The third-harmonic generation(THG)coefficient for a spherical quantum dot system with inversely quadratic Hellmann plus inversely quadratic potential is investigated theoretically,considering the regulation of quantum...The third-harmonic generation(THG)coefficient for a spherical quantum dot system with inversely quadratic Hellmann plus inversely quadratic potential is investigated theoretically,considering the regulation of quantum size,confinement potential depth and the external environment.The numerical simulation results indicate that the THG coefficient can reach the order of 10~(-12)m~2V~(-2),which strongly relies on the tunable factor,with its resonant peak experiencing a redshift or blueshift.Interestingly,the effect of temperature on the THG coefficient in terms of peak location and size is consistent with the quantum dot radius but contrasts with the hydrostatic pressure.Thus,it is crucial to focus on the influence of internal and external parameters on nonlinear optical effects,and to implement the theory in practical experiments and the manufacture of optoelectronic devices.展开更多
After briefly breaching 65 cents/lb, the December contract fell sharply over a few of days in mid-August before finding support near 58 cents/lb. In the weeks since, December futures have gradually drifted upwards,
In this study,we propose a framework for the inverse design of amorphous alloys based on multi-objective properties,which not only constructs the potential relationship between material properties and composition,but ...In this study,we propose a framework for the inverse design of amorphous alloys based on multi-objective properties,which not only constructs the potential relationship between material properties and composition,but also verifies the inherent constraints between different objective properties.The framework uses the variational autoencoder and conditional variational autoencoder as the core generation model,and combines the grid filter,quantum network,and machine learning algorithm to filter and recommend the generated data,thereby improving the feasibility of practical applications.To validate and analyze the performance of the proposed framework,we implemented an amorphous alloy design scheme covering three key properties:saturation magnetic strength,coercivity,and glass transition temperature.The experimental results show that the framework can generate alloy compositions that are extremely similar to the experimental data under the same property conditions,thus validating its excellent generation capability.More importantly,our material inverse design framework has good scalability.This allows it to flexibly respond to the increasingly complex and specific property requirements of various materials in the future.展开更多
Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations a...Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.展开更多
In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honey...In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.展开更多
Recycling of waste rubber(WR)is crucial for the sustainable development of the rubber industry.The enhancement of interfacial interactions is the main strategy for waste polymer recycling.However,there is a lack of me...Recycling of waste rubber(WR)is crucial for the sustainable development of the rubber industry.The enhancement of interfacial interactions is the main strategy for waste polymer recycling.However,there is a lack of methods for enhancing the interfacial interactions for WR recycling because WR contains abundant inert C―H bonds.Herein,we designed thioctic acid inverse vulcanization copolymers to endow recycled WR with dynamic disulfide interfacial interactions,significantly improving the mechanical properties of recycled WR.These disulfide interfacial interactions among the recycled WR tend to exchange,which dramatically increases the fractocohesive length and prevents stress concentration near the crack tips.When recycled WR is subjected to external stress,the loads are redistributed across a broad region of adjacent regions instead of being concentrated on a limited length scale,which resists crack propagation.This work effectively recycled WR,providing a strategy for solvent-free reaction-derived inverse vulcanization copolymers to improve the toughness of WR recycling.展开更多
In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep le...In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.展开更多
Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral mole...Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral molecules L/D-PF1 and L/D-PF2 with pyrene groups were synthesized and its chiroptical properties upon co-assembly with achiral compound octafluoronaphthalene(OFN) through AP interaction were systemically studied.The co-assembly of L/D-PF1/OFN and L/D-PF2/OFN exhibited distinct chiroptical properties such as circular dichroism(CD) and circularly polarized luminescence(CPL) signals.Chirality transfer from the chirality center of L/D-PF1 and L/D-PF2 to the achiral OFN and chiral amplification were successfully achieved.Besides,no significant CPL signal was observed in the self-assembly of L/DPF1 or L/D-PF2 while co-assembly with OFN exhibited obvious CPL amplification induced by AP interaction.Notably,a reversal CD signal and CPL signal could be observed in L/D-PF2/OFN when the molar ratio changed from 1:1 to 1:2 while not found in L/D-PF1/OFN,indicating that that minor structural changes of molecules could cause large changes in assembly.In addition,a series of computational calculations were conducted to verify the AP interaction between L-PF1/L-PF2 and OFN.This work demonstrated that arene-perfluoroarene interaction could drive chiral transfer,chiral amplification and chiral inversion and provided a new method for the preparation of chiroptical materials.展开更多
Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which req...Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which requires iteratively solving nonlinear hyperelasticity problems,is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians.In this study,we present an inverse material parameter identification strategy that integrates deep neural networks(DNNs)with FEA,namely inverse DNN-FEA.In this framework,a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution,which aims to reduce susceptibility to local optima that often arise in heterogeneous nonlinear hyperelastic problems.Consequently,inverse DNN-FEA enables identification of material parameters at the element level.For validation,we applied DNN-FEA to identify four spatially varying passive Holzapfel-Ogden material parameters of the left ventricular myocardium in synthetic benchmark cases with a clinically-derived geometry.To evaluate the benefit of DNN integration,a baseline FEA-only solver implemented in PyTorch was used for comparison.Results demonstrated that DNN-FEA achieved substantially lower average errors in parameter identification compared to FEA(case 1,DNN-FEA:0.37%~2.15%vs.FEA:2.64%~12.91%).The results also demonstrate that the same DNN architecture is capable of identifying a different spatial material property distribution(case 2,DNN-FEA:0.03%~0.60%vs.FEA:0.93%~16.25%).These findings suggest that DNN-FEA provides an accurate framework for inverse identification of heterogeneous myocardial material properties.This approach may facilitate future applications in patient-specific modeling based on in vivo clinical imaging and could be extended to other biomechanical simulation problems.展开更多
The complexity of the seismicity pattern for the subduction zone along the oceanic plate triggered the outer rise events and revealed cyclic tectonic deformation conditions along the plate subduction zones.The outer r...The complexity of the seismicity pattern for the subduction zone along the oceanic plate triggered the outer rise events and revealed cyclic tectonic deformation conditions along the plate subduction zones.The outer rise earthquakes have been observed along the Sunda arc,following the estimated rupture area of the 2005 M_(W)8.6 Nias earthquakes.Here,we used kinematic waveform inversion(KIWI)to obtain the source parameters of the 14 May 2021 M_(W)6.6 event off the west coast of northern Sumatra and to define the fault plane that triggered this outer rise event.The KIWI algorithm allows two types of seismic source to be configured:the moment tensor model to describe the type of shear with six moment tensor components and the Eikonal model for the rupture of pure double-couple sources.This method was chosen for its flexibility to be applied for different sources of seismicity and also for the automated full-moment tensor solution with real-time monitoring.We used full waveform traces from 8 broadband seismic stations within 1000 km epicentral distances sourced from the Incorporated Research Institutions for Seismology(IRIS-IDA)and Geofon GFZ seismic record databases.The initial origin time and hypocenter values are obtained from the IRIS-IDA.The synthetic seismograms used in the inversion process are based on the existing regional green function database model and were accessed from the KIWI Tools Green's Function Database.The obtained scalar seismic moment value is 1.18×10^(19)N·m,equivalent to a moment magnitude M_(W)6.6.The source parameters are 140°,44°,and−99°for the strike,dip,and rake values at a centroid depth of 10.2 km,indicating that this event is a normal fault earthquake that occurred in the outer rise area.The outer rise events with normal faults typically occur at the shallow part of the plate,with nodal-plane dips predominantly in the range of 30°-60°on the weak oceanic lithosphere due to hydrothermal alteration.The stress regime around the plate subduction zone varies both temporally and spatially due to the cyclic influences of megathrust earthquakes.Tensional outer rise earthquakes tend to occur after the megathrust events.The relative timing of these events is not known due to the viscous relaxation of the down going slab and poroelastic response in the trench slope region.The occurrence of the 14 May 2021 earthquake shows the seismicity in the outer rise region in the strongly coupled Sunda arc subduction zone due to elastic bending stress within the duration of the seismic cycle.展开更多
Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces th...Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces the accuracy of conventional methods.This article proposes a user-friendly software for PSD analysis,GranuSAS,which employs an algorithm that integrates truncated singular value decomposition(TSVD)with the Chahine method.This approach employs TSVD for data preprocessing,generating a set of initial solutions with noise suppression.A high-quality initial solution is subsequently selected via the L-curve method.This selected candidate solution is then iteratively refined by the Chahine algorithm,enforcing constraints such as non-negativity and improving physical interpretability.Most importantly,GranuSAS employs a parallel architecture that simultaneously yields inversion results from multiple shape models and,by evaluating the accuracy of each model's reconstructed scattering curve,offers a suggestion for model selection in material systems.To systematically validate the accuracy and efficiency of the software,verification was performed using both simulated and experimental datasets.The results demonstrate that the proposed software delivers both satisfactory accuracy and reliable computational efficiency.It provides an easy-to-use and reliable tool for researchers in materials science,helping them fully exploit the potential of SAXS in nanoparticle characterization.展开更多
This study examines how native pore structures and loading conditions influencethe fracture size distribution and the predictability of catastrophic failure in rocks.Four lithologies with distinct pore characteristics...This study examines how native pore structures and loading conditions influencethe fracture size distribution and the predictability of catastrophic failure in rocks.Four lithologies with distinct pore characteristics,i.e.granite,limestone,red sandstone,and marble,were tested under uniaxial compression and Brazilian splitting.Nuclear magnetic resonance(NMR)was used to characterize pore structures,while acoustic emission(AE)monitoring captured the temporal evolution of microcracking.The relationships among pore properties,AE b-values,and failure predictability were systematically evaluated.Results show that the overall b-value is primarily controlled by native pore size rather than loading condition.Rocks with larger pores display higher b-value and greater temporal variability,whereas those with smaller pores exhibit lower and more stable b-value.To assess failure predictability,the AE count rate was incorporated into an inverse power law model.The model demonstrates higher predictive accuracy for high-porosity rocks.The average predicted failure time(t_(p))decreases monotonically with porosity:under uniaxial compression,t_(p)for granite,marble,limestone,and sandstone are 2.32,1.82,1.42,and 0.03,respectively;under Brazilian splitting,3.54,3.30,0.10,and 0.03.Among the four rock types,sandstone with the highest porosity exhibits the smallest discrepancy between predicted and actual failure time,whereas granite with the lowest porosity shows the largest.As porosity decreases,prediction accuracy progressively declines for limestone and marble.Overall,the findings indicate that native pore heterogeneity governs both fracture scaling behavior and failure predictability,and that these effects are largely independent of the loading conditions examined in this study.展开更多
Physics-informed neural networks(PINNs),as a novel artificial intelligence method for solving partial differential equations,are applicable to solve both forward and inverse problems.This study evaluates the performan...Physics-informed neural networks(PINNs),as a novel artificial intelligence method for solving partial differential equations,are applicable to solve both forward and inverse problems.This study evaluates the performance of PINNs in solving the temperature diffusion equation of the seawater across six scenarios,including forward and inverse problems under three different boundary conditions.Results demonstrate that PINNs achieved consistently higher accuracy with the Dirichlet and Neumann boundary conditions compared to the Robin boundary condition for both forward and inverse problems.Inaccurate weighting of terms in the loss function can reduce model accuracy.Additionally,the sensitivity of model performance to the positioning of sampling points varied between different boundary conditions.In particular,the model under the Dirichlet boundary condition exhibited superior robustness to variations in point positions during the solutions of inverse problems.In contrast,for the Neumann and Robin boundary conditions,accuracy declines when points were sampled from identical positions or at the same time.Subsequently,the Argo observations were used to reconstruct the vertical diffusion of seawater temperature in the north-central Pacific for the applicability of PINNs in the real ocean.The PINNs successfully captured the vertical diffusion characteristics of seawater temperature,reflected the seasonal changes of vertical temperature under different topographic conditions,and revealed the influence of topography on the temperature diffusion coefficient.The PINNs were proved effective in solving the temperature diffusion equation of seawater with limited data,providing a promising technique for simulating or predicting ocean phenomena using sparse observations.展开更多
Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This stu...Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.展开更多
We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semicon...We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semiconductor lasers,as well as Raman and semiconductor optical amplifiers.Although inverse design approaches for optical detectors remain relatively underexplored,we examine optical layers,particularly metamaterial absorbers,as promising candidates for high-performance optical detection.In addition,we underscore advancements in inverse designing passive optical components,including beam splitters,gratings,and optical fibers.These optical blocks are fundamental in developing next-generation standalone optical communication systems and optical sensing networks,including integrated sensing and communication technologies.While categorizing various reported deep learning architectures across five paradigms,we offer a paradigm-based perspective that reveals how different ML techniques function within modern inverse design methods and enable fast,data-driven solutions that significantly reduce design time and computational demands compared with traditional optimization methods.展开更多
Terahertz metasurfaces have great applications for efficient terahertz modulation, but there are still problems in designing terahertz metadevices in terms of complexity and inefficiency. Herein, we demonstrate an inv...Terahertz metasurfaces have great applications for efficient terahertz modulation, but there are still problems in designing terahertz metadevices in terms of complexity and inefficiency. Herein, we demonstrate an inversely-designed terahertz metasurface with double electromagnetically induced transparency(EIT)-like windows by incorporating a particle swarm optimization(PSO) algorithm with the finite-difference time-domain method. We prepared and tested the metadevices, and the experimental terahertz signals are close to the designed results. By hybridizing amorphous germanium film with the inversely-designed metasurface, two EIT-like windows, including transmission and slow-light effect, exhibit ultrafast modulation behavior in 25 ps excited by a femtosecond laser. The modulation depths of transmission in two transparency windows are 74% and 65%, respectively. The numerical simulations also illustrate the ultrafast dynamic process and modulation mechanism, which match well with the experiment results. Our work thus offers opportunities for designing other objective functions of the terahertz metadevice.展开更多
Independent manipulation of transmitted and reflected light fields is a key technology for the realization of multifunctional optical applications,which can be implemented based on multilayered plasmonic or supercell ...Independent manipulation of transmitted and reflected light fields is a key technology for the realization of multifunctional optical applications,which can be implemented based on multilayered plasmonic or supercell subwavelength structures.However,the former is not suitable for the optical bands,while the latter is insufficient in generating large phase gradients.Here,an adjoint-optimization-based inverse design methodology is proposed,which utilizes the polarization-selective local interference between individual meta-atoms and enables monolayer dielectric metasurfaces to decouple the wavefront of transmitted and reflected optical fields.Moreover,this methodology serves to mitigate the aperiodic electromagnetic crosstalk inherent between adjacent meta-atoms,consequently leading to a significant enhancement in the performance of meta-devices.We analyzed the physical mechanism of adjoint optimization and proposed the concept of phase factors,highlighting their importance in the rapid inverse design of meta-devices—an aspect often overlooked in previous research.To demonstrate the feasibility and robustness of our method,we optimize monolayer metasurfaces with different initial structures.These devices efficiently focus and deflect x-linearly and y-linearly polarized incident light in transmission and reflection spaces,respectively.Overall,this methodology holds immense potential for designing multifunctional,high-performing metasurfaces that meet multiple constraints,opening up broad prospects for applications.展开更多
As an advanced device for observing atmospheric winds,the spaceborne Doppler Asymmetric Spatial Heterodyne(DASH)interferometer also encounters challenges associated with phase distortion,par-ticularly in limb sounding...As an advanced device for observing atmospheric winds,the spaceborne Doppler Asymmetric Spatial Heterodyne(DASH)interferometer also encounters challenges associated with phase distortion,par-ticularly in limb sounding scenarios.This paper discusses interferogram modeling and phase distortion cor-rection techniques for spaceborne DASH interferometers.The modeling of phase distortion interferograms with and without Doppler shift for limb observation was conducted,and the effectiveness of the analytical expression was verified through numerical simulation.The simulation results indicate that errors propagate layer by layer while using the onion-peeling inversion algorithm to handle phase-distorted interferograms.In contrast,the phase distortion correction algorithm can achieve effective correction.This phase correction method can be successfully applied to correct phase distortions in the interferograms of the spaceborne DASH interferometer,providing a feasible solution to enhance its measurement accuracy.展开更多
To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions tha...To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current methods.Firstly,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic environments.With a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic environments.Particularly,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.展开更多
Improving the accuracy of anthropogenic volatile organic compounds(VOCs)emission inventory is crucial for reducing atmospheric pollution and formulating control policy of air pollution.In this study,an anthropogenic s...Improving the accuracy of anthropogenic volatile organic compounds(VOCs)emission inventory is crucial for reducing atmospheric pollution and formulating control policy of air pollution.In this study,an anthropogenic speciated VOCs emission inventory was established for Central China represented by Henan Province at a 3 km×3 km spatial resolution based on the emission factormethod.The 2019 VOCs emission in Henan Provincewas 1003.5 Gg,while industrial process source(33.7%)was the highest emission source,Zhengzhou(17.9%)was the city with highest emission and April and August were the months with the more emissions.High VOCs emission regions were concentrated in downtown areas and industrial parks.Alkanes and aromatic hydrocarbons were the main VOCs contribution groups.The species composition,source contribution and spatial distribution were verified and evaluated through tracer ratio method(TR),Positive Matrix Factorization Model(PMF)and remote sensing inversion(RSI).Results show that both the emission results by emission inventory(EI)(15.7 Gg)and by TRmethod(13.6 Gg)and source contribution by EI and PMF are familiar.The spatial distribution of HCHO primary emission based on RSI is basically consistent with that of HCHO emission based on EI with a R-value of 0.73.The verification results show that the VOCs emission inventory and speciated emission inventory established in this study are relatively reliable.展开更多
基金National Natural Science Foundation of China(Grant Nos.11674312,52174161,51702003,12174161 and 61775087)Anhui University of Science and Technology(Grant No.2023CX2141)。
文摘The third-harmonic generation(THG)coefficient for a spherical quantum dot system with inversely quadratic Hellmann plus inversely quadratic potential is investigated theoretically,considering the regulation of quantum size,confinement potential depth and the external environment.The numerical simulation results indicate that the THG coefficient can reach the order of 10~(-12)m~2V~(-2),which strongly relies on the tunable factor,with its resonant peak experiencing a redshift or blueshift.Interestingly,the effect of temperature on the THG coefficient in terms of peak location and size is consistent with the quantum dot radius but contrasts with the hydrostatic pressure.Thus,it is crucial to focus on the influence of internal and external parameters on nonlinear optical effects,and to implement the theory in practical experiments and the manufacture of optoelectronic devices.
文摘After briefly breaching 65 cents/lb, the December contract fell sharply over a few of days in mid-August before finding support near 58 cents/lb. In the weeks since, December futures have gradually drifted upwards,
基金supported by the National Natural Science Foundation of China(Grant No.U23A2065)the National Key R&D Program of China(Grant No.2024YFB3411500)。
文摘In this study,we propose a framework for the inverse design of amorphous alloys based on multi-objective properties,which not only constructs the potential relationship between material properties and composition,but also verifies the inherent constraints between different objective properties.The framework uses the variational autoencoder and conditional variational autoencoder as the core generation model,and combines the grid filter,quantum network,and machine learning algorithm to filter and recommend the generated data,thereby improving the feasibility of practical applications.To validate and analyze the performance of the proposed framework,we implemented an amorphous alloy design scheme covering three key properties:saturation magnetic strength,coercivity,and glass transition temperature.The experimental results show that the framework can generate alloy compositions that are extremely similar to the experimental data under the same property conditions,thus validating its excellent generation capability.More importantly,our material inverse design framework has good scalability.This allows it to flexibly respond to the increasingly complex and specific property requirements of various materials in the future.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3209504)Natural Science Foundation of Wuhan(Grant No.2024040801020271)the Fundamental Research Funds for Central Public Welfare Research Institutes(Grant No.CKSF2025718/YT).
文摘Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.
基金the financial supports from National Key R&D Program for Young Scientists of China(Grant No.2022YFC3080900)National Natural Science Foundation of China(Grant No.52374181)+1 种基金BIT Research and Innovation Promoting Project(Grant No.2024YCXZ017)supported by Science and Technology Innovation Program of Beijing institute of technology under Grant No.2022CX01025。
文摘In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.
基金financially supported by the National Natural Science Foundation of China(No.52363007)。
文摘Recycling of waste rubber(WR)is crucial for the sustainable development of the rubber industry.The enhancement of interfacial interactions is the main strategy for waste polymer recycling.However,there is a lack of methods for enhancing the interfacial interactions for WR recycling because WR contains abundant inert C―H bonds.Herein,we designed thioctic acid inverse vulcanization copolymers to endow recycled WR with dynamic disulfide interfacial interactions,significantly improving the mechanical properties of recycled WR.These disulfide interfacial interactions among the recycled WR tend to exchange,which dramatically increases the fractocohesive length and prevents stress concentration near the crack tips.When recycled WR is subjected to external stress,the loads are redistributed across a broad region of adjacent regions instead of being concentrated on a limited length scale,which resists crack propagation.This work effectively recycled WR,providing a strategy for solvent-free reaction-derived inverse vulcanization copolymers to improve the toughness of WR recycling.
基金sponsored by the National Key Scientific Instrument and Equipment Development Projects of China(Grant No.62027823)the National Natural Science Foun-dation of China(Grant No.61775048).
文摘In recent years,the use of deep learning to replace traditional numerical methods for electromagnetic propagation has shown tremendous potential in the rapid design of photonic devices.However,most research on deep learning has focused on single-layer grating couplers,and the accuracy of multi-layer grating couplers has not yet reached a high level.This paper proposes and demonstrates a novel deep learning network-assisted strategy for inverse design.The network model is based on a multi-layer perceptron(MLP)and incorporates convolutional neural networks(CNNs)and transformers.Through the stacking of multiple layers,it achieves a high-precision design for both multi-layer and single-layer raster couplers with various functionalities.The deep learning network exhibits exceptionally high predictive accuracy,with an average absolute error across the full wavelength range of 1300–1700 nm being only 0.17%,and an even lower predictive absolute error below 0.09%at the specific wavelength of 1550 nm.By combining the deep learning network with the genetic algorithm,we can efficiently design grating couplers that perform different functions.Simulation results indicate that the designed single-wavelength grating couplers achieve coupling efficiencies exceeding 80%at central wavelengths of 1550 nm and 1310 nm.The performance of designed dual-wavelength and broadband grating couplers also reaches high industry standards.Furthermore,the network structure and inverse design method are highly scalable and can be applied not only to multi-layer grating couplers but also directly to the prediction and design of single-layer grating couplers,providing a new perspective for the innovative development of photonic devices.
基金financially supported by the National Natural Science Foundation of China (Nos.22171165 and 22371170)Natural Science Foundation of Shandong Province (No.ZR2022MB080)Scientific and Technological Frontiers in Project of Henan Province(No.242102110192)。
文摘Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral molecules L/D-PF1 and L/D-PF2 with pyrene groups were synthesized and its chiroptical properties upon co-assembly with achiral compound octafluoronaphthalene(OFN) through AP interaction were systemically studied.The co-assembly of L/D-PF1/OFN and L/D-PF2/OFN exhibited distinct chiroptical properties such as circular dichroism(CD) and circularly polarized luminescence(CPL) signals.Chirality transfer from the chirality center of L/D-PF1 and L/D-PF2 to the achiral OFN and chiral amplification were successfully achieved.Besides,no significant CPL signal was observed in the self-assembly of L/DPF1 or L/D-PF2 while co-assembly with OFN exhibited obvious CPL amplification induced by AP interaction.Notably,a reversal CD signal and CPL signal could be observed in L/D-PF2/OFN when the molar ratio changed from 1:1 to 1:2 while not found in L/D-PF1/OFN,indicating that that minor structural changes of molecules could cause large changes in assembly.In addition,a series of computational calculations were conducted to verify the AP interaction between L-PF1/L-PF2 and OFN.This work demonstrated that arene-perfluoroarene interaction could drive chiral transfer,chiral amplification and chiral inversion and provided a new method for the preparation of chiroptical materials.
基金supported in part by the National Science Foundation under GrantsDMS 2436630 and 2436629.
文摘Patient-specific finite element analysis(FEA)is a promising tool for noninvasive quantification of cardiac and vascular structural mechanics in vivo.However,inverse material property identification using FEA,which requires iteratively solving nonlinear hyperelasticity problems,is computationally expensive which limits the ability to provide timely patient-specific insights to clinicians.In this study,we present an inverse material parameter identification strategy that integrates deep neural networks(DNNs)with FEA,namely inverse DNN-FEA.In this framework,a DNN encodes the spatial distribution of material parameters and effectively regularizes the inverse solution,which aims to reduce susceptibility to local optima that often arise in heterogeneous nonlinear hyperelastic problems.Consequently,inverse DNN-FEA enables identification of material parameters at the element level.For validation,we applied DNN-FEA to identify four spatially varying passive Holzapfel-Ogden material parameters of the left ventricular myocardium in synthetic benchmark cases with a clinically-derived geometry.To evaluate the benefit of DNN integration,a baseline FEA-only solver implemented in PyTorch was used for comparison.Results demonstrated that DNN-FEA achieved substantially lower average errors in parameter identification compared to FEA(case 1,DNN-FEA:0.37%~2.15%vs.FEA:2.64%~12.91%).The results also demonstrate that the same DNN architecture is capable of identifying a different spatial material property distribution(case 2,DNN-FEA:0.03%~0.60%vs.FEA:0.93%~16.25%).These findings suggest that DNN-FEA provides an accurate framework for inverse identification of heterogeneous myocardial material properties.This approach may facilitate future applications in patient-specific modeling based on in vivo clinical imaging and could be extended to other biomechanical simulation problems.
基金supported by the National Natural Science Foundation of China(Grant No.42130312)。
文摘The complexity of the seismicity pattern for the subduction zone along the oceanic plate triggered the outer rise events and revealed cyclic tectonic deformation conditions along the plate subduction zones.The outer rise earthquakes have been observed along the Sunda arc,following the estimated rupture area of the 2005 M_(W)8.6 Nias earthquakes.Here,we used kinematic waveform inversion(KIWI)to obtain the source parameters of the 14 May 2021 M_(W)6.6 event off the west coast of northern Sumatra and to define the fault plane that triggered this outer rise event.The KIWI algorithm allows two types of seismic source to be configured:the moment tensor model to describe the type of shear with six moment tensor components and the Eikonal model for the rupture of pure double-couple sources.This method was chosen for its flexibility to be applied for different sources of seismicity and also for the automated full-moment tensor solution with real-time monitoring.We used full waveform traces from 8 broadband seismic stations within 1000 km epicentral distances sourced from the Incorporated Research Institutions for Seismology(IRIS-IDA)and Geofon GFZ seismic record databases.The initial origin time and hypocenter values are obtained from the IRIS-IDA.The synthetic seismograms used in the inversion process are based on the existing regional green function database model and were accessed from the KIWI Tools Green's Function Database.The obtained scalar seismic moment value is 1.18×10^(19)N·m,equivalent to a moment magnitude M_(W)6.6.The source parameters are 140°,44°,and−99°for the strike,dip,and rake values at a centroid depth of 10.2 km,indicating that this event is a normal fault earthquake that occurred in the outer rise area.The outer rise events with normal faults typically occur at the shallow part of the plate,with nodal-plane dips predominantly in the range of 30°-60°on the weak oceanic lithosphere due to hydrothermal alteration.The stress regime around the plate subduction zone varies both temporally and spatially due to the cyclic influences of megathrust earthquakes.Tensional outer rise earthquakes tend to occur after the megathrust events.The relative timing of these events is not known due to the viscous relaxation of the down going slab and poroelastic response in the trench slope region.The occurrence of the 14 May 2021 earthquake shows the seismicity in the outer rise region in the strongly coupled Sunda arc subduction zone due to elastic bending stress within the duration of the seismic cycle.
基金Project supported by the Project of the Anhui Provincial Natural Science Foundation(Grant No.2308085MA19)Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA0410401)+2 种基金the National Natural Science Foundation of China(Grant No.52202120)the National Key Research and Development Program of China(Grant No.2023YFA1609800)USTC Research Funds of the Double First-Class Initiative(Grant No.YD2310002013)。
文摘Small angle x-ray scattering(SAXS)is an advanced technique for characterizing the particle size distribution(PSD)of nanoparticles.However,the ill-posed nature of inverse problems in SAXS data analysis often reduces the accuracy of conventional methods.This article proposes a user-friendly software for PSD analysis,GranuSAS,which employs an algorithm that integrates truncated singular value decomposition(TSVD)with the Chahine method.This approach employs TSVD for data preprocessing,generating a set of initial solutions with noise suppression.A high-quality initial solution is subsequently selected via the L-curve method.This selected candidate solution is then iteratively refined by the Chahine algorithm,enforcing constraints such as non-negativity and improving physical interpretability.Most importantly,GranuSAS employs a parallel architecture that simultaneously yields inversion results from multiple shape models and,by evaluating the accuracy of each model's reconstructed scattering curve,offers a suggestion for model selection in material systems.To systematically validate the accuracy and efficiency of the software,verification was performed using both simulated and experimental datasets.The results demonstrate that the proposed software delivers both satisfactory accuracy and reliable computational efficiency.It provides an easy-to-use and reliable tool for researchers in materials science,helping them fully exploit the potential of SAXS in nanoparticle characterization.
基金supported by the National Natural Science Foundation of China(Grant No.42172316)the Major National Science and Technology Project for Deep Earth(Grant No.2024ZD100380X)the Natural Science Foundation of Hunan Province of China(2025JJ20030).
文摘This study examines how native pore structures and loading conditions influencethe fracture size distribution and the predictability of catastrophic failure in rocks.Four lithologies with distinct pore characteristics,i.e.granite,limestone,red sandstone,and marble,were tested under uniaxial compression and Brazilian splitting.Nuclear magnetic resonance(NMR)was used to characterize pore structures,while acoustic emission(AE)monitoring captured the temporal evolution of microcracking.The relationships among pore properties,AE b-values,and failure predictability were systematically evaluated.Results show that the overall b-value is primarily controlled by native pore size rather than loading condition.Rocks with larger pores display higher b-value and greater temporal variability,whereas those with smaller pores exhibit lower and more stable b-value.To assess failure predictability,the AE count rate was incorporated into an inverse power law model.The model demonstrates higher predictive accuracy for high-porosity rocks.The average predicted failure time(t_(p))decreases monotonically with porosity:under uniaxial compression,t_(p)for granite,marble,limestone,and sandstone are 2.32,1.82,1.42,and 0.03,respectively;under Brazilian splitting,3.54,3.30,0.10,and 0.03.Among the four rock types,sandstone with the highest porosity exhibits the smallest discrepancy between predicted and actual failure time,whereas granite with the lowest porosity shows the largest.As porosity decreases,prediction accuracy progressively declines for limestone and marble.Overall,the findings indicate that native pore heterogeneity governs both fracture scaling behavior and failure predictability,and that these effects are largely independent of the loading conditions examined in this study.
基金Supported by the National Key Research and Development Program of China(No.2023YFC3008200)the Independent Research Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(No.SML2022SP505)。
文摘Physics-informed neural networks(PINNs),as a novel artificial intelligence method for solving partial differential equations,are applicable to solve both forward and inverse problems.This study evaluates the performance of PINNs in solving the temperature diffusion equation of the seawater across six scenarios,including forward and inverse problems under three different boundary conditions.Results demonstrate that PINNs achieved consistently higher accuracy with the Dirichlet and Neumann boundary conditions compared to the Robin boundary condition for both forward and inverse problems.Inaccurate weighting of terms in the loss function can reduce model accuracy.Additionally,the sensitivity of model performance to the positioning of sampling points varied between different boundary conditions.In particular,the model under the Dirichlet boundary condition exhibited superior robustness to variations in point positions during the solutions of inverse problems.In contrast,for the Neumann and Robin boundary conditions,accuracy declines when points were sampled from identical positions or at the same time.Subsequently,the Argo observations were used to reconstruct the vertical diffusion of seawater temperature in the north-central Pacific for the applicability of PINNs in the real ocean.The PINNs successfully captured the vertical diffusion characteristics of seawater temperature,reflected the seasonal changes of vertical temperature under different topographic conditions,and revealed the influence of topography on the temperature diffusion coefficient.The PINNs were proved effective in solving the temperature diffusion equation of seawater with limited data,providing a promising technique for simulating or predicting ocean phenomena using sparse observations.
基金supported by the Malaysia Ministry of Higher Education under Fundamental Research Grant Scheme with Project Code:FRGS/1/2024/TK07/USM/02/3.
文摘Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.
基金the School of Engineering and Built Environment at Anglia Ruskin University,UK,for the supportthe support of IRC-CSS and the Electrical Engineering Department,KFUPM,Saudi Arabia。
文摘We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semiconductor lasers,as well as Raman and semiconductor optical amplifiers.Although inverse design approaches for optical detectors remain relatively underexplored,we examine optical layers,particularly metamaterial absorbers,as promising candidates for high-performance optical detection.In addition,we underscore advancements in inverse designing passive optical components,including beam splitters,gratings,and optical fibers.These optical blocks are fundamental in developing next-generation standalone optical communication systems and optical sensing networks,including integrated sensing and communication technologies.While categorizing various reported deep learning architectures across five paradigms,we offer a paradigm-based perspective that reveals how different ML techniques function within modern inverse design methods and enable fast,data-driven solutions that significantly reduce design time and computational demands compared with traditional optimization methods.
基金supported by the National Natural Science Foundation of China (No. 62075240)the National Key Research and Development Program of China (No. 2020YFB2205800)。
文摘Terahertz metasurfaces have great applications for efficient terahertz modulation, but there are still problems in designing terahertz metadevices in terms of complexity and inefficiency. Herein, we demonstrate an inversely-designed terahertz metasurface with double electromagnetically induced transparency(EIT)-like windows by incorporating a particle swarm optimization(PSO) algorithm with the finite-difference time-domain method. We prepared and tested the metadevices, and the experimental terahertz signals are close to the designed results. By hybridizing amorphous germanium film with the inversely-designed metasurface, two EIT-like windows, including transmission and slow-light effect, exhibit ultrafast modulation behavior in 25 ps excited by a femtosecond laser. The modulation depths of transmission in two transparency windows are 74% and 65%, respectively. The numerical simulations also illustrate the ultrafast dynamic process and modulation mechanism, which match well with the experiment results. Our work thus offers opportunities for designing other objective functions of the terahertz metadevice.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFB2805800)the National Natural Science Foundation of China(Grant Nos.62175242,and U20A20217)the Sichuan Science and Technology Program(Grant No.2021ZYCD002)。
文摘Independent manipulation of transmitted and reflected light fields is a key technology for the realization of multifunctional optical applications,which can be implemented based on multilayered plasmonic or supercell subwavelength structures.However,the former is not suitable for the optical bands,while the latter is insufficient in generating large phase gradients.Here,an adjoint-optimization-based inverse design methodology is proposed,which utilizes the polarization-selective local interference between individual meta-atoms and enables monolayer dielectric metasurfaces to decouple the wavefront of transmitted and reflected optical fields.Moreover,this methodology serves to mitigate the aperiodic electromagnetic crosstalk inherent between adjacent meta-atoms,consequently leading to a significant enhancement in the performance of meta-devices.We analyzed the physical mechanism of adjoint optimization and proposed the concept of phase factors,highlighting their importance in the rapid inverse design of meta-devices—an aspect often overlooked in previous research.To demonstrate the feasibility and robustness of our method,we optimize monolayer metasurfaces with different initial structures.These devices efficiently focus and deflect x-linearly and y-linearly polarized incident light in transmission and reflection spaces,respectively.Overall,this methodology holds immense potential for designing multifunctional,high-performing metasurfaces that meet multiple constraints,opening up broad prospects for applications.
文摘As an advanced device for observing atmospheric winds,the spaceborne Doppler Asymmetric Spatial Heterodyne(DASH)interferometer also encounters challenges associated with phase distortion,par-ticularly in limb sounding scenarios.This paper discusses interferogram modeling and phase distortion cor-rection techniques for spaceborne DASH interferometers.The modeling of phase distortion interferograms with and without Doppler shift for limb observation was conducted,and the effectiveness of the analytical expression was verified through numerical simulation.The simulation results indicate that errors propagate layer by layer while using the onion-peeling inversion algorithm to handle phase-distorted interferograms.In contrast,the phase distortion correction algorithm can achieve effective correction.This phase correction method can be successfully applied to correct phase distortions in the interferograms of the spaceborne DASH interferometer,providing a feasible solution to enhance its measurement accuracy.
基金supported by the Beijing Natural Science Foundation(No.L212029)the National Natural Science Foundation of China(No.62271043).
文摘To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current methods.Firstly,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic environments.With a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic environments.Particularly,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.
基金supported by Zhengzhou PM_(2.5)and O_(3)Collaborative Control and Monitoring Project(No.20220347A)the 2020 National Supercomputing Zhengzhou Center Innovation Ecosystem Construction Technology Project(No.201400210700).
文摘Improving the accuracy of anthropogenic volatile organic compounds(VOCs)emission inventory is crucial for reducing atmospheric pollution and formulating control policy of air pollution.In this study,an anthropogenic speciated VOCs emission inventory was established for Central China represented by Henan Province at a 3 km×3 km spatial resolution based on the emission factormethod.The 2019 VOCs emission in Henan Provincewas 1003.5 Gg,while industrial process source(33.7%)was the highest emission source,Zhengzhou(17.9%)was the city with highest emission and April and August were the months with the more emissions.High VOCs emission regions were concentrated in downtown areas and industrial parks.Alkanes and aromatic hydrocarbons were the main VOCs contribution groups.The species composition,source contribution and spatial distribution were verified and evaluated through tracer ratio method(TR),Positive Matrix Factorization Model(PMF)and remote sensing inversion(RSI).Results show that both the emission results by emission inventory(EI)(15.7 Gg)and by TRmethod(13.6 Gg)and source contribution by EI and PMF are familiar.The spatial distribution of HCHO primary emission based on RSI is basically consistent with that of HCHO emission based on EI with a R-value of 0.73.The verification results show that the VOCs emission inventory and speciated emission inventory established in this study are relatively reliable.
