the high-order version of DMD technique(known as HODMD)is employed.A theoretical derivation is conducted to interpret how a spatial highorder DMD analysis can extract the growth rate of the unsteady perturbations.The new method is validated by transition predictions of flows over a low-speed Natural-Laminar-Flow(NLF)airfoil NLF0416 at various angles of attack and a transonic NLF airfoil NPU-LSC-72613.The transition locations predicted by our HODMD/e^Nmethod agree well with experimental data and compare favorably to those obtained by some existing methods■.It is shown that the proposed method is able to predict transition locations for flows over different types of airfoils and offers the potential for application to 3D wings as well as more complex configurations.展开更多
Based on the study of the relationship between structure and feedback of China’s natural gas demand system, this paper establishes a system dynamics model. In order to simulate the total demand and consumption struct...Based on the study of the relationship between structure and feedback of China’s natural gas demand system, this paper establishes a system dynamics model. In order to simulate the total demand and consumption structure of natural gas in China, we set up seven scenarios by changing some of the parameters of the model. The results showed that the total demand of natural gas would increase steadily year by year and reach in the range from 3600 to 4500 billion cubic meters in 2035. Furthermore, in terms of consumption structure, urban gas consumption would still be the largest term, followed by the gas consumption as industrial fuel, gas power generation and natural gas chemical industry. In addition, compared with the population growth, economic development still plays a dominant role in the natural gas demand growth, the impact of urbanization on urban gas consumption is significant, and the promotion of natural gas utilization technology can effectively reduce the total consumption of natural gas.展开更多
Based on the theory of formation dynamics of oil/gas pools, the Dongying sag can be divided into three dynamic systems regarding the accumulation of oil and gas： the superpressure closed system, the semi-closed syste...Based on the theory of formation dynamics of oil/gas pools, the Dongying sag can be divided into three dynamic systems regarding the accumulation of oil and gas： the superpressure closed system, the semi-closed system and the normal pressure open system. Based on the analysis of genesis of superpressure in the superpressure closed system and the rule of hydrocarbon expulsion, it is found that hydrocarbon generation is related to superpressure, which is the main driving factor of hydrocarbon migration. Micro fractures formed by superpressure are the main channels for hydrocarbon migration. There are three dynamic patterns for hydrocarbon expulsion： free water drainage, hydrocarbon accumulation and drainage through micro fissures. In the superpressure closed system, the oil-driving-water process and oil/gas accumulation were completed in lithologic traps by way of such two dynamic patterns as episodic evolution of superpressure systems and episodic pressure release of faults. The oil-bearing capacity of lithologic traps is intimately related to reservoir-forming dynamic force. Quantitative evaluation of dynamic conditions for pool formation can effectively predict the oil-bearing capability of traps.展开更多
This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is intro...This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.展开更多
Background Cardiovascular disease(CVD)remains a major health challenge globally,particularly in aging populations.Using data from the China Health and Retirement Longitudinal Study(CHARLS),this study examines the Trig...Background Cardiovascular disease(CVD)remains a major health challenge globally,particularly in aging populations.Using data from the China Health and Retirement Longitudinal Study(CHARLS),this study examines the Triglyceride-glucose(TyG)index dynamics,a marker for insulin resistance,and its relationship with CVD in Chinese adults aged 45 and older.Methods This reanalysis utilized five waves of CHARLS data with multistage sampling.From 17,705 participants,5,625 with TyG index and subsequent CVD data were included,excluding those lacking 2011 and 2015 TyG data.TyG derived from glucose and triglyceride levels,CVD outcomes via self-reports and records.Participants divided into four groups based on TyG changes(2011–2015):low-low,low-high,high-low,high-high TyG groups.Results Adjusting for covariates,stable high group showed a significantly higher risk of incident CVD compared to stable low group,with an HR of 1.18(95%CI:1.03–1.36).Similarly,for stroke risk,stable high group had a HR of 1.45(95%CI:1.11–1.89).Survival curves indicated that individuals with stable high TyG levels had a significantly increased CVD risk compared to controls.The dynamic TyG change showed a greater risk for CVD than abnormal glucose metabolism,notably for stroke.However,there was no statistical difference in single incidence risk of heart disease between stable low and stable high group.Subgroup analyses underscored demographic disparities,with stable high group consistently showing elevated risks,particularly among<65 years individuals,females,and those with higher education,lower BMI,or higher depression scores.Machine learning models,including random forest,XGBoost,CoxBoost,Deepsurv and GBM,underscored the predictive superiority of dynamic TyG over abnormal glucose metabolism for CVD.Conclusions Dynamic TyG change correlate with CVD risks.Monitoring these changes could predict and manage cardiovascular health in middle-aged and older adults.Targeted interventions based on TyG index trends are crucial for reducing CVD risks in this population.展开更多
The early fatigue damage in the van-body of the semi-trailer is often caused by the unique mechanical characteristics and the dynamic impact of the loads.The traditional finite element method with static strength anal...The early fatigue damage in the van-body of the semi-trailer is often caused by the unique mechanical characteristics and the dynamic impact of the loads.The traditional finite element method with static strength analysis cannot support the fatigue design of van-body;thus,the dynamics analysis should be adopted for the endurance performance.The accurate dynamics model to describe the transient impacts of all kinds of uneven road and the proper system transfer functions to calculate the load transfer effects from tire to van-body are two critical factors for transient dynamics analysis.In order to evaluate the dynamic performance,the dynamics model of the trailer with the air suspension is brought forward.Then the analysis method of the power spectral density （PSD） is set up to study the transient responses of the road dynamic impacts.The transient responses transferred from axles to van-body are calculated,such as dynamic stress,dynamic RMS acceleration,and dynamic load factors.Based on the above dynamic responses,the fatigue life of van-body is predicted with the finite element analysis （FEA） method.Applying the test parameters of the trailer with air suspension,the simulation system with Matlab/Simulink is constructed to describe the dynamic responses of the impacts of the tested PSD of the vehicle axles,and then the fatigue life is predicted with FEA method.The simulated results show that the vibration level of the van-body with air suspension is reduced and the fatigue life is improved.The real vehicle tests on different roads are carried out,and the test results validate the accuracy of the simulation system.The proposed fatigue life prediction method is effective for the virtual design of auto-body.展开更多
基金J.YANG was supported by funding from the National Natural Science Foundation of China(Grant Nos.42475022,42261144671)the National Key R&D Program of China(Project No.2024YFC3013100)+2 种基金the Fundamental Research Funds for the Central UniversitiesM.LU was supported by the Otto Poon Centre of Climate Resilience and Sustainability at HKUST and the Hong Kong Research Grant Committee(Project No.16300424)Data processing and storage were supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).
基金supported in part by the National Natural Science Foundation of China(52175528)in part by the National Key Research and Development Program of China,the Chinese Ministry of Science and Technology(2018YFB1703200).
文摘Milling force is key to the understanding of cutting mechanism and the control of machining process.Traditional milling force models have limited prediction accuracy due to their simplified conditions and incomplete knowledge contained for model construction.On the other hand,due to the lack of guidance from physics,the data-driven models lack interpretability,making them challenging to generalize to practical applications.To meet these difficulties,a deep network model guided by milling dynamics is proposed in this study to predict the instantaneous milling force and spindle vibration under varying cutting conditions.The model uses a milling dynamics model to generate data sets to pre-train the deep network and then integrates the experimental data for fine-tuning to improve the model’s generalization and accuracy.Additionally,the vibration equation is incorporated into the loss function as the physical constraint,enhancing the model’s interpretability.A milling experiment is conducted to validate the effectiveness of the proposed model,and the results indicate that the physics incorporated could improve the network learning capability and interpretability.The predicted results are in good agreement with the measured values,with an average error as low as 2.6705%.The prediction accuracy is increased by 24.4367%compared to the pure data-driven model.
基金supported by the Beijing Natural Science Foundation(Grant No.8252012)the National Natural Science Foundation of China(Grant No.52378475).
文摘Accurate acquisition of the lithological composition of a tunnel face is crucial for efficient tunneling and hazard prevention in large-diameter slurry shield tunnels.While widely applied,current data-driven methods often face challenges such as indirect prediction,data sparsity,and data drift,which limit their accuracy and generalizability.This study develops an integrated method that combines a knowledge-driven method to directly compute distribution patterns of lithological components,which are used as a priori knowledge to guide the development of a data-driven method.Coupled Markov chain(CMC)and deep neural networks(DNNs)serve as the knowledge-driven and data-driven components,respectively.Additionally,a dynamic prediction strategy is proposed,where the model is continuously optimized as construction progresses and training samples accumulate,rather than being statically trained on post-construction data,as is common in data-driven methods.Finally,the proposed method is evaluated using a real-world project.The evaluation results show that the integrated method outperforms both individual data-and knowledge-driven methods,demonstrating higher predictive performance,greater stability,and greater robustness to data scarcity and data drift.Furthermore,the dynamic prediction strategy better captures the effects of gradual data accumulation and lithological spatial variability on prediction performance during construction,providing new insights for real-time prediction in practical tunneling applications.
基金the financial support provided by the Key Laboratory of Urban Underground Engineering of Ministry of Education,Beijing Jiaotong University(Grant Nos.TUL2024-05)。
文摘Water inflow into mountain tunnels exhibits high variability and nonlinear seepage behavior,leading to significant prediction inaccuracies and poor pattern recognition when conventional analytical methods are applied.This study proposes a dynamic water inflow prediction method specifically designed for mountain tunnels.The method is based on groundwater dynamics theory,employing nonDarcian law as the governing equation and deriving analytical solutions applicable to both confined and phreatic aquifer conditions.The method incorporates spatiotemporal variations along the tunnel alignment,enabling both short-term and long-term dynamic predictions of water inflow.The study examines the nonlinear characteristics of the seepage field during tunnel water inrush.The research findings indicate that the predictive results are consistent with the hypothesized two-stage water inflow pattern,with relative errors for key parameters,such as maximum water inflow,normal water inflow,and duration of water inflow,remaining within 10%.The magnitude of water inflow is positively correlated with the permeability coefficient,head height;it is negatively correlated with the axial distance to the tunnel face and the non-Darcian influence coefficient.Both water inflow and water pressure are subject to non-Darcian effects within a defined influence zone extending approximately 1.3 times the tunnel diameter.Comparisons with established predictive methods,numerical simulations,and data from existing tunnel projects confirm the effectiveness of the proposed method.Moreover,the method was successfully applied to a mountain tunnel in the Tibet Plateau region in southwestern China,where it achieved prediction errors within 3%to 8%,demonstrating high reliability.
文摘A mathematical model of principal elements of the aircraft hydraulic system is presented based on the heat transfer theory. The dynamic heat transfer process of the hydraulic oil and the pump shells within an aircraft hydraulic system are analyzed by the difference method. A kind of means for the prediction to variational trends of the aircraft hydraulic system temperature is provided during operation. The numerical prediction and simulation under the operational conditions are presented for ground trial running and the decelerated operation in flight. Computational results show that there is a good coincidence between the experimental data and the numerical predictions.
基金supported by the National Key Research and Development Program of China under Grant 2022YFB3608300in part by the National Nature Science Foundation of China(NSFC)under Grants 62404050,U2341218,62574056,62204052。
文摘Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications.
基金National Natural Science Foundation of China,Grant/Award Numbers:12072363,12372373,51934007,52104234,52174091。
文摘The coal dynamic characteristic stress identification under dynamic load is important for guiding underground mineral mining and predicting underground dynamic disasters.In this article,the dynamic compression test of anthracite under five strain rates is carried out,the evolution law of three kinds of crack characteristic stress is analyzed,and a prediction model of the crack characteristic stress threshold considering the strain rate effect is established.Then,the rationality of crack characteristic stress under dynamic loading is discussed from the damage evolution standpoint,and the crack extension response mechanism during dynamic compression of anthracite is discussed.The result shows that the crack characteristic stress threshold is significantly influenced by the strain rate.The three characteristic stress thresholds are positively correlated with the strain rate,but the ratios to the crest stress gradually decrease.The increase in the strain rate strongly contributes to the crack extension behavior of anthracite.In the crack unstable extension phase,because of the increase of the strain rate,anthracite shows more energy dissipation under the same deformation in association with the stress concentration effect and the dynamic strength enhancement effect.The crack propagation rate is increased,the crack propagation path of the section is more complex,and more severe damage occurs before the dynamic failure of anthracite,which leads to even more severe damage.
文摘Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.
基金supported by the Tianjin Manufacturing High Quality Development Special Foundation(No.20232185)the Roycom Foundation(No.70306901).
文摘Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.
文摘In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.
基金supported by the Postgraduate Research&Practice Innovation Program of Nanjing University of Aeronautics and Astronautics(NUAA)(No.xcxjh20251502)。
文摘Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable dynamic gaps,resulting in conservative and suboptimal trajectories.To address these challenges,this paper proposes a hierarchical reinforcement learning(RL)framework that integrates global path guidance,local trajectory generation,predictive safety evaluation,and neural network-based decision-making.Specifically,the global planner provides long-term navigation guidance,and the local module then utilizes an improved 3D dynamic window approach(DWA)to generate dynamically feasible candidate trajectories.To enhance safety in dense dynamic scenarios,the algorithm introduces a predictive axis-aligned bounding box(AABB)strategy to model the future occupancy of obstacles,combined with convex hull verification for efficient trajectory safety assessment.Furthermore,a double deep Q-network(DDQN)is employed with structured feature encoding,enabling the neural network to reliably select the optimal trajectory from the candidate set,thereby improving robustness and generalization.Comparative experiments conducted in a high-fidelity simulation environment show that the algorithm outperforms existing algorithms,reducing the average number of collisions to 0.2 while shortening the average task completion time by approximately 15%,and achieving a success rate of 97%.
基金supported by the National Natural Science Foundation of China (Grant No. 41130103)the special Fund for Public Welfare Industry (Meteorology) (Grant No. GYHY201306026)+1 种基金the National Natural Science Foundation for Distinguished Young Scientists of China (Grant No. 41325018)the National Basic Research Program of China (Grant No. 2010CB951901)
文摘To study the prediction of the anomalous precipitation and general circulation for the summer（June–July–August） of1998, the Community Climate System Model Version 4.0（CCSM4.0） integrations were used to drive version 3.2 of the Weather Research and Forecasting（WRF3.2） regional climate model to produce hindcasts at 60 km resolution. The results showed that the WRF model produced improved summer precipitation simulations. The systematic errors in the east of the Tibetan Plateau were removed, while in North China and Northeast China the systematic errors still existed. The improvements in summer precipitation interannual increment prediction also had regional characteristics. There was a marked improvement over the south of the Yangtze River basin and South China, but no obvious improvement over North China and Northeast China. Further analysis showed that the improvement was present not only for the seasonal mean precipitation, but also on a sub-seasonal timescale. The two occurrences of the Mei-yu rainfall agreed better with the observations in the WRF model,but were not resolved in CCSM. These improvements resulted from both the higher resolution and better topography of the WRF model.
文摘Based on the hindcast results of summer rainfall anomalies over China for the period 1981-2000 by the Dynamical Climate Prediction System （IAP-DCP） developed by the Institute of Atmospheric Physics, a correction method that can account for the dependence of model＇s systematic biases on SST anomalies is proposed. It is shown that this correction method can improve the hindcast skill of the IAP-DCP for summer rainfall anomalies over China, especially in western China and southeast China, which may imply its potential application to real-time seasonal prediction.
基金supported by Hunan Provincial Natural Science Foundation of China (Grant Nos. 10JJ2040, 11JJ3055)National Major Science and Technology Special Projects of China (Grant No.2012ZX04011-011)+1 种基金Postdoctoral Science Funded Project of China (GrantNo. 20110490261)Hunan Provincial 12th Five-year Plan Key Disciplines of China (Grant No. 2012-42)
文摘Currently, the modeling of cutting process mainly focuses on two aspects: one is the setup of the universal cutting force model that can be adapted to a broader cutting condition; the other is the setup of the exact cutting force model that can accurately reflect a true cutting process. However, there is little research on the prediction of chatter stablity in milling. Based on the generalized mathematical model of inserted cutters introduced by ENGIN, an improved geometrical, mechanical and dynamic model for the vast variety of inserted cutters widely used in engineering applications is presented, in which the average directional cutting force coefficients are obtained by means of a numerical approach, thus leading to an analytical determination of stability lobes diagram (SLD) on the axial depth of cut. A new kind of SLD on the radial depth of cut is also created to satisfy the special requirement of inserted cutter milling. The corresponding algorithms used for predicting cutting forces, vibrations, dimensional surface finish and stability lobes in inserted cutter milling under different cutting conditions are put forward. Thereafter, a dynamic simulation module of inserted cutter milling is implemented by using hybrid program of Matlab with Visual Basic. Verification tests are conducted on a vertical machine center for Aluminum alloy LC4 by using two different types of inserted cutters, and the effectiveness of the model and the algorithm is verified by the good agreement of simulation result with that of cutting tests under different cutting conditions. The proposed model can predict the cutting process accurately under a variety of cutting conditions, and a high efficient and chatter-free milling operation can be achieved by a cutting condition optimization in industry applications.
基金supported by the National Natural Science Foundation of China (No. 11772261)the Aeronautical Science Foundation of China (No. 2016ZA53011)+1 种基金the ATCFD Project (No. 2015-F-016)the 111 Project of China (No. B17037)
文摘This article presents a novel approach for predicting transition locations over airfoils,which are used to activate turbulence model in a Reynolds-averaged Navier-Stokes flow solver.This approach combines Dynamic Mode Decomposition(DMD)with e^Ncriterion.The core idea is to use a spatial DMD analysis to extract the modes of unstable perturbations from a steady flowfield and substitute the local Linear Stability Theory(LST)analysis to quantify the spatial growth of Tollmien–Schlichting(TS)waves.Transition is assumed to take place at the stream-wise location where the most amplified mode’s N-factor reaches a prescribed threshold and a turbulence model is activated thereafter.To improve robustness,the high-order version of DMD technique(known as HODMD)is employed.A theoretical derivation is conducted to interpret how a spatial highorder DMD analysis can extract the growth rate of the unsteady perturbations.The new method is validated by transition predictions of flows over a low-speed Natural-Laminar-Flow(NLF)airfoil NLF0416 at various angles of attack and a transonic NLF airfoil NPU-LSC-72613.The transition locations predicted by our HODMD/e^Nmethod agree well with experimental data and compare favorably to those obtained by some existing methods■.It is shown that the proposed method is able to predict transition locations for flows over different types of airfoils and offers the potential for application to 3D wings as well as more complex configurations.
基金financially supported by the National Natural Science Foundation of China (Grant Nos. 71273021 and 7167030506)
文摘Based on the study of the relationship between structure and feedback of China’s natural gas demand system, this paper establishes a system dynamics model. In order to simulate the total demand and consumption structure of natural gas in China, we set up seven scenarios by changing some of the parameters of the model. The results showed that the total demand of natural gas would increase steadily year by year and reach in the range from 3600 to 4500 billion cubic meters in 2035. Furthermore, in terms of consumption structure, urban gas consumption would still be the largest term, followed by the gas consumption as industrial fuel, gas power generation and natural gas chemical industry. In addition, compared with the population growth, economic development still plays a dominant role in the natural gas demand growth, the impact of urbanization on urban gas consumption is significant, and the promotion of natural gas utilization technology can effectively reduce the total consumption of natural gas.
文摘Based on the theory of formation dynamics of oil/gas pools, the Dongying sag can be divided into three dynamic systems regarding the accumulation of oil and gas： the superpressure closed system, the semi-closed system and the normal pressure open system. Based on the analysis of genesis of superpressure in the superpressure closed system and the rule of hydrocarbon expulsion, it is found that hydrocarbon generation is related to superpressure, which is the main driving factor of hydrocarbon migration. Micro fractures formed by superpressure are the main channels for hydrocarbon migration. There are three dynamic patterns for hydrocarbon expulsion： free water drainage, hydrocarbon accumulation and drainage through micro fissures. In the superpressure closed system, the oil-driving-water process and oil/gas accumulation were completed in lithologic traps by way of such two dynamic patterns as episodic evolution of superpressure systems and episodic pressure release of faults. The oil-bearing capacity of lithologic traps is intimately related to reservoir-forming dynamic force. Quantitative evaluation of dynamic conditions for pool formation can effectively predict the oil-bearing capability of traps.
基金Supported by the Shandong Natural Science Foundation(ZR2013BL008)
文摘This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.
基金the National Natural Science Foundation of China(grant numbers 82070434,LYQ)。
文摘Background Cardiovascular disease(CVD)remains a major health challenge globally,particularly in aging populations.Using data from the China Health and Retirement Longitudinal Study(CHARLS),this study examines the Triglyceride-glucose(TyG)index dynamics,a marker for insulin resistance,and its relationship with CVD in Chinese adults aged 45 and older.Methods This reanalysis utilized five waves of CHARLS data with multistage sampling.From 17,705 participants,5,625 with TyG index and subsequent CVD data were included,excluding those lacking 2011 and 2015 TyG data.TyG derived from glucose and triglyceride levels,CVD outcomes via self-reports and records.Participants divided into four groups based on TyG changes(2011–2015):low-low,low-high,high-low,high-high TyG groups.Results Adjusting for covariates,stable high group showed a significantly higher risk of incident CVD compared to stable low group,with an HR of 1.18(95%CI:1.03–1.36).Similarly,for stroke risk,stable high group had a HR of 1.45(95%CI:1.11–1.89).Survival curves indicated that individuals with stable high TyG levels had a significantly increased CVD risk compared to controls.The dynamic TyG change showed a greater risk for CVD than abnormal glucose metabolism,notably for stroke.However,there was no statistical difference in single incidence risk of heart disease between stable low and stable high group.Subgroup analyses underscored demographic disparities,with stable high group consistently showing elevated risks,particularly among<65 years individuals,females,and those with higher education,lower BMI,or higher depression scores.Machine learning models,including random forest,XGBoost,CoxBoost,Deepsurv and GBM,underscored the predictive superiority of dynamic TyG over abnormal glucose metabolism for CVD.Conclusions Dynamic TyG change correlate with CVD risks.Monitoring these changes could predict and manage cardiovascular health in middle-aged and older adults.Targeted interventions based on TyG index trends are crucial for reducing CVD risks in this population.
基金supported by National Natural Science Foundation of China (Grant No. 50905092)
文摘The early fatigue damage in the van-body of the semi-trailer is often caused by the unique mechanical characteristics and the dynamic impact of the loads.The traditional finite element method with static strength analysis cannot support the fatigue design of van-body;thus,the dynamics analysis should be adopted for the endurance performance.The accurate dynamics model to describe the transient impacts of all kinds of uneven road and the proper system transfer functions to calculate the load transfer effects from tire to van-body are two critical factors for transient dynamics analysis.In order to evaluate the dynamic performance,the dynamics model of the trailer with the air suspension is brought forward.Then the analysis method of the power spectral density （PSD） is set up to study the transient responses of the road dynamic impacts.The transient responses transferred from axles to van-body are calculated,such as dynamic stress,dynamic RMS acceleration,and dynamic load factors.Based on the above dynamic responses,the fatigue life of van-body is predicted with the finite element analysis （FEA） method.Applying the test parameters of the trailer with air suspension,the simulation system with Matlab/Simulink is constructed to describe the dynamic responses of the impacts of the tested PSD of the vehicle axles,and then the fatigue life is predicted with FEA method.The simulated results show that the vibration level of the van-body with air suspension is reduced and the fatigue life is improved.The real vehicle tests on different roads are carried out,and the test results validate the accuracy of the simulation system.The proposed fatigue life prediction method is effective for the virtual design of auto-body.
