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
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
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
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
The article "Data-driven soft sensors in blast furnace ironmaking:a survey,"written by Yueyang LUO,Xinmin ZHANG,Manabu KANO,Long DENG,Chunjie YANG,and Zhihuan SONG,was originally published electronically on ...The article "Data-driven soft sensors in blast furnace ironmaking:a survey,"written by Yueyang LUO,Xinmin ZHANG,Manabu KANO,Long DENG,Chunjie YANG,and Zhihuan SONG,was originally published electronically on the publisher's Internet portal on Mar.27,2023 without open access.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
The discovery of new superconducting materials,particularly those exhibiting high critical temperature(Tc),has been a vibrant area of study within the field of condensed matter physics.Conventional approaches primaril...The discovery of new superconducting materials,particularly those exhibiting high critical temperature(Tc),has been a vibrant area of study within the field of condensed matter physics.Conventional approaches primarily rely on physical intuition to search for potential superconductors within the existing databases.However,the known materials only scratch the surface of the extensive array of possibilities within the realm of materials.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
Social behaviors,including social support and mating,play a critical role in survival and reproduction.Animals must make adaptive social decisions based on internal states and external contexts[1].The sex of a social ...Social behaviors,including social support and mating,play a critical role in survival and reproduction.Animals must make adaptive social decisions based on internal states and external contexts[1].The sex of a social partner is a crucial factor that shapes social decision-making,as oppositesex interactions are vital for fulfilling reproductive needs,whereas same-sex interactions are essential for both collaborative support and competitive behaviors.Under normal circumstances,mice typically exhibit a variety of prosocial behaviors that strengthen social bonds within their groups.展开更多
The 2025 National Energy Conference(hereinafter referred to as“the conference”)was held in Beijing on December 15,2024.The conference highlighted that“next year and the‘15th Five-Year Plan’period will be crucial ...The 2025 National Energy Conference(hereinafter referred to as“the conference”)was held in Beijing on December 15,2024.The conference highlighted that“next year and the‘15th Five-Year Plan’period will be crucial for accelerating the construction of a new energy system and promoting high-quality energy development and high-level security”.This marks the 16th national energy conference since the annual national energy conference was reinstated in January 2009 after a hiatus of over a decade.展开更多
Introduction Early cancer detection represents a critical evolution in healthcare,addressing a significant pain point in cancer treatment:the tendency for diagnoses to occur at advanced stages.Traditionally,many cance...Introduction Early cancer detection represents a critical evolution in healthcare,addressing a significant pain point in cancer treatment:the tendency for diagnoses to occur at advanced stages.Traditionally,many cancers are not identified until they have progressed to late stages,where treatment options become limited,less effective,and more costly.This late detection results in poorer prognoses,higher mortality rates,and increased healthcare costs.Without early detection tools like Fluorescence In Situ Hybridization(FISH),these challenges persist,leaving patients with fewer opportunities for successful outcomes.展开更多
Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs.However,pure electric-driven mining excavators are prone to unexpected ...Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs.However,pure electric-driven mining excavators are prone to unexpected power outages in mines because of drastic changes in load power,leading to significant fluctuations in the power demand of the grid,which in turn affects production.To solve the above problem,a pure electric-driven mining hydraulic excavator based on electric-motor-driven swing platform and hydraulic pumps was used as the research object.Moreover,supercapacitors and DC/DC converter,as the energy storage system(ESS)adjust the output power of the grid and recover the braking kinetic energy of the swing platform.Subsequently,a novel integrated energy management strategy for a DC bus voltage predictive controller based on the power feedforward of fuzzy rules is proposed to run mining excavators efficiently and reliably.Specifically,the working modes of the ESS are determined by the DC bus voltage and state of charge(SOC)of the supercapacitor.Next,the output power of the supercapacitor and the DC bus voltage were controlled by adjusting the charging and discharging currents of the DC/DC converter using a predictive controller and fuzzy rules.In addition,a digital prototype of the excavator was verified using an original machine test.The performance of the different strategies and driven systems were analyzed using digital prototypes.The results showed that,compared with traditional excavators with diesel engines,the operational cost of the developed excavators was reduced by 54.02%.Compared to pure electric-driven excavators without an ESS,the peak power of the grid for the developed excavators was reduced by 10%.This study designed an integrated energy management strategy for a pure electric mining excavator that can regulate the power output of the grid and maintain the stability of the bus voltage and SOC of the ESS.展开更多
Dear Editor,This letter studies the bipartite consensus tracking problem for heterogeneous multi-agent systems with actuator faults and a leader's unknown time-varying control input. To handle such a problem, the ...Dear Editor,This letter studies the bipartite consensus tracking problem for heterogeneous multi-agent systems with actuator faults and a leader's unknown time-varying control input. To handle such a problem, the continuous fault-tolerant control protocol via observer design is developed. In addition, it is strictly proved that the multi-agent system driven by the designed controllers can still achieve bipartite consensus tracking after faults occur.展开更多
Improving the computational efficiency of multi-physics simulation and constructing a real-time online simulation method is an important way to realise the virtual-real fusion of entities and data of power equipment w...Improving the computational efficiency of multi-physics simulation and constructing a real-time online simulation method is an important way to realise the virtual-real fusion of entities and data of power equipment with digital twin.In this paper,a datadriven fast calculation method for the temperature field of resin impregnated paper(RIP)bushing used in converter transformer valve-side is proposed,which combines the data dimensionality reduction technology and the surrogate model.After applying the finite element algorithm to obtain the temperature field distribution of RIP bushing under different operation conditions as the input dataset,the proper orthogonal decomposition(POD)algorithm is adopted to reduce the order and obtain the low-dimensional projection of the temperature data.On this basis,the surrogate model is used to construct the mapping relationship between the sensor monitoring data and the low-dimensional projection,so that it can achieve the fast calculation and reconstruction of temperature field distribution.The results show that this method can effectively and quickly calculate the overall temperature field distribution of the RIP bushing.The maximum relative error and the average relative error are less than 4.5%and 0.25%,respectively.The calculation speed is at the millisecond level,meeting the needs of digitalisation of power equipment.展开更多
Plp1-lineage Schwann cells(SCs)of peripheral nerve play a critical role in vascular remodeling and osteogenic differentiation during the early stage of bone healing,and the abnormal plasticity of SCs would jeopardize ...Plp1-lineage Schwann cells(SCs)of peripheral nerve play a critical role in vascular remodeling and osteogenic differentiation during the early stage of bone healing,and the abnormal plasticity of SCs would jeopardize the bone regeneration.However,how Plp1-lineage cells respond to injury and initiate the vascularized osteogenesis remains incompletely understood.Here,by employing single-cell transcriptional profiling combined with lineage-specific tracing models,we uncover that Plp1-lineage cells undergoing injury-induced glia-to-MSCs transition contributed to osteogenesis and revascularization in the initial stage of bone injury.Importantly,our data demonstrated that the Sonic hedgehog(Shh)signaling was responsible for the transition process initiation,which was strongly activated by c-Jun/SIRT6/BAF170 complex-driven Shh enhancers.Collectively,these findings depict an injuryspecific niche signal-mediated Plp1-lineage cells transition towards Gli1+MSCs and may be instructive for approaches to promote bone regeneration during aging or other bone diseases.展开更多
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.
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.
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The article "Data-driven soft sensors in blast furnace ironmaking:a survey,"written by Yueyang LUO,Xinmin ZHANG,Manabu KANO,Long DENG,Chunjie YANG,and Zhihuan SONG,was originally published electronically on the publisher's Internet portal on Mar.27,2023 without open access.
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.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.62476278,12434009,and 12204533)the National Key R&D Program of China(Grant No.2024YFA1408601)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302402)。
文摘The discovery of new superconducting materials,particularly those exhibiting high critical temperature(Tc),has been a vibrant area of study within the field of condensed matter physics.Conventional approaches primarily rely on physical intuition to search for potential superconductors within the existing databases.However,the known materials only scratch the surface of the extensive array of possibilities within the realm of materials.
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supported by grants from the National Natural Science Foundation of China(32471074 and 32200825)the STI2030-Major Projects(2021ZD0203000 and 2021ZD0203002)+1 种基金the Shandong Provincial Taishan Scholars Project(tsqn202306174)the Shandong Provincial Natural Science Foundation(ZR2022QC173).
文摘Social behaviors,including social support and mating,play a critical role in survival and reproduction.Animals must make adaptive social decisions based on internal states and external contexts[1].The sex of a social partner is a crucial factor that shapes social decision-making,as oppositesex interactions are vital for fulfilling reproductive needs,whereas same-sex interactions are essential for both collaborative support and competitive behaviors.Under normal circumstances,mice typically exhibit a variety of prosocial behaviors that strengthen social bonds within their groups.
文摘The 2025 National Energy Conference(hereinafter referred to as“the conference”)was held in Beijing on December 15,2024.The conference highlighted that“next year and the‘15th Five-Year Plan’period will be crucial for accelerating the construction of a new energy system and promoting high-quality energy development and high-level security”.This marks the 16th national energy conference since the annual national energy conference was reinstated in January 2009 after a hiatus of over a decade.
基金supported by Guangzhou Development Zone Science and Technology(2021GH10,2020GH10,2023GH02)the University of Macao(MYRG2022-00271-FST)The Science and Technology Development Fund(FDCT)of Macao(0032/2022/A).
文摘Introduction Early cancer detection represents a critical evolution in healthcare,addressing a significant pain point in cancer treatment:the tendency for diagnoses to occur at advanced stages.Traditionally,many cancers are not identified until they have progressed to late stages,where treatment options become limited,less effective,and more costly.This late detection results in poorer prognoses,higher mortality rates,and increased healthcare costs.Without early detection tools like Fluorescence In Situ Hybridization(FISH),these challenges persist,leaving patients with fewer opportunities for successful outcomes.
基金Supported by National Natural Science Foundation of ChinaShanxi Coalbased Low-Carbon Joint Fund(Grant No.U1910211)。
文摘Using electric motors instead of diesel engines as the driving system for mining excavators can reduce the energy consumption and operating costs.However,pure electric-driven mining excavators are prone to unexpected power outages in mines because of drastic changes in load power,leading to significant fluctuations in the power demand of the grid,which in turn affects production.To solve the above problem,a pure electric-driven mining hydraulic excavator based on electric-motor-driven swing platform and hydraulic pumps was used as the research object.Moreover,supercapacitors and DC/DC converter,as the energy storage system(ESS)adjust the output power of the grid and recover the braking kinetic energy of the swing platform.Subsequently,a novel integrated energy management strategy for a DC bus voltage predictive controller based on the power feedforward of fuzzy rules is proposed to run mining excavators efficiently and reliably.Specifically,the working modes of the ESS are determined by the DC bus voltage and state of charge(SOC)of the supercapacitor.Next,the output power of the supercapacitor and the DC bus voltage were controlled by adjusting the charging and discharging currents of the DC/DC converter using a predictive controller and fuzzy rules.In addition,a digital prototype of the excavator was verified using an original machine test.The performance of the different strategies and driven systems were analyzed using digital prototypes.The results showed that,compared with traditional excavators with diesel engines,the operational cost of the developed excavators was reduced by 54.02%.Compared to pure electric-driven excavators without an ESS,the peak power of the grid for the developed excavators was reduced by 10%.This study designed an integrated energy management strategy for a pure electric mining excavator that can regulate the power output of the grid and maintain the stability of the bus voltage and SOC of the ESS.
基金supported by the National Natural Science Foundation of China(62325304,U22B2046,62073079,62376029)the Jiangsu Provincial Scientific Research Center of Applied Mathematics(BK20233002)the China Postdoctoral Science Foundation(2023M730255,2024T171123)
文摘Dear Editor,This letter studies the bipartite consensus tracking problem for heterogeneous multi-agent systems with actuator faults and a leader's unknown time-varying control input. To handle such a problem, the continuous fault-tolerant control protocol via observer design is developed. In addition, it is strictly proved that the multi-agent system driven by the designed controllers can still achieve bipartite consensus tracking after faults occur.
基金supported by China Postdoctoral Science Foundation,Grant 2024M753544Science and Technology Project of CSG,Grant GDKJXM2022106.
文摘Improving the computational efficiency of multi-physics simulation and constructing a real-time online simulation method is an important way to realise the virtual-real fusion of entities and data of power equipment with digital twin.In this paper,a datadriven fast calculation method for the temperature field of resin impregnated paper(RIP)bushing used in converter transformer valve-side is proposed,which combines the data dimensionality reduction technology and the surrogate model.After applying the finite element algorithm to obtain the temperature field distribution of RIP bushing under different operation conditions as the input dataset,the proper orthogonal decomposition(POD)algorithm is adopted to reduce the order and obtain the low-dimensional projection of the temperature data.On this basis,the surrogate model is used to construct the mapping relationship between the sensor monitoring data and the low-dimensional projection,so that it can achieve the fast calculation and reconstruction of temperature field distribution.The results show that this method can effectively and quickly calculate the overall temperature field distribution of the RIP bushing.The maximum relative error and the average relative error are less than 4.5%and 0.25%,respectively.The calculation speed is at the millisecond level,meeting the needs of digitalisation of power equipment.
基金supported by the National Natural Science Foundation of China(grants 81970910 and 82370931)Jiangsu Province Capability Improvement Project through Science,Technology and Education-Jiangsu Provincial Research Hospital Cultivation Unit(YJXYYJSDW4)Jiangsu Provincial Medical Innovation Center(CXZX202227).
文摘Plp1-lineage Schwann cells(SCs)of peripheral nerve play a critical role in vascular remodeling and osteogenic differentiation during the early stage of bone healing,and the abnormal plasticity of SCs would jeopardize the bone regeneration.However,how Plp1-lineage cells respond to injury and initiate the vascularized osteogenesis remains incompletely understood.Here,by employing single-cell transcriptional profiling combined with lineage-specific tracing models,we uncover that Plp1-lineage cells undergoing injury-induced glia-to-MSCs transition contributed to osteogenesis and revascularization in the initial stage of bone injury.Importantly,our data demonstrated that the Sonic hedgehog(Shh)signaling was responsible for the transition process initiation,which was strongly activated by c-Jun/SIRT6/BAF170 complex-driven Shh enhancers.Collectively,these findings depict an injuryspecific niche signal-mediated Plp1-lineage cells transition towards Gli1+MSCs and may be instructive for approaches to promote bone regeneration during aging or other bone diseases.
