Single-phase,non-isolated microinverters used in photovoltaic(PV)systems commonly encounter two persistent challenges:High-frequency leakage current and fluctuating power delivery.This paper presents a novel single-ph...Single-phase,non-isolated microinverters used in photovoltaic(PV)systems commonly encounter two persistent challenges:High-frequency leakage current and fluctuating power delivery.This paper presents a novel single-phase,non-isolated,multi-input microinverter topology with a common-ground structure that effectively eliminates ground leakage current without requiring additional active components.The proposed microinverter architecture integrates a dual-boost configuration and uses only four active switches.This is especially advantageous in terms of the component count,which is beneficial to enhance reliability,reduce cost,and simplify the overall system design.With one,two,or four PV inputs,it can operate without interruption under unbalanced voltage or partial shading and even if some inputs drop to zero.A tailored modulation scheme minimizes conduction losses while maintaining a stable direct-current(DC)-link voltage,and a decoupling capacitor efficiently absorbs the single-phase pulsating power,thus overcoming one major limitation in existing microinverter designs.By validating with a 1-kW GaN-based prototype,both the simulated and experimental results demonstrate its high efficiency,robustness,and practical suitability for cost-effective PV applications,with a peak efficiency value of 94.8%.展开更多
The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measu...The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has...0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has largely been discipline-based,relying on field investigations,data collection,experimental analyses,and data interpretation to study individual components of the Earth system.展开更多
In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structu...In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.展开更多
In order to explore the safe operation of UAVs in non-segregated airspace,a collision risk model for cylindrical UAVs based on conflict areas was constructed and the risk of conflict between manned and unmanned aerial...In order to explore the safe operation of UAVs in non-segregated airspace,a collision risk model for cylindrical UAVs based on conflict areas was constructed and the risk of conflict between manned and unmanned aerial vehicles was researched.According to the results of risk analysis,a strategy for solving the conflict of aircraft is proposed,and the risk assessment experiment of unmanned aerial vehicle(UAV)in non-isolated airspace conflict is carried out.The results show that under the experimental conditions,large unmanned aerial vehicles equipped with ADS-B,TCAS and other airborne sensing systems will indeed interfere with other aircraft in airspace when they enter non-isolated airspace.Especially when the number of aircraft in airspace is large,the automatic avoidance system of UAV will increase the avoidance time and trigger the safety alarm,but the safety level is still acceptable.This indicates that it is relatively safe for UAVs to enter non-isolated airspace under limited conditions.The results can be used as a reference for the safe operation of unmanned aerial vehicle(UAV)in non-isolated airspace.展开更多
The work illustrates the impossibility of decreasing entropy in a strictly random thermodynamic process in a non-isolated system using the example of heating a planet by solar radiation flux without and taking into ac...The work illustrates the impossibility of decreasing entropy in a strictly random thermodynamic process in a non-isolated system using the example of heating a planet by solar radiation flux without and taking into account its rotation around its own axis. That is, the second law of thermodynamics formulated for isolated systems continues to govern such systems. We have shown that in order to achieve a stationary state at lower values of temperature and entropy far from thermodynamic equilibrium at a maximum of temperature and entropy, it is necessary to have regular factors of nonrandom nature, one of which in this example is the rotation of the planet around its own axis. This means that the reason for the appearance of ordered structured objects in non-isolated thermodynamic systems is not the random process itself, but the action of dynamic control mechanisms, such as periodic external influences, nonlinear elements with positive feedback, catalysts for chemical reactions, etc. We present the plots with dependences of temperature and entropy versus time in non-isolated systems with purely random processes and in the presence of a control factor of non-random nature-rotation.展开更多
This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key de...This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.展开更多
Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpret...Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpretability.A promising approach to overcoming these challenges is to embed domain knowledge into the ML pipeline,enhancing the model with additional pattern information.In this paper,we review the latest developments in PHM,encapsulated under the concept of Knowledge Driven Machine Learning(KDML).We propose a hierarchical framework to define KDML in PHM,which includes scientific paradigms,knowledge sources,knowledge representations,and knowledge embedding methods.Using this framework,we examine current research to demonstrate how various forms of knowledge can be integrated into the ML pipeline and provide roadmap to specific usage.Furthermore,we present several case studies that illustrate specific implementations of KDML in the PHM domain,including inductive experience,physical model,and signal processing.We analyze the improvements in generalization capability and interpretability that KDML can achieve.Finally,we discuss the challenges,potential applications,and usage recommendations of KDML in PHM,with a particular focus on the critical need for interpretability to ensure trustworthy deployment of artificial intelligence in PHM.展开更多
Acute lung injury(ALI)was characterized by excessive reactive oxygen species(ROS)levels and inflammatory response in the lung.Scavenging ROS could inhibit the excessive inflammatory response,further treating ALI.Herei...Acute lung injury(ALI)was characterized by excessive reactive oxygen species(ROS)levels and inflammatory response in the lung.Scavenging ROS could inhibit the excessive inflammatory response,further treating ALI.Herein,we designed a novel nanozyme(P@Co)comprised of polydopamine(PDA)nanoparticles(NPs)loading with ultra-small Co,combining with near infrared(NIR)irradiation,which could efficiently scavenge intracellular ROS and suppress inflammatory responses against ALI.For lipopolysaccharide(LPS)induced macrophages,P@Co+NIR presented excellent antioxidant and anti-inflammatory capacities through lowering intracellular ROS levels,decreasing the expression levels of interleukin-6(IL-6)and tumor necrosis factor-α(TNF-α)as well as inducing macrophage M2 directional polarization.Significantly,it displayed the outstanding activities of lowering acute lung inflammation,relieving diffuse alveolar damage,and up-regulating heat shock protein 70(HSP70)expression,resulting in synergistic enhanced ALI therapy effect.It offers a novel strategy for the clinical treatment of ROS related diseases.展开更多
Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve ...Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.展开更多
Methane(CH4),the predominant component of natural gas and shale gas,is regarded as a promising carbon feedstock for chemical synthesis[1].However,considering the extreme stability of CH4 molecules,it's quite chall...Methane(CH4),the predominant component of natural gas and shale gas,is regarded as a promising carbon feedstock for chemical synthesis[1].However,considering the extreme stability of CH4 molecules,it's quite challenging in simultaneously achieving high activity and selectivity for target products under mild conditions,especially when synthesizing high-value C2t chemicals such as ethanol[2].The conversion of methane to ethanol by photocatalysis is promising for achieving transformation under ambient temperature and pressure conditions.Currently,the apparent quantum efficiency(AQE)of solar-driven methane-to-ethanol conversion is generally below 0.5%[3,4].Furthermore,the stability of photocatalysts remains inadequate,offering substantial potential for further improvement.展开更多
The conventional Kibble–Zurek mechanism,describing driven dynamics across critical points based on the adiabatic-impulse scenario(AIS),has attracted broad attention.However,the driven dynamics at the tricritical poin...The conventional Kibble–Zurek mechanism,describing driven dynamics across critical points based on the adiabatic-impulse scenario(AIS),has attracted broad attention.However,the driven dynamics at the tricritical point with two independent relevant directions have not been adequately studied.Here,we employ the time-dependent variational principle to study the driven critical dynamics at a one-dimensional supersymmetric Ising tricritical point.For the relevant direction along the Ising critical line,the AIS apparently breaks down.Nevertheless,we find that the critical dynamics can still be described by finite-time scaling in which the driving rate has a dimension of r_(μ)=z+1/v_(μ)with z and v_(μ)being the dynamic exponent and correlation length exponent in this direction,respectively.For driven dynamics along another direction,the driving rate has a dimension of r_(p)=z+1/v_(p)with v_(p)being another correlation length exponent.Our work brings a new fundamental perspective into nonequilibrium critical dynamics near the tricritical point,which could be realized in programmable quantum processors in Rydberg atomic systems.展开更多
As the number of distributed power supplies increases on the user side,smart grids are becoming larger and more complex.These changes bring new security challenges,especially with the widespread adop-tion of data-driv...As the number of distributed power supplies increases on the user side,smart grids are becoming larger and more complex.These changes bring new security challenges,especially with the widespread adop-tion of data-driven control methods.This paper introduces a novel black-box false data injection attack(FDIA)method that exploits the measurement modules of distributed power supplies within smart grids,highlighting its effectiveness in bypassing conventional security measures.Unlike traditional methods that focus on data manipulation within communication networks,this approach directly injects false data at the point of measurement,using a generative adversarial network(GAN)to generate stealthy attack vectors.This method requires no detailed knowledge of the target system,making it practical for real-world attacks.The attack’s impact on power system stability is demonstrated through experiments,high-lighting the significant cybersecurity risks introduced by data-driven algorithms in smart grids.展开更多
Driven critical dynamics in quantum phase transitions holds significant theoretical importance,and also has practical applications in fast-developing quantum devices.While scaling corrections have been shown to play i...Driven critical dynamics in quantum phase transitions holds significant theoretical importance,and also has practical applications in fast-developing quantum devices.While scaling corrections have been shown to play important roles in fully characterizing equilibrium quantum criticality,their impact on nonequilibrium critical dynamics has not been extensively explored.In this work,we investigate the driven critical dynamics in a two-dimensional quantum Heisenberg model.We find that in this model the scaling corrections arising from both finite system size and finite driving rate must be incorporated into the finite-time scaling form in order to properly describe the nonequilibrium scaling behaviors.In addition,improved scaling relations are obtained from the expansion of the full scaling form.We numerically verify these scaling forms and improved scaling relations for different starting states using the nonequilibrium quantum Monte Carlo algorithm.展开更多
Current research on robot calibration can be roughly classified into two categories,and both of them have certain inherent limitations.Model-based methods are difficult to model and compensate the pose errors arising ...Current research on robot calibration can be roughly classified into two categories,and both of them have certain inherent limitations.Model-based methods are difficult to model and compensate the pose errors arising from configuration-dependent geometric and non-geometric source errors,whereas the accuracy of data-driven methods depends on a large amount of measurement data.Using a 5-DOF(degrees of freedom)hybrid machining robot as an exemplar,this study presents a model data-driven approach for the calibration of robotic manipulators.An f-DOF realistic robot containing various source errors is visualized as a 6-DOF fictitious robot having error-free parameters,but erroneous actuated/virtual joint motions.The calibration process essentially involves four steps:(1)formulating the linear map relating the pose error twist to the joint motion errors,(2)parameterizing the joint motion errors using second-order polynomials in terms of nominal actuated joint variables,(3)identifying the polynomial coefficients using the weighted least squares plus principal component analysis,and(4)compensating the compensable pose errors by updating the nominal actuated joint variables.The merit of this approach is that it enables compensation of the pose errors caused by configuration-dependent geometric and non-geometric source errors using finite measurement configurations.Experimental studies on a prototype machine illustrate the effectiveness of the proposed approach.展开更多
Correction to:J.Iron Steel Res.Int.https://doi.org/10.1007/s42243-025-01545-x The publication of this article unfortunately contained mistakes.Equation(14)was not correct.The corrected equation is given below.
Additive manufacturing(AM)technology has revolutionized engineering field by enabling the creation of intricate,high-performance structures that were once difficult or impossible to fabricate.This transformative techn...Additive manufacturing(AM)technology has revolutionized engineering field by enabling the creation of intricate,high-performance structures that were once difficult or impossible to fabricate.This transformative technology has particularly advanced the development of metamaterials-engineered materials whose unique properties arise from their structure rather than composition-unlocking immense potential in fields ranging from aerospace to biomedical engineering.展开更多
Tunnel facility management(FM)is crucial for ensuring safety,efficiency,and resilience of tunnel infrastructure.Current FM practices,such as reactive and preventive maintenance,have limitations-reactive maintenance ca...Tunnel facility management(FM)is crucial for ensuring safety,efficiency,and resilience of tunnel infrastructure.Current FM practices,such as reactive and preventive maintenance,have limitations-reactive maintenance can't prevent failures,and preventive maintenance can't predict asset maintenance needs,leading to costly and inefficient processes.In addition,existing computerized tunnel FM systems face various challenges,including the lack of integrated real-time monitoring information,visualization of assets in a three-dimensional(3D)environment,supporting predictive maintenance,and scaling into long infrastructure with complicated spatiotemporal relationships.This study addresses these limitations by proposing a data-driven digital twin(DT)-based framework that supports predictive maintenance to improve tunnel FM processes and enhance resilience.The proposed framework consists of six layers,allowing the integration of data from monitoring system,FM system,and building information modeling(BIM)models.The framework proposes a flexible tunnel data model and classification system that hierarchically divides the tunnel models,ensuring an efficient data connection from the physical twin to the DT.The system was implemented in a tunnel case study that generates maintenance plans and work orders using historical and current condition monitoring data,and the 3D visualization technology suggests maintenance and repair processes,making the FM decision process more effective.The proposed system detected and predicted the twin state based on a data-driven analysis,and the prediction accuracy of the machine learning models was sufficiently high for use in real scenarios to make FM plans in advance and prevent asset failures.The proposed framework is contributing to the infrastructure resilience by enhancing the tunnel system ability to predict the maintenance tasks and prevent failures using data-driven DT technology.展开更多
Accurate identification and effective support of key blocks are crucial for ensuring the stability and safety of rock slopes.The number of structural planes and rock blocks were reduced in previous studies.This impair...Accurate identification and effective support of key blocks are crucial for ensuring the stability and safety of rock slopes.The number of structural planes and rock blocks were reduced in previous studies.This impairs the ability to characterize complex rock slopes accurately and inhibits the identification of key blocks.In this paper,a knowledge-data dually driven paradigm for accurate identification of key blocks in complex rock slopes is proposed.Our basic idea is to integrate key block theory into data-driven models based on finely characterizing structural features to identify key blocks in complex rock slopes accurately.The proposed novel paradigm consists of(1)representing rock slopes as graph-structured data based on complex systems theory,(2)identifying key nodes in the graph-structured data using graph deep learning,and(3)mapping the key nodes of graph-structured data to corresponding key blocks in the rock slope.Verification experiments and real-case applications are conducted by the proposed method.The verification results demonstrate excellent model performance,strong generalization capability,and effective classification results.Moreover,the real case application is conducted on the northern slope of the Yanqianshan Iron Mine.The results show that the proposed method can accurately identify key blocks in complex rock slopes,which can provide a decision-making basis and rational recommendations for effective support and instability prevention of rock slopes,thereby ensuring the stability of rock engineering and the safety of life and property.展开更多
基金supported by Libyan Cultural Affair/London,Libya under Grant No.13840.
文摘Single-phase,non-isolated microinverters used in photovoltaic(PV)systems commonly encounter two persistent challenges:High-frequency leakage current and fluctuating power delivery.This paper presents a novel single-phase,non-isolated,multi-input microinverter topology with a common-ground structure that effectively eliminates ground leakage current without requiring additional active components.The proposed microinverter architecture integrates a dual-boost configuration and uses only four active switches.This is especially advantageous in terms of the component count,which is beneficial to enhance reliability,reduce cost,and simplify the overall system design.With one,two,or four PV inputs,it can operate without interruption under unbalanced voltage or partial shading and even if some inputs drop to zero.A tailored modulation scheme minimizes conduction losses while maintaining a stable direct-current(DC)-link voltage,and a decoupling capacitor efficiently absorbs the single-phase pulsating power,thus overcoming one major limitation in existing microinverter designs.By validating with a 1-kW GaN-based prototype,both the simulated and experimental results demonstrate its high efficiency,robustness,and practical suitability for cost-effective PV applications,with a peak efficiency value of 94.8%.
文摘The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
基金supported by National Key R&D Program of China(No.2021YFF0501301)the National Natural Science Foundation of China(No.42172231)。
文摘0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has largely been discipline-based,relying on field investigations,data collection,experimental analyses,and data interpretation to study individual components of the Earth system.
文摘In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.
基金The authors would like to thank National Nature Science Foundation of China under Grant(71701202)The special funding project of Civil Aviation University of China for the basic scientific research services of the Central University(3122013Z006).
文摘In order to explore the safe operation of UAVs in non-segregated airspace,a collision risk model for cylindrical UAVs based on conflict areas was constructed and the risk of conflict between manned and unmanned aerial vehicles was researched.According to the results of risk analysis,a strategy for solving the conflict of aircraft is proposed,and the risk assessment experiment of unmanned aerial vehicle(UAV)in non-isolated airspace conflict is carried out.The results show that under the experimental conditions,large unmanned aerial vehicles equipped with ADS-B,TCAS and other airborne sensing systems will indeed interfere with other aircraft in airspace when they enter non-isolated airspace.Especially when the number of aircraft in airspace is large,the automatic avoidance system of UAV will increase the avoidance time and trigger the safety alarm,but the safety level is still acceptable.This indicates that it is relatively safe for UAVs to enter non-isolated airspace under limited conditions.The results can be used as a reference for the safe operation of unmanned aerial vehicle(UAV)in non-isolated airspace.
文摘The work illustrates the impossibility of decreasing entropy in a strictly random thermodynamic process in a non-isolated system using the example of heating a planet by solar radiation flux without and taking into account its rotation around its own axis. That is, the second law of thermodynamics formulated for isolated systems continues to govern such systems. We have shown that in order to achieve a stationary state at lower values of temperature and entropy far from thermodynamic equilibrium at a maximum of temperature and entropy, it is necessary to have regular factors of nonrandom nature, one of which in this example is the rotation of the planet around its own axis. This means that the reason for the appearance of ordered structured objects in non-isolated thermodynamic systems is not the random process itself, but the action of dynamic control mechanisms, such as periodic external influences, nonlinear elements with positive feedback, catalysts for chemical reactions, etc. We present the plots with dependences of temperature and entropy versus time in non-isolated systems with purely random processes and in the presence of a control factor of non-random nature-rotation.
基金supported by Poongsan-KAIST Future Research Center Projectthe fund support provided by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(Grant No.2023R1A2C2005661)。
文摘This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.
基金Supported in part by Science Center for Gas Turbine Project(Project No.P2022-DC-I-003-001)National Natural Science Foundation of China(Grant No.52275130).
文摘Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpretability.A promising approach to overcoming these challenges is to embed domain knowledge into the ML pipeline,enhancing the model with additional pattern information.In this paper,we review the latest developments in PHM,encapsulated under the concept of Knowledge Driven Machine Learning(KDML).We propose a hierarchical framework to define KDML in PHM,which includes scientific paradigms,knowledge sources,knowledge representations,and knowledge embedding methods.Using this framework,we examine current research to demonstrate how various forms of knowledge can be integrated into the ML pipeline and provide roadmap to specific usage.Furthermore,we present several case studies that illustrate specific implementations of KDML in the PHM domain,including inductive experience,physical model,and signal processing.We analyze the improvements in generalization capability and interpretability that KDML can achieve.Finally,we discuss the challenges,potential applications,and usage recommendations of KDML in PHM,with a particular focus on the critical need for interpretability to ensure trustworthy deployment of artificial intelligence in PHM.
基金financially supported by the Key Research&Development Program of Guangxi(No.GuiKeAB22080088)the Joint Project on Regional High-Incidence Diseases Research of Guangxi Natural Science Foundation(No.2023GXNSFDA026023)+3 种基金the Natural Science Foundation of Guangxi(No.2023JJA140322)the National Natural Science Foundation of China(No.82360372)the High-level Medical Expert Training Program of Guangxi“139 Plan Funding(No.G202003010)the Medical Appropriate Technology Development and Popularization and Application Project of Guangxi(No.S2020099)。
文摘Acute lung injury(ALI)was characterized by excessive reactive oxygen species(ROS)levels and inflammatory response in the lung.Scavenging ROS could inhibit the excessive inflammatory response,further treating ALI.Herein,we designed a novel nanozyme(P@Co)comprised of polydopamine(PDA)nanoparticles(NPs)loading with ultra-small Co,combining with near infrared(NIR)irradiation,which could efficiently scavenge intracellular ROS and suppress inflammatory responses against ALI.For lipopolysaccharide(LPS)induced macrophages,P@Co+NIR presented excellent antioxidant and anti-inflammatory capacities through lowering intracellular ROS levels,decreasing the expression levels of interleukin-6(IL-6)and tumor necrosis factor-α(TNF-α)as well as inducing macrophage M2 directional polarization.Significantly,it displayed the outstanding activities of lowering acute lung inflammation,relieving diffuse alveolar damage,and up-regulating heat shock protein 70(HSP70)expression,resulting in synergistic enhanced ALI therapy effect.It offers a novel strategy for the clinical treatment of ROS related diseases.
基金the National Natural Science Foundation of China (Grants No. 12072090 and No.12302056) to provide fund for conducting experiments。
文摘Recently, high-precision trajectory prediction of ballistic missiles in the boost phase has become a research hotspot. This paper proposes a trajectory prediction algorithm driven by data and knowledge(DKTP) to solve this problem. Firstly, the complex dynamics characteristics of ballistic missile in the boost phase are analyzed in detail. Secondly, combining the missile dynamics model with the target gravity turning model, a knowledge-driven target three-dimensional turning(T3) model is derived. Then, the BP neural network is used to train the boost phase trajectory database in typical scenarios to obtain a datadriven state parameter mapping(SPM) model. On this basis, an online trajectory prediction framework driven by data and knowledge is established. Based on the SPM model, the three-dimensional turning coefficients of the target are predicted by using the current state of the target, and the state of the target at the next moment is obtained by combining the T3 model. Finally, simulation verification is carried out under various conditions. The simulation results show that the DKTP algorithm combines the advantages of data-driven and knowledge-driven, improves the interpretability of the algorithm, reduces the uncertainty, which can achieve high-precision trajectory prediction of ballistic missile in the boost phase.
基金the support from the National Natural Science Foundation of China(52202306)Program from Guangdong Introducing Innovative and Entrepreneurial Teams(2019ZT08L101 and RCTDPT-2020-001)+1 种基金Shenzhen Key Laboratory of Eco-materials and Renewable Energy(ZDSYS20200922160400001)the Provincial Talent Plan of Guangdong(2023TB0012).
文摘Methane(CH4),the predominant component of natural gas and shale gas,is regarded as a promising carbon feedstock for chemical synthesis[1].However,considering the extreme stability of CH4 molecules,it's quite challenging in simultaneously achieving high activity and selectivity for target products under mild conditions,especially when synthesizing high-value C2t chemicals such as ethanol[2].The conversion of methane to ethanol by photocatalysis is promising for achieving transformation under ambient temperature and pressure conditions.Currently,the apparent quantum efficiency(AQE)of solar-driven methane-to-ethanol conversion is generally below 0.5%[3,4].Furthermore,the stability of photocatalysts remains inadequate,offering substantial potential for further improvement.
基金supported by the National Natural Science Foundation of China(Grant Nos.12222515,12075324 for S.Yin,and 12347107,1257-4160 for Y.F.Jiang)the National Key R&D Program of China(Grant No.2022YFA1402703 for Y.F.Jiang)+1 种基金the Science and Technology Projects in Guangdong Province(Grant No.2021QN02X561 for S.Yin)the Science and Technology Projects in Guangzhou City(Grant No.2025A04J5408 for S.Yin)。
文摘The conventional Kibble–Zurek mechanism,describing driven dynamics across critical points based on the adiabatic-impulse scenario(AIS),has attracted broad attention.However,the driven dynamics at the tricritical point with two independent relevant directions have not been adequately studied.Here,we employ the time-dependent variational principle to study the driven critical dynamics at a one-dimensional supersymmetric Ising tricritical point.For the relevant direction along the Ising critical line,the AIS apparently breaks down.Nevertheless,we find that the critical dynamics can still be described by finite-time scaling in which the driving rate has a dimension of r_(μ)=z+1/v_(μ)with z and v_(μ)being the dynamic exponent and correlation length exponent in this direction,respectively.For driven dynamics along another direction,the driving rate has a dimension of r_(p)=z+1/v_(p)with v_(p)being another correlation length exponent.Our work brings a new fundamental perspective into nonequilibrium critical dynamics near the tricritical point,which could be realized in programmable quantum processors in Rydberg atomic systems.
基金supported by the National Natural Science Foundation of China(62302234).
文摘As the number of distributed power supplies increases on the user side,smart grids are becoming larger and more complex.These changes bring new security challenges,especially with the widespread adop-tion of data-driven control methods.This paper introduces a novel black-box false data injection attack(FDIA)method that exploits the measurement modules of distributed power supplies within smart grids,highlighting its effectiveness in bypassing conventional security measures.Unlike traditional methods that focus on data manipulation within communication networks,this approach directly injects false data at the point of measurement,using a generative adversarial network(GAN)to generate stealthy attack vectors.This method requires no detailed knowledge of the target system,making it practical for real-world attacks.The attack’s impact on power system stability is demonstrated through experiments,high-lighting the significant cybersecurity risks introduced by data-driven algorithms in smart grids.
基金supported by the National Natural Science Foundation of China(Grant Nos.12104109,12222515,and 12075324)the Science and Technology Projects in Guangzhou(Grant No.2024A04J2092)the Science and Technology Projects in Guangdong Province(Grant No.211193863020).
文摘Driven critical dynamics in quantum phase transitions holds significant theoretical importance,and also has practical applications in fast-developing quantum devices.While scaling corrections have been shown to play important roles in fully characterizing equilibrium quantum criticality,their impact on nonequilibrium critical dynamics has not been extensively explored.In this work,we investigate the driven critical dynamics in a two-dimensional quantum Heisenberg model.We find that in this model the scaling corrections arising from both finite system size and finite driving rate must be incorporated into the finite-time scaling form in order to properly describe the nonequilibrium scaling behaviors.In addition,improved scaling relations are obtained from the expansion of the full scaling form.We numerically verify these scaling forms and improved scaling relations for different starting states using the nonequilibrium quantum Monte Carlo algorithm.
基金Supported by National Natural Science Foundation of China(Grant Nos.52325501,U24B2047).
文摘Current research on robot calibration can be roughly classified into two categories,and both of them have certain inherent limitations.Model-based methods are difficult to model and compensate the pose errors arising from configuration-dependent geometric and non-geometric source errors,whereas the accuracy of data-driven methods depends on a large amount of measurement data.Using a 5-DOF(degrees of freedom)hybrid machining robot as an exemplar,this study presents a model data-driven approach for the calibration of robotic manipulators.An f-DOF realistic robot containing various source errors is visualized as a 6-DOF fictitious robot having error-free parameters,but erroneous actuated/virtual joint motions.The calibration process essentially involves four steps:(1)formulating the linear map relating the pose error twist to the joint motion errors,(2)parameterizing the joint motion errors using second-order polynomials in terms of nominal actuated joint variables,(3)identifying the polynomial coefficients using the weighted least squares plus principal component analysis,and(4)compensating the compensable pose errors by updating the nominal actuated joint variables.The merit of this approach is that it enables compensation of the pose errors caused by configuration-dependent geometric and non-geometric source errors using finite measurement configurations.Experimental studies on a prototype machine illustrate the effectiveness of the proposed approach.
文摘Correction to:J.Iron Steel Res.Int.https://doi.org/10.1007/s42243-025-01545-x The publication of this article unfortunately contained mistakes.Equation(14)was not correct.The corrected equation is given below.
文摘Additive manufacturing(AM)technology has revolutionized engineering field by enabling the creation of intricate,high-performance structures that were once difficult or impossible to fabricate.This transformative technology has particularly advanced the development of metamaterials-engineered materials whose unique properties arise from their structure rather than composition-unlocking immense potential in fields ranging from aerospace to biomedical engineering.
文摘Tunnel facility management(FM)is crucial for ensuring safety,efficiency,and resilience of tunnel infrastructure.Current FM practices,such as reactive and preventive maintenance,have limitations-reactive maintenance can't prevent failures,and preventive maintenance can't predict asset maintenance needs,leading to costly and inefficient processes.In addition,existing computerized tunnel FM systems face various challenges,including the lack of integrated real-time monitoring information,visualization of assets in a three-dimensional(3D)environment,supporting predictive maintenance,and scaling into long infrastructure with complicated spatiotemporal relationships.This study addresses these limitations by proposing a data-driven digital twin(DT)-based framework that supports predictive maintenance to improve tunnel FM processes and enhance resilience.The proposed framework consists of six layers,allowing the integration of data from monitoring system,FM system,and building information modeling(BIM)models.The framework proposes a flexible tunnel data model and classification system that hierarchically divides the tunnel models,ensuring an efficient data connection from the physical twin to the DT.The system was implemented in a tunnel case study that generates maintenance plans and work orders using historical and current condition monitoring data,and the 3D visualization technology suggests maintenance and repair processes,making the FM decision process more effective.The proposed system detected and predicted the twin state based on a data-driven analysis,and the prediction accuracy of the machine learning models was sufficiently high for use in real scenarios to make FM plans in advance and prevent asset failures.The proposed framework is contributing to the infrastructure resilience by enhancing the tunnel system ability to predict the maintenance tasks and prevent failures using data-driven DT technology.
基金supported by the National Natural Science Foundation of China(Grant Nos.42277161,42230709).
文摘Accurate identification and effective support of key blocks are crucial for ensuring the stability and safety of rock slopes.The number of structural planes and rock blocks were reduced in previous studies.This impairs the ability to characterize complex rock slopes accurately and inhibits the identification of key blocks.In this paper,a knowledge-data dually driven paradigm for accurate identification of key blocks in complex rock slopes is proposed.Our basic idea is to integrate key block theory into data-driven models based on finely characterizing structural features to identify key blocks in complex rock slopes accurately.The proposed novel paradigm consists of(1)representing rock slopes as graph-structured data based on complex systems theory,(2)identifying key nodes in the graph-structured data using graph deep learning,and(3)mapping the key nodes of graph-structured data to corresponding key blocks in the rock slope.Verification experiments and real-case applications are conducted by the proposed method.The verification results demonstrate excellent model performance,strong generalization capability,and effective classification results.Moreover,the real case application is conducted on the northern slope of the Yanqianshan Iron Mine.The results show that the proposed method can accurately identify key blocks in complex rock slopes,which can provide a decision-making basis and rational recommendations for effective support and instability prevention of rock slopes,thereby ensuring the stability of rock engineering and the safety of life and property.
