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
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
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
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
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
We investigate electron mesoscopic transport in a three-terminal setup with coupled quantum dots and a magnetic flux.By mapping the original transport problem into a non-Hermitian Hamiltonian form,we study the interpl...We investigate electron mesoscopic transport in a three-terminal setup with coupled quantum dots and a magnetic flux.By mapping the original transport problem into a non-Hermitian Hamiltonian form,we study the interplay between the coherent couplings between quantum dots,the magnetic flux,and the dissipation due to the tunnel coupling with the reservoirs.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
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
The surrogate model serves as an efficient simulation tool during the slope parameter inversion process.However,the creep constitutive model integrated with dynamic damage evolution poses challenges in development of ...The surrogate model serves as an efficient simulation tool during the slope parameter inversion process.However,the creep constitutive model integrated with dynamic damage evolution poses challenges in development of the required surrogate model.In this study,a novel physics knowledge-based surrogate model framework is proposed.In this framework,a Transformer module is employed to capture straindriven softening-hardening physical mechanisms.Positional encoding and self-attention are utilized to transform the constitutive parameters associated with shear strain,which are not directly time-related,into intermediate latent features for physical loss calculation.Next,a multi-layer stacked GRU(gated recurrent unit)network is built to provide input interfaces for time-dependent intermediate latent features,hydraulic boundary conditions,and water-rock interaction degradation equations,with static parameters introduced via external fully-connected layers.Finally,a combined loss function is constructed to facilitate the collaborative training of physical and data loss,introducing time-dependent weight adjustments to focus the surrogate model on accurate deformation predictions during critical phases.Based on the deformation of a reservoir bank landslide triggered by impoundment and subsequent restabilization,an elasto-viscoplastic constitutive model that considers water effect and sliding state dependencies is developed to validate the proposed surrogate model framework.The results indicate that the framework exhibits good performance in capturing physical mechanisms and predicting creep behavior,reducing errors by about 30 times compared to baseline models such as GRU and LSTM(long short-term memory),meeting the precision requirements for parameter inversion.Ablation experiments also confirmed the effectiveness of the framework.This framework can also serve as a reference for constructing other creep surrogate models that involve non-time-related across dimensions.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
Taking the core content of classical mechanics,Newton′s laws of motion,which students are familiar with,as the breakthrough point,this paper deeply explores the ideological and political elements therein,which helps ...Taking the core content of classical mechanics,Newton′s laws of motion,which students are familiar with,as the breakthrough point,this paper deeply explores the ideological and political elements therein,which helps to achieve the organic unity of knowledge imparting and value guidance.When teaching,it will also introduce the history of the development of physics,the ideas and methods used by physicists to study problems,and intersperse some ideological and political elements such as excellent qualities and scientific spirit of physicists for edification.In addition,combining with China′s national conditions,the ideological and political teaching content related to cultural inheritance,national pride and other aspects is infiltrated,which has an important and positive impact on students for establishing the correct worldviews,outlooks on life and values.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
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
Jetting-based bioprinting facilitates contactless drop-on-demand deposition of subnanoliter droplets at well-defined positions to control the spatial arrangement of cells,growth factors,drugs,and biomaterials in a hig...Jetting-based bioprinting facilitates contactless drop-on-demand deposition of subnanoliter droplets at well-defined positions to control the spatial arrangement of cells,growth factors,drugs,and biomaterials in a highly automated layer-by-layer fabrication approach.Due to its immense versatility,jetting-based bioprinting has been used for various applications,including tissue engineering and regenerative medicine,wound healing,and drug development.A lack of in-depth understanding exists in the processes that occur during jetting-based bioprinting.This review paper will comprehensively discuss the physical considerations for bioinks and printing conditions used in jetting-based bioprinting.We first present an overview of different jetting-based bioprinting techniques such as inkjet bioprinting,laser-induced forward transfer bioprinting,electrohydrodynamic jet bioprinting,acoustic bioprinting and microvalve bioprinting.Next,we provide an in-depth discussion of various considerations for bioink formulation relating to cell deposition,print chamber design,droplet formation and droplet impact.Finally,we highlight recent accomplishments in jetting-based bioprinting.We present the advantages and challenges of each method,discuss considerations relating to cell viability and protein stability,and conclude by providing insights into future directions of jetting-based bioprinting.展开更多
Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and cou...Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.展开更多
Estimating gas enrichments is a key objective in exploring sweet spots within tight sandstone gas reservoirs.However,the low sensitivity of elastic parameters to gas saturations in such formations makes it a significa...Estimating gas enrichments is a key objective in exploring sweet spots within tight sandstone gas reservoirs.However,the low sensitivity of elastic parameters to gas saturations in such formations makes it a significant challenge to reliably estimate gas enrichments using seismic methods.Through rock physical modeling and reservoir parameter analyses conducted in this study,a more suitable indicator for estimating gas enrichment,termed the gas content indicator,has been proposed.This indicator is formulated based on effective fluid bulk modulus and shear modulus and demonstrates a clear positive correlation with gas content in tight sandstones.Moreover,a new seismic amplitude variation versus offset(AVO)equation is derived to directly extract reservoir properties,such as the gas content indicator and porosity,from prestack seismic data.The accuracy of this proposed AVO equation is validated through comparison with the exact solutions provided by the Zoeppritz equation.To ensure reliable estimations of reservoir properties from partial angle-stacked seismic data,the proposed AVO equation is reformulated within the elastic impedance inversion framework.The estimated gas content indicator and porosity exhibit favorable agreement with logging data,suggesting that the obtained results are suitable for reliable predictions of tight sandstones with high gas enrichments.Furthermore,the proposed methods have the potential to stimulate the advancement of other suitable inversion techniques for directly estimating reservoir properties from seismic data across various petroleum resources.展开更多
Quantitative prediction of reservoir properties(e.g., gas saturation, porosity, and shale content) of tight reservoirs is of great significance for resource evaluation and well placements. However, the complex pore st...Quantitative prediction of reservoir properties(e.g., gas saturation, porosity, and shale content) of tight reservoirs is of great significance for resource evaluation and well placements. However, the complex pore structures, poor pore connectivity, and uneven fluid distribution of tight sandstone reservoirs make the correlation between reservoir parameters and elastic properties more complicated and thus pose a major challenge in seismic reservoir characterization. We have developed a partially connected double porosity model to calculate elastic properties by considering the pore structure and connectivity, and to analyze these factors' influences on the elastic behaviors of tight sandstone reservoirs. The modeling results suggest that the bulk modulus is likely to be affected by the pore connectivity coefficient, while the shear modulus is sensitive to the volumetric fraction of stiff pores. By comparing the model predictions with the acoustic measurements of the dry and saturated quartz sandstone samples, the volumetric fraction of stiff pores and the pore connectivity coefficient can be determined. Based on the calibrated model, we have constructed a 3D rock physics template that accounts for the reservoir properties' impacts on the P-wave impedance, S-wave impedance, and density. The template combined with Bayesian inverse theory is used to quantify gas saturation, porosity, clay content, and their corresponding uncertainties from elastic parameters. The application of well-log and seismic data demonstrates that our 3D rock physics template-based probabilistic inversion approach performs well in predicting the spatial distribution of high-quality tight sandstone reservoirs in southwestern China.展开更多
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.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.
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.
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supported by the National Key R&D Program of China(Grant No.2022YFA1404400)the National Natural Science Foundation of China(Grant No.12125504 and 12305050)+2 种基金Zhejiang Provincial Natural Science Foundation of China(Grant No.LZ25A050001)the Hundred Talents Program of the Chinese Academy of Sciencesthe Natural Science Foundation of Jiangsu Higher Education Institutions of China(Grant No.23KJB140017)。
文摘We investigate electron mesoscopic transport in a three-terminal setup with coupled quantum dots and a magnetic flux.By mapping the original transport problem into a non-Hermitian Hamiltonian form,we study the interplay between the coherent couplings between quantum dots,the magnetic flux,and the dissipation due to the tunnel coupling with the reservoirs.
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.
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 No.41961134032).
文摘The surrogate model serves as an efficient simulation tool during the slope parameter inversion process.However,the creep constitutive model integrated with dynamic damage evolution poses challenges in development of the required surrogate model.In this study,a novel physics knowledge-based surrogate model framework is proposed.In this framework,a Transformer module is employed to capture straindriven softening-hardening physical mechanisms.Positional encoding and self-attention are utilized to transform the constitutive parameters associated with shear strain,which are not directly time-related,into intermediate latent features for physical loss calculation.Next,a multi-layer stacked GRU(gated recurrent unit)network is built to provide input interfaces for time-dependent intermediate latent features,hydraulic boundary conditions,and water-rock interaction degradation equations,with static parameters introduced via external fully-connected layers.Finally,a combined loss function is constructed to facilitate the collaborative training of physical and data loss,introducing time-dependent weight adjustments to focus the surrogate model on accurate deformation predictions during critical phases.Based on the deformation of a reservoir bank landslide triggered by impoundment and subsequent restabilization,an elasto-viscoplastic constitutive model that considers water effect and sliding state dependencies is developed to validate the proposed surrogate model framework.The results indicate that the framework exhibits good performance in capturing physical mechanisms and predicting creep behavior,reducing errors by about 30 times compared to baseline models such as GRU and LSTM(long short-term memory),meeting the precision requirements for parameter inversion.Ablation experiments also confirmed the effectiveness of the framework.This framework can also serve as a reference for constructing other creep surrogate models that involve non-time-related across dimensions.
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.
基金supported by Perspective of Industry-Education Integration,a Project under the 2024 Annual Planning of the"14th Five-Year Plan"for the Educational Science of Shanxi Province(GH-240186)。
文摘Taking the core content of classical mechanics,Newton′s laws of motion,which students are familiar with,as the breakthrough point,this paper deeply explores the ideological and political elements therein,which helps to achieve the organic unity of knowledge imparting and value guidance.When teaching,it will also introduce the history of the development of physics,the ideas and methods used by physicists to study problems,and intersperse some ideological and political elements such as excellent qualities and scientific spirit of physicists for edification.In addition,combining with China′s national conditions,the ideological and political teaching content related to cultural inheritance,national pride and other aspects is infiltrated,which has an important and positive impact on students for establishing the correct worldviews,outlooks on life and values.
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.
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support from NTU Presidential Postdoctoral Fellowship.
文摘Jetting-based bioprinting facilitates contactless drop-on-demand deposition of subnanoliter droplets at well-defined positions to control the spatial arrangement of cells,growth factors,drugs,and biomaterials in a highly automated layer-by-layer fabrication approach.Due to its immense versatility,jetting-based bioprinting has been used for various applications,including tissue engineering and regenerative medicine,wound healing,and drug development.A lack of in-depth understanding exists in the processes that occur during jetting-based bioprinting.This review paper will comprehensively discuss the physical considerations for bioinks and printing conditions used in jetting-based bioprinting.We first present an overview of different jetting-based bioprinting techniques such as inkjet bioprinting,laser-induced forward transfer bioprinting,electrohydrodynamic jet bioprinting,acoustic bioprinting and microvalve bioprinting.Next,we provide an in-depth discussion of various considerations for bioink formulation relating to cell deposition,print chamber design,droplet formation and droplet impact.Finally,we highlight recent accomplishments in jetting-based bioprinting.We present the advantages and challenges of each method,discuss considerations relating to cell viability and protein stability,and conclude by providing insights into future directions of jetting-based bioprinting.
基金supported by the National Key Research and Development Program (2022YFF0609504)the National Natural Science Foundation of China (61974126,51902273,62005230,62001405)the Natural Science Foundation of Fujian Province of China (No.2021J06009)
文摘Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.
基金supported by the National Natural Science Foundation of China(Grant Nos.42074153 and 42274160)。
文摘Estimating gas enrichments is a key objective in exploring sweet spots within tight sandstone gas reservoirs.However,the low sensitivity of elastic parameters to gas saturations in such formations makes it a significant challenge to reliably estimate gas enrichments using seismic methods.Through rock physical modeling and reservoir parameter analyses conducted in this study,a more suitable indicator for estimating gas enrichment,termed the gas content indicator,has been proposed.This indicator is formulated based on effective fluid bulk modulus and shear modulus and demonstrates a clear positive correlation with gas content in tight sandstones.Moreover,a new seismic amplitude variation versus offset(AVO)equation is derived to directly extract reservoir properties,such as the gas content indicator and porosity,from prestack seismic data.The accuracy of this proposed AVO equation is validated through comparison with the exact solutions provided by the Zoeppritz equation.To ensure reliable estimations of reservoir properties from partial angle-stacked seismic data,the proposed AVO equation is reformulated within the elastic impedance inversion framework.The estimated gas content indicator and porosity exhibit favorable agreement with logging data,suggesting that the obtained results are suitable for reliable predictions of tight sandstones with high gas enrichments.Furthermore,the proposed methods have the potential to stimulate the advancement of other suitable inversion techniques for directly estimating reservoir properties from seismic data across various petroleum resources.
基金supported by the National Natural Science Foundation of China (42104121)the Scientific Research and Technology Development Project of the CNPC (2021DJ0606)。
文摘Quantitative prediction of reservoir properties(e.g., gas saturation, porosity, and shale content) of tight reservoirs is of great significance for resource evaluation and well placements. However, the complex pore structures, poor pore connectivity, and uneven fluid distribution of tight sandstone reservoirs make the correlation between reservoir parameters and elastic properties more complicated and thus pose a major challenge in seismic reservoir characterization. We have developed a partially connected double porosity model to calculate elastic properties by considering the pore structure and connectivity, and to analyze these factors' influences on the elastic behaviors of tight sandstone reservoirs. The modeling results suggest that the bulk modulus is likely to be affected by the pore connectivity coefficient, while the shear modulus is sensitive to the volumetric fraction of stiff pores. By comparing the model predictions with the acoustic measurements of the dry and saturated quartz sandstone samples, the volumetric fraction of stiff pores and the pore connectivity coefficient can be determined. Based on the calibrated model, we have constructed a 3D rock physics template that accounts for the reservoir properties' impacts on the P-wave impedance, S-wave impedance, and density. The template combined with Bayesian inverse theory is used to quantify gas saturation, porosity, clay content, and their corresponding uncertainties from elastic parameters. The application of well-log and seismic data demonstrates that our 3D rock physics template-based probabilistic inversion approach performs well in predicting the spatial distribution of high-quality tight sandstone reservoirs in southwestern China.
