Multimodality image registration and fusion are essential steps in building 3-D models from remotesensing data. We present in this paper a neural network technique for the registration and fusion of multimodali-ty rem...Multimodality image registration and fusion are essential steps in building 3-D models from remotesensing data. We present in this paper a neural network technique for the registration and fusion of multimodali-ty remote sensing data for the reconstruction of 3-D models of terrain regions. A FeedForward neural network isused to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data arepresented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusionresults.展开更多
In this paper,a single-shot 360-degree cranial deformity detection system using digital image correlation(DIC)is presented to quickly obtain and detect accurate 3D data of infants’cra-nium.By introducing plane mirror...In this paper,a single-shot 360-degree cranial deformity detection system using digital image correlation(DIC)is presented to quickly obtain and detect accurate 3D data of infants’cra-nium.By introducing plane mirrors into a stereo 3D DIC measurement system,a multi-view 3D imaging model is established to convert 3D data from real and virtual perspectives into 360-degree 3D data of the tested infant cranium,achieving single-shot and panoramic 3D measurement.Exper-imental results showed that the performance and measurement accuracy of the proposed system can meet the requirements for cranial deformity detection,which provides a fast,accurate,and low-cost solution medically.展开更多
INTRODUCTION.On January 7,2025,at 9:05 AM BJT,a MS6.8 earthquake(CENC epicenter:28.50°N,87.45°E)struck Dingri County,Xizang Province(hereinafter referred to as the Dingri mainshock).The inferred moment magni...INTRODUCTION.On January 7,2025,at 9:05 AM BJT,a MS6.8 earthquake(CENC epicenter:28.50°N,87.45°E)struck Dingri County,Xizang Province(hereinafter referred to as the Dingri mainshock).The inferred moment magnitude,based on regional/teleseismic waveform inversion and back-projection,is approximately MW7.1.Focal mechanism solutions,aftershock distribution,and field surveys indicate that the Dingri mainshock was a normal-faulting event,with a nearly north-south strike and a westward-dipping fault plane.展开更多
The lithospheric magnetic field is an important component of the geomagnetic field,and the oceanic lithosphere exhibits distinct characteristics.Because of its formation mechanisms,evolutionary history,and geomagnetic...The lithospheric magnetic field is an important component of the geomagnetic field,and the oceanic lithosphere exhibits distinct characteristics.Because of its formation mechanisms,evolutionary history,and geomagnetic field polarity reversals,the oceanic lithosphere has significant remanent magnetization,which causes magnetic anomaly stripes parallel to the mid-ocean ridges.However,it is difficult to construct a high-resolution lithospheric magnetic field model in oceanic regions with relatively sparse data or no data.Using forward calculated lithospheric magnetic field data based on an oceanic remanent magnetization(ORM) model with physical and geological foundations as a supplement is a feasible approach.We first collect the latest available oceanic crust age grid,plate motion model,geomagnetic polarity timescale,and oceanic lithosphere thermal structure.Combining the assumptions that the paleo geomagnetic field is a geocentric axial dipole field and that the normal oceanic crust moves only in the horizontal direction,we construct a vertically integrated ORM model of the normal oceanic crust with a known age,including the intensity,inclination,and declination.Both the ORM model and the global induced magnetization(GIM) model are then scaled from two aspects between their forward calculated results and the lithospheric magnetic field model LCS-1.One aspect is the difference in their spherical harmonic power spectra,and the other is the misfit between the grid data over the oceans.We last compare the forward calculated lithospheric magnetic anomaly from the scaled ORM and GIM models with the Macao Science Satellite-1(MSS-1) observed data.The comparison results show that the magnetic anomalies over the normal oceanic crust regions at satellite altitude are mainly contributed by the high-intensity remanent magnetization corresponding to the Cretaceous magnetic quiet period.In these regions,the predicted and observed anomalies show good consistency in spatial distribution,whereas their amplitude differences vary across regions.This result suggests that regional ORM construction should be attempted in future work to address these amplitude discrepancies.展开更多
As a means of quantitative interpretation,forward calculations of the global lithospheric magnetic field in the Spherical Harmonic(SH)domain have been widely used to reveal geophysical,lithological,and geothermal vari...As a means of quantitative interpretation,forward calculations of the global lithospheric magnetic field in the Spherical Harmonic(SH)domain have been widely used to reveal geophysical,lithological,and geothermal variations in the lithosphere.Traditional approaches either do not consider the non-axial dipolar terms of the inducing field and its radial variation or do so by means of complicated formulae.Moreover,existing methods treat the magnetic lithosphere either as an infinitesimally thin layer or as a radially uniform spherical shell of constant thickness.Here,we present alternative forward formulae that account for an arbitrarily high maximum degree of the inducing field and for a magnetic lithosphere of variable thickness.Our simulations based on these formulae suggest that the satellite magnetic anomaly field is sensitive to the non-axial dipolar terms of the inducing field but not to its radial variation.Therefore,in forward and inverse calculations of satellite magnetic anomaly data,the non-axial dipolar terms of the inducing field should not be ignored.Furthermore,our results show that the satellite magnetic anomaly field is sensitive to variability in the lateral thickness of the magnetized shell.In particular,we show that for a given vertically integrated susceptibility distribution,underestimating the thickness of the magnetic layer overestimates the induced magnetic field.This discovery bridges the greatest part of the alleged gap between the susceptibility values measured from rock samples and the susceptibility values required to match the observed magnetic field signal.We expect the formulae and conclusions of this study to be a valuable tool for the quantitative interpretation of the Earth's global lithospheric magnetic field,through an inverse or forward modelling approach.展开更多
The equivalent source(ES)method in the spherical coordinate system has been widely applied to processing,reduction,field modeling,and geophysical and geological interpretation of satellite magnetic anomaly data.Howeve...The equivalent source(ES)method in the spherical coordinate system has been widely applied to processing,reduction,field modeling,and geophysical and geological interpretation of satellite magnetic anomaly data.However,the inversion for the ES model suffers from nonuniqueness and instability,which remain unresolved.To mitigate these issues,we introduce both the minimum and flattest models into the model objective function as an alternative regularization approach in the spherical ES method.We first present the methods,then analyze the accuracy of forward calculation and test the proposed ES method in this study by using synthetic data.The experimental results from simulation data indicate that our proposed regularization effectively suppresses the Backus effect and mitigates inversion instability in the low-latitude region.Finally,we apply the proposed method to magnetic anomaly data from China Seismo-Electromagnetic Satellite-1(CSES-1)and Macao Science Satellite-1(MSS-1)magnetic measurements over Africa by constructing an ES model of the large-scale lithospheric magnetic field.Compared with existing global lithospheric magnetic field models,our ES model demonstrates good consistency at high altitudes and predicts more stable fields at low altitudes.Furthermore,we derive the reduction to the pole(RTP)magnetic anomaly fields and the apparent susceptibility contrast distribution based on the ES model.The latter correlates well with the regional tectonic framework in Africa and surroundings.展开更多
By combining data from the Challenging Minisatellite Payload(CHAMP),Swarm-A,and newest Macao Science Satellite-1(MSS-1) missions,we constructed a lithospheric magnetic field model up to spherical harmonic degree N = 1...By combining data from the Challenging Minisatellite Payload(CHAMP),Swarm-A,and newest Macao Science Satellite-1(MSS-1) missions,we constructed a lithospheric magnetic field model up to spherical harmonic degree N = 100.To isolate the lithospheric magnetic field signals,we utilized the latest CHAOS-8(CHAMP,Φrsted,and SAC-C 8) model and MGFM(Multisource Geomagnetic Field Model) to remove nonlithospheric sources,including the core field,magnetospheric field,ocean tidal field,and ocean circulation field.Subsequently,orbit-by-orbit processing was applied to both scalar and vector data,such as spherical harmonic high-pass filtering,singular spectrum analysis,and line leveling,to suppress noise and residual signals along the satellite tracks.With an orbital inclination of only 41°,MSS-1 effectively captures fine-scale lithospheric magnetic field signals in mid-to low-latitude regions.Its data exhibit a root mean square error of only 0.77 nT relative to the final model,confirming the high quality and utility of lithospheric field modeling.The resulting model exhibits excellent consistency with the MF7(Magnetic Field Model 7),maintaining a high correlation up to N = 90 and still exceeding 0.65 at N = 100.These results demonstrate the reliability and value of MSS-1 data in global lithospheric magnetic field modeling.展开更多
The CUG_CLMFM3D series comprises high-resolution three-dimensional lithospheric magnetic field models for China and its surroundings.The first version,CUG_CLMFM3Dv1,is a spherical cap harmonic model integrating the WD...The CUG_CLMFM3D series comprises high-resolution three-dimensional lithospheric magnetic field models for China and its surroundings.The first version,CUG_CLMFM3Dv1,is a spherical cap harmonic model integrating the WDMAMv2(World Digital Magnetic Anomaly Map version 2)global magnetic anomaly grid and nearly a decade of CHAMP(Challenging Minisatellite Payload for Geophysical Research and Application)satellite vector data.It achieves a~5.7 km resolution but has limitations:the WDMAMv2 grid lacks high-resolution data in the southern Xinjiang and Tibet regions,which leads to missing small-to medium-scale anomalies,and unfiltered CHAMP data introduce low-frequency conflicts with global spherical harmonic models.Above the altitude of 150 km,correlations with global models drop below 0.9.The second version,CUG_CLMFM3Dv2,addresses these issues by incorporating 5-km-resolution aeromagnetic data and rigorously processed satellite data from CHAMP,Swarm,CSES-1(China Seismo-Electromagnetic Satellite 1),and MSS-1(Macao Science Satellite 1).The comparison analysis shows that the CUG_CLMFM3Dv2 captures finer high-frequency details and more stable long-wavelength signals,offering improved magnetic anomaly maps for further geological and geophysical studies.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
Quantitative phase imaging(QPI)enables non-invasive cellular analysis by utilizing cell thickness and refractive index as intrinsic probes,revolutionizing label-free microscopy in cellular research.Differential phase ...Quantitative phase imaging(QPI)enables non-invasive cellular analysis by utilizing cell thickness and refractive index as intrinsic probes,revolutionizing label-free microscopy in cellular research.Differential phase contrast(DPC),a non-interferometric QPI technique,requires only four intensity images under asymmetric illumination to recover the phase of a sample,offering the advantages of being label-free,non-coherent and highly robust.Its phase reconstruction result relies on precise modeling of the phase transfer function(PTF).However,in real optical systems,the PTF will deviate from its theoretical ideal due to the unknown wavefront aberrations,which will lead to significant artifacts and distortions in the reconstructed phase.We propose an aberration-corrected DPC(ACDPC)method that utilizes three intensity images under annular illumination to jointly retrieve the aberration and the phase,achieving high-quality QPI with minimal raw data.By employing three annular illuminations precisely matched to the numerical aperture of the objective lens,the object information is transmitted into the acquired intensity with a high signal-to-noise ratio.Phase retrieval is achieved by an iterative deconvolution algorithm that uses simulated annealing to estimate the aberration and further employs regularized deconvolution to reconstruct the phase,ultimately obtaining a refined complex pupil function and an aberration-corrected quantitative phase.We demonstrate that ACDPC is robust to multi-order aberrations without any priori knowledge,and can effectively retrieve and correct system aberrations to obtain high-quality quantitative phase.Experimental results show that ACDPC can clearly reproduce subcellular structures such as vesicles and lipid droplets with higher resolution than conventional DPC,which opens up new possibilities for more accurate subcellular structure analysis in cell biology.展开更多
Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficie...Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.展开更多
Single-atom nanozymes(SAzymes)hold significant potential for tumor catalytic therapy,but their effectiveness is often compromised by low catalytic efficiency within tumor microenvironment.This efficiency is mainly inf...Single-atom nanozymes(SAzymes)hold significant potential for tumor catalytic therapy,but their effectiveness is often compromised by low catalytic efficiency within tumor microenvironment.This efficiency is mainly influenced by key factors including hydrogen peroxide(H_(2)O_(2))availability,acidity,and temperature.Simultaneous optimization of these key factors presents a significant challenge for tumor catalytic therapy.In this study,we developed a comprehensive strategy to refine single-atom catalytic kinetics for enhancing tumor catalytic therapy through dual-enzyme-driven cascade reactions.Iridium(Ir)SAzymes with high catalytic activity and natural enzyme glucose oxidase(GOx)were utilized to construct the cascade reaction system.GOx was loaded by Ir SAzymes due to its large surface area.Then,the dual-enzyme-driven cascade reaction system was modified by cancer cell membranes for improving biocompatibility and achieving tumor homologous targeting ability.GOx catalysis reaction could produce abundant H2O2 and lower the local p H,thereby optimizing key reaction-limiting factors.Additionally,upon laser irradiation,Ir SAzymes could raise local temperature,further enhancing the catalytic efficiency of dual-enzyme system.This comprehensive optimization maximized the performance of Ir SAzymes,significantly improving the efficiency of catalytic therapy.Our findings present a strategy of refining single-atom catalytic kinetics for tumor homologous-targeted catalytic therapy.展开更多
Recent advancements in artificial intelligence have transformed three-dimensional(3D)optical imaging and metrology,enabling high-resolution and high-precision 3D surface geometry measurements from one single fringe pa...Recent advancements in artificial intelligence have transformed three-dimensional(3D)optical imaging and metrology,enabling high-resolution and high-precision 3D surface geometry measurements from one single fringe pattern projection.However,the imaging speed of conventional fringe projection profilometry(FPP)remains limited by the native sensor refresh rates due to the inherent"one-to-one"synchronization mechanism between pattern projection and image acquisition in standard structured light techniques.Here,we present dual-frequency angular-multiplexed fringe projection profilometry(DFAMFPP),a deep learning-enabled 3D imaging technique that achieves high-speed,high-precision,and large-depth-range absolute 3D surface measurements at speeds 16 times faster than the sensor's native frame rate.By encoding multi-timeframe 3D information into a single multiplexed image using multiple pairs of dual-frequency fringes,high-accuracy absolute phase maps are reconstructed using specially trained two-stage number-theoretical-based deep neural networks.We validate the effectiveness of DFAMFPP through dynamic scene measurements,achieving 10,000 Hz 3D imaging of a running turbofan engine prototype with only a 625 Hz camera.By overcoming the sensor hardware bottleneck,DFAMFPP significantly advances high-speed and ultra-high-speed 3D imaging,opening new avenues for exploring dynamic processes across diverse scientific disciplines.展开更多
The remarkable biological activities ofγ-aminobutyric acid derivatives(GABAs)spurred the exploration of green and efficient synthetic methods to construct these scaffolds.Herein,we have developed a catalyst-free phot...The remarkable biological activities ofγ-aminobutyric acid derivatives(GABAs)spurred the exploration of green and efficient synthetic methods to construct these scaffolds.Herein,we have developed a catalyst-free photoinduced strategy for the redox-neutral three-component carboimination of alkenes,enabling efficient and modular assembly of a wide range ofγ-aminobutyric acid derivatives.Mechanistic studies indicate that this reaction is initiated with an electron donor-acceptor complex between deprotonated malonates and O-aryl oximes.Furthermore,the resulting products could be further converted to functionalizedγ-lactam derivatives through an acidic lactamization process.展开更多
The Earth's magnetic field,which has been extensively observed from ground to satellite altitudes over several decades,originates from multiple sources,such as the core dynamo,the conductive mantle,the magnetized ...The Earth's magnetic field,which has been extensively observed from ground to satellite altitudes over several decades,originates from multiple sources,such as the core dynamo,the conductive mantle,the magnetized lithosphere,and the space current systems.Modeling of the lithospheric contribution plays an important role in the geophysical studies and industrial applications.In this paper,we propose a new method for global and regional modeling of the lithospheric magnetic field based on the cubed-sphere.An equivalent dipole source method on a quasi-uniform cubed-sphere grid is employed in the forward modeling.The dipole directions are fixed according to a priori magnetization and the relative intensities are estimated by an inversion procedure of least-squares fitting with minimum model regularization.Several numerical tests are performed to validate the accuracy and efficiency of both forward modeling and inversion procedure.The proposed method is applied to the global and regional modeling based on the latest magnetic data from Swarm Alpha satellite and MSS-1 mission.The model results indicate that the proposed method works quite well for realistic satellite data and MSS-1 data is consistent with the Swarm data in terms of lithospheric field modeling.展开更多
The data of marine-controlled source electromagnetic exploration collected in shallow waters are considerably influenced by airwaves.Thus,finding ways to eliminate this influence is important.Decomposing the electroma...The data of marine-controlled source electromagnetic exploration collected in shallow waters are considerably influenced by airwaves.Thus,finding ways to eliminate this influence is important.Decomposing the electromagnetic field into the upgoing and downgoing fields is an effective method to resolve this problem.By utilizing the Stratton-Chu integral transform,this study proposes a novel method that can separate a 3D electromagnetic field into upgoing and downgoing electromagnetic fields through rigorous mathematical deduction.We examine the spectral characteristics to determine the effectiveness of the method.The results show that a practical digital filter can be achieved by selecting a reasonable window size and spatial step,as demonstrated through spectral comparisons with an analytical filter.展开更多
文摘Multimodality image registration and fusion are essential steps in building 3-D models from remotesensing data. We present in this paper a neural network technique for the registration and fusion of multimodali-ty remote sensing data for the reconstruction of 3-D models of terrain regions. A FeedForward neural network isused to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data arepresented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusionresults.
基金supported by the National Natural Science Found-ation of China(No.62075096)Leading Technology of Ji-angsu Basic Research Plan(No.BK20192003)+4 种基金National De-fense Science and Technology Foundation of China(No.2019-JCJQ-JJ-381)“333 Engineering”Research Project of Jiangsu Province(No.BRA2016407)Jiangsu Provincial“One Belt and One Road”Innovation Cooperation Project(No.BZ2020007)Fundamental Research Funds for the Central Universities(Nos.30921011208,30919011222 and 30920032101)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(No.JS-GP202105).
文摘In this paper,a single-shot 360-degree cranial deformity detection system using digital image correlation(DIC)is presented to quickly obtain and detect accurate 3D data of infants’cra-nium.By introducing plane mirrors into a stereo 3D DIC measurement system,a multi-view 3D imaging model is established to convert 3D data from real and virtual perspectives into 360-degree 3D data of the tested infant cranium,achieving single-shot and panoramic 3D measurement.Exper-imental results showed that the performance and measurement accuracy of the proposed system can meet the requirements for cranial deformity detection,which provides a fast,accurate,and low-cost solution medically.
基金supported by the“CUG Scholar”Scientific Research Funds at China University of Geosciences(Wuhan)(No.2021230)supported by the National Natural Science Foundation of China(Nos.41922025,42204062)。
文摘INTRODUCTION.On January 7,2025,at 9:05 AM BJT,a MS6.8 earthquake(CENC epicenter:28.50°N,87.45°E)struck Dingri County,Xizang Province(hereinafter referred to as the Dingri mainshock).The inferred moment magnitude,based on regional/teleseismic waveform inversion and back-projection,is approximately MW7.1.Focal mechanism solutions,aftershock distribution,and field surveys indicate that the Dingri mainshock was a normal-faulting event,with a nearly north-south strike and a westward-dipping fault plane.
基金supported by the National Natural Science Foundation of China (41804067, 42174090, 42250101, and 42250103)the Science Research Project of the Hebei Education Department (BJK2024107)+3 种基金the Hebei Natural Science Foundation (D2022403044)the Opening Fund of the Key Laboratory of Geological Survey and Evaluation of the Ministry of Education (GLAB2023ZR02)the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources (MSFGPMR2022-4)the Excellent Young Scientist Fund of Hebei GEO University (YQ202403)。
文摘The lithospheric magnetic field is an important component of the geomagnetic field,and the oceanic lithosphere exhibits distinct characteristics.Because of its formation mechanisms,evolutionary history,and geomagnetic field polarity reversals,the oceanic lithosphere has significant remanent magnetization,which causes magnetic anomaly stripes parallel to the mid-ocean ridges.However,it is difficult to construct a high-resolution lithospheric magnetic field model in oceanic regions with relatively sparse data or no data.Using forward calculated lithospheric magnetic field data based on an oceanic remanent magnetization(ORM) model with physical and geological foundations as a supplement is a feasible approach.We first collect the latest available oceanic crust age grid,plate motion model,geomagnetic polarity timescale,and oceanic lithosphere thermal structure.Combining the assumptions that the paleo geomagnetic field is a geocentric axial dipole field and that the normal oceanic crust moves only in the horizontal direction,we construct a vertically integrated ORM model of the normal oceanic crust with a known age,including the intensity,inclination,and declination.Both the ORM model and the global induced magnetization(GIM) model are then scaled from two aspects between their forward calculated results and the lithospheric magnetic field model LCS-1.One aspect is the difference in their spherical harmonic power spectra,and the other is the misfit between the grid data over the oceans.We last compare the forward calculated lithospheric magnetic anomaly from the scaled ORM and GIM models with the Macao Science Satellite-1(MSS-1) observed data.The comparison results show that the magnetic anomalies over the normal oceanic crust regions at satellite altitude are mainly contributed by the high-intensity remanent magnetization corresponding to the Cretaceous magnetic quiet period.In these regions,the predicted and observed anomalies show good consistency in spatial distribution,whereas their amplitude differences vary across regions.This result suggests that regional ORM construction should be attempted in future work to address these amplitude discrepancies.
基金supported by the National Natural Science Foundation of China(Grant Nos.42250103 and 42174090)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB2023ZR02)the Ministry of Science and Technology(MOST)Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources(Grant No.MSFGPMR2022-4)。
文摘As a means of quantitative interpretation,forward calculations of the global lithospheric magnetic field in the Spherical Harmonic(SH)domain have been widely used to reveal geophysical,lithological,and geothermal variations in the lithosphere.Traditional approaches either do not consider the non-axial dipolar terms of the inducing field and its radial variation or do so by means of complicated formulae.Moreover,existing methods treat the magnetic lithosphere either as an infinitesimally thin layer or as a radially uniform spherical shell of constant thickness.Here,we present alternative forward formulae that account for an arbitrarily high maximum degree of the inducing field and for a magnetic lithosphere of variable thickness.Our simulations based on these formulae suggest that the satellite magnetic anomaly field is sensitive to the non-axial dipolar terms of the inducing field but not to its radial variation.Therefore,in forward and inverse calculations of satellite magnetic anomaly data,the non-axial dipolar terms of the inducing field should not be ignored.Furthermore,our results show that the satellite magnetic anomaly field is sensitive to variability in the lateral thickness of the magnetized shell.In particular,we show that for a given vertically integrated susceptibility distribution,underestimating the thickness of the magnetic layer overestimates the induced magnetic field.This discovery bridges the greatest part of the alleged gap between the susceptibility values measured from rock samples and the susceptibility values required to match the observed magnetic field signal.We expect the formulae and conclusions of this study to be a valuable tool for the quantitative interpretation of the Earth's global lithospheric magnetic field,through an inverse or forward modelling approach.
基金supported by the National Natural Science Foundation of China(Grant Nos.42250103 and 42174090)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB2023ZR02)the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources(Grant No.MSFGPMR2022-4).
文摘The equivalent source(ES)method in the spherical coordinate system has been widely applied to processing,reduction,field modeling,and geophysical and geological interpretation of satellite magnetic anomaly data.However,the inversion for the ES model suffers from nonuniqueness and instability,which remain unresolved.To mitigate these issues,we introduce both the minimum and flattest models into the model objective function as an alternative regularization approach in the spherical ES method.We first present the methods,then analyze the accuracy of forward calculation and test the proposed ES method in this study by using synthetic data.The experimental results from simulation data indicate that our proposed regularization effectively suppresses the Backus effect and mitigates inversion instability in the low-latitude region.Finally,we apply the proposed method to magnetic anomaly data from China Seismo-Electromagnetic Satellite-1(CSES-1)and Macao Science Satellite-1(MSS-1)magnetic measurements over Africa by constructing an ES model of the large-scale lithospheric magnetic field.Compared with existing global lithospheric magnetic field models,our ES model demonstrates good consistency at high altitudes and predicts more stable fields at low altitudes.Furthermore,we derive the reduction to the pole(RTP)magnetic anomaly fields and the apparent susceptibility contrast distribution based on the ES model.The latter correlates well with the regional tectonic framework in Africa and surroundings.
基金the support of the National Natural Science Foundation of China (Nos. 42250103, 41974073, and 41404053)the Macao Foundation and the preresearch project of Civil Aerospace Technologies (Nos. D020308 and D020303)funded by China’s National Space Administration, and the Specialized Research Fund for State Key Laboratories。
文摘By combining data from the Challenging Minisatellite Payload(CHAMP),Swarm-A,and newest Macao Science Satellite-1(MSS-1) missions,we constructed a lithospheric magnetic field model up to spherical harmonic degree N = 100.To isolate the lithospheric magnetic field signals,we utilized the latest CHAOS-8(CHAMP,Φrsted,and SAC-C 8) model and MGFM(Multisource Geomagnetic Field Model) to remove nonlithospheric sources,including the core field,magnetospheric field,ocean tidal field,and ocean circulation field.Subsequently,orbit-by-orbit processing was applied to both scalar and vector data,such as spherical harmonic high-pass filtering,singular spectrum analysis,and line leveling,to suppress noise and residual signals along the satellite tracks.With an orbital inclination of only 41°,MSS-1 effectively captures fine-scale lithospheric magnetic field signals in mid-to low-latitude regions.Its data exhibit a root mean square error of only 0.77 nT relative to the final model,confirming the high quality and utility of lithospheric field modeling.The resulting model exhibits excellent consistency with the MF7(Magnetic Field Model 7),maintaining a high correlation up to N = 90 and still exceeding 0.65 at N = 100.These results demonstrate the reliability and value of MSS-1 data in global lithospheric magnetic field modeling.
基金supported by the National Natural Science Foundation of China(Grant Nos.42250103,42174090,42250101,42250102,and 41774091)the Macao Foundation+1 种基金the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB2023ZR02)the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources(Grant No.MSFGPMR2022-4)。
文摘The CUG_CLMFM3D series comprises high-resolution three-dimensional lithospheric magnetic field models for China and its surroundings.The first version,CUG_CLMFM3Dv1,is a spherical cap harmonic model integrating the WDMAMv2(World Digital Magnetic Anomaly Map version 2)global magnetic anomaly grid and nearly a decade of CHAMP(Challenging Minisatellite Payload for Geophysical Research and Application)satellite vector data.It achieves a~5.7 km resolution but has limitations:the WDMAMv2 grid lacks high-resolution data in the southern Xinjiang and Tibet regions,which leads to missing small-to medium-scale anomalies,and unfiltered CHAMP data introduce low-frequency conflicts with global spherical harmonic models.Above the altitude of 150 km,correlations with global models drop below 0.9.The second version,CUG_CLMFM3Dv2,addresses these issues by incorporating 5-km-resolution aeromagnetic data and rigorously processed satellite data from CHAMP,Swarm,CSES-1(China Seismo-Electromagnetic Satellite 1),and MSS-1(Macao Science Satellite 1).The comparison analysis shows that the CUG_CLMFM3Dv2 captures finer high-frequency details and more stable long-wavelength signals,offering improved magnetic anomaly maps for further geological and geophysical studies.
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 National Natural Science Foundation of China(62305162,62227818,62361136588)China Postdoctoral Science Foundation(2023TQ0160,2023M731683)+5 种基金Nanjing University of Science and Technology independent research project(30923010305)National Key Research and Development Program of China(2024YFE0101300)Biomedical Competition Foundation of Jiangsu Province(BE2022847)Key National Industrial Technology Cooperation Foundation of Jiangsu Province(BZ2022039)Fundamental Research Funds for the Central Universities(2023102001)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGP202105,JSGP202201,JSGPCXZNGZ202401)。
文摘Quantitative phase imaging(QPI)enables non-invasive cellular analysis by utilizing cell thickness and refractive index as intrinsic probes,revolutionizing label-free microscopy in cellular research.Differential phase contrast(DPC),a non-interferometric QPI technique,requires only four intensity images under asymmetric illumination to recover the phase of a sample,offering the advantages of being label-free,non-coherent and highly robust.Its phase reconstruction result relies on precise modeling of the phase transfer function(PTF).However,in real optical systems,the PTF will deviate from its theoretical ideal due to the unknown wavefront aberrations,which will lead to significant artifacts and distortions in the reconstructed phase.We propose an aberration-corrected DPC(ACDPC)method that utilizes three intensity images under annular illumination to jointly retrieve the aberration and the phase,achieving high-quality QPI with minimal raw data.By employing three annular illuminations precisely matched to the numerical aperture of the objective lens,the object information is transmitted into the acquired intensity with a high signal-to-noise ratio.Phase retrieval is achieved by an iterative deconvolution algorithm that uses simulated annealing to estimate the aberration and further employs regularized deconvolution to reconstruct the phase,ultimately obtaining a refined complex pupil function and an aberration-corrected quantitative phase.We demonstrate that ACDPC is robust to multi-order aberrations without any priori knowledge,and can effectively retrieve and correct system aberrations to obtain high-quality quantitative phase.Experimental results show that ACDPC can clearly reproduce subcellular structures such as vesicles and lipid droplets with higher resolution than conventional DPC,which opens up new possibilities for more accurate subcellular structure analysis in cell biology.
文摘Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.
基金financially supported by National Natural Science Foundation of China(U23A2097,82372116,22474079,22104094,82302362)Shenzhen Medical Research Fund(B2302047)+3 种基金Basic Research Program of Shenzhen(KQTD20190929172538530,JCYJ20220818095806014,JCYJ20240813142810014)Natural Science Foundation of Guangdong Province(2024A1515012677)Research Team Cultivation Program of Shenzhen University(2023QNT017,2023QNT019)Shenzhen University 2035 Program for Excellent Research(2024C004)。
文摘Single-atom nanozymes(SAzymes)hold significant potential for tumor catalytic therapy,but their effectiveness is often compromised by low catalytic efficiency within tumor microenvironment.This efficiency is mainly influenced by key factors including hydrogen peroxide(H_(2)O_(2))availability,acidity,and temperature.Simultaneous optimization of these key factors presents a significant challenge for tumor catalytic therapy.In this study,we developed a comprehensive strategy to refine single-atom catalytic kinetics for enhancing tumor catalytic therapy through dual-enzyme-driven cascade reactions.Iridium(Ir)SAzymes with high catalytic activity and natural enzyme glucose oxidase(GOx)were utilized to construct the cascade reaction system.GOx was loaded by Ir SAzymes due to its large surface area.Then,the dual-enzyme-driven cascade reaction system was modified by cancer cell membranes for improving biocompatibility and achieving tumor homologous targeting ability.GOx catalysis reaction could produce abundant H2O2 and lower the local p H,thereby optimizing key reaction-limiting factors.Additionally,upon laser irradiation,Ir SAzymes could raise local temperature,further enhancing the catalytic efficiency of dual-enzyme system.This comprehensive optimization maximized the performance of Ir SAzymes,significantly improving the efficiency of catalytic therapy.Our findings present a strategy of refining single-atom catalytic kinetics for tumor homologous-targeted catalytic therapy.
基金supported by National Key Research and Development Program of China(2022YFB2804603,2022YFB2804605)National Natural Science Foundation of China(U21B2033)+4 种基金Fundamental Research Funds forthe Central Universities(2023102001,2024202002)National Key Laborato-ry of Shock Wave and Detonation Physics(JCKYS2024212111)China Post-doctoral Science Fund(2023T160318)Open Research Fund of JiangsuKey Laboratory of Spectral Imaging&Intelligent Sense(JSGP202105,JSGP202201)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX25_0695,SJCX25_0188)。
文摘Recent advancements in artificial intelligence have transformed three-dimensional(3D)optical imaging and metrology,enabling high-resolution and high-precision 3D surface geometry measurements from one single fringe pattern projection.However,the imaging speed of conventional fringe projection profilometry(FPP)remains limited by the native sensor refresh rates due to the inherent"one-to-one"synchronization mechanism between pattern projection and image acquisition in standard structured light techniques.Here,we present dual-frequency angular-multiplexed fringe projection profilometry(DFAMFPP),a deep learning-enabled 3D imaging technique that achieves high-speed,high-precision,and large-depth-range absolute 3D surface measurements at speeds 16 times faster than the sensor's native frame rate.By encoding multi-timeframe 3D information into a single multiplexed image using multiple pairs of dual-frequency fringes,high-accuracy absolute phase maps are reconstructed using specially trained two-stage number-theoretical-based deep neural networks.We validate the effectiveness of DFAMFPP through dynamic scene measurements,achieving 10,000 Hz 3D imaging of a running turbofan engine prototype with only a 625 Hz camera.By overcoming the sensor hardware bottleneck,DFAMFPP significantly advances high-speed and ultra-high-speed 3D imaging,opening new avenues for exploring dynamic processes across diverse scientific disciplines.
基金supported by Sichuan Science and Technology Program(No.2024NSFSC2100)the National Natural Science Foundation of China(No.22171233)Collaborative Fund of Luzhou Government and Southwest Medical University(Nos.2023LZXNYDJ019 and 2024LZXNYDJ050).
文摘The remarkable biological activities ofγ-aminobutyric acid derivatives(GABAs)spurred the exploration of green and efficient synthetic methods to construct these scaffolds.Herein,we have developed a catalyst-free photoinduced strategy for the redox-neutral three-component carboimination of alkenes,enabling efficient and modular assembly of a wide range ofγ-aminobutyric acid derivatives.Mechanistic studies indicate that this reaction is initiated with an electron donor-acceptor complex between deprotonated malonates and O-aryl oximes.Furthermore,the resulting products could be further converted to functionalizedγ-lactam derivatives through an acidic lactamization process.
基金supported by the National Natural Science Foundation of China(42250101,42250102,42250103,12250013)the Macao Foundation。
文摘The Earth's magnetic field,which has been extensively observed from ground to satellite altitudes over several decades,originates from multiple sources,such as the core dynamo,the conductive mantle,the magnetized lithosphere,and the space current systems.Modeling of the lithospheric contribution plays an important role in the geophysical studies and industrial applications.In this paper,we propose a new method for global and regional modeling of the lithospheric magnetic field based on the cubed-sphere.An equivalent dipole source method on a quasi-uniform cubed-sphere grid is employed in the forward modeling.The dipole directions are fixed according to a priori magnetization and the relative intensities are estimated by an inversion procedure of least-squares fitting with minimum model regularization.Several numerical tests are performed to validate the accuracy and efficiency of both forward modeling and inversion procedure.The proposed method is applied to the global and regional modeling based on the latest magnetic data from Swarm Alpha satellite and MSS-1 mission.The model results indicate that the proposed method works quite well for realistic satellite data and MSS-1 data is consistent with the Swarm data in terms of lithospheric field modeling.
基金supported by the National Natural Science Foundation of China(42074085).
文摘The data of marine-controlled source electromagnetic exploration collected in shallow waters are considerably influenced by airwaves.Thus,finding ways to eliminate this influence is important.Decomposing the electromagnetic field into the upgoing and downgoing fields is an effective method to resolve this problem.By utilizing the Stratton-Chu integral transform,this study proposes a novel method that can separate a 3D electromagnetic field into upgoing and downgoing electromagnetic fields through rigorous mathematical deduction.We examine the spectral characteristics to determine the effectiveness of the method.The results show that a practical digital filter can be achieved by selecting a reasonable window size and spatial step,as demonstrated through spectral comparisons with an analytical filter.
