This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred...This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.展开更多
Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current t...Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.展开更多
In response to the capabilities presented by the High-Intensity Heavy Ion Accelerator Facility(HIAF) and the Accelerator-Driven Subcritical System(Ci ADS), as well as the proposed Chinese Advanced Nuclear Physics Rese...In response to the capabilities presented by the High-Intensity Heavy Ion Accelerator Facility(HIAF) and the Accelerator-Driven Subcritical System(Ci ADS), as well as the proposed Chinese Advanced Nuclear Physics Research Facility(CNUF), we are assembling a consortium of experts in relevant disciplines, both domestically and internationally,to delineate high-precision physics experiments that leverage the state-of-the-art research environment afforded by CNUF.Our focus encompasses six primary domains of inquiry: hadron physics—including endeavors such as the super eta factory and investigations into light hadron structures;muon physics;neutrino physics;neutron physics;the testing of fundamental symmetries;and the exploration of quantum effects within nuclear physics, along with the utilization of vortex accelerators.We aim to foster a well-rounded portfolio of large, medium, and small-scale projects, thus unlocking new scientific avenues and optimizing the potential of the Huizhou large scientific facility. The aspiration for international leadership in scientific research will be a guiding principle in our strategic planning. This initiative will serve as a foundational reference for the Institute of Modern Physics in its strategic planning and goal-setting, ensuring alignment with its developmental objectives while striving to secure a competitive edge in technological advancement. Our ambition is to engage in substantive research within these realms of high-precision physics, to pursue groundbreaking discoveries, and to stimulate progress in China's nuclear physics landscape, positioning Huizhou as a preeminent global hub for advanced nuclear physics research.展开更多
We study the trimer state in a three-body system,where two of the atoms are subject to Rashba-type spin-orbit coupling and spin-dependent loss while interacting spin-selectively with the third atom.The short-time cond...We study the trimer state in a three-body system,where two of the atoms are subject to Rashba-type spin-orbit coupling and spin-dependent loss while interacting spin-selectively with the third atom.The short-time conditional dynamics of the three-body system is effectively governed by a non-Hermitian Hamiltonian with an imaginary Zeeman field.Remarkably,the interplay of non-Hermitian single particle dispersion and the spin-selective interaction results in a Borromean state and an enlarged trimer phase.The stability of trimer state can be reflected by the imaginary part of trimer energy and the momentum distribution of trimer wave function.We also show the phase diagram of the three-body system under both real and imaginary Zeeman fields.Our results illustrate the interesting consequence of non-Hermitian spectral symmetry on the few-body level,which may be readily observable in current cold-atom experiments.展开更多
Low-dimensional physics provides profound insights into strongly correlated interactions,leading to enhancedquantum effects and the emergence of exotic quantum states.The Ln_(3)ScBi_(5)family stands out as a chemicall...Low-dimensional physics provides profound insights into strongly correlated interactions,leading to enhancedquantum effects and the emergence of exotic quantum states.The Ln_(3)ScBi_(5)family stands out as a chemicallyversatile kagome platform with mixed low-dimensional structural framework and tunable physical properties.Ourresearch initiates with a comprehensive evaluation of the currently known Ln_(3)ScBi_(5)(Ln=La-Nd,Sm)materials,providing a robust methodology for assessing their stability frontiers within this system.Focusing on Pr_(3)ScBi_(5),we investigate the influence of the zigzag chains of quasi-one-dimensional(Q1D)motifs and the distorted kagomelayers of quasi-two-dimensional(Q2D)networks in the mixed-dimensional structure on the intricate magneticground states and unique spin fluctuations.Our study reveals that the noncollinear antiferromagnetic(AFM)moments of Pr^(3+)ions are confined within the Q2D kagome planes,displaying minimal in-plane anisotropy.Incontrast,a strong AFM coupling is observed within the Q1D zigzag chains,significantly constraining spin motion.Notably,magnetic frustration is partially a consequence of coupling to conduction electrons via Ruderman-Kittel-Kasuya-Yosida interaction,highlighting a promising framework for future investigations into mixed-dimensional frustration in Ln_(3)ScBi_(5) systems.展开更多
Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process...Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process.An inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping phenomenon.Deep learning methods have been applied in musculoskeletal imaging,but need a large amount of data for training.Inspired by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and density.In the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue models.The results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best performance.The specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.展开更多
ABOUT THIS JOURNAL Launched in 1988,the Chinese Journal of Chemical Physics (CJCP)is devoted to reporting new and original experimental and theoretical research on interdisciplinary areas,with chemistry and physics gr...ABOUT THIS JOURNAL Launched in 1988,the Chinese Journal of Chemical Physics (CJCP)is devoted to reporting new and original experimental and theoretical research on interdisciplinary areas,with chemistry and physics groundwork of interest to researchers,faculty and students domestic and abroad in the fields of chemistry,physics,material and biological sciences and their interdisciplinary areas.As one of the 24 peer-reviewed journals under the Chinese Physical Society (CPS),CJCP has been covered in ISI products (SCIE) as well as other major indexes.CJCP is currently a bimonthly journal,and it publishes in English with Chinese abstract as of 2006.展开更多
Deep learning(DL)is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy(PAM).Off-the-shelf DL...Deep learning(DL)is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy(PAM).Off-the-shelf DL models,however,do not necessarily obey the fundamental governing laws of PAM physical systems,nor do they generalize well to scenarios on which they have not been trained.In this work,a physics-embedded degeneration learning(PEDL)approach is proposed to enhance the image quality of PAM with a self-attention enhanced U-Net network,which obtains greater physical consistency,improves data efficiency,and higher adaptability.The proposed method is demonstrated on both synthetic and real datasets,including animal experiments in vivo(blood vessels of mouse's ear and brain).And the results show that compared with previous DL methods,the PEDL algorithm exhibits good performance in recovering PAM images qualitatively and quantitatively.It overcomes the challenges related to training data,accuracy,and robustness which a typical data-driven approach encounters,whose exemplary application envisions to provide a new perspective for existing DL tools of enhanced PAM.展开更多
With the development of educational digitalization,how to effectively apply digital animation technology to traditional classroom teaching has become an urgent problem to be solved.This study explores the application ...With the development of educational digitalization,how to effectively apply digital animation technology to traditional classroom teaching has become an urgent problem to be solved.This study explores the application of Manim in the course of Mathematical Methods for Physics.Taking the visualization of Fourier series,complex numbers,and other content as examples,it improves students’understanding of complex and abstract mathematical physics concepts through dynamic and visual teaching methods.The teaching effect shows that Manim helps to enhance students’learning experience,improve teaching efficiency and effectiveness,and has a positive impact on students’active learning ability.The research in this paper can provide references and inspiration for the educational digitalization of higher education.展开更多
Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.Howev...Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.展开更多
Against the backdrop of China’s strong emphasis on cultivating top innovative talents,the high school physics curriculum,as a core component of scientific education,bears the crucial mission of fostering students’sc...Against the backdrop of China’s strong emphasis on cultivating top innovative talents,the high school physics curriculum,as a core component of scientific education,bears the crucial mission of fostering students’scientific thinking,creativity,and practical ability.However,current physics teaching still faces issues such as fragmented content systems,weak experimental and inquiry components,and single evaluation methods,which fail to align with the objectives of top-talent cultivation[1].Guided by the top-talent training model,this study systematically explores reform paths for the high school physics curriculum in terms of educational philosophy,course content,teaching methods,and evaluation mechanisms.The research proposes reconstructing the curriculum system around the main thread of“core concepts-inquiry practice-innovative application,”strengthening interdisciplinary integration and the inclusion of frontier modern physics topics.In teaching methodology,it advocates for a shift toward problem-driven,project-based,and self-directed learning models,leveraging information and intelligent technologies to enhance classroom effectiveness.In evaluation,it suggests building a comprehensive system centered on formative assessment and innovation capability evaluation.Based on the reform practice of a key high school’s top-talent experimental class,the findings show significant improvements in students’scientific inquiry skills and creative thinking,as well as optimization of teaching philosophy and classroom ecology.The results provide theoretical and practical references for high school physics curriculum reform in the new era and offer insights into the construction of top-talent cultivation systems.展开更多
Deep Learning(DL)model has been widely used in the field of Synthetic Aperture Radar Automatic Target Recognition(SAR-ATR)and has achieved excellent performance.However,the black-box nature of DL models has been the f...Deep Learning(DL)model has been widely used in the field of Synthetic Aperture Radar Automatic Target Recognition(SAR-ATR)and has achieved excellent performance.However,the black-box nature of DL models has been the focus of criticism,especially in the application of SARATR,which is closely associated with the national defense and security domain.To address these issues,a new interpretable recognition model Physics-Guided BagNet(PGBN)is proposed in this article.The model adopts an interpretable convolutional neural network framework and uses time–frequency analysis to extract physical scattering features in SAR images.Based on the physical scattering features,an unsupervised segmentation method is proposed to distinguish targets from the background in SAR images.On the basis of the segmentation result,a structure is designed,which constrains the model's spatial attention to focus more on the targets themselves rather than the background,thereby making the model's decision-making more in line with physical principles.In contrast to previous interpretable research methods,this model combines interpretable structure with physical interpretability,further reducing the model's risk of error recognition.Experiments on the MSTAR dataset verify that the PGBN model exhibits excellent interpretability and recognition performance,and comparative experiments with heatmaps indicate that the physical feature guidance module presented in this article can constrain the model to focus more on the target itself rather than the background.展开更多
ABOUT THIS JOURNALLaunched in 1988,the Chinese Journal of Chemical Physics(CJCP)is devoted to reporting new and original experimental and theoretical research on interdisciplinary areas,with chemistry and physicsgroun...ABOUT THIS JOURNALLaunched in 1988,the Chinese Journal of Chemical Physics(CJCP)is devoted to reporting new and original experimental and theoretical research on interdisciplinary areas,with chemistry and physicsgroundwork of interest to researchers,faculty and students domesticand abroad in the fields of chemistry,physics,material and biological sciences and their interdisciplinary areas.展开更多
Scientists have been searching for possible new particles beyond the standard model(SM),the theory that has predicted the building bricks that have constituted the known matter world today,including the Higgs-“the l...Scientists have been searching for possible new particles beyond the standard model(SM),the theory that has predicted the building bricks that have constituted the known matter world today,including the Higgs-“the last”SM particle.展开更多
The Beijing 325 m meteorological tower stands as a pivotal research platform for exploring atmospheric boundary layer physics and atmospheric chemistry.With a legacy spanning 45 years,the tower has played a crucial ro...The Beijing 325 m meteorological tower stands as a pivotal research platform for exploring atmospheric boundary layer physics and atmospheric chemistry.With a legacy spanning 45 years,the tower has played a crucial role in unraveling the complexities of urban air pollution,atmospheric processes,and climate change in Beijing,China.This review paper provides a comprehensive overview of the measurements on the tower over the past two decades.Through long-term comprehensive observations,researchers have elucidated the intricate relationships between anthropogenic emissions,meteorological dynamics,and atmospheric composition,shedding light on the drivers of air pollution and its impacts on public health.The vertical measurements on the tower also enable detailed investigations into boundary layer dynamics,turbulent mixing,and pollutant dispersion,providing invaluable data for validating chemical transport models.Key findings from the tower’s research include the identification of positive feedback mechanisms between aerosols and the boundary layer,the characterization of pollutant sources and transport pathways,the determination of fluxes of gaseous and particulate species,and the assessment of the effectiveness of pollution control measures.Additionally,isotopic measurements have provided new insights into the sources and formation processes of particulate matter and reactive nitrogen species.Finally,the paper outlines future directions for tower-based research,emphasizing the need for long-term comprehensive measurements,the development of innovative tower platforms,and integration of emerging technologies.展开更多
Traditional educational paradigms prioritize age-based progression and early specialization as key indicators of academic potential,especially in STEM.This study challenges this norm by analyzing university entrance a...Traditional educational paradigms prioritize age-based progression and early specialization as key indicators of academic potential,especially in STEM.This study challenges this norm by analyzing university entrance ages of 226 Nobel Physics Laureates(1901-2024).Results reveal a right-skewed distribution(Median=18;Mean=18.8;SD=2.4)with substantial variance(14-25 years),including outliers like Lev Landau(14)and Arthur Ashkin(24).Notably,figures such as Guglielmo Marconi achieved breakthroughs without formal university entry,relying on self-directed learning.Using survival analysis and multinomial regression,we find“non-traditional”timelines,accelerated,delayed,or non-formal pathways,correlate with distinct creative advantages.This suggests current“timeliness”metrics poorly predict transformative scientific achievement.We propose an“Optimal Chrono-Diversity”framework advocating flexible entry systems,enhanced adult learner support,and recognition of autodidactic potential to inform educational policy and cultivate innovative STEM talent.展开更多
Active matter encompasses all systems in which each individual constituent independently dissipates energy in its environment.This definition brings together biological systems such as cellular tissues,bacterial colon...Active matter encompasses all systems in which each individual constituent independently dissipates energy in its environment.This definition brings together biological systems such as cellular tissues,bacterial colonies,cytoskeletal filaments driven by molecular motors and animal groups,as well as collections of inert self-propelled particles such as Janus particles,[1]colloidal rollers[2]or vibrated grains.[3]Because of the local persistent drive,these systems are far from thermal equilibrium and cannot be described in terms of thermodynamic potentials.This leads to surprising physics that defies some of the basic intuitions that we have from passive systems,including longrange order in two dimensions[4]and phase-separation in absence of attractive interactions.展开更多
基金supported by the National Key R&D Program of China[grant number 2023YFC3008004]。
文摘This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.
基金the North Dakota Industrial Commission (NDIC) for their financial supportprovided by the University of North Dakota Computational Research Center。
文摘Accurate estimation of mineralogy from geophysical well logs is crucial for characterizing geological formations,particularly in hydrocarbon exploration,CO_(2) sequestration,and geothermal energy development.Current techniques,such as multimineral petrophysical analysis,offer details into mineralogical distribution.However,it is inherently time-intensive and demands substantial geological expertise for accurate model evaluation.Furthermore,traditional machine learning techniques often struggle to predict mineralogy accurately and sometimes produce estimations that violate fundamental physical principles.To address this,we present a new approach using Physics-Integrated Neural Networks(PINNs),that combines data-driven learning with domain-specific physical constraints,embedding petrophysical relationships directly into the neural network architecture.This approach enforces that predictions adhere to physical laws.The methodology is applied to the Broom Creek Deep Saline aquifer,a CO_(2) sequestration site in the Williston Basin,to predict the volumes of key mineral constituents—quartz,dolomite,feldspar,anhydrite,illite—along with porosity.Compared to traditional artificial neural networks (ANN),the PINN approach demonstrates higher accuracy and better generalizability,significantly enhancing predictive performance on unseen well datasets.The average mean error across the three blind wells is 0.123 for ANN and 0.042 for PINN,highlighting the superior accuracy of the PINN approach.This method reduces uncertainties in reservoir characterization by improving the reliability of mineralogy and porosity predictions,providing a more robust tool for decision-making in various subsurface geoscience applications.
基金supported by the National Natural Science Foundation of China (Grant No.12075326)the Guangdong Basic and Applied Basic Research Foundation (Grant No.2025A1515010669)+2 种基金the Natural Science Foundation of Guangzhou (Grant No.2024A04J6243)the Fundamental Research Funds for the Central Universities in Sun Yat-sen University (No.23xkjc017)the Innovation Training Program for bachelor students in Sun Yat-sen University。
文摘In response to the capabilities presented by the High-Intensity Heavy Ion Accelerator Facility(HIAF) and the Accelerator-Driven Subcritical System(Ci ADS), as well as the proposed Chinese Advanced Nuclear Physics Research Facility(CNUF), we are assembling a consortium of experts in relevant disciplines, both domestically and internationally,to delineate high-precision physics experiments that leverage the state-of-the-art research environment afforded by CNUF.Our focus encompasses six primary domains of inquiry: hadron physics—including endeavors such as the super eta factory and investigations into light hadron structures;muon physics;neutrino physics;neutron physics;the testing of fundamental symmetries;and the exploration of quantum effects within nuclear physics, along with the utilization of vortex accelerators.We aim to foster a well-rounded portfolio of large, medium, and small-scale projects, thus unlocking new scientific avenues and optimizing the potential of the Huizhou large scientific facility. The aspiration for international leadership in scientific research will be a guiding principle in our strategic planning. This initiative will serve as a foundational reference for the Institute of Modern Physics in its strategic planning and goal-setting, ensuring alignment with its developmental objectives while striving to secure a competitive edge in technological advancement. Our ambition is to engage in substantive research within these realms of high-precision physics, to pursue groundbreaking discoveries, and to stimulate progress in China's nuclear physics landscape, positioning Huizhou as a preeminent global hub for advanced nuclear physics research.
基金supported by the National Natural Science Foundation of China(Grant No.11974331)。
文摘We study the trimer state in a three-body system,where two of the atoms are subject to Rashba-type spin-orbit coupling and spin-dependent loss while interacting spin-selectively with the third atom.The short-time conditional dynamics of the three-body system is effectively governed by a non-Hermitian Hamiltonian with an imaginary Zeeman field.Remarkably,the interplay of non-Hermitian single particle dispersion and the spin-selective interaction results in a Borromean state and an enlarged trimer phase.The stability of trimer state can be reflected by the imaginary part of trimer energy and the momentum distribution of trimer wave function.We also show the phase diagram of the three-body system under both real and imaginary Zeeman fields.Our results illustrate the interesting consequence of non-Hermitian spectral symmetry on the few-body level,which may be readily observable in current cold-atom experiments.
基金supported by the National Key R&D Program of China(Grant Nos.2024YFA1408400 and 2021YFA1400401)the National Natural Science Foundation of China(Grant Nos.U22A6005 and 52271238)+2 种基金the China Postdoctoral Science Foundation(Grant No.2025M770186)the Center for Materials Genome,and the Synergetic Extreme Condition User Facility(SECUF)supported by the AI-driven experiments,simulations and model training on the robotic AI-Scientist platform from Chinese Academy of Sciences and the Research Funds for the Central Universities(Grant No.N25ZLE007).
文摘Low-dimensional physics provides profound insights into strongly correlated interactions,leading to enhancedquantum effects and the emergence of exotic quantum states.The Ln_(3)ScBi_(5)family stands out as a chemicallyversatile kagome platform with mixed low-dimensional structural framework and tunable physical properties.Ourresearch initiates with a comprehensive evaluation of the currently known Ln_(3)ScBi_(5)(Ln=La-Nd,Sm)materials,providing a robust methodology for assessing their stability frontiers within this system.Focusing on Pr_(3)ScBi_(5),we investigate the influence of the zigzag chains of quasi-one-dimensional(Q1D)motifs and the distorted kagomelayers of quasi-two-dimensional(Q2D)networks in the mixed-dimensional structure on the intricate magneticground states and unique spin fluctuations.Our study reveals that the noncollinear antiferromagnetic(AFM)moments of Pr^(3+)ions are confined within the Q2D kagome planes,displaying minimal in-plane anisotropy.Incontrast,a strong AFM coupling is observed within the Q1D zigzag chains,significantly constraining spin motion.Notably,magnetic frustration is partially a consequence of coupling to conduction electrons via Ruderman-Kittel-Kasuya-Yosida interaction,highlighting a promising framework for future investigations into mixed-dimensional frustration in Ln_(3)ScBi_(5) systems.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12122403 and 12327807).
文摘Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal disease.However,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging process.An inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping phenomenon.Deep learning methods have been applied in musculoskeletal imaging,but need a large amount of data for training.Inspired by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and density.In the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue models.The results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best performance.The specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.
文摘ABOUT THIS JOURNAL Launched in 1988,the Chinese Journal of Chemical Physics (CJCP)is devoted to reporting new and original experimental and theoretical research on interdisciplinary areas,with chemistry and physics groundwork of interest to researchers,faculty and students domestic and abroad in the fields of chemistry,physics,material and biological sciences and their interdisciplinary areas.As one of the 24 peer-reviewed journals under the Chinese Physical Society (CPS),CJCP has been covered in ISI products (SCIE) as well as other major indexes.CJCP is currently a bimonthly journal,and it publishes in English with Chinese abstract as of 2006.
基金supported by National Natural Science Foundation of China(62227818,12204239,62275121)Youth Foundation of Jiangsu Province(BK20220946)+1 种基金Fundamental Research Funds for the Central Universities(30923011024)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGP202201).
文摘Deep learning(DL)is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy(PAM).Off-the-shelf DL models,however,do not necessarily obey the fundamental governing laws of PAM physical systems,nor do they generalize well to scenarios on which they have not been trained.In this work,a physics-embedded degeneration learning(PEDL)approach is proposed to enhance the image quality of PAM with a self-attention enhanced U-Net network,which obtains greater physical consistency,improves data efficiency,and higher adaptability.The proposed method is demonstrated on both synthetic and real datasets,including animal experiments in vivo(blood vessels of mouse's ear and brain).And the results show that compared with previous DL methods,the PEDL algorithm exhibits good performance in recovering PAM images qualitatively and quantitatively.It overcomes the challenges related to training data,accuracy,and robustness which a typical data-driven approach encounters,whose exemplary application envisions to provide a new perspective for existing DL tools of enhanced PAM.
基金supported by the Teaching Reform Research Project of Shaanxi University of Science&Technology(23Y083)the Project of National University Association for Mathematical Methods in Physics(JZW-23-SL-02)+3 种基金the Graduate Course Construction Project of Shaanxi University of Science&Technology(KC2024Y03)the 2024 National Higher Education University Physics Reform Research Project(2024PR064)the Teaching Reform Research Project of the International Office of Shaanxi University of Science&Technology(YB202410)Graduate Education and Teaching Reform Research Project of Shaanxi University of Science&Technology(JG2025Y18).
文摘With the development of educational digitalization,how to effectively apply digital animation technology to traditional classroom teaching has become an urgent problem to be solved.This study explores the application of Manim in the course of Mathematical Methods for Physics.Taking the visualization of Fourier series,complex numbers,and other content as examples,it improves students’understanding of complex and abstract mathematical physics concepts through dynamic and visual teaching methods.The teaching effect shows that Manim helps to enhance students’learning experience,improve teaching efficiency and effectiveness,and has a positive impact on students’active learning ability.The research in this paper can provide references and inspiration for the educational digitalization of higher education.
基金supported by the National Natural Science Foundation of China(Grant Nos.1217211 and 12372244).
文摘Physics informed neural networks(PINNs)are a deep learning approach designed to solve partial differential equations(PDEs).Accurately learning the initial conditions is crucial when employing PINNs to solve PDEs.However,simply adjusting weights and imposing hard constraints may not always lead to better learning of the initial conditions;sometimes it even makes it difficult for the neural networks to converge.To enhance the accuracy of PINNs in learning the initial conditions,this paper proposes a novel strategy named causally enhanced initial conditions(CEICs).This strategy works by embedding a new loss in the loss function:the loss is constructed by the derivative of the initial condition and the derivative of the neural network at the initial condition.Furthermore,to respect the causality in learning the derivative,a novel causality coefficient is introduced for the training when selecting multiple derivatives.Additionally,because CEICs can provide more accurate pseudo-labels in the first subdomain,they are compatible with the temporal-marching strategy.Experimental results demonstrate that CEICs outperform hard constraints and improve the overall accuracy of pre-training PINNs.For the 1D-Korteweg–de Vries,reaction and convection equations,the CEIC method proposed in this paper reduces the relative error by at least 60%compared to the previous methods.
文摘Against the backdrop of China’s strong emphasis on cultivating top innovative talents,the high school physics curriculum,as a core component of scientific education,bears the crucial mission of fostering students’scientific thinking,creativity,and practical ability.However,current physics teaching still faces issues such as fragmented content systems,weak experimental and inquiry components,and single evaluation methods,which fail to align with the objectives of top-talent cultivation[1].Guided by the top-talent training model,this study systematically explores reform paths for the high school physics curriculum in terms of educational philosophy,course content,teaching methods,and evaluation mechanisms.The research proposes reconstructing the curriculum system around the main thread of“core concepts-inquiry practice-innovative application,”strengthening interdisciplinary integration and the inclusion of frontier modern physics topics.In teaching methodology,it advocates for a shift toward problem-driven,project-based,and self-directed learning models,leveraging information and intelligent technologies to enhance classroom effectiveness.In evaluation,it suggests building a comprehensive system centered on formative assessment and innovation capability evaluation.Based on the reform practice of a key high school’s top-talent experimental class,the findings show significant improvements in students’scientific inquiry skills and creative thinking,as well as optimization of teaching philosophy and classroom ecology.The results provide theoretical and practical references for high school physics curriculum reform in the new era and offer insights into the construction of top-talent cultivation systems.
基金co-supported by the National Natural Science Foundation of China(No.62001507)the Youth Talent Lifting Project of the China Association for Science and Technology(No.2021-JCJQ-QT-018)+1 种基金the Program of the Youth Innovation Team of Shaanxi Universitiesthe Natural Science Basic Research Plan in Shaanxi Province of China(No.2023-JC-YB-491)。
文摘Deep Learning(DL)model has been widely used in the field of Synthetic Aperture Radar Automatic Target Recognition(SAR-ATR)and has achieved excellent performance.However,the black-box nature of DL models has been the focus of criticism,especially in the application of SARATR,which is closely associated with the national defense and security domain.To address these issues,a new interpretable recognition model Physics-Guided BagNet(PGBN)is proposed in this article.The model adopts an interpretable convolutional neural network framework and uses time–frequency analysis to extract physical scattering features in SAR images.Based on the physical scattering features,an unsupervised segmentation method is proposed to distinguish targets from the background in SAR images.On the basis of the segmentation result,a structure is designed,which constrains the model's spatial attention to focus more on the targets themselves rather than the background,thereby making the model's decision-making more in line with physical principles.In contrast to previous interpretable research methods,this model combines interpretable structure with physical interpretability,further reducing the model's risk of error recognition.Experiments on the MSTAR dataset verify that the PGBN model exhibits excellent interpretability and recognition performance,and comparative experiments with heatmaps indicate that the physical feature guidance module presented in this article can constrain the model to focus more on the target itself rather than the background.
文摘ABOUT THIS JOURNALLaunched in 1988,the Chinese Journal of Chemical Physics(CJCP)is devoted to reporting new and original experimental and theoretical research on interdisciplinary areas,with chemistry and physicsgroundwork of interest to researchers,faculty and students domesticand abroad in the fields of chemistry,physics,material and biological sciences and their interdisciplinary areas.
文摘Scientists have been searching for possible new particles beyond the standard model(SM),the theory that has predicted the building bricks that have constituted the known matter world today,including the Higgs-“the last”SM particle.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0760200)the National Natural Science Foundation of China(Grant Nos.42330605 and 42377101).
文摘The Beijing 325 m meteorological tower stands as a pivotal research platform for exploring atmospheric boundary layer physics and atmospheric chemistry.With a legacy spanning 45 years,the tower has played a crucial role in unraveling the complexities of urban air pollution,atmospheric processes,and climate change in Beijing,China.This review paper provides a comprehensive overview of the measurements on the tower over the past two decades.Through long-term comprehensive observations,researchers have elucidated the intricate relationships between anthropogenic emissions,meteorological dynamics,and atmospheric composition,shedding light on the drivers of air pollution and its impacts on public health.The vertical measurements on the tower also enable detailed investigations into boundary layer dynamics,turbulent mixing,and pollutant dispersion,providing invaluable data for validating chemical transport models.Key findings from the tower’s research include the identification of positive feedback mechanisms between aerosols and the boundary layer,the characterization of pollutant sources and transport pathways,the determination of fluxes of gaseous and particulate species,and the assessment of the effectiveness of pollution control measures.Additionally,isotopic measurements have provided new insights into the sources and formation processes of particulate matter and reactive nitrogen species.Finally,the paper outlines future directions for tower-based research,emphasizing the need for long-term comprehensive measurements,the development of innovative tower platforms,and integration of emerging technologies.
基金Inner Mongolia Natural Science Foundation of China(Project No.:2023QN01015).
文摘Traditional educational paradigms prioritize age-based progression and early specialization as key indicators of academic potential,especially in STEM.This study challenges this norm by analyzing university entrance ages of 226 Nobel Physics Laureates(1901-2024).Results reveal a right-skewed distribution(Median=18;Mean=18.8;SD=2.4)with substantial variance(14-25 years),including outliers like Lev Landau(14)and Arthur Ashkin(24).Notably,figures such as Guglielmo Marconi achieved breakthroughs without formal university entry,relying on self-directed learning.Using survival analysis and multinomial regression,we find“non-traditional”timelines,accelerated,delayed,or non-formal pathways,correlate with distinct creative advantages.This suggests current“timeliness”metrics poorly predict transformative scientific achievement.We propose an“Optimal Chrono-Diversity”framework advocating flexible entry systems,enhanced adult learner support,and recognition of autodidactic potential to inform educational policy and cultivate innovative STEM talent.
文摘Active matter encompasses all systems in which each individual constituent independently dissipates energy in its environment.This definition brings together biological systems such as cellular tissues,bacterial colonies,cytoskeletal filaments driven by molecular motors and animal groups,as well as collections of inert self-propelled particles such as Janus particles,[1]colloidal rollers[2]or vibrated grains.[3]Because of the local persistent drive,these systems are far from thermal equilibrium and cannot be described in terms of thermodynamic potentials.This leads to surprising physics that defies some of the basic intuitions that we have from passive systems,including longrange order in two dimensions[4]and phase-separation in absence of attractive interactions.
