In the mid-to-late stages of gas reservoir development,liquid loading in gas wells becomes a common challenge.Plunger lift,as an intermittent production technique,is widely used for deliquification in gas wells.With t...In the mid-to-late stages of gas reservoir development,liquid loading in gas wells becomes a common challenge.Plunger lift,as an intermittent production technique,is widely used for deliquification in gas wells.With the advancement of big data and artificial intelligence,the future of oil and gas field development is trending towards intelligent,unmanned,and automated operations.Currently,the optimization of plunger lift working systems is primarily based on expert experience and manual control,focusing mainly on the success of the plunger lift without adequately considering the impact of different working systems on gas production.Additionally,liquid loading in gas wells is a dynamic process,and the intermittent nature of plunger lift requires accurate modeling;using constant inflow dynamics to describe reservoir flow introduces significant errors.To address these challenges,this study establishes a coupled wellbore-reservoir model for plunger lift wells and validates the computational wellhead pressure results against field measurements.Building on this model,a novel optimization control algorithm based on the deep deterministic policy gradient(DDPG)framework is proposed.The algorithm aims to optimize plunger lift working systems to balance overall reservoir pressure,stabilize gas-water ratios,and maximize gas production.Through simulation experiments in three different production optimization scenarios,the effectiveness of reinforcement learning algorithms(including RL,PPO,DQN,and the proposed DDPG)and traditional optimization algorithms(including GA,PSO,and Bayesian optimization)in enhancing production efficiency is compared.The results demonstrate that the coupled model provides highly accurate calculations and can precisely describe the transient production of wellbore and gas reservoir systems.The proposed DDPG algorithm achieves the highest reward value during training with minimal error,leading to a potential increase in cumulative gas production by up to 5%and cumulative liquid production by 252%.The DDPG algorithm exhibits robustness across different optimization scenarios,showcasing excellent adaptability and generalization capabilities.展开更多
This study proposes an automatic control system for Autonomous Underwater Vehicle(AUV)docking,utilizing a digital twin(DT)environment based on the HoloOcean platform,which integrates six-degree-of-freedom(6-DOF)motion...This study proposes an automatic control system for Autonomous Underwater Vehicle(AUV)docking,utilizing a digital twin(DT)environment based on the HoloOcean platform,which integrates six-degree-of-freedom(6-DOF)motion equations and hydrodynamic coefficients to create a realistic simulation.Although conventional model-based and visual servoing approaches often struggle in dynamic underwater environments due to limited adaptability and extensive parameter tuning requirements,deep reinforcement learning(DRL)offers a promising alternative.In the positioning stage,the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm is employed for synchronized depth and heading control,which offers stable training,reduced overestimation bias,and superior handling of continuous control compared to other DRL methods.During the searching stage,zig-zag heading motion combined with a state-of-the-art object detection algorithm facilitates docking station localization.For the docking stage,this study proposes an innovative Image-based DDPG(I-DDPG),enhanced and trained in a Unity-MATLAB simulation environment,to achieve visual target tracking.Furthermore,integrating a DT environment enables efficient and safe policy training,reduces dependence on costly real-world tests,and improves sim-to-real transfer performance.Both simulation and real-world experiments were conducted,demonstrating the effectiveness of the system in improving AUV control strategies and supporting the transition from simulation to real-world operations in underwater environments.The results highlight the scalability and robustness of the proposed system,as evidenced by the TD3 controller achieving 25%less oscillation than the adaptive fuzzy controller when reaching the target depth,thereby demonstrating superior stability,accuracy,and potential for broader and more complex autonomous underwater tasks.展开更多
The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address th...The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address this critical challenge,this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient(ZSG-MAD3PG).The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient(MAD3PG)algorithm and incorporates defensive deception(DD)strategies to achieve adaptive and efficient protection.While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,the proposed ZSG-MAD3PG framework mitigates these limitations through multi-stage game modeling and adaptive learning,enabling more efficient resource utilization and faster response times.The Stackelberg-based architecture allows defenders to dynamically optimize packet sampling strategies,while attackers adjust their tactics to reach rapid equilibrium.Furthermore,dynamic deception techniques reduce the time required for the concealment of attacks and the overall system burden.A lightweight behavioral fingerprinting detection mechanism further enhances real-time zero-day attack identification within industrial device clusters.ZSG-MAD3PG demonstrates higher true positive rates(TPR)and lower false alarm rates(FAR)compared to existing methods,while also achieving improved latency,resource efficiency,and stealth adaptability in IIoT zero-day defense scenarios.展开更多
GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieve...GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieved by several parameters,such as polynomial terms,periodic terms,offsets,and post-seismic models.The latter contains some stochastic noises,which can be affected by detecting the former parameters.If there are not enough parameters assumed,modeling errors will occur and adversely affect the analysis results.In this study,we propose a processing strategy in which the commonly-used 1-order of the polynomial term can be replaced with different orders for better fitting GNSS time series of the Crustal Movement Network of China(CMONOC)stations.Initially,we use the Bayesian Information Criterion(BIC)to identify the best order within the range of 1-4 during the fitting process using the white noise plus power-law noise(WN+PL)model.Then,we compare the 1-order and the optimal order on the effect of deterministic models in GNSS time series,including the velocity and its uncertainty,amplitudes,and initial phases of the annual signals.The results indicate that the first-order polynomial in the GNSS time series is not the primary factor.The root mean square(RMS)reduction rates of almost all station components are positive,which means the new fitting of optimal-order polynomial helps to reduce the RMS of residual series.Most stations maintain the velocity difference(VD)within ±1 mm/yr,with percentages of 85.6%,81.9%and 63.4%in the North,East,and Up components,respectively.As for annual signals,the numbers of amplitude difference(AD)remained at ±0.2 mm are 242,239,and 200 in three components,accounting for 99.6%,98.4%,and 82.3%,respectively.This finding reminds us that the detection of the optimal-order polynomial is necessary when we aim to acquire an accurate understanding of the crustal movement features.展开更多
The rise in construction activities within mountainous regions has significantly increased the frequency of rockfalls.Statistical models for rockfall hazard assessment often struggle to achieve high precision on a lar...The rise in construction activities within mountainous regions has significantly increased the frequency of rockfalls.Statistical models for rockfall hazard assessment often struggle to achieve high precision on a large scale.This limitation arises primarily from the scarcity of historical rockfall data and the inadequacy of conventional assessment indicators in capturing the physical and structural characteristics of rockfalls.This study proposes a physically based deterministic model designed to accurately quantify rockfall hazards at a large scale.The model accounts for multiple rockfall failure modes and incorporates the key physical and structural parameters of the rock mass.Rockfall hazard is defined as the product of three factors:the rockfall failure probability,the probability of reaching a specific position,and the corresponding impact intensity.The failure probability includes probabilities of formation and instability of rock blocks under different failure modes,modeled based on the combination patterns of slope surfaces and rock discontinuities.The Monte Carlo method is employed to account for the randomness of mechanical and geometric parameters when quantifying instability probabilities.Additionally,the rock trajectories and impact energies simulated using Flow-R software are combined with rockfall failure probability to enable regional rockfall hazard zoning.A case study was conducted in Tiefeng,Chongqing,China,considering four types of rockfall failure modes.Hazard zoning results identified the steep and elevated terrains of the northern and southern anaclinal slopes as areas of highest rockfall hazard.These findings align with observed conditions,providing detailed hazard zoning and validating the effectiveness and potential of the proposed model.展开更多
For the linear deterministic system with unknown orders and coefficients adaptive controlsare given so that the closed-loop system is stabilized and the unknown parameters are consistentlyestimated. Moreover, if the p...For the linear deterministic system with unknown orders and coefficients adaptive controlsare given so that the closed-loop system is stabilized and the unknown parameters are consistentlyestimated. Moreover, if the parameter estimation is ignored, then the system input and outputcan be reduced to zero with an exponential rate.展开更多
Recently, an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems. In this paper, a fault detection scheme is proposed for a clas...Recently, an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems. In this paper, a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning. By using a discrete-time extension of deterministic learning algorithm, the general fault functions （i.e., the internal dynamics） underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function （RBF） networks. Then, a bank of estimators with the obtained knowledge of system dynamics embedded is constructed, and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems. A fault detection decision scheme is presented according to the smallest residual principle, i.e., the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals. The fault detectability analysis is carried out and the upper bound of detection time is derived. A simulation example is given to illustrate the effectiveness of the proposed scheme.展开更多
In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity pen...In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model. The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity. Under a few weak assumptions, it is proved that though the derived state estimator is biased, the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded. Numerical simulations show that the obtained robust filter has relatively nice estimation performances.展开更多
Background:Combined knee valgus and tibial internal rotation(VL+IR)moments have been shown to stress the anterior cruciate ligament(ACL)in several in vitro cadaveric studies.To utilize this knowledge for non-contact A...Background:Combined knee valgus and tibial internal rotation(VL+IR)moments have been shown to stress the anterior cruciate ligament(ACL)in several in vitro cadaveric studies.To utilize this knowledge for non-contact ACL injury prevention in sports,it is necessary to elucidate how the ground reaction force(GRF)acting point(center of pressure(CoP))in the stance foot produces combined knee VL+IR moments in risky maneuvers,such as cuttings.However,the effects of the GRF acting point on the development of the combined knee VL+IR moment in cutting are still unknown.Methods:We first established the deterministic mechanical condition that the CoP position relative to the tibial rotational axis differentiates the GRF vector’s directional probability for developing the combined knee VL+IR moment,and theoretically predicted that when the CoP is posterior to the tibial rotational axis,the GRF vector is more likely to produce the combined knee VL+IR moment than when the CoP is anterior to the tibial rotational axis.Then,we tested a stochastic aspect of our theory in a lab-controlled in vivo experiment.Fourteen females performed 60˚cutting under forefoot/rearfoot strike conditions(10 trials each).The positions of lower limb markers and GRF data were measured,and the knee moment due to GRF vector was calculated.The trials were divided into anterior-and posterior-CoP groups depending on the CoP position relative to the tibial rotational axis at each 10 ms interval from 0 to 100 ms after foot strike,and the occurrence rate of the combined knee VL+IR moment was compared between trial groups.Results:The posterior-CoP group showed significantly higher occurrence rates of the combined knee VL+IR moment(maximum of 82.8%)at every time point than those of the anterior-CoP trials,as theoretically predicted by the deterministic mechanical condition.Conclusion:The rearfoot strikes inducing the posterior CoP should be avoided to reduce the risk of non-contact ACL injury associated with the combined knee VL+IR stress.展开更多
Emerging long-range industrial IoT applications(e.g.,remote patient monitoring)have increasingly higher requirements for global deterministic delay.Although many existing methods have built deterministic networks in s...Emerging long-range industrial IoT applications(e.g.,remote patient monitoring)have increasingly higher requirements for global deterministic delay.Although many existing methods have built deterministic networks in small-scale networks through centralized computing and resource reservation,they cannot be applied on a global scale.The emerging mega-constellations enable new opportunities for realizing deterministic delay globally.As one constellation(e.g.,Starlink)might be managed by a single operator(e.g.,SpaceX),packets can be routed within deterministic number of hops.Moreover,the path diversity brought by the highly symmetrical network structure in mega-constellations can help to construct a congestion free network by routing.This paper leverages these unique characteristics of mega-constellations to avoid the traditional network congestion caused by multiple inputs and single output,and to determine the routing hops,and thus realizing a global deterministic network(DETSPACE).The model based on the 2D Markov chain theoretically verifies the correctness of DETSPACE.The effectiveness of DETSPACE in different traffic load con-ditions is also verified by extensive simulations.展开更多
Optimal deterministic disturbances rejection control problem for singularly perturbed linear systems is considered. By using the slow-fast decomposition theory of singular perturbation, the existent and unique conditi...Optimal deterministic disturbances rejection control problem for singularly perturbed linear systems is considered. By using the slow-fast decomposition theory of singular perturbation, the existent and unique conditions of the feedforward and feedback composite control （FFCC） laws for both infinite-time and finite-time are proposed, and the design approaches are given. A disturbance observer is introduced to make the FFCC laws realizable physically. Simulation results indicate that the FFCC laws are robust with respect to external disturbances.展开更多
This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances.To deal with the unknown deterministic disturbances,two strategies are implemented to construct the ...This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances.To deal with the unknown deterministic disturbances,two strategies are implemented to construct the row space that can be used to approximately represent the unknown deterministic disturbances using the trigonometric functions or Bernstein polynomials depending on whether the disturbance frequencies are known.For closed-loop identification,CCF-N4SID is extended to the case with unknown deterministic disturbances using the oblique projection.In addition,a proper Bernstein polynomial order can be determined using the Akaike information criterion(AIC)or the Bayesian information criterion(BIC).Numerical simulation results demonstrate the effectiveness of the proposed identification method for both periodic and aperiodic deterministic disturbances.展开更多
Autonomous driving has witnessed rapid advancement;however,ensuring safe and efficient driving in intricate scenarios remains a critical challenge.In particular,traffic roundabouts bring a set of challenges to autonom...Autonomous driving has witnessed rapid advancement;however,ensuring safe and efficient driving in intricate scenarios remains a critical challenge.In particular,traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles,susceptibility to traffic flow bottlenecks,and imperfect data in perceiving environmental information,rendering them a vital issue in the practical application of autonomous driving.To address the traffic challenges,this work focused on complex roundabouts with multi-lane and proposed a Perception EnhancedDeepDeterministic Policy Gradient(PE-DDPG)for AutonomousDriving in the Roundabouts.Specifically,themodel incorporates an enhanced variational autoencoder featuring an integrated spatial attention mechanism alongside the Deep Deterministic Policy Gradient framework,enhancing the vehicle’s capability to comprehend complex roundabout environments and make decisions.Furthermore,the PE-DDPG model combines a dynamic path optimization strategy for roundabout scenarios,effectively mitigating traffic bottlenecks and augmenting throughput efficiency.Extensive experiments were conducted with the collaborative simulation platform of CARLA and SUMO,and the experimental results show that the proposed PE-DDPG outperforms the baseline methods in terms of the convergence capacity of the training process,the smoothness of driving and the traffic efficiency with diverse traffic flow patterns and penetration rates of autonomous vehicles(AVs).Generally,the proposed PE-DDPGmodel could be employed for autonomous driving in complex scenarios with imperfect data.展开更多
The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural design.However,their complex and nonlinear dynamic behavior presents a significant challenge for cont...The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural design.However,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in flight.Various strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control systems.This paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental constraints.To address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini drone.The results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.展开更多
Naturally fractured rocks contain most of the world's petroleum reserves.This significant amount of oil can be recovered efficiently by gas assisted gravity drainage(GAGD).Although,GAGD is known as one of the most...Naturally fractured rocks contain most of the world's petroleum reserves.This significant amount of oil can be recovered efficiently by gas assisted gravity drainage(GAGD).Although,GAGD is known as one of the most effective recovery methods in reservoir engineering,the lack of available simulation and mathematical models is considerable in these kinds of reservoirs.The main goal of this study is to provide efficient and accurate methods for predicting the GAGD recovery factor using data driven techniques.The proposed models are developed to relate GAGD recovery factor to the various parameters including model height,matrix porosity and permeability,fracture porosity and permeability,dip angle,viscosity and density of wet and non-wet phases,injection rate,and production time.In this investigation,by considering the effective parameters on GAGD recovery factor,three different efficient,smart,and fast models including artificial neural network(ANN),least square support vector machine(LSSVM),and multi-gene genetic programming(MGGP)are developed and compared in both fractured and homogenous porous media.Buckinghamπtheorem is also used to generate dimensionless numbers to reduce the number of input and output parameters.The efficiency of the proposed models is examined through statistical analysis of R-squared,RMSE,MSE,ARE,and AARE.Moreover,the performance of the generated MGGP correlation is compared to the traditional models.Results demonstrate that the ANN model predicts the GAGD recovery factor more accurately than the LSSVM and MGGP models.The maximum R^(2)of 0.9677 and minimum RMSE of 0.0520 values are obtained by the ANN model.Although the MGGP model has the lowest performance among the other used models(the R2 of 0.896 and the RMSE of 0.0846),the proposed MGGP correlation can predict the GAGD recovery factor in fractured and homogenous reservoirs with high accuracy and reliability compared to the traditional models.Results reveal that the employed models can easily predict GAGD recovery factor without requiring complicate governing equations or running complex and time-consuming simulation models.The approach of this research work improves our understanding about the most significant parameters on GAGD recovery and helps to optimize the stages of the process,and make appropriate economic decisions.展开更多
In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be describ...In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive estimation methods are used to solve the parametric estimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the parametric estimation of a stochastic interconnected nonlinear hydraulic system is treated.展开更多
Estimate of the Deterministic Neutron RBE for Radiation-induced Pseudo-Pelger Huët Cell Formation R.E.Goans1,2,C.J.Iddins1,R.E.Goans,Jr.3(1.Radiation Emergency Assistance Center/Training Site,Oak Ridge,TN;2.MJW C...Estimate of the Deterministic Neutron RBE for Radiation-induced Pseudo-Pelger Huët Cell Formation R.E.Goans1,2,C.J.Iddins1,R.E.Goans,Jr.3(1.Radiation Emergency Assistance Center/Training Site,Oak Ridge,TN;2.MJW Corporation,Amherst,NY;3.LMU Debusk School of Medicine,Harrogate,TN)Abstract:Using archival peripheral blood slides from radiation accident patients,we have recently described the pseudo-Pelger Huët anomaly(PPHA)in neutrophils as a new radiation-induced biomarker,useful for dosimetry not only immediately after a radiation incident but also potentially helpful as a tool in retrospective dosimetry.展开更多
Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary a...Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.展开更多
Effective vegetation reconstruction plays a vital role in the restoration of desert ecosystems.However,in reconstruction of different vegetation types,the community characteristics,assembly processes,and functions of ...Effective vegetation reconstruction plays a vital role in the restoration of desert ecosystems.However,in reconstruction of different vegetation types,the community characteristics,assembly processes,and functions of different soil microbial taxa under environmental changes are still disputed,which limits the understanding of the sustainability of desert restoration.Hence,we investigated the soil microbial community characteristics and functional attributes of grassland desert(GD),desert steppe(DS),typical steppe(TS),and artificial forest(AF)in the Mu Us Desert,China.Our findings confirmed the geographical conservation of soil microbial composition but highlighted decreased microbial diversity in TS.Meanwhile,the abundance of rare taxa and microbial community stability in TS improved.Heterogeneous and homogeneous selection determined the assembly of rare and abundant bacterial taxa,respectively,with both being significantly influenced by soil moisture.In contrast,fungal communities displayed stochastic processes and exhibited sensitivity to soil nutrient conditions.Furthermore,our investigation revealed a noteworthy augmentation in bacterial metabolic functionality in TS,aligning with improved vegetation restoration and the assemblage of abundant bacterial taxa.However,within nutrient-limited soils(GD,DS,and AF),the assembly dynamics of rare fungal taxa assumed a prominent role in augmenting their metabolic capacity and adaptability to desert ecosystems.These results highlighted the variations in the assembly processes and metabolic functions of soil microorganisms during vegetation reestablishment and provided corresponding theoretical support for anthropogenic revegetation of desert ecosystems.展开更多
Background:Climate change is accelerating alterations in forest species and community composition worldwide,especially following extreme events like severe droughts and windstorms.Understanding these effects on subtro...Background:Climate change is accelerating alterations in forest species and community composition worldwide,especially following extreme events like severe droughts and windstorms.Understanding these effects on subtropical forests is crucial for conservation and forest management,but it remains unclear whether the impacts are stochastic or deterministic.Methods:We analyzed a unique dataset from a 1-ha permanent plot in a subtropical monsoon broadleaf evergreen forest in China,monitored over 26 years with six surveys from 1994 to 2020.The forest has been free from anthropogenic disturbances for over 400 years.In each survey,we measured all trees with a diameter at breast height(DBH)≥1 cm,and recorded 11 plant functional traits relating to photosynthesis,wood properties,water use,and nutrient dynamics.Using this data,we calculated species and trait dispersion,assessing short-term(~5years)and long-term(26 years)trends in species and trait composition following severe droughts and windstorm events.Results:Severe droughts,and subsequent droughts,increased both species and trait dispersion,while species composition converged,and trait dispersion remained relatively stable throughout the recovery period.Windstorm events led to increased species dispersion but decreased trait dispersion.We observed a clear directional shift in both species and trait composition under these climatic stressors,with a more pronounced increase in trait dispersion compared to species dispersion.Conclusion:In the short term(~5 years),severe droughts and windstorms increased species composition divergence,while trait composition responses varied.Over 26 years,deterministic processes mainly drove community composition changes,especially for trait composition,although stochastic processes also played a role.These findings suggest enhancing forest resilience to climatic stressors by protecting adaptive species or increasing species diversity in management practices.展开更多
基金support from Science Foundation of China University of Petroleum,Beijing(No.2462023YJRC019)National Natural Science Foundation of China(No.52204059)Key Core Technology Research Project Foundation of PetroChina Group(No.2023ZG18).
文摘In the mid-to-late stages of gas reservoir development,liquid loading in gas wells becomes a common challenge.Plunger lift,as an intermittent production technique,is widely used for deliquification in gas wells.With the advancement of big data and artificial intelligence,the future of oil and gas field development is trending towards intelligent,unmanned,and automated operations.Currently,the optimization of plunger lift working systems is primarily based on expert experience and manual control,focusing mainly on the success of the plunger lift without adequately considering the impact of different working systems on gas production.Additionally,liquid loading in gas wells is a dynamic process,and the intermittent nature of plunger lift requires accurate modeling;using constant inflow dynamics to describe reservoir flow introduces significant errors.To address these challenges,this study establishes a coupled wellbore-reservoir model for plunger lift wells and validates the computational wellhead pressure results against field measurements.Building on this model,a novel optimization control algorithm based on the deep deterministic policy gradient(DDPG)framework is proposed.The algorithm aims to optimize plunger lift working systems to balance overall reservoir pressure,stabilize gas-water ratios,and maximize gas production.Through simulation experiments in three different production optimization scenarios,the effectiveness of reinforcement learning algorithms(including RL,PPO,DQN,and the proposed DDPG)and traditional optimization algorithms(including GA,PSO,and Bayesian optimization)in enhancing production efficiency is compared.The results demonstrate that the coupled model provides highly accurate calculations and can precisely describe the transient production of wellbore and gas reservoir systems.The proposed DDPG algorithm achieves the highest reward value during training with minimal error,leading to a potential increase in cumulative gas production by up to 5%and cumulative liquid production by 252%.The DDPG algorithm exhibits robustness across different optimization scenarios,showcasing excellent adaptability and generalization capabilities.
基金supported by the National Science and Technology Council,Taiwan[Grant NSTC 111-2628-E-006-005-MY3]supported by the Ocean Affairs Council,Taiwansponsored in part by Higher Education Sprout Project,Ministry of Education to the Headquarters of University Advancement at National Cheng Kung University(NCKU).
文摘This study proposes an automatic control system for Autonomous Underwater Vehicle(AUV)docking,utilizing a digital twin(DT)environment based on the HoloOcean platform,which integrates six-degree-of-freedom(6-DOF)motion equations and hydrodynamic coefficients to create a realistic simulation.Although conventional model-based and visual servoing approaches often struggle in dynamic underwater environments due to limited adaptability and extensive parameter tuning requirements,deep reinforcement learning(DRL)offers a promising alternative.In the positioning stage,the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm is employed for synchronized depth and heading control,which offers stable training,reduced overestimation bias,and superior handling of continuous control compared to other DRL methods.During the searching stage,zig-zag heading motion combined with a state-of-the-art object detection algorithm facilitates docking station localization.For the docking stage,this study proposes an innovative Image-based DDPG(I-DDPG),enhanced and trained in a Unity-MATLAB simulation environment,to achieve visual target tracking.Furthermore,integrating a DT environment enables efficient and safe policy training,reduces dependence on costly real-world tests,and improves sim-to-real transfer performance.Both simulation and real-world experiments were conducted,demonstrating the effectiveness of the system in improving AUV control strategies and supporting the transition from simulation to real-world operations in underwater environments.The results highlight the scalability and robustness of the proposed system,as evidenced by the TD3 controller achieving 25%less oscillation than the adaptive fuzzy controller when reaching the target depth,thereby demonstrating superior stability,accuracy,and potential for broader and more complex autonomous underwater tasks.
基金funded in part by the Humanities and Social Sciences Planning Foundation of Ministry of Education of China under Grant No.24YJAZH123National Undergraduate Innovation and Entrepreneurship Training Program of China under Grant No.202510347069the Huzhou Science and Technology Planning Foundation under Grant No.2023GZ04.
文摘The Industrial Internet of Things(IIoT)is increasingly vulnerable to sophisticated cyber threats,particularly zero-day attacks that exploit unknown vulnerabilities and evade traditional security measures.To address this critical challenge,this paper proposes a dynamic defense framework named Zero-day-aware Stackelberg Game-based Multi-Agent Distributed Deep Deterministic Policy Gradient(ZSG-MAD3PG).The framework integrates Stackelberg game modeling with the Multi-Agent Distributed Deep Deterministic Policy Gradient(MAD3PG)algorithm and incorporates defensive deception(DD)strategies to achieve adaptive and efficient protection.While conventional methods typically incur considerable resource overhead and exhibit higher latency due to static or rigid defensive mechanisms,the proposed ZSG-MAD3PG framework mitigates these limitations through multi-stage game modeling and adaptive learning,enabling more efficient resource utilization and faster response times.The Stackelberg-based architecture allows defenders to dynamically optimize packet sampling strategies,while attackers adjust their tactics to reach rapid equilibrium.Furthermore,dynamic deception techniques reduce the time required for the concealment of attacks and the overall system burden.A lightweight behavioral fingerprinting detection mechanism further enhances real-time zero-day attack identification within industrial device clusters.ZSG-MAD3PG demonstrates higher true positive rates(TPR)and lower false alarm rates(FAR)compared to existing methods,while also achieving improved latency,resource efficiency,and stealth adaptability in IIoT zero-day defense scenarios.
基金supported by the National Natural Science Foundation of China(Grant Nos.42404017,42122025 and 42174030).
文摘GNSS time series analysis provides an effective method for research on the earth's surface deformation,and it can be divided into two parts,deterministic models and stochastic models.The former part can be achieved by several parameters,such as polynomial terms,periodic terms,offsets,and post-seismic models.The latter contains some stochastic noises,which can be affected by detecting the former parameters.If there are not enough parameters assumed,modeling errors will occur and adversely affect the analysis results.In this study,we propose a processing strategy in which the commonly-used 1-order of the polynomial term can be replaced with different orders for better fitting GNSS time series of the Crustal Movement Network of China(CMONOC)stations.Initially,we use the Bayesian Information Criterion(BIC)to identify the best order within the range of 1-4 during the fitting process using the white noise plus power-law noise(WN+PL)model.Then,we compare the 1-order and the optimal order on the effect of deterministic models in GNSS time series,including the velocity and its uncertainty,amplitudes,and initial phases of the annual signals.The results indicate that the first-order polynomial in the GNSS time series is not the primary factor.The root mean square(RMS)reduction rates of almost all station components are positive,which means the new fitting of optimal-order polynomial helps to reduce the RMS of residual series.Most stations maintain the velocity difference(VD)within ±1 mm/yr,with percentages of 85.6%,81.9%and 63.4%in the North,East,and Up components,respectively.As for annual signals,the numbers of amplitude difference(AD)remained at ±0.2 mm are 242,239,and 200 in three components,accounting for 99.6%,98.4%,and 82.3%,respectively.This finding reminds us that the detection of the optimal-order polynomial is necessary when we aim to acquire an accurate understanding of the crustal movement features.
基金supported by the National Natural Science Foundation of China(Grant Nos.42172318 and 42377186)the National Key R&D Program of China(Grant No.2023YFC3007201).
文摘The rise in construction activities within mountainous regions has significantly increased the frequency of rockfalls.Statistical models for rockfall hazard assessment often struggle to achieve high precision on a large scale.This limitation arises primarily from the scarcity of historical rockfall data and the inadequacy of conventional assessment indicators in capturing the physical and structural characteristics of rockfalls.This study proposes a physically based deterministic model designed to accurately quantify rockfall hazards at a large scale.The model accounts for multiple rockfall failure modes and incorporates the key physical and structural parameters of the rock mass.Rockfall hazard is defined as the product of three factors:the rockfall failure probability,the probability of reaching a specific position,and the corresponding impact intensity.The failure probability includes probabilities of formation and instability of rock blocks under different failure modes,modeled based on the combination patterns of slope surfaces and rock discontinuities.The Monte Carlo method is employed to account for the randomness of mechanical and geometric parameters when quantifying instability probabilities.Additionally,the rock trajectories and impact energies simulated using Flow-R software are combined with rockfall failure probability to enable regional rockfall hazard zoning.A case study was conducted in Tiefeng,Chongqing,China,considering four types of rockfall failure modes.Hazard zoning results identified the steep and elevated terrains of the northern and southern anaclinal slopes as areas of highest rockfall hazard.These findings align with observed conditions,providing detailed hazard zoning and validating the effectiveness and potential of the proposed model.
基金This project is supported by the National Natural Science Foundation of China
文摘For the linear deterministic system with unknown orders and coefficients adaptive controlsare given so that the closed-loop system is stabilized and the unknown parameters are consistentlyestimated. Moreover, if the parameter estimation is ignored, then the system input and outputcan be reduced to zero with an exponential rate.
基金This work was supported by the National Science Fund for Distinguished Young Scholars （No. 61225014）, the National Major Scientific Instruments Development Project （No. 61527811）, the National Natural Science Foundation of China （Nos. 61304084, 61374119）, the Guangdong Natural Science Foundation （No. 2014A030312005）, and the Space Intelligent Control Key Laboratory of Science and Technology for National Defense.
文摘Recently, an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems. In this paper, a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning. By using a discrete-time extension of deterministic learning algorithm, the general fault functions （i.e., the internal dynamics） underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function （RBF） networks. Then, a bank of estimators with the obtained knowledge of system dynamics embedded is constructed, and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems. A fault detection decision scheme is presented according to the smallest residual principle, i.e., the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals. The fault detectability analysis is carried out and the upper bound of detection time is derived. A simulation example is given to illustrate the effectiveness of the proposed scheme.
基金supported in part by the 973 Program(Nos.2009CB320602,2012CB316504)in part by the National Natural Science Foundation of China(Nos.61174122,61021063,60721003,60625305)in part by the Specialized Research Fund for the Doctoral Program of Higher Education,China(No.20110002110045)
文摘In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model. The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity. Under a few weak assumptions, it is proved that though the derived state estimator is biased, the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded. Numerical simulations show that the obtained robust filter has relatively nice estimation performances.
基金supported by the Grant-in-Aid for Young Scientists(B)Project(Grant No.24700716)funded by the Ministry of Education,Culture,Sports,Science and Technology,Japan.
文摘Background:Combined knee valgus and tibial internal rotation(VL+IR)moments have been shown to stress the anterior cruciate ligament(ACL)in several in vitro cadaveric studies.To utilize this knowledge for non-contact ACL injury prevention in sports,it is necessary to elucidate how the ground reaction force(GRF)acting point(center of pressure(CoP))in the stance foot produces combined knee VL+IR moments in risky maneuvers,such as cuttings.However,the effects of the GRF acting point on the development of the combined knee VL+IR moment in cutting are still unknown.Methods:We first established the deterministic mechanical condition that the CoP position relative to the tibial rotational axis differentiates the GRF vector’s directional probability for developing the combined knee VL+IR moment,and theoretically predicted that when the CoP is posterior to the tibial rotational axis,the GRF vector is more likely to produce the combined knee VL+IR moment than when the CoP is anterior to the tibial rotational axis.Then,we tested a stochastic aspect of our theory in a lab-controlled in vivo experiment.Fourteen females performed 60˚cutting under forefoot/rearfoot strike conditions(10 trials each).The positions of lower limb markers and GRF data were measured,and the knee moment due to GRF vector was calculated.The trials were divided into anterior-and posterior-CoP groups depending on the CoP position relative to the tibial rotational axis at each 10 ms interval from 0 to 100 ms after foot strike,and the occurrence rate of the combined knee VL+IR moment was compared between trial groups.Results:The posterior-CoP group showed significantly higher occurrence rates of the combined knee VL+IR moment(maximum of 82.8%)at every time point than those of the anterior-CoP trials,as theoretically predicted by the deterministic mechanical condition.Conclusion:The rearfoot strikes inducing the posterior CoP should be avoided to reduce the risk of non-contact ACL injury associated with the combined knee VL+IR stress.
基金This work is supported by National Key Research and Development Plan of China(2022YFB3105204).
文摘Emerging long-range industrial IoT applications(e.g.,remote patient monitoring)have increasingly higher requirements for global deterministic delay.Although many existing methods have built deterministic networks in small-scale networks through centralized computing and resource reservation,they cannot be applied on a global scale.The emerging mega-constellations enable new opportunities for realizing deterministic delay globally.As one constellation(e.g.,Starlink)might be managed by a single operator(e.g.,SpaceX),packets can be routed within deterministic number of hops.Moreover,the path diversity brought by the highly symmetrical network structure in mega-constellations can help to construct a congestion free network by routing.This paper leverages these unique characteristics of mega-constellations to avoid the traditional network congestion caused by multiple inputs and single output,and to determine the routing hops,and thus realizing a global deterministic network(DETSPACE).The model based on the 2D Markov chain theoretically verifies the correctness of DETSPACE.The effectiveness of DETSPACE in different traffic load con-ditions is also verified by extensive simulations.
基金This project was supported by the National Natural Science Foundation of China (60574023), the Natural Science Foundation of Shandong Province (Z2005G01), and the Natural Science Foundation of Qingdao City (05-1-JC-94).
文摘Optimal deterministic disturbances rejection control problem for singularly perturbed linear systems is considered. By using the slow-fast decomposition theory of singular perturbation, the existent and unique conditions of the feedforward and feedback composite control （FFCC） laws for both infinite-time and finite-time are proposed, and the design approaches are given. A disturbance observer is introduced to make the FFCC laws realizable physically. Simulation results indicate that the FFCC laws are robust with respect to external disturbances.
基金partially supported by National Key Research and Development Program of China(2019YFC1510902)National Natural Science Foundation of China(62073104)+1 种基金Natural Science Foundation of Heilongjiang Province(LH2022F024)China Postdoctoral Science Foundation(2022M710965)。
文摘This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances.To deal with the unknown deterministic disturbances,two strategies are implemented to construct the row space that can be used to approximately represent the unknown deterministic disturbances using the trigonometric functions or Bernstein polynomials depending on whether the disturbance frequencies are known.For closed-loop identification,CCF-N4SID is extended to the case with unknown deterministic disturbances using the oblique projection.In addition,a proper Bernstein polynomial order can be determined using the Akaike information criterion(AIC)or the Bayesian information criterion(BIC).Numerical simulation results demonstrate the effectiveness of the proposed identification method for both periodic and aperiodic deterministic disturbances.
基金supported in part by the projects of the National Natural Science Foundation of China(62376059,41971340)Fujian Provincial Department of Science and Technology(2023XQ008,2023I0024,2021Y4019),Fujian Provincial Department of Finance(GY-Z230007,GYZ23012)Fujian Key Laboratory of Automotive Electronics and Electric Drive(KF-19-22001).
文摘Autonomous driving has witnessed rapid advancement;however,ensuring safe and efficient driving in intricate scenarios remains a critical challenge.In particular,traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles,susceptibility to traffic flow bottlenecks,and imperfect data in perceiving environmental information,rendering them a vital issue in the practical application of autonomous driving.To address the traffic challenges,this work focused on complex roundabouts with multi-lane and proposed a Perception EnhancedDeepDeterministic Policy Gradient(PE-DDPG)for AutonomousDriving in the Roundabouts.Specifically,themodel incorporates an enhanced variational autoencoder featuring an integrated spatial attention mechanism alongside the Deep Deterministic Policy Gradient framework,enhancing the vehicle’s capability to comprehend complex roundabout environments and make decisions.Furthermore,the PE-DDPG model combines a dynamic path optimization strategy for roundabout scenarios,effectively mitigating traffic bottlenecks and augmenting throughput efficiency.Extensive experiments were conducted with the collaborative simulation platform of CARLA and SUMO,and the experimental results show that the proposed PE-DDPG outperforms the baseline methods in terms of the convergence capacity of the training process,the smoothness of driving and the traffic efficiency with diverse traffic flow patterns and penetration rates of autonomous vehicles(AVs).Generally,the proposed PE-DDPGmodel could be employed for autonomous driving in complex scenarios with imperfect data.
基金supported by Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R135)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural design.However,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in flight.Various strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control systems.This paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental constraints.To address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini drone.The results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.
文摘Naturally fractured rocks contain most of the world's petroleum reserves.This significant amount of oil can be recovered efficiently by gas assisted gravity drainage(GAGD).Although,GAGD is known as one of the most effective recovery methods in reservoir engineering,the lack of available simulation and mathematical models is considerable in these kinds of reservoirs.The main goal of this study is to provide efficient and accurate methods for predicting the GAGD recovery factor using data driven techniques.The proposed models are developed to relate GAGD recovery factor to the various parameters including model height,matrix porosity and permeability,fracture porosity and permeability,dip angle,viscosity and density of wet and non-wet phases,injection rate,and production time.In this investigation,by considering the effective parameters on GAGD recovery factor,three different efficient,smart,and fast models including artificial neural network(ANN),least square support vector machine(LSSVM),and multi-gene genetic programming(MGGP)are developed and compared in both fractured and homogenous porous media.Buckinghamπtheorem is also used to generate dimensionless numbers to reduce the number of input and output parameters.The efficiency of the proposed models is examined through statistical analysis of R-squared,RMSE,MSE,ARE,and AARE.Moreover,the performance of the generated MGGP correlation is compared to the traditional models.Results demonstrate that the ANN model predicts the GAGD recovery factor more accurately than the LSSVM and MGGP models.The maximum R^(2)of 0.9677 and minimum RMSE of 0.0520 values are obtained by the ANN model.Although the MGGP model has the lowest performance among the other used models(the R2 of 0.896 and the RMSE of 0.0846),the proposed MGGP correlation can predict the GAGD recovery factor in fractured and homogenous reservoirs with high accuracy and reliability compared to the traditional models.Results reveal that the employed models can easily predict GAGD recovery factor without requiring complicate governing equations or running complex and time-consuming simulation models.The approach of this research work improves our understanding about the most significant parameters on GAGD recovery and helps to optimize the stages of the process,and make appropriate economic decisions.
基金supported by the Ministry of Higher Education and Scientific Research of Tunisia
文摘In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive estimation methods are used to solve the parametric estimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the parametric estimation of a stochastic interconnected nonlinear hydraulic system is treated.
文摘Estimate of the Deterministic Neutron RBE for Radiation-induced Pseudo-Pelger Huët Cell Formation R.E.Goans1,2,C.J.Iddins1,R.E.Goans,Jr.3(1.Radiation Emergency Assistance Center/Training Site,Oak Ridge,TN;2.MJW Corporation,Amherst,NY;3.LMU Debusk School of Medicine,Harrogate,TN)Abstract:Using archival peripheral blood slides from radiation accident patients,we have recently described the pseudo-Pelger Huët anomaly(PPHA)in neutrophils as a new radiation-induced biomarker,useful for dosimetry not only immediately after a radiation incident but also potentially helpful as a tool in retrospective dosimetry.
文摘Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.
基金supported by the National Natural Science Foundation of China(No.42007428)the National Forage Industry Technology System Program of China(No.CARS34)+1 种基金the Key Research and Development Program of Shaanxi,China(No.2022SF-285)Shaanxi Province Forestry Science and Technology Innovation Program,China(No.SXLK2022-02-14)。
文摘Effective vegetation reconstruction plays a vital role in the restoration of desert ecosystems.However,in reconstruction of different vegetation types,the community characteristics,assembly processes,and functions of different soil microbial taxa under environmental changes are still disputed,which limits the understanding of the sustainability of desert restoration.Hence,we investigated the soil microbial community characteristics and functional attributes of grassland desert(GD),desert steppe(DS),typical steppe(TS),and artificial forest(AF)in the Mu Us Desert,China.Our findings confirmed the geographical conservation of soil microbial composition but highlighted decreased microbial diversity in TS.Meanwhile,the abundance of rare taxa and microbial community stability in TS improved.Heterogeneous and homogeneous selection determined the assembly of rare and abundant bacterial taxa,respectively,with both being significantly influenced by soil moisture.In contrast,fungal communities displayed stochastic processes and exhibited sensitivity to soil nutrient conditions.Furthermore,our investigation revealed a noteworthy augmentation in bacterial metabolic functionality in TS,aligning with improved vegetation restoration and the assemblage of abundant bacterial taxa.However,within nutrient-limited soils(GD,DS,and AF),the assembly dynamics of rare fungal taxa assumed a prominent role in augmenting their metabolic capacity and adaptability to desert ecosystems.These results highlighted the variations in the assembly processes and metabolic functions of soil microorganisms during vegetation reestablishment and provided corresponding theoretical support for anthropogenic revegetation of desert ecosystems.
基金supported by National Natural Science Foundation of China(Nos.42130506,42071031)the Special Technology Innovation Fund of Carbon Peak and Carbon Neutrality in Jiangsu Province(No.BK20231515)。
文摘Background:Climate change is accelerating alterations in forest species and community composition worldwide,especially following extreme events like severe droughts and windstorms.Understanding these effects on subtropical forests is crucial for conservation and forest management,but it remains unclear whether the impacts are stochastic or deterministic.Methods:We analyzed a unique dataset from a 1-ha permanent plot in a subtropical monsoon broadleaf evergreen forest in China,monitored over 26 years with six surveys from 1994 to 2020.The forest has been free from anthropogenic disturbances for over 400 years.In each survey,we measured all trees with a diameter at breast height(DBH)≥1 cm,and recorded 11 plant functional traits relating to photosynthesis,wood properties,water use,and nutrient dynamics.Using this data,we calculated species and trait dispersion,assessing short-term(~5years)and long-term(26 years)trends in species and trait composition following severe droughts and windstorm events.Results:Severe droughts,and subsequent droughts,increased both species and trait dispersion,while species composition converged,and trait dispersion remained relatively stable throughout the recovery period.Windstorm events led to increased species dispersion but decreased trait dispersion.We observed a clear directional shift in both species and trait composition under these climatic stressors,with a more pronounced increase in trait dispersion compared to species dispersion.Conclusion:In the short term(~5 years),severe droughts and windstorms increased species composition divergence,while trait composition responses varied.Over 26 years,deterministic processes mainly drove community composition changes,especially for trait composition,although stochastic processes also played a role.These findings suggest enhancing forest resilience to climatic stressors by protecting adaptive species or increasing species diversity in management practices.
