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
Specially shaped permanent magnet structures can satisfy the requirements of equipment with limited space or unique shapes.Thereby,these optimize the distribution of magnetic fields.However,traditional manufacturing m...Specially shaped permanent magnet structures can satisfy the requirements of equipment with limited space or unique shapes.Thereby,these optimize the distribution of magnetic fields.However,traditional manufacturing methods are limited by the mold design and insufficient material utilization.In this study,a polymer-based Nd_(2)Fe_(14)B(NdFeB)magnetic slurry was developed based on direct ink writing(DIW)3D printing technology.A rapidly volatilizable magnetic slurry was used to achieve 3D oriented controllable layering,thus realizing the direct molding fabrication of NdFeB permanent magnets with complex structures.By exploring and optimizing the 3D printing process parameters,specially shaped bonded NdFeB permanent magnet structures with high precision and shape fidelity were prepared.The test results indicated that the remnant magnetization of the printed magnets was proportional to the NdFeB content in the slurry,the coercivity closely matched that of the original powder,and the mechanical properties of the printed magnets were favorable.Building on this,a magnetically driven helical-structure robot was designed and printed to achieve stable motion in low-Reynolds-number fluids.This paper presents a new,low-cost solution for the room-temperature preparation of shape-bonded NdFeB permanent magnets.展开更多
The accelerated pace of natural and human-driven climate change presents profound challenges for Earth's systems.Oceans and ice sheets are critical regulators of climate systems,functioning as carbon sinks and the...The accelerated pace of natural and human-driven climate change presents profound challenges for Earth's systems.Oceans and ice sheets are critical regulators of climate systems,functioning as carbon sinks and thermal reservoirs.However,they are increasingly vulnerable to warming and greenhouse gas emissions.展开更多
Designing transition metal nickel-cobalt-based battery-type electrode materials driven by anions is crucial for achieving rapid OH-ion transport under electrochemical activation conditions,thereby improving capacitanc...Designing transition metal nickel-cobalt-based battery-type electrode materials driven by anions is crucial for achieving rapid OH-ion transport under electrochemical activation conditions,thereby improving capacitance performance.Herein,borate anions are selected through theoretical calculations,and twodimensional(2D)defect-rich amorphous nickel-cobalt-based borate is synthesized via a facile chemical reduction method.Under potentiostatic modification,activated products(NCB-G-E)are obtained.In situ Raman spectra reveal that electron-deficient borate extracts electrons from metal centers,facilitating the oxidation state transition of Ni and Co.Theoretical calculations show that in situ adsorbed borate regulates the d-band centers of metal sites,enhancing OH^(-)intermediate adsorption.Meanwhile,borate anion adsorption accelerates the deprotonation and activation processes.Electrochemical tests demonstrate that NCB-G-E displays superior capacitance performance,with a high quality specific capacity of383.3 mA h g^(-1)and 65% retention rate at 30 A g^(-1),surpassing most nickel-cobalt-based electrodes.The assembled asymmetric supercapacitor presents an impressive energy density of 68.2 Wh kg^(-1)and good cycling stability.This work highlights the role of electron-deficient borate in tuning metal band structure and promoting oxidation state transition through synergistic defect advantages,offering new prospects for advanced battery-type energy storage materials.展开更多
Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience...Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience-dependent mechanisms.The pruning process involves multiple molecular signals and a series of regulatory activities governing the“eat me”and“don't eat me”states.Under physiological conditions,the interaction between glial cells and neurons results in the clearance of unnecessary synapses,maintaining normal neural circuit functionality via synaptic pruning.Alterations in genetic and environmental factors can lead to imbalanced synaptic pruning,thus promoting the occurrence and development of autism spectrum disorder,schizophrenia,Alzheimer's disease,and other neurological disorders.In this review,we investigated the molecular mechanisms responsible for synaptic pruning during neural development.We focus on how synaptic pruning can regulate neural circuits and its association with neurological disorders.Furthermore,we discuss the application of emerging optical and imaging technologies to observe synaptic structure and function,as well as their potential for clinical translation.Our aim was to enhance our understanding of synaptic pruning during neural development,including the molecular basis underlying the regulation of synaptic function and the dynamic changes in synaptic density,and to investigate the potential role of these mechanisms in the pathophysiology of neurological diseases,thus providing a theoretical foundation for the treatment of neurological disorders.展开更多
As a tool for quantifying individuals’visual attention and information processing,eye-tracking technology is gradually being applied in the reform of higher education.This paper focuses on issues in university mathem...As a tool for quantifying individuals’visual attention and information processing,eye-tracking technology is gradually being applied in the reform of higher education.This paper focuses on issues in university mathematics teaching,such as heavy cognitive load,delayed feedback,and insufficient adaptability.Based on theories of cognitive psychology,the study explores application pathways of this technology in cognitive diagnosis,instructional optimization,classroom regulation,personalized support,and teaching assessment.Research shows that eye-tracking data can reveal key cognitive features during the learning process,enhance the visualization of instructional feedback,and improve the scientific basis of decision-making.This provides both theoretical support and practical reference for data-driven and precise transformation in university mathematics education.展开更多
China’s modern corporate system fuels sustainable growth by merging profit-driven innovation with social responsibility,turning rural villages into prosperous microcosms of equitable development.
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
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
As NPC performances surge in popularity,destinations are recruiting actors and building story-driven scenes to reinvent traditional tourism Eight days,seven cities-a whirlwind tour that defined actor Zheng Guolin’s w...As NPC performances surge in popularity,destinations are recruiting actors and building story-driven scenes to reinvent traditional tourism Eight days,seven cities-a whirlwind tour that defined actor Zheng Guolin’s work schedule during the National Day holiday a month ago.From 1 to 8 October,he maintained a relentless pace,not just logging miles but also switching between roles.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
China’s enhanced intellectual property rights(IPR)protection framework presents a promising prospect for European SMEs to pursue strategic growth and market expansion within the country’s innovation-driven economy.
Academic exchanges,training,and technology transfer under China-Africa cooperation are heralding a sea change in rural Tanzania.Iden Revocatus Stephano,based in Morogoro Region,is a shining example of this youth-drive...Academic exchanges,training,and technology transfer under China-Africa cooperation are heralding a sea change in rural Tanzania.Iden Revocatus Stephano,based in Morogoro Region,is a shining example of this youth-driven transition.A graduate of Sokoine University of Agriculture,26-year-old Stephano runs a flourishing mushroom farm-a testament to the growing wave of young Africans reshaping rural economies with global insight and local innovation.展开更多
Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reas...Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reasonable manual inspection capacity.Given that idlers typically have a lifespan of 1-2 years,there is an urgent need for a rapid,cost-effective,and intelligent safety monitoring system.However,current embedded systems face prohibitive replacement costs,while conventional monitoring technologies suffer from inefficiency at low rotational speeds and lack systematic structural optimization frameworks for diverse idler types and parameters.To address these challenges,this paper introduces an integrated,on-site detachable self-powered idler condition monitoring system(ICMS).This system combines energy harvesting based on the magnetic modulation technology with wireless condition monitoring capabilities.Specifically,it develops a data-driven model integrating convolutional neural networks(CNNs) with genetic algorithms(GAs).The conventional testing results show that the data-driven model not only significantly accelerates the parameter response time,but also achieves a prediction accuracy of 92.95%.The in-situ experiments conducted in coal mines demonstrate the system's reliability and monitoring functionality under both no-load and fullload conditions.This research provides an innovative self-powered condition monitoring solution and develops an efficient data-driven model,offering feasible online monitoring approaches for smart mine construction.展开更多
Monitoring of the earth’s surface has been significantly improved thanks to optical remote sensing by satellites such as SPOT,Landsat and Sentinel-2,which produce vast datasets.The processing of this data,often refer...Monitoring of the earth’s surface has been significantly improved thanks to optical remote sensing by satellites such as SPOT,Landsat and Sentinel-2,which produce vast datasets.The processing of this data,often referred to as Big Data,is essential for decision-making,requiring the application of advanced algorithms to analyze changes in land cover.In the age of artificial intelligence,supervised machine learning algorithms are widely used,although their application in urban contexts remains complex.Researchers have to evaluate and tune various algorithms according to assumptions and experiments,which requires time and resources.This paper presents a meta-modeling approach for urban satellite image classification,using model-driven engineering techniques.The aim is to provide urban planners with standardized solutions for geospatial processing,promoting reusability and interoperability.Formalization includes the creation of a knowledge base and the modeling of processing chains to analyze land use.展开更多
Additive Manufacturing(AM)has significantly impacted the development of high-performance materials and structures,offering new possibilities for industries ranging from aerospace to biomedicine.This special issue feat...Additive Manufacturing(AM)has significantly impacted the development of high-performance materials and structures,offering new possibilities for industries ranging from aerospace to biomedicine.This special issue features pioneering research that integrates AI-driven methods with AM,enabling the design and fabrication of complex,optimized structures with enhanced properties.展开更多
Accurate prediction of ground surface settlement(GSS)adjacent to an excavation is important to prevent potential damage to the surrounding environment.Previous studies have extensively delved into this topic but all u...Accurate prediction of ground surface settlement(GSS)adjacent to an excavation is important to prevent potential damage to the surrounding environment.Previous studies have extensively delved into this topic but all under the limitations of either imprecise theories or insufficient data.In the present study,we proposed a physics-constrained neural network(PhyNN)for predicting excavation-induced GSS to fully integrate the theory of elasticity with observations and make full use of the strong fitting ability of neural networks(NNs).This model incorporates an analytical solution as an additional regularization term in the loss function to guide the training of NN.Moreover,we introduced three trainable parameters into the analytical solution so that it can be adaptively modified during the training process.The performance of the proposed PhyNN model is verified using data from a case study project.Results show that our PhyNN model achieves higher prediction accuracy,better generalization ability,and robustness than the purely data-driven NN model when confronted with data containing noise and outliers.Remarkably,by incorporating physical constraints,the admissible solution space of PhyNN is significantly narrowed,leading to a substantial reduction in the need for the amount of training data.The proposed PhyNN can be utilized as a general framework for integrating physical constraints into data-driven machine-learning models.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
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 National Natural Science Foundation of China(Grant Nos.52375348,52175331)National Natural Science Foundation of Shandong Province(Grant Nos.ZR2022ME014,ZR2020ZD04).
文摘Specially shaped permanent magnet structures can satisfy the requirements of equipment with limited space or unique shapes.Thereby,these optimize the distribution of magnetic fields.However,traditional manufacturing methods are limited by the mold design and insufficient material utilization.In this study,a polymer-based Nd_(2)Fe_(14)B(NdFeB)magnetic slurry was developed based on direct ink writing(DIW)3D printing technology.A rapidly volatilizable magnetic slurry was used to achieve 3D oriented controllable layering,thus realizing the direct molding fabrication of NdFeB permanent magnets with complex structures.By exploring and optimizing the 3D printing process parameters,specially shaped bonded NdFeB permanent magnet structures with high precision and shape fidelity were prepared.The test results indicated that the remnant magnetization of the printed magnets was proportional to the NdFeB content in the slurry,the coercivity closely matched that of the original powder,and the mechanical properties of the printed magnets were favorable.Building on this,a magnetically driven helical-structure robot was designed and printed to achieve stable motion in low-Reynolds-number fluids.This paper presents a new,low-cost solution for the room-temperature preparation of shape-bonded NdFeB permanent magnets.
文摘The accelerated pace of natural and human-driven climate change presents profound challenges for Earth's systems.Oceans and ice sheets are critical regulators of climate systems,functioning as carbon sinks and thermal reservoirs.However,they are increasingly vulnerable to warming and greenhouse gas emissions.
基金supported by the National Natural Science Foundation of China(22478422,22238012,and 22178384)Science Foundation of China University of Petroleum,Beijing(2462024QNXZ003)CHN Energy Investment Group(GJNY23-23)。
文摘Designing transition metal nickel-cobalt-based battery-type electrode materials driven by anions is crucial for achieving rapid OH-ion transport under electrochemical activation conditions,thereby improving capacitance performance.Herein,borate anions are selected through theoretical calculations,and twodimensional(2D)defect-rich amorphous nickel-cobalt-based borate is synthesized via a facile chemical reduction method.Under potentiostatic modification,activated products(NCB-G-E)are obtained.In situ Raman spectra reveal that electron-deficient borate extracts electrons from metal centers,facilitating the oxidation state transition of Ni and Co.Theoretical calculations show that in situ adsorbed borate regulates the d-band centers of metal sites,enhancing OH^(-)intermediate adsorption.Meanwhile,borate anion adsorption accelerates the deprotonation and activation processes.Electrochemical tests demonstrate that NCB-G-E displays superior capacitance performance,with a high quality specific capacity of383.3 mA h g^(-1)and 65% retention rate at 30 A g^(-1),surpassing most nickel-cobalt-based electrodes.The assembled asymmetric supercapacitor presents an impressive energy density of 68.2 Wh kg^(-1)and good cycling stability.This work highlights the role of electron-deficient borate in tuning metal band structure and promoting oxidation state transition through synergistic defect advantages,offering new prospects for advanced battery-type energy storage materials.
基金supported by the National Natural Science Foundation of China,No.31760290,82160688the Key Development Areas Project of Ganzhou Science and Technology,No.2022B-SF9554(all to XL)。
文摘Synaptic pruning is a crucial process in synaptic refinement,eliminating unstable synaptic connections in neural circuits.This process is triggered and regulated primarily by spontaneous neural activity and experience-dependent mechanisms.The pruning process involves multiple molecular signals and a series of regulatory activities governing the“eat me”and“don't eat me”states.Under physiological conditions,the interaction between glial cells and neurons results in the clearance of unnecessary synapses,maintaining normal neural circuit functionality via synaptic pruning.Alterations in genetic and environmental factors can lead to imbalanced synaptic pruning,thus promoting the occurrence and development of autism spectrum disorder,schizophrenia,Alzheimer's disease,and other neurological disorders.In this review,we investigated the molecular mechanisms responsible for synaptic pruning during neural development.We focus on how synaptic pruning can regulate neural circuits and its association with neurological disorders.Furthermore,we discuss the application of emerging optical and imaging technologies to observe synaptic structure and function,as well as their potential for clinical translation.Our aim was to enhance our understanding of synaptic pruning during neural development,including the molecular basis underlying the regulation of synaptic function and the dynamic changes in synaptic density,and to investigate the potential role of these mechanisms in the pathophysiology of neurological diseases,thus providing a theoretical foundation for the treatment of neurological disorders.
基金The 2024 Education and Teaching Reform Project,“Exploration and Practice of University Mathematics Teaching Reform Driven by Eye-Tracking Technology”(Project No.:JG2024047)。
文摘As a tool for quantifying individuals’visual attention and information processing,eye-tracking technology is gradually being applied in the reform of higher education.This paper focuses on issues in university mathematics teaching,such as heavy cognitive load,delayed feedback,and insufficient adaptability.Based on theories of cognitive psychology,the study explores application pathways of this technology in cognitive diagnosis,instructional optimization,classroom regulation,personalized support,and teaching assessment.Research shows that eye-tracking data can reveal key cognitive features during the learning process,enhance the visualization of instructional feedback,and improve the scientific basis of decision-making.This provides both theoretical support and practical reference for data-driven and precise transformation in university mathematics education.
文摘China’s modern corporate system fuels sustainable growth by merging profit-driven innovation with social responsibility,turning rural villages into prosperous microcosms of equitable development.
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.
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.
文摘As NPC performances surge in popularity,destinations are recruiting actors and building story-driven scenes to reinvent traditional tourism Eight days,seven cities-a whirlwind tour that defined actor Zheng Guolin’s work schedule during the National Day holiday a month ago.From 1 to 8 October,he maintained a relentless pace,not just logging miles but also switching between roles.
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China’s enhanced intellectual property rights(IPR)protection framework presents a promising prospect for European SMEs to pursue strategic growth and market expansion within the country’s innovation-driven economy.
文摘Academic exchanges,training,and technology transfer under China-Africa cooperation are heralding a sea change in rural Tanzania.Iden Revocatus Stephano,based in Morogoro Region,is a shining example of this youth-driven transition.A graduate of Sokoine University of Agriculture,26-year-old Stephano runs a flourishing mushroom farm-a testament to the growing wave of young Africans reshaping rural economies with global insight and local innovation.
基金supported by the National Natural Science Foundation of China(Nos.12172248,12302022,12021002,and 12132010)the Tianjin Research Program of Application Foundation and Advanced Technology of China(No.23JCZDJC00950)。
文摘Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reasonable manual inspection capacity.Given that idlers typically have a lifespan of 1-2 years,there is an urgent need for a rapid,cost-effective,and intelligent safety monitoring system.However,current embedded systems face prohibitive replacement costs,while conventional monitoring technologies suffer from inefficiency at low rotational speeds and lack systematic structural optimization frameworks for diverse idler types and parameters.To address these challenges,this paper introduces an integrated,on-site detachable self-powered idler condition monitoring system(ICMS).This system combines energy harvesting based on the magnetic modulation technology with wireless condition monitoring capabilities.Specifically,it develops a data-driven model integrating convolutional neural networks(CNNs) with genetic algorithms(GAs).The conventional testing results show that the data-driven model not only significantly accelerates the parameter response time,but also achieves a prediction accuracy of 92.95%.The in-situ experiments conducted in coal mines demonstrate the system's reliability and monitoring functionality under both no-load and fullload conditions.This research provides an innovative self-powered condition monitoring solution and develops an efficient data-driven model,offering feasible online monitoring approaches for smart mine construction.
文摘Monitoring of the earth’s surface has been significantly improved thanks to optical remote sensing by satellites such as SPOT,Landsat and Sentinel-2,which produce vast datasets.The processing of this data,often referred to as Big Data,is essential for decision-making,requiring the application of advanced algorithms to analyze changes in land cover.In the age of artificial intelligence,supervised machine learning algorithms are widely used,although their application in urban contexts remains complex.Researchers have to evaluate and tune various algorithms according to assumptions and experiments,which requires time and resources.This paper presents a meta-modeling approach for urban satellite image classification,using model-driven engineering techniques.The aim is to provide urban planners with standardized solutions for geospatial processing,promoting reusability and interoperability.Formalization includes the creation of a knowledge base and the modeling of processing chains to analyze land use.
文摘Additive Manufacturing(AM)has significantly impacted the development of high-performance materials and structures,offering new possibilities for industries ranging from aerospace to biomedicine.This special issue features pioneering research that integrates AI-driven methods with AM,enabling the design and fabrication of complex,optimized structures with enhanced properties.
基金funded by the National Natural Science Foundation of China(Grant No.52090082)support provided by the China Scholarship Council(Grant No.202206260206).
文摘Accurate prediction of ground surface settlement(GSS)adjacent to an excavation is important to prevent potential damage to the surrounding environment.Previous studies have extensively delved into this topic but all under the limitations of either imprecise theories or insufficient data.In the present study,we proposed a physics-constrained neural network(PhyNN)for predicting excavation-induced GSS to fully integrate the theory of elasticity with observations and make full use of the strong fitting ability of neural networks(NNs).This model incorporates an analytical solution as an additional regularization term in the loss function to guide the training of NN.Moreover,we introduced three trainable parameters into the analytical solution so that it can be adaptively modified during the training process.The performance of the proposed PhyNN model is verified using data from a case study project.Results show that our PhyNN model achieves higher prediction accuracy,better generalization ability,and robustness than the purely data-driven NN model when confronted with data containing noise and outliers.Remarkably,by incorporating physical constraints,the admissible solution space of PhyNN is significantly narrowed,leading to a substantial reduction in the need for the amount of training data.The proposed PhyNN can be utilized as a general framework for integrating physical constraints into data-driven machine-learning models.
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.
