In this paper,we propose a physics-informed neural network extrapolation method that leverages machine learning techniques to reconstruct coronal magnetic fields.We enhance the classical neural network structure by in...In this paper,we propose a physics-informed neural network extrapolation method that leverages machine learning techniques to reconstruct coronal magnetic fields.We enhance the classical neural network structure by introducing the concept of a quasi-output layer to address the challenge of preserving physical constraints during the neural network extrapolation process.Furthermore,we employ second-order optimization methods for training the neural network,which are more efficient compared to the first-order optimization methods commonly used in classical machine learning.Our approach is evaluated on the widely recognized semi-analytical model proposed by Low and Lou.The results demonstrate that the deep learning method achieves high accuracy in reconstructing the semianalytical model across multiple evaluation metrics.In addition,we validate the effectiveness of our method on the observed magnetogram of active region.展开更多
以95%乙醇为溶剂,采用超声法提取桑白皮活性成分黄酮,以黄酮提取率为评价指标,在单因素实验的基础上,采用响应面法优化了提取工艺,并研究了桑白皮黄酮提取液的体外抗氧化活性。结果表明,桑白皮黄酮的最优提取条件为:提取温度45.78℃、...以95%乙醇为溶剂,采用超声法提取桑白皮活性成分黄酮,以黄酮提取率为评价指标,在单因素实验的基础上,采用响应面法优化了提取工艺,并研究了桑白皮黄酮提取液的体外抗氧化活性。结果表明,桑白皮黄酮的最优提取条件为:提取温度45.78℃、提取时间38.33 min、料液比1∶25.52(g∶mL),在此条件下,黄酮提取率为2.211%。体外抗氧化实验表明,2.00 mL 0.50 mg·mL^(-1)桑白皮黄酮提取液对4.00 mL 0.05 mg·mL^(-1)DPPH自由基的清除率为62.21%,低于同浓度的阳性对照VC(清除率81.39%);抗氧化实验的精密度、稳定性、重复性良好,RSD值分别为0.05%、0.15%、0.69%(n=5)。该提取方法简单、高效、可靠,为桑白皮的后续开发提供了一定理论基础。展开更多
Amide-and alkyl-modified nanosilicas(AANPs)were synthesized and introduced into Xanthan gum(XG)solution,aiming to improve the temperature/salt tolerance and oil recovery.The rheological behaviors of XG/AANP hybrid dis...Amide-and alkyl-modified nanosilicas(AANPs)were synthesized and introduced into Xanthan gum(XG)solution,aiming to improve the temperature/salt tolerance and oil recovery.The rheological behaviors of XG/AANP hybrid dispersions were systematically studied at different concentrations,temperatures and inorganic salts.At high temperature(75C)and high salinity(10,000 mg,L1 NaCl),AANPs increase the apparent viscosity and dynamic modulus of the XG solution,and XG/AANP hybrid dispersion exhibits elastic-dominant properties.The most effective concentrations of XG and AANP interacting with each other are 1750 mg·L^(-1) and 0.74 wt%,respectively.The temperature tolerance of XG solution is not satisfactory,and high temperature further weakens the salt tolerance of XG.However,the AANPs significantly enhance the viscoelasticity the XG solution through hydrogen bonds and hydrophobic effect.Under reservoir conditions,XG/AANP hybrid recovers approximately 18.5%more OOIP(original oil in place)than AANP and 11.3%more OOIP than XG.The enhanced oil recovery mechanism of the XG/AANP hybrid is mainly increasing the sweep coefficient,the contribution from the reduction of oil-water interfacial tension is less.展开更多
The Atmospheric Imaging Assembly(AIA)onboard the Solar Dynamics Observatory(SDO)captures full-disk solar images in seven extreme ultraviolet wave bands.As a violent solar flare occurs,incoming photoflux may exceed the...The Atmospheric Imaging Assembly(AIA)onboard the Solar Dynamics Observatory(SDO)captures full-disk solar images in seven extreme ultraviolet wave bands.As a violent solar flare occurs,incoming photoflux may exceed the threshold of an optical imaging system,resulting in regional saturation/overexposure of images.Fortunately,the lost signal can be partially retrieved from non-local unsaturated regions of an image according to scattering and diffraction principle,which is well consistent with the attention mechanism in deep learning.Thus,an attention augmented convolutional neural network(AANet)is proposed to perform image desaturation of SDO/AIA in this paper.It is built on a U-Net backbone network with partial convolution and adversarial learning.In addition,a lightweight attention model,namely criss-cross attention,is embedded between each two convolution layers to enhance the backbone network.Experimental results validate the superiority of the proposed AANet beyond state-of-the-arts from both quantitative and qualitative comparisons.展开更多
Learning the mapping of magnetograms and EUV images is important for understanding the solar eruption mechanism and space weather forecasting.Previous works are mainly based on the pix2pix model for full-disk magnetog...Learning the mapping of magnetograms and EUV images is important for understanding the solar eruption mechanism and space weather forecasting.Previous works are mainly based on the pix2pix model for full-disk magnetograms generation and obtain good performance.However,in general,we are more concerned with the magnetic field distribution in the active regions where various solar storms such as the solar flare and coronal mass ejection happen.In this paper,we fuse the self-attention mechanism with the pix2pix model which allows more computation resource and greater weight for strong magnetic regions.In addition,the attention features are concatenated by the Residual Hadamard Production(RHP) with the abstracted features after the encoder.We named our model as RHP-attention pix2pix.From the experiments,we can find that the proposed model can generate magnetograms with finer strong magnetic structures,such as sunspots.In addition,the polarity distribution of generated magnetograms at strong magnetic regions is more consistent with observed ones.展开更多
A sky model from CLEAN deconvolution is a particularly effective high dynamic range reconstruction in radio astronomy,which can effectively model the sky and remove the sidelobes of the point spread function(PSF)cause...A sky model from CLEAN deconvolution is a particularly effective high dynamic range reconstruction in radio astronomy,which can effectively model the sky and remove the sidelobes of the point spread function(PSF)caused by incomplete sampling in the spatial frequency domain.Compared to scale-free and multi-scale sky models,adaptive-scale sky modeling,which can model both compact and diffuse features,has been proven to have better sky modeling capabilities in narrowband simulated data,especially for large-scale features in high-sensitivity observations which are exactly one of the challenges of data processing for the Square Kilometre Array(SKA).However,adaptive scale CLEAN algorithms have not been verified by real observation data and allow negative components in the model.In this paper,we propose an adaptive scale model algorithm with non-negative constraint and wideband imaging capacities,and it is applied to simulated SKA data and real observation data from the Karl G.Jansky Very Large Array(JVLA),an SKA precursor.Experiments show that the new algorithm can reconstruct more physical models with rich details.This work is a step forward for future SKA image reconstruction and developing SKA imaging pipelines.展开更多
We propose a scheme that utilizes weak-field-induced quantum beats to investigate the electronic coherences of atoms driven by a strong attosecond extreme ultraviolet(XUV)pulse.The technique involves using a strong XU...We propose a scheme that utilizes weak-field-induced quantum beats to investigate the electronic coherences of atoms driven by a strong attosecond extreme ultraviolet(XUV)pulse.The technique involves using a strong XUV pump pulse to excite and ionize atoms and a time-delayed weak short pulse to probe the photoelectron signal.Our theoretical analysis demonstrates that the information regarding the bound states,initiated by the strong pump pulse,can be precisely reconstructed from the weak-field-induced quantum beat spectrum.To examine this scheme,we apply it to the attosecond XUV laser-induced ionization of hydrogen atoms by solving a three-dimensional time-dependent Schr?dinger equation.This work provides an essential reference for reconstructing the ultrafast dynamics of bound states induced by strong XUV attosecond pulses.展开更多
基金supported by the National Key R&D Program of China(Nos.2021YFA1600504,2022YFE0133700,2022YFF0503900)the National Natural Science Foundation of China(NSFC,Grant Nos.11790305 and 11973058)。
文摘In this paper,we propose a physics-informed neural network extrapolation method that leverages machine learning techniques to reconstruct coronal magnetic fields.We enhance the classical neural network structure by introducing the concept of a quasi-output layer to address the challenge of preserving physical constraints during the neural network extrapolation process.Furthermore,we employ second-order optimization methods for training the neural network,which are more efficient compared to the first-order optimization methods commonly used in classical machine learning.Our approach is evaluated on the widely recognized semi-analytical model proposed by Low and Lou.The results demonstrate that the deep learning method achieves high accuracy in reconstructing the semianalytical model across multiple evaluation metrics.In addition,we validate the effectiveness of our method on the observed magnetogram of active region.
文摘以95%乙醇为溶剂,采用超声法提取桑白皮活性成分黄酮,以黄酮提取率为评价指标,在单因素实验的基础上,采用响应面法优化了提取工艺,并研究了桑白皮黄酮提取液的体外抗氧化活性。结果表明,桑白皮黄酮的最优提取条件为:提取温度45.78℃、提取时间38.33 min、料液比1∶25.52(g∶mL),在此条件下,黄酮提取率为2.211%。体外抗氧化实验表明,2.00 mL 0.50 mg·mL^(-1)桑白皮黄酮提取液对4.00 mL 0.05 mg·mL^(-1)DPPH自由基的清除率为62.21%,低于同浓度的阳性对照VC(清除率81.39%);抗氧化实验的精密度、稳定性、重复性良好,RSD值分别为0.05%、0.15%、0.69%(n=5)。该提取方法简单、高效、可靠,为桑白皮的后续开发提供了一定理论基础。
基金We gratefully acknowledge financial supports from the Major Program of National Natural Science Foundation of China(Grant No.42090024)the National Natural Science Foundation of China(Grant No.52004322)the Natural Science Foundation of Shandong Province,China(Grant No.ZR2020QE108).
文摘Amide-and alkyl-modified nanosilicas(AANPs)were synthesized and introduced into Xanthan gum(XG)solution,aiming to improve the temperature/salt tolerance and oil recovery.The rheological behaviors of XG/AANP hybrid dispersions were systematically studied at different concentrations,temperatures and inorganic salts.At high temperature(75C)and high salinity(10,000 mg,L1 NaCl),AANPs increase the apparent viscosity and dynamic modulus of the XG solution,and XG/AANP hybrid dispersion exhibits elastic-dominant properties.The most effective concentrations of XG and AANP interacting with each other are 1750 mg·L^(-1) and 0.74 wt%,respectively.The temperature tolerance of XG solution is not satisfactory,and high temperature further weakens the salt tolerance of XG.However,the AANPs significantly enhance the viscoelasticity the XG solution through hydrogen bonds and hydrophobic effect.Under reservoir conditions,XG/AANP hybrid recovers approximately 18.5%more OOIP(original oil in place)than AANP and 11.3%more OOIP than XG.The enhanced oil recovery mechanism of the XG/AANP hybrid is mainly increasing the sweep coefficient,the contribution from the reduction of oil-water interfacial tension is less.
基金supported by the National Key R&D Program of China(Nos.2021YFA1600504 and 2022YFE0133700)the National Natural Science Foundation of China(NSFC)(Nos.11790305,11873060 and 11963003)。
文摘The Atmospheric Imaging Assembly(AIA)onboard the Solar Dynamics Observatory(SDO)captures full-disk solar images in seven extreme ultraviolet wave bands.As a violent solar flare occurs,incoming photoflux may exceed the threshold of an optical imaging system,resulting in regional saturation/overexposure of images.Fortunately,the lost signal can be partially retrieved from non-local unsaturated regions of an image according to scattering and diffraction principle,which is well consistent with the attention mechanism in deep learning.Thus,an attention augmented convolutional neural network(AANet)is proposed to perform image desaturation of SDO/AIA in this paper.It is built on a U-Net backbone network with partial convolution and adversarial learning.In addition,a lightweight attention model,namely criss-cross attention,is embedded between each two convolution layers to enhance the backbone network.Experimental results validate the superiority of the proposed AANet beyond state-of-the-arts from both quantitative and qualitative comparisons.
基金funded by the National Key R&D Program of China(Nos.2021YFA1600504 and 2022YFE0133700)the National Natural Science Foundation of China(NSFC)(Nos.11790305,11963003,12273007 and 61902371)。
文摘Learning the mapping of magnetograms and EUV images is important for understanding the solar eruption mechanism and space weather forecasting.Previous works are mainly based on the pix2pix model for full-disk magnetograms generation and obtain good performance.However,in general,we are more concerned with the magnetic field distribution in the active regions where various solar storms such as the solar flare and coronal mass ejection happen.In this paper,we fuse the self-attention mechanism with the pix2pix model which allows more computation resource and greater weight for strong magnetic regions.In addition,the attention features are concatenated by the Residual Hadamard Production(RHP) with the abstracted features after the encoder.We named our model as RHP-attention pix2pix.From the experiments,we can find that the proposed model can generate magnetograms with finer strong magnetic structures,such as sunspots.In addition,the polarity distribution of generated magnetograms at strong magnetic regions is more consistent with observed ones.
基金partially supported by the National Key R&D Program of China(2018YFA0404602 and 2018YFA0404603)the National SKA Program of China(2020SKA0110300)+3 种基金the National Natural Science Foundation of China(NSFC,11963003,11763002,61572461,11790305,U1831204,U1931141,11961141001 and 11903009)the Guizhou Science&Technology Plan Project(Platform Talent No.[2017]5788,[2017]5781)the Youth Science&Technology Talents Development Project of Guizhou Education Department(No.KY[2018]119 and[2018]433)the Guizhou University Talent Research Fund(No.(2018)60)。
文摘A sky model from CLEAN deconvolution is a particularly effective high dynamic range reconstruction in radio astronomy,which can effectively model the sky and remove the sidelobes of the point spread function(PSF)caused by incomplete sampling in the spatial frequency domain.Compared to scale-free and multi-scale sky models,adaptive-scale sky modeling,which can model both compact and diffuse features,has been proven to have better sky modeling capabilities in narrowband simulated data,especially for large-scale features in high-sensitivity observations which are exactly one of the challenges of data processing for the Square Kilometre Array(SKA).However,adaptive scale CLEAN algorithms have not been verified by real observation data and allow negative components in the model.In this paper,we propose an adaptive scale model algorithm with non-negative constraint and wideband imaging capacities,and it is applied to simulated SKA data and real observation data from the Karl G.Jansky Very Large Array(JVLA),an SKA precursor.Experiments show that the new algorithm can reconstruct more physical models with rich details.This work is a step forward for future SKA image reconstruction and developing SKA imaging pipelines.
基金supported by the National Natural Science Foundation of China(Nos.12088101,12047548,12074265,and U2330401)Science Challenge Project(No.TZ2018005)Guangdong Basic and Applied Basic Research Foundation(No.2022A1515010329)。
文摘We propose a scheme that utilizes weak-field-induced quantum beats to investigate the electronic coherences of atoms driven by a strong attosecond extreme ultraviolet(XUV)pulse.The technique involves using a strong XUV pump pulse to excite and ionize atoms and a time-delayed weak short pulse to probe the photoelectron signal.Our theoretical analysis demonstrates that the information regarding the bound states,initiated by the strong pump pulse,can be precisely reconstructed from the weak-field-induced quantum beat spectrum.To examine this scheme,we apply it to the attosecond XUV laser-induced ionization of hydrogen atoms by solving a three-dimensional time-dependent Schr?dinger equation.This work provides an essential reference for reconstructing the ultrafast dynamics of bound states induced by strong XUV attosecond pulses.
