The combination of dual-main-phase(DMP)(Nd,Ce)-Fe-B magnets and grain boundary diffusion process(GBDP)is currently a research topic for obtaining high-cost performance materials in rare earth permanent magnet fields.T...The combination of dual-main-phase(DMP)(Nd,Ce)-Fe-B magnets and grain boundary diffusion process(GBDP)is currently a research topic for obtaining high-cost performance materials in rare earth permanent magnet fields.The novel structural features of GBDP(Nd,Ce)-Fe-B magnets give a version of different domain reversal processes from those of non-diffused magnets.In this work,the in-situ magnetic domain evolution of the DMP magnets was observed at elevated temperatures,and the temperature demagnetization and coercivity mechanism of the GBDP dual-main-phase(Nd,Ce)-Fe-B magnets are discussed.The results show that the shell composition of different types of grains in DMP magnets is similar,while the magnetic microstructure results indicate the Ce-rich grains tend to demagnetize first.Dy-rich shell with a high anisotropic field caused by GBDP leads to an increase in the nucleation field,which enhances the coercivity.It is found that much more grains exhibit single domain characteristics in the remanent state for GBDP dual-main-phase(Nd,Ce)-Fe-B magnets.In addition,the grains that undergo demagnetization first are Ce-rich or Nd-rich grains,which is different from that of non-diffused magnets.These results were not found in previous studies but can be intuitively characterized from the perspective of magnetic domains in this work,providing a new perspective and understanding of the performance improvement of magnetic materials.展开更多
In this work,we proposed a method to enhance the magnetic properties of(Nd,Ce)-Fe-B magnets with Ce/TRE ratios below 25 wt%by introducing a moderate amount of La elements.The segregation behavior of La elements toward...In this work,we proposed a method to enhance the magnetic properties of(Nd,Ce)-Fe-B magnets with Ce/TRE ratios below 25 wt%by introducing a moderate amount of La elements.The segregation behavior of La elements towards grain boundaries(GBs)was utilized to optimize the GB phase structure.Incorporation of La atoms into the main phase induces lattice expansion,leading to an increased formation of Ce^(3+)ions with enhanced magnetic moments.Comparative analysis with the original magnet(La/Ce=0 wt%)demonstrates that the magnet with a La/Ce ratio of 10 wt%exhibits improvements of 0.3%in remanence,12.6%in coercivity,and 0.6%in maximum energy produ ct.These results underscore that the moderate addition of La elements enhances the fluidity of the rare earth-rich phase and optimizes the distribution of lamellar GB,consequently reinforcing the magnetic isolation effect.Furthermore,the promotion of the transformation from Ce^(4+)to Ce^(3+)ons contributes to the comprehensive enhancement of the magnetic properties.This research offers a novel strategy for fabricating high-performance and resource-e fficient sintered magnets based on LaCe alloys.展开更多
Preterm birth(PTB)is defined as delivery before 37 weeks of gestation.PTB is associated with increased cardiovascular risk,neurodevelopmental disorders,and other diseases in infancy,childhood,and adulthood[1].Globally...Preterm birth(PTB)is defined as delivery before 37 weeks of gestation.PTB is associated with increased cardiovascular risk,neurodevelopmental disorders,and other diseases in infancy,childhood,and adulthood[1].Globally,approximately 15 million PTB cases are reported annually,posing a huge burden on individual families and the community economy[2].In the context of climate warming,O_(3) pollution has continuously increased in many countries in recent years,including China;therefore,scientific communities and government agencies must strive to mitigate ozone pollution.展开更多
The catalytic diesel particulate filter(CDPF)is the most widely used after-treatment device for controlling diesel engine soot emissions.The development of cost-effective catalysts is crucial for diesel engines to com...The catalytic diesel particulate filter(CDPF)is the most widely used after-treatment device for controlling diesel engine soot emissions.The development of cost-effective catalysts is crucial for diesel engines to comply with future ultra-low emission regulations.This paper studies a new type of Ce/La modified Cs-V non-noble metal CDPF catalyst.Three test catalysts(Cs-V,Cs-V-5%Ce,and Cs-V-5%La)were formulated to explore the physical properties,activity,and sulfur resistance through XRD,SEM,XPS,and TPO tests.And TGA tests with different catalyst-to-soot mass ratios were designed to analyze the reaction kinetics.The results show that the soot oxidation process is divided into three stages:slow oxidation,rapid oxidation,and soot burnout.SEM and XRD results show that,compared with Ce doping,La-doped catalysts have less damage to the microstructure of the first active component,Cs_(2)V_(4)O_(11).XPS results show that the introduction of Ce and La is beneficial to the formation of oxygen vacancies and lattice distortion,increasing the proportion of active oxygen species,thereby improving the soot oxidation activity,among which La-doped active oxygen species have the highest proportion(94%).And the Cs-V-5%La catalyst has the best effect on improving the soot conversion of the three stages.The fresh state has the best low-temperature activity index,the lowest characteristic temperature(T_(50) of 374℃)and activation energy(115.01 kJ/mol),and excellent sulfur resistance.The soot conversion and oxidation speed of the three stages decreases,duration lengthens,and activation energy increases by more than 100 kJ/mol as catalyst-to-soot mass ratios decrease.展开更多
The microstructure of(Nd,Ce)-Fe-B sintered magnets with different diffusion depths was characterized by a magnetic force microscope,and the relationship between the magnetic properties and the local structure of grain...The microstructure of(Nd,Ce)-Fe-B sintered magnets with different diffusion depths was characterized by a magnetic force microscope,and the relationship between the magnetic properties and the local structure of grain boundary diffused magnets is discussed.The domains perpendicular to the c-axis(easy magnetization direction)show a typical maze-like pattern,while those parallel to the c-axis show the characte ristics of plate domains.The significant gradient change is shown in the concentration of Dy with the direction of diffusion from the surface to the interior.Dy diffuses along grain boundaries and(Dy,Nd)_(2)Fe_(14)B layer with a high anisotropy field formed around the grains.Through in-situ electron probe micro-analysis/magnetic force microscopy(EPMA/MFM),it is found that the average domain width decreases,and the proportion of single domain grains increases as diffusion depth increases.This is caused by both the change of concentration and distribution of Dy.The grain boundary diffusion process changes the microstructure and microchemistry inside the magnet,and these local magnetism differences can be reflected by the configuration of the magnetic domain structure.展开更多
The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the tradit...The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.展开更多
As the mining depth of coal resources increases,resulting in frequent mine earthquakes during mining.In this study,the rolling window ratio method is firstly chosen as the seismic phase recognition method to read the ...As the mining depth of coal resources increases,resulting in frequent mine earthquakes during mining.In this study,the rolling window ratio method is firstly chosen as the seismic phase recognition method to read the mine earthquake data received by the microseismic sensor.Secondly,the improved genetic algorithm is used as the optimization algorithm of the objective function to build the algorithmic framework of accurate inverse localization of mine earthquake.Finally,the accuracy of this algorithm for seismic source localization is validated using actual engineering cases.Results show that the first arrival time extraction by the rolling window ratio method has the advantages of high accuracy and fast algorithm operation speed.The Fast Fourier Transform-Butterworth joint noise reduction method has a good noise reduction effect,which successfully suppressing noise outside the mine earthquake signal and effectively improving the issue of excessive noise in the mine earthquake signal.Compared to microseismic monitoring data,the localization error for mine earthquakes remains within 5%.展开更多
This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networ...This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networks.Unlike conventional Deep Reinforcement Learning(DRL)-based approaches for CW size adjustment,which often suffer from overestimation bias and limited exploration diversity,leading to suboptimal throughput and collision performance.Our framework integrates the Gumbel distribution and extreme value theory to systematically enhance action selection under varying network conditions.First,SETL adopts a DDQN architecture(SETL-DDQN)to improve Q-value estimation accuracy and enhance training stability.Second,we incorporate a Gumbel distribution-driven exploration mechanism,forming SETL-DDQN(Gumbel),which employs the extreme value theory to promote diverse action selection,replacing the conventional-greedy exploration that undergoes early convergence to suboptimal solutions.Both models are evaluated through extensive simulations in static and time-varying IEEE 802.11 network scenarios.The results demonstrate that our approach consistently achieves higher throughput,lower collision rates,and improved adaptability,even under abrupt fluctuations in traffic load and network conditions.In particular,the Gumbel-based mechanism enhances the balance between exploration and exploitation,facilitating faster adaptation to varying congestion levels.These findings position Gumbel-enhanced DRL as an effective and robust solution for CW optimization in wireless networks,offering notable gains in efficiency and reliability over existing methods.展开更多
The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic ...The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic customer demands.These uncertainties make traditional deterministic models inadequate,often leading to suboptimal or infeasible solutions.To address these challenges,this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms(GA)with Local Search(LS),while incorporating stochastic uncertainty modeling through probabilistic travel times.The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance.This adaptivity enhances the algorithm’s ability to balance exploration and exploitation during the optimization process.Travel time uncertainties are modeled using Gaussian noise,and solution robustness is evaluated through scenario-based simulations.We test our method on a set of benchmark problems from Solomon’s instance suite,comparing its performance under deterministic and stochastic conditions.Results show that the proposed hybrid approach achieves up to a 9%reduction in expected total travel time and a 40% reduction in time window violations compared to baseline methods,including classical GA and non-adaptive hybrids.Additionally,the algorithm demonstrates strong robustness,with lower solution variance across uncertainty scenarios,and converges faster than competing approaches.These findings highlight the method’s suitability for practical logistics applications such as last-mile delivery and real-time transportation planning,where uncertainty and service-level constraints are critical.The flexibility and effectiveness of the proposed framework make it a promising candidate for deployment in dynamic,uncertainty-aware supply chain environments.展开更多
基金supported by the National Key Research and Development Program of China(Nos.2021YFB3503003,2021YFB3503100,and 2022YFB3505401).
文摘The combination of dual-main-phase(DMP)(Nd,Ce)-Fe-B magnets and grain boundary diffusion process(GBDP)is currently a research topic for obtaining high-cost performance materials in rare earth permanent magnet fields.The novel structural features of GBDP(Nd,Ce)-Fe-B magnets give a version of different domain reversal processes from those of non-diffused magnets.In this work,the in-situ magnetic domain evolution of the DMP magnets was observed at elevated temperatures,and the temperature demagnetization and coercivity mechanism of the GBDP dual-main-phase(Nd,Ce)-Fe-B magnets are discussed.The results show that the shell composition of different types of grains in DMP magnets is similar,while the magnetic microstructure results indicate the Ce-rich grains tend to demagnetize first.Dy-rich shell with a high anisotropic field caused by GBDP leads to an increase in the nucleation field,which enhances the coercivity.It is found that much more grains exhibit single domain characteristics in the remanent state for GBDP dual-main-phase(Nd,Ce)-Fe-B magnets.In addition,the grains that undergo demagnetization first are Ce-rich or Nd-rich grains,which is different from that of non-diffused magnets.These results were not found in previous studies but can be intuitively characterized from the perspective of magnetic domains in this work,providing a new perspective and understanding of the performance improvement of magnetic materials.
基金Project supported by the National Natural Science Foundation of China(52071004,52301228,51971005,52171168)the Program of Top Disciplines Construction in Beijing(PXM2019_014204_500031)the International Research Cooperation Seed Fund of Beijing University of Technology(2021B23)。
文摘In this work,we proposed a method to enhance the magnetic properties of(Nd,Ce)-Fe-B magnets with Ce/TRE ratios below 25 wt%by introducing a moderate amount of La elements.The segregation behavior of La elements towards grain boundaries(GBs)was utilized to optimize the GB phase structure.Incorporation of La atoms into the main phase induces lattice expansion,leading to an increased formation of Ce^(3+)ions with enhanced magnetic moments.Comparative analysis with the original magnet(La/Ce=0 wt%)demonstrates that the magnet with a La/Ce ratio of 10 wt%exhibits improvements of 0.3%in remanence,12.6%in coercivity,and 0.6%in maximum energy produ ct.These results underscore that the moderate addition of La elements enhances the fluidity of the rare earth-rich phase and optimizes the distribution of lamellar GB,consequently reinforcing the magnetic isolation effect.Furthermore,the promotion of the transformation from Ce^(4+)to Ce^(3+)ons contributes to the comprehensive enhancement of the magnetic properties.This research offers a novel strategy for fabricating high-performance and resource-e fficient sintered magnets based on LaCe alloys.
基金supported by the Natural Science Foundation of Henan Province[grant number:242300420115]Key Scientific Research Projects in Universities of Henan Province[grant number:23A330006].
文摘Preterm birth(PTB)is defined as delivery before 37 weeks of gestation.PTB is associated with increased cardiovascular risk,neurodevelopmental disorders,and other diseases in infancy,childhood,and adulthood[1].Globally,approximately 15 million PTB cases are reported annually,posing a huge burden on individual families and the community economy[2].In the context of climate warming,O_(3) pollution has continuously increased in many countries in recent years,including China;therefore,scientific communities and government agencies must strive to mitigate ozone pollution.
基金supported by the National Natural Science Foundation of China(No.52206167)the Science and Technology Talents and Platform Program(Academician ExpertWorkstation)(No.202305AF150109)+1 种基金Shanghai Sailing Program(No.21YF1448900)the Introduced and co-builded high-level research and development institutions of Jiangxi Province(No.20212CCH45004).
文摘The catalytic diesel particulate filter(CDPF)is the most widely used after-treatment device for controlling diesel engine soot emissions.The development of cost-effective catalysts is crucial for diesel engines to comply with future ultra-low emission regulations.This paper studies a new type of Ce/La modified Cs-V non-noble metal CDPF catalyst.Three test catalysts(Cs-V,Cs-V-5%Ce,and Cs-V-5%La)were formulated to explore the physical properties,activity,and sulfur resistance through XRD,SEM,XPS,and TPO tests.And TGA tests with different catalyst-to-soot mass ratios were designed to analyze the reaction kinetics.The results show that the soot oxidation process is divided into three stages:slow oxidation,rapid oxidation,and soot burnout.SEM and XRD results show that,compared with Ce doping,La-doped catalysts have less damage to the microstructure of the first active component,Cs_(2)V_(4)O_(11).XPS results show that the introduction of Ce and La is beneficial to the formation of oxygen vacancies and lattice distortion,increasing the proportion of active oxygen species,thereby improving the soot oxidation activity,among which La-doped active oxygen species have the highest proportion(94%).And the Cs-V-5%La catalyst has the best effect on improving the soot conversion of the three stages.The fresh state has the best low-temperature activity index,the lowest characteristic temperature(T_(50) of 374℃)and activation energy(115.01 kJ/mol),and excellent sulfur resistance.The soot conversion and oxidation speed of the three stages decreases,duration lengthens,and activation energy increases by more than 100 kJ/mol as catalyst-to-soot mass ratios decrease.
基金Project supported by the National Key Research and Development Program of China(2021YFB3503003,2021YFB3503100,2022YFB3505401)。
文摘The microstructure of(Nd,Ce)-Fe-B sintered magnets with different diffusion depths was characterized by a magnetic force microscope,and the relationship between the magnetic properties and the local structure of grain boundary diffused magnets is discussed.The domains perpendicular to the c-axis(easy magnetization direction)show a typical maze-like pattern,while those parallel to the c-axis show the characte ristics of plate domains.The significant gradient change is shown in the concentration of Dy with the direction of diffusion from the surface to the interior.Dy diffuses along grain boundaries and(Dy,Nd)_(2)Fe_(14)B layer with a high anisotropy field formed around the grains.Through in-situ electron probe micro-analysis/magnetic force microscopy(EPMA/MFM),it is found that the average domain width decreases,and the proportion of single domain grains increases as diffusion depth increases.This is caused by both the change of concentration and distribution of Dy.The grain boundary diffusion process changes the microstructure and microchemistry inside the magnet,and these local magnetism differences can be reflected by the configuration of the magnetic domain structure.
基金supported by National Natural Science Foundation of China(42364008,41804110)in part by Guizhou Provincial Basic Research Program(Natural Science)(ZK[2022]060)+1 种基金in part by China Postdoctoral Science Foundation(2022M723127)in part by Youth Innovation Team Project of Shandong Provincial Education Department(2022KJ141).
文摘The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.
基金supported by the Shandong Energy Group(No.SNKJ2022A01-R26).
文摘As the mining depth of coal resources increases,resulting in frequent mine earthquakes during mining.In this study,the rolling window ratio method is firstly chosen as the seismic phase recognition method to read the mine earthquake data received by the microseismic sensor.Secondly,the improved genetic algorithm is used as the optimization algorithm of the objective function to build the algorithmic framework of accurate inverse localization of mine earthquake.Finally,the accuracy of this algorithm for seismic source localization is validated using actual engineering cases.Results show that the first arrival time extraction by the rolling window ratio method has the advantages of high accuracy and fast algorithm operation speed.The Fast Fourier Transform-Butterworth joint noise reduction method has a good noise reduction effect,which successfully suppressing noise outside the mine earthquake signal and effectively improving the issue of excessive noise in the mine earthquake signal.Compared to microseismic monitoring data,the localization error for mine earthquakes remains within 5%.
文摘This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networks.Unlike conventional Deep Reinforcement Learning(DRL)-based approaches for CW size adjustment,which often suffer from overestimation bias and limited exploration diversity,leading to suboptimal throughput and collision performance.Our framework integrates the Gumbel distribution and extreme value theory to systematically enhance action selection under varying network conditions.First,SETL adopts a DDQN architecture(SETL-DDQN)to improve Q-value estimation accuracy and enhance training stability.Second,we incorporate a Gumbel distribution-driven exploration mechanism,forming SETL-DDQN(Gumbel),which employs the extreme value theory to promote diverse action selection,replacing the conventional-greedy exploration that undergoes early convergence to suboptimal solutions.Both models are evaluated through extensive simulations in static and time-varying IEEE 802.11 network scenarios.The results demonstrate that our approach consistently achieves higher throughput,lower collision rates,and improved adaptability,even under abrupt fluctuations in traffic load and network conditions.In particular,the Gumbel-based mechanism enhances the balance between exploration and exploitation,facilitating faster adaptation to varying congestion levels.These findings position Gumbel-enhanced DRL as an effective and robust solution for CW optimization in wireless networks,offering notable gains in efficiency and reliability over existing methods.
文摘The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic customer demands.These uncertainties make traditional deterministic models inadequate,often leading to suboptimal or infeasible solutions.To address these challenges,this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms(GA)with Local Search(LS),while incorporating stochastic uncertainty modeling through probabilistic travel times.The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance.This adaptivity enhances the algorithm’s ability to balance exploration and exploitation during the optimization process.Travel time uncertainties are modeled using Gaussian noise,and solution robustness is evaluated through scenario-based simulations.We test our method on a set of benchmark problems from Solomon’s instance suite,comparing its performance under deterministic and stochastic conditions.Results show that the proposed hybrid approach achieves up to a 9%reduction in expected total travel time and a 40% reduction in time window violations compared to baseline methods,including classical GA and non-adaptive hybrids.Additionally,the algorithm demonstrates strong robustness,with lower solution variance across uncertainty scenarios,and converges faster than competing approaches.These findings highlight the method’s suitability for practical logistics applications such as last-mile delivery and real-time transportation planning,where uncertainty and service-level constraints are critical.The flexibility and effectiveness of the proposed framework make it a promising candidate for deployment in dynamic,uncertainty-aware supply chain environments.
