The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
In the application of clean energy heating,the development of a low-carbon winter heat supply in severe cold regions of China is hindered by the stability of heat sources.To ensure the smooth transformation of traditi...In the application of clean energy heating,the development of a low-carbon winter heat supply in severe cold regions of China is hindered by the stability of heat sources.To ensure the smooth transformation of traditional energy to clean energy heating modes,the feasibility of a heating system coupling traditional and clean energies was studied using the heating system of an office building in a cold region of China as the research object.The air-source heat pump(ASHP)heating system used in the office building in this study was field-tested.The problems existing in the heating system were analysed using testing data combined with the existing conditions of the building.Solar-air source heat pump coupled heating system based on heat grid(NH-SASHP)was proposed,and the system model was established using TRNSYS software.The operation effect of the NH-SASHP coupling system was analysed,and the control strategy of the coupling system was optimized.The results showed that NH-SASHP system possessed certain advantages over the ASHP system during the heating season,and the energy saving rate is 50.79%compared with the ASHP system.Under the most unfavourable working conditions in the middle of severe cold,the indoor temperature compliance rate was 100%.The average coefficient of performance of the heat pump system(COPsys)of NH-SASHP system and ASHP system were 6.27 and 3.55,respectively.The operating cost of the NH-SASHP system is approximately 72.3%of the ASHP system.展开更多
Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status.However,the presentation of the data lacks structural information.Existing single-netwo...Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status.However,the presentation of the data lacks structural information.Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously.Therefore,there is a need for complementary methods to address these deficiencies.To address these limitations,this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system.A dual information network is constructed to assess the degree of operational deviation considering the planning tasks.To validate the effectiveness,discussions are conducted through a modified cosine similarity calculation on theoretical analysis,delay level description,and the ability to identify abnormal dates.Compared to some state-of-the-art methods,the proposed method achieves an average Spearman delay correlation of 0.847 and a relative distance of 3.477.Furthermore,case analyses are invested in regions of China's Mainland,Europe,and the United States,investigating both the overall and sub-regional network fluctuations.To represent the impact of network fluctuations in sub-regions,a response loss value was developed.The times that are prone to fluctuations are also discussed through the classification of time series data.The research can offer a novel approach to system monitoring,providing a research direction that utilizes individual data combined to represent macroscopic states.Our code will be released at https://github.com/daozhong/STPN.git.展开更多
With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Further...With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.展开更多
Lhasa,one of the world's highest cities,confronts the challenge of harmonizing cultural heritage preservation with ecological protection.Assessing the spatiotemporal dynamics of ecosystem service value(ESV)in its ...Lhasa,one of the world's highest cities,confronts the challenge of harmonizing cultural heritage preservation with ecological protection.Assessing the spatiotemporal dynamics of ecosystem service value(ESV)in its central urban area is therefore critical for informing future urban planning and land management.This study systematically analyzed land use evolution,the spatiotemporal characteristics of ecosystem services,and ecological network construction within Lhasa's central urban area.It integrated multi-source data,including Landsat remote sensing imagery from 2000,2010,and 2023,with multiple modeling methods such as the InVEST model,MaxEnt for cultural service assessment,the Minimum Cumulative Resistance(MCR)model,and circuit theory.Based on these analyses,optimization strategies were proposed.The results indicate that from 2000 to 2023,areas of cultivated land,grassland,and water bodies decreased by 7.47%,6.85%,and 0.68%,respectively,while wetland and forest areas expanded by 1.44%and 0.64%.Construction land exhibited significant expansion(12.94%),leading to an overall ESV reduction of 462.8×10^(5)yuan.Vegetation coverage was identified as the pivotal factor influencing ESV distribution,with higher values concentrated in the Lhasa River Basin and near the Lhalu Wetland,diminishing towards the urban core.Furthermore,spatial autocorrelation analysis revealed significant positive spatial clustering,with low-low aggregation in the eastern and central regions and high-high aggregation in the Lhasa River Basin and its surrounding water bodies.Moreover,based on a comprehensive ecosystem service assessment,11 ecological source sites were identified,primarily in the southwestern mountains and northeastern foothills.A comprehensive resistance surface,incorporating factors such as elevation,Normalized Difference Vegetation Index(NDVI),and land use,facilitated the extraction of 23 potential ecological corridors totaling 124.96 km in length.Topological network analysis indicated high redundancy and connectivity;however,marginal source sites relying on single connections exhibited significant vulnerability to rupture.Additionally,the application of circuit theory identified 30 ecological pinch points(current density≥1.5 A/km^(2))and 23 obstacle points,revealing significant blockages to ecological flow along the Qinghai-Xizang Highway,within the old city,and in other areas of high-intensity human activity.To address the identified network deficiencies—‘scattered cores,fragmented corridors,and insufficient resilience’—this study proposes an optimization strategy conceptualized as‘one vein,three corridors,and multiple cores’.Recommendations for enhancing network resilience include the delineation of ecological protection red lines,the integration of plateau-adapted technologies,and the fostering of community governance mechanisms.This approach aims to provide a scientific basis for constructing an ecological security pattern and promoting sustainable development in plateau cities.Ultimately,this research contributes to the enhancement of ecological well-being in the Himalayan region.展开更多
The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynam...The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynamic agents belong to distinct groups.Interactions are cooperative-antagonistic within each group and sign-in-degree balanced across the inter-groups.Firstly,a state feedback control protocol is designed to ensure that the trajectories of followers in diverse groups can converge to distinct convex hulls formed by their corresponding leaders,respectively.As an extension,the bipartite control problem with time-variant formation for the multi-agent system(MAS)is also considered,and a corresponding control protocol with formation compensation vectors is given.Finally,in view of Lyapunov stability theory and matrix inequality,the sufficient conditions for realizing the bipartite containment control are obtained,and several simulations are provided to verify the validity of the above methods.展开更多
Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted featur...Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.展开更多
The Sichuan Yanting Agro-ecosystem Research Station in Chinese National Ecosystem Research Network(CERN),also named the Yanting Agroecological Experimental Station of Purple Soil,Chinese Academy of Science,is situated...The Sichuan Yanting Agro-ecosystem Research Station in Chinese National Ecosystem Research Network(CERN),also named the Yanting Agroecological Experimental Station of Purple Soil,Chinese Academy of Science,is situated in Yanting County(105°27'E,31°16'N)in the north-central Sichuan Basin—a region characterized by hilly landscapes.The area features exposed purple sand-mudstone strata from the Jurassic and Cretaceous periods.The station represents a typical subtropical farmland ecosystem dominated by purple soils.Established in 1980,the station joined the CERN in 1991.It was designated a National Key Field Research Station in 2005,and in 2007,it was recognized as a Soil and Water Conservation Science and Technology Demonstration Park by the Ministry of Water Resources.It also serves as an observation site for the Global Terrestrial Observation System(GTOS)and is a key field station under the Chinese Ministry of Agriculture.展开更多
Leakage events occurring at multiple locations simultaneously generate overlapping and topologydependent pressure signatures,making reliable detection and subsequent restoration planning a persistent challenge in wate...Leakage events occurring at multiple locations simultaneously generate overlapping and topologydependent pressure signatures,making reliable detection and subsequent restoration planning a persistent challenge in water distribution systems(WDSs).While recent data-driven techniques have improved the ability to identify anomalous hydraulic behavior,most approaches remain limited to the detection stage and offer little guidance on how utilities should prioritize repairs once multiple failures are identified.To bridge this gap,this study proposes an integrated framework that links topology-aware leakage detection with quantitative restoration prioritization.First,a multi-task learning framework based on Graph Attention Networks(GAT)is employed to jointly detect both the location and magnitude of multiple leakages by explicitly incorporating hydraulic responses and network topology into the learning process.The model’s detection robustness is evaluated across networks with contrasting looped,branched,and hybrid topologies to examine how structural characteristics influence detection accuracy under multievent conditions.Second,the study develops a restoration-planning module that constructs a two-objective decision space combining restoration cost and segment vulnerability,where the latter accounts for disruption potential arising from hydraulic importance and local service connectivity.Non-dominated sorting is used to derive Pareto-optimal restoration sequences,enabling explicit quantification of the trade-offs between operational cost and service disruption.This provides decision-makers with a ranked set of restoration orders that reflect both hydraulic impact and functional risk,rather than relying on heuristics or cost-only criteria.Notably,the proposed framework separates offline training from online inference,requiring only a single forward pass for real-time decision-making without the need for iterative hydraulic simulations.Results demonstrate that topology strongly governs both detection performance and the structure of optimal repair sequences,underscoring the importance of integrating network-aware learning with multi-criteria restoration evaluation.展开更多
The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by...The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.展开更多
This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method...This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.展开更多
Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectiv...Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.展开更多
Soil microbial communities are key factors in maintaining ecosystem multifunctionality(EMF).However,the distribution patterns of bacterial diversity and how the different bacterial taxa and their diversity dimensions ...Soil microbial communities are key factors in maintaining ecosystem multifunctionality(EMF).However,the distribution patterns of bacterial diversity and how the different bacterial taxa and their diversity dimensions affect EMF remain largely unknown.Here,we investigated variation in three measures of diversity(alpha diversity,community composition and network complexity)among rare,intermediate,and abundant taxa across a latitudinal gradient spanning five forest plots in Yunnan Province,China and examined their contributions on EMF.We aimed to characterize the diversity distributions of bacterial groups across latitudes and to assess the differences in the mechanisms underlying their contributions to EMF.We found that multifaceted diversity(i.e.,diversity assessed by the three different metrics)of rare,intermediate,and abundant bacteria generally decreased with increasing latitude.More importantly,we found that rare bacterial taxa tended to be more diverse,but they contributed less to EMF than intermediate or abundant bacteria.Among the three dimensions of diversity we assessed,only community composition significantly affected EMF across all locations,while alpha diversity had a negative effect,and network complexity showed no significant impact.Our study further emphasizes the importance of intermediate and abundant bacterial taxa as well as community composition to EMF and provides a theoretical basis for investigating the mechanisms by which belowground microorganisms drive EMF along a latitudinal gradient.展开更多
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.T...In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.The designed distributed controller consists of two parts:a dynamic average consensus part that asymptotically reproduces the unknown NE,and an adaptive reference-tracking module responsible for steering EL systems’positions to track a desired trajectory.The generalized Barbalat’s Lemma is used to overcome the discontinuity of the closed-loop system caused by the switching networks.The proposed algorithm is illustrated by a sensor network deployment problem.展开更多
Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiv...Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.展开更多
The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation.While existing vehicl...The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation.While existing vehicleto-infrastructure coordination frameworks partially address congestion mitigation,they often neglect priority-aware optimization and exhibit algorithmic bias toward dominant vehicle classes—critical limitations in mixed-priority scenarios involving emergency vehicles.To bridge this gap,this study proposes a preference game-theoretic coordination framework with adaptive strategy transfer protocol,explicitly balancing system-wide efficiency(measured by network throughput)with priority vehicle rights protection(quantified via time-sensitive utility functions).The approach innovatively combines(1)a multi-vehicle dynamic routing model with quantifiable preference weights,and(2)a distributed Nash equilibrium solver updated using replicator sub-dynamic models.The framework was evaluated on an urban road network containing 25 intersections with mixed priority ratios(10%–30%of vehicles with priority access demand),and the framework showed consistent benefits on four benchmarks(Social routing algorithm,Shortest path algorithm,The comprehensive path optimisation model,The emergency vehicle timing collaborative evolution path optimization method)showed consistent benefits.Results showthat across different traffic demand configurations,the proposed method reduces the average vehicle traveling time by at least 365 s,increases the road network throughput by 48.61%,and effectively balances the road loads.This approach successfully meets the diverse traffic demands of various vehicle types while optimizing road resource allocations.The proposed coordination paradigm advances theoretical foundations for fairness-aware traffic optimization while offering implementable strategies for next-generation cooperative vehicle-road systems,particularly in smart city deployments requiring mixed-priority mobility guarantees.展开更多
Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 3...Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET estimation.The findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological processes.These hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET estimation.The growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional methods.Despite the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data scarcity.Future research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder trust.Additionally,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world applications.Approaches such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these challenges.This study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable regions.By integrating CNNs for automatic feature extraction and leveraging hybrid architectures,HANNs offer considerable advantages for optimizing water management,particularly agriculture.Addressing challenges related to interpretability and scalability will be vital to ensuring the widespread deployment and operational success of HANNs in global water resource management.展开更多
Projective synchronization problems of a drive system and a particular response network were investigated,where the drive system is an arbitrary system with n+1 dimensions;it may be a linear or nonlinear system,and ev...Projective synchronization problems of a drive system and a particular response network were investigated,where the drive system is an arbitrary system with n+1 dimensions;it may be a linear or nonlinear system,and even a chaotic or hyperchaotic system,the response network is complex system coupled by N nodes,and every node is showed by the approximately linear part of the drive system.Only controlling any one node of the response network by designed controller can achieve the projective synchronization.Some numerical examples were employed to verify the effectiveness and correctness of the designed controller.展开更多
In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained f...In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
文摘In the application of clean energy heating,the development of a low-carbon winter heat supply in severe cold regions of China is hindered by the stability of heat sources.To ensure the smooth transformation of traditional energy to clean energy heating modes,the feasibility of a heating system coupling traditional and clean energies was studied using the heating system of an office building in a cold region of China as the research object.The air-source heat pump(ASHP)heating system used in the office building in this study was field-tested.The problems existing in the heating system were analysed using testing data combined with the existing conditions of the building.Solar-air source heat pump coupled heating system based on heat grid(NH-SASHP)was proposed,and the system model was established using TRNSYS software.The operation effect of the NH-SASHP coupling system was analysed,and the control strategy of the coupling system was optimized.The results showed that NH-SASHP system possessed certain advantages over the ASHP system during the heating season,and the energy saving rate is 50.79%compared with the ASHP system.Under the most unfavourable working conditions in the middle of severe cold,the indoor temperature compliance rate was 100%.The average coefficient of performance of the heat pump system(COPsys)of NH-SASHP system and ASHP system were 6.27 and 3.55,respectively.The operating cost of the NH-SASHP system is approximately 72.3%of the ASHP system.
文摘Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status.However,the presentation of the data lacks structural information.Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously.Therefore,there is a need for complementary methods to address these deficiencies.To address these limitations,this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system.A dual information network is constructed to assess the degree of operational deviation considering the planning tasks.To validate the effectiveness,discussions are conducted through a modified cosine similarity calculation on theoretical analysis,delay level description,and the ability to identify abnormal dates.Compared to some state-of-the-art methods,the proposed method achieves an average Spearman delay correlation of 0.847 and a relative distance of 3.477.Furthermore,case analyses are invested in regions of China's Mainland,Europe,and the United States,investigating both the overall and sub-regional network fluctuations.To represent the impact of network fluctuations in sub-regions,a response loss value was developed.The times that are prone to fluctuations are also discussed through the classification of time series data.The research can offer a novel approach to system monitoring,providing a research direction that utilizes individual data combined to represent macroscopic states.Our code will be released at https://github.com/daozhong/STPN.git.
基金supported by the National Natural Science Foundation of China under Grant 62471493supported by the Natural Science Foundation of Shandong Province under Grant ZR2023LZH017,ZR2024MF066。
文摘With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.
基金National Natural Science Foundation of China Youth Fund Project:Research on the Construction of Ecological Security Pattern in the Transition Zone of Nature Reserves along the Sichuan-Xizang Railway(Western Sichuan Section)(51908470).
文摘Lhasa,one of the world's highest cities,confronts the challenge of harmonizing cultural heritage preservation with ecological protection.Assessing the spatiotemporal dynamics of ecosystem service value(ESV)in its central urban area is therefore critical for informing future urban planning and land management.This study systematically analyzed land use evolution,the spatiotemporal characteristics of ecosystem services,and ecological network construction within Lhasa's central urban area.It integrated multi-source data,including Landsat remote sensing imagery from 2000,2010,and 2023,with multiple modeling methods such as the InVEST model,MaxEnt for cultural service assessment,the Minimum Cumulative Resistance(MCR)model,and circuit theory.Based on these analyses,optimization strategies were proposed.The results indicate that from 2000 to 2023,areas of cultivated land,grassland,and water bodies decreased by 7.47%,6.85%,and 0.68%,respectively,while wetland and forest areas expanded by 1.44%and 0.64%.Construction land exhibited significant expansion(12.94%),leading to an overall ESV reduction of 462.8×10^(5)yuan.Vegetation coverage was identified as the pivotal factor influencing ESV distribution,with higher values concentrated in the Lhasa River Basin and near the Lhalu Wetland,diminishing towards the urban core.Furthermore,spatial autocorrelation analysis revealed significant positive spatial clustering,with low-low aggregation in the eastern and central regions and high-high aggregation in the Lhasa River Basin and its surrounding water bodies.Moreover,based on a comprehensive ecosystem service assessment,11 ecological source sites were identified,primarily in the southwestern mountains and northeastern foothills.A comprehensive resistance surface,incorporating factors such as elevation,Normalized Difference Vegetation Index(NDVI),and land use,facilitated the extraction of 23 potential ecological corridors totaling 124.96 km in length.Topological network analysis indicated high redundancy and connectivity;however,marginal source sites relying on single connections exhibited significant vulnerability to rupture.Additionally,the application of circuit theory identified 30 ecological pinch points(current density≥1.5 A/km^(2))and 23 obstacle points,revealing significant blockages to ecological flow along the Qinghai-Xizang Highway,within the old city,and in other areas of high-intensity human activity.To address the identified network deficiencies—‘scattered cores,fragmented corridors,and insufficient resilience’—this study proposes an optimization strategy conceptualized as‘one vein,three corridors,and multiple cores’.Recommendations for enhancing network resilience include the delineation of ecological protection red lines,the integration of plateau-adapted technologies,and the fostering of community governance mechanisms.This approach aims to provide a scientific basis for constructing an ecological security pattern and promoting sustainable development in plateau cities.Ultimately,this research contributes to the enhancement of ecological well-being in the Himalayan region.
基金National Natural Science Foundation of China(No.12071370)。
文摘The bipartite containment control problem for heterogeneous nonlinear multi-agent systems(HNMASs)within multi-group networks under signed digraphs is investigated,where the first-order and second-order nonlinear dynamic agents belong to distinct groups.Interactions are cooperative-antagonistic within each group and sign-in-degree balanced across the inter-groups.Firstly,a state feedback control protocol is designed to ensure that the trajectories of followers in diverse groups can converge to distinct convex hulls formed by their corresponding leaders,respectively.As an extension,the bipartite control problem with time-variant formation for the multi-agent system(MAS)is also considered,and a corresponding control protocol with formation compensation vectors is given.Finally,in view of Lyapunov stability theory and matrix inequality,the sufficient conditions for realizing the bipartite containment control are obtained,and several simulations are provided to verify the validity of the above methods.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant no.(GPIP:1074-612-2024).
文摘Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.
文摘The Sichuan Yanting Agro-ecosystem Research Station in Chinese National Ecosystem Research Network(CERN),also named the Yanting Agroecological Experimental Station of Purple Soil,Chinese Academy of Science,is situated in Yanting County(105°27'E,31°16'N)in the north-central Sichuan Basin—a region characterized by hilly landscapes.The area features exposed purple sand-mudstone strata from the Jurassic and Cretaceous periods.The station represents a typical subtropical farmland ecosystem dominated by purple soils.Established in 1980,the station joined the CERN in 1991.It was designated a National Key Field Research Station in 2005,and in 2007,it was recognized as a Soil and Water Conservation Science and Technology Demonstration Park by the Ministry of Water Resources.It also serves as an observation site for the Global Terrestrial Observation System(GTOS)and is a key field station under the Chinese Ministry of Agriculture.
基金supported by the Korea Environmental Industry&Technology Institute(KEITI)through Water Management Program for Drought,funded by Korea Ministry of Environment(MOE)(RS-2023-00231944)supported by the research grant of the Gyeongsang National University in 2023。
文摘Leakage events occurring at multiple locations simultaneously generate overlapping and topologydependent pressure signatures,making reliable detection and subsequent restoration planning a persistent challenge in water distribution systems(WDSs).While recent data-driven techniques have improved the ability to identify anomalous hydraulic behavior,most approaches remain limited to the detection stage and offer little guidance on how utilities should prioritize repairs once multiple failures are identified.To bridge this gap,this study proposes an integrated framework that links topology-aware leakage detection with quantitative restoration prioritization.First,a multi-task learning framework based on Graph Attention Networks(GAT)is employed to jointly detect both the location and magnitude of multiple leakages by explicitly incorporating hydraulic responses and network topology into the learning process.The model’s detection robustness is evaluated across networks with contrasting looped,branched,and hybrid topologies to examine how structural characteristics influence detection accuracy under multievent conditions.Second,the study develops a restoration-planning module that constructs a two-objective decision space combining restoration cost and segment vulnerability,where the latter accounts for disruption potential arising from hydraulic importance and local service connectivity.Non-dominated sorting is used to derive Pareto-optimal restoration sequences,enabling explicit quantification of the trade-offs between operational cost and service disruption.This provides decision-makers with a ranked set of restoration orders that reflect both hydraulic impact and functional risk,rather than relying on heuristics or cost-only criteria.Notably,the proposed framework separates offline training from online inference,requiring only a single forward pass for real-time decision-making without the need for iterative hydraulic simulations.Results demonstrate that topology strongly governs both detection performance and the structure of optimal repair sequences,underscoring the importance of integrating network-aware learning with multi-criteria restoration evaluation.
基金described in this paper has been developed with in the project PRESECREL(PID2021-124502OB-C43)。
文摘The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.
基金The National Natural Science Foundation of China(W2431048)The Science and Technology Research Program of Chongqing Municipal Education Commission,China(KJZDK202300807)The Chongqing Natural Science Foundation,China(CSTB2024NSCQQCXMX0052).
文摘This paper addresses the consensus problem of nonlinear multi-agent systems subject to external disturbances and uncertainties under denial-ofservice(DoS)attacks.Firstly,an observer-based state feedback control method is employed to achieve secure control by estimating the system's state in real time.Secondly,by combining a memory-based adaptive eventtriggered mechanism with neural networks,the paper aims to approximate the nonlinear terms in the networked system and efficiently conserve system resources.Finally,based on a two-degree-of-freedom model of a vehicle affected by crosswinds,this paper constructs a multi-unmanned ground vehicle(Multi-UGV)system to validate the effectiveness of the proposed method.Simulation results show that the proposed control strategy can effectively handle external disturbances such as crosswinds in practical applications,ensuring the stability and reliable operation of the Multi-UGV system.
基金supported in part by the National Natural Science Foundation of China(62173255,62188101)Shenzhen Key Laboratory of Control Theory and Intelligent Systems(ZDSYS20220330161800001)
文摘Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.
基金supported by the Fundamental Research Funds of Chinese Academy of Forestry(Nos.CAFYBB2022SY037,CAFYBB2021ZA002 and CAFYBB2022QC002)the Basic Research Foundation of Yunnan Province(Grant No.202201AT070264).
文摘Soil microbial communities are key factors in maintaining ecosystem multifunctionality(EMF).However,the distribution patterns of bacterial diversity and how the different bacterial taxa and their diversity dimensions affect EMF remain largely unknown.Here,we investigated variation in three measures of diversity(alpha diversity,community composition and network complexity)among rare,intermediate,and abundant taxa across a latitudinal gradient spanning five forest plots in Yunnan Province,China and examined their contributions on EMF.We aimed to characterize the diversity distributions of bacterial groups across latitudes and to assess the differences in the mechanisms underlying their contributions to EMF.We found that multifaceted diversity(i.e.,diversity assessed by the three different metrics)of rare,intermediate,and abundant bacteria generally decreased with increasing latitude.More importantly,we found that rare bacterial taxa tended to be more diverse,but they contributed less to EMF than intermediate or abundant bacteria.Among the three dimensions of diversity we assessed,only community composition significantly affected EMF across all locations,while alpha diversity had a negative effect,and network complexity showed no significant impact.Our study further emphasizes the importance of intermediate and abundant bacterial taxa as well as community composition to EMF and provides a theoretical basis for investigating the mechanisms by which belowground microorganisms drive EMF along a latitudinal gradient.
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
基金supported by the Research Grants Council of the Hong Kong Special Administration Region under the Grant No.14201621。
文摘In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.The designed distributed controller consists of two parts:a dynamic average consensus part that asymptotically reproduces the unknown NE,and an adaptive reference-tracking module responsible for steering EL systems’positions to track a desired trajectory.The generalized Barbalat’s Lemma is used to overcome the discontinuity of the closed-loop system caused by the switching networks.The proposed algorithm is illustrated by a sensor network deployment problem.
基金founded by the Open Project Program of Anhui Province Key Laboratory of Metallurgical Engineering and Resources Recycling(Anhui University of Technology)(No.SKF21-06)Research Fund for Young Teachers of Anhui University of Technology in 2020(No.QZ202001).
文摘Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.
基金funded by the National Key Research and Development Program Project 2022YFB4300404.
文摘The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation.While existing vehicleto-infrastructure coordination frameworks partially address congestion mitigation,they often neglect priority-aware optimization and exhibit algorithmic bias toward dominant vehicle classes—critical limitations in mixed-priority scenarios involving emergency vehicles.To bridge this gap,this study proposes a preference game-theoretic coordination framework with adaptive strategy transfer protocol,explicitly balancing system-wide efficiency(measured by network throughput)with priority vehicle rights protection(quantified via time-sensitive utility functions).The approach innovatively combines(1)a multi-vehicle dynamic routing model with quantifiable preference weights,and(2)a distributed Nash equilibrium solver updated using replicator sub-dynamic models.The framework was evaluated on an urban road network containing 25 intersections with mixed priority ratios(10%–30%of vehicles with priority access demand),and the framework showed consistent benefits on four benchmarks(Social routing algorithm,Shortest path algorithm,The comprehensive path optimisation model,The emergency vehicle timing collaborative evolution path optimization method)showed consistent benefits.Results showthat across different traffic demand configurations,the proposed method reduces the average vehicle traveling time by at least 365 s,increases the road network throughput by 48.61%,and effectively balances the road loads.This approach successfully meets the diverse traffic demands of various vehicle types while optimizing road resource allocations.The proposed coordination paradigm advances theoretical foundations for fairness-aware traffic optimization while offering implementable strategies for next-generation cooperative vehicle-road systems,particularly in smart city deployments requiring mixed-priority mobility guarantees.
文摘Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET estimation.The findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological processes.These hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET estimation.The growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional methods.Despite the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data scarcity.Future research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder trust.Additionally,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world applications.Approaches such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these challenges.This study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable regions.By integrating CNNs for automatic feature extraction and leveraging hybrid architectures,HANNs offer considerable advantages for optimizing water management,particularly agriculture.Addressing challenges related to interpretability and scalability will be vital to ensuring the widespread deployment and operational success of HANNs in global water resource management.
基金Supported by the National Natural Science Foundation of China (11161027)。
文摘Projective synchronization problems of a drive system and a particular response network were investigated,where the drive system is an arbitrary system with n+1 dimensions;it may be a linear or nonlinear system,and even a chaotic or hyperchaotic system,the response network is complex system coupled by N nodes,and every node is showed by the approximately linear part of the drive system.Only controlling any one node of the response network by designed controller can achieve the projective synchronization.Some numerical examples were employed to verify the effectiveness and correctness of the designed controller.
基金Supported by the National Natural Science Foundation of China(11971458,11471310)。
文摘In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.
