To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is deve...To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is developed to identify the geometric parameters.The study utilizes a common precast element for highway bridges as the research subject.First,edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology.Subsequently,a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output.A dataset is generated by varying the control parameters and noise levels for model training.Finally,field measurements are conducted to validate the accuracy of the developed method.The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components,with an error rate maintained within 5%.展开更多
Tissue clearing combined with high-resolution confocal imaging is a cutting-edge approach for dissecting the three-dimensional(3D)architecture of tissues and deciphering cellular spatial interactions under physiologic...Tissue clearing combined with high-resolution confocal imaging is a cutting-edge approach for dissecting the three-dimensional(3D)architecture of tissues and deciphering cellular spatial interactions under physiological and pathological conditions.Deciphering the spatial interaction of leptin receptor-expressing(LepR^(+))stromal cells with other compartments in the bone marrow is crucial for a deeper understanding of the stem cell niche and the skeletal tissue.In this study,we introduce an optimized protocol for the 3D analysis of skeletal tissues,enabling the visualization of hematopoietic and stromal cells,especially LepR+stromal cells,within optically cleared bone hemisections.Our method preserves the 3D tissue architecture and is extendable to other hematopoietic sites such as calvaria and vertebrae.The protocol entails tissue fixation,decalcification,and cryosectioning to reveal the marrow cavity.Completed within approximately 12 days,this process yields highly transparent tissues that maintain genetically encoded or antibody-stained fluorescent signals.The bone hemisections are compatible with diverse antibody labeling strategies.Confocal microscopy of these transparent samples allows for qualitative and quantitative image analysis using Aivia or Bitplane Imaris software,assessing a spectrum of parameters.With proper storage,the fluorescent signal in the stained and cleared bone hemisections remains intact for at least 2–3 months.This protocol is robust,straightforward to implement,and highly reproducible,offering a valuable tool for tissue architecture and cellular interaction studies.展开更多
The identification and characterization of rock types is a core activity in geology and related fields,including mining,petroleum,environmental science,industry,and construction.Traditionally,this task is performed by...The identification and characterization of rock types is a core activity in geology and related fields,including mining,petroleum,environmental science,industry,and construction.Traditionally,this task is performed by human specialists who analyze and describe the type,composition,texture,shape,and other properties of rock samples,whether collected in-situ or prepared in a laboratory.However,the process is subjective,dependent on the specialist’s experience,and time-consuming.This study proposes an artificial intelligence-based approach that combines computer vision and natural language processing to generate both textual and verbal descriptions from images of rock thin sections.A dataset of images and corresponding textual descriptions is used to train a hybrid deep learning model.Features extracted from the images using EfficientNetB7 are processed by a Transformer network to generate textual descriptions,which are then converted into verbal responses using a speech synthesis service.The experimental results show an accuracy of 0.892 and a BLEU score of 0.71.This model offers potential utility for research,professional,and academic applications and has been deployed as a web application for public use.展开更多
The direct and dissociative ionizations of oxygen molecule are investigated experimen-tally by electron collision with energies from 350 eV to 8000 eV.The absolute ionization cross sections for the product ions(O_(2)^...The direct and dissociative ionizations of oxygen molecule are investigated experimen-tally by electron collision with energies from 350 eV to 8000 eV.The absolute ionization cross sections for the product ions(O_(2)^(2+),O_(2)^(2+)O^(+),O^(2+),and their total)and two Coulomb explosion channels(O^(+)+O^(+)and O^(2+)+O^(+))are obtained by putting the data of O^(2+)on the scale of Ar+from O_(2)and Ar gases mixed with a fixed relative flow ratio of 1:1.The experimental errors are assessed by taking uncertainties of various factors into account.The present absolute cross sections are well consistent with the previous data in the overlapped energy range below 1000 eV.展开更多
Convolutional neural networks have been widely used for analyzing image data in industry,especially in the oil and gas area.Brazil has an extensive hydrocarbon reserve on its coast and has also benefited from these ne...Convolutional neural networks have been widely used for analyzing image data in industry,especially in the oil and gas area.Brazil has an extensive hydrocarbon reserve on its coast and has also benefited from these neural network models.Image data from petrographic thin section can be essential to provide information about reservoir quality,highlighting important features such as carbonate lithology.However,the automatic identification of lithology in reservoir rocks is still a significant challenge,mainly due to the heterogeneity that is part of the lithologies of the Brazilian pre-salt.Within this context,this work presents an approach using one-class or specialist models to identify four classes of lithology present in reservoir rocks in the Brazilian pre-salt.The proposed methodology had the challenge of dealing with a small number of images for training the neural networks,in addition to the complexity involved in the analyzed data.An auto-machine learning tool called AutoKeras was used to define the hyperparameters of the implemented models.The results found were satisfactory and presented an accuracy greater than 70%for image samples belonging to other wells not seen during the model building,which increases the applicability of the implemented model.Finally,a comparison was made between the proposed methodology and multiple-class models,demonstrating the superiority of one-class models.展开更多
Here,we report the spin-orbit state-resolved differential cross sections(DCSs)for the prototype barrierless reaction S(^(1)D)+HD.Both product channels,namely H+SD(^(2)Π_(3/2,1/2))and D+SH(^(2)Π_(3/2,1/2)),were measu...Here,we report the spin-orbit state-resolved differential cross sections(DCSs)for the prototype barrierless reaction S(^(1)D)+HD.Both product channels,namely H+SD(^(2)Π_(3/2,1/2))and D+SH(^(2)Π_(3/2,1/2)),were measured by high-resolution crossed molecular beam experiments.The DCSs of the two product channels show an overall forward-backward symmetry,in accordance with statistical model predictions.However,the DCSs for different spin-orbit manifolds show different preferences in forward or backward scattering directions at the same collision energies.This study reveals that,even though the title reaction proceeds via the long-lived complex mechanism,the spin-orbit coupling effects in the product channels play an important role in the reaction process.展开更多
Inelastic collisions are the dominant cause of energy loss in radiotherapy.In the energy range around the Bragg peak,single ionization(SI)and single-electron capture(SC)are the primary inelastic collisions that lead t...Inelastic collisions are the dominant cause of energy loss in radiotherapy.In the energy range around the Bragg peak,single ionization(SI)and single-electron capture(SC)are the primary inelastic collisions that lead to energy loss.This study employs the classical trajectory Monte Carlo method to study the SI and SC processes of H_(2)O molecules using He^(2+) and C^(6+) projectiles in the energy range of 10 keV/u to 10 MeV/u.The total cross sections,single differential cross sections,impact parameter dependence of SI and SC,and fragmentation cross sections were investigated.Results illustrate that the cross section for SI is the highest when the projectile energy is close to the Bragg peak energy.When the projectile energy is below the Bragg peak energy,the ionized electrons in the forward direction dominate,and the removal of electrons can be associated with large impact parameters.As the projectile energy increases,the emission angle of the electrons gradually transitions from small angles(60°~120°)to large angles(60°~120°),and the removal of electrons is associated with small impact parameters.The energy distributions of the ionized electron are similar when the projectile energy is equal to,below or above the Bragg peak energy.The fragmentation cross sections after SI and SC in the energy range around the Bragg peak were also estimated.展开更多
The neutron capture cross section for^(165)Ho was measured at the backstreaming white neutron beam line(Back-n)of the China Spallation Neutron Source(CSNS)using total energy detection systems,composed of a set of four...The neutron capture cross section for^(165)Ho was measured at the backstreaming white neutron beam line(Back-n)of the China Spallation Neutron Source(CSNS)using total energy detection systems,composed of a set of four C_(6)D_(6)scintillator detectors coupled with pulse height weighting techniques.The resonance parameters were extracted using the multilevel multichannel R-matrix code SAMMY to fit the measured capture yields of the^(165)Ho(n,γ)reaction in the neutron energy range below100 eV.Subsequently,the resonance region’s capture cross sections were reconstructed based on the obtained parameters.Furthermore,the unresolved resonance average cross section of the^(165)Ho(n,γ)reaction was determined relative to that of the standard sample^(197)Au within the neutron energy range of 2 keV to 1 MeV.The experimental data were compared with the recommended nuclear data from the ENDF/B-VIII.0 library,as well as with results of calculations performed using the TALYS-1.9 code.The comparison revealed agreement between the measured^(165)Ho(n,γ)cross sections and these data.The present results are crucial for evaluating the^(165)Ho neutron capture cross section and thus enhance the quality of evaluated nuclear data libraries.They provide valuable guidance for nuclear theoretical models and nuclear astrophysical studies.展开更多
基金The National Natural Science Foundation of China(No.52338011,52378291)Young Elite Scientists Sponsorship Program by CAST(No.2022-2024QNRC0101).
文摘To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is developed to identify the geometric parameters.The study utilizes a common precast element for highway bridges as the research subject.First,edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology.Subsequently,a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output.A dataset is generated by varying the control parameters and noise levels for model training.Finally,field measurements are conducted to validate the accuracy of the developed method.The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components,with an error rate maintained within 5%.
基金National Natural Science Foundation of China(grant number 82272563 to B.S.)National Science and Technology Major Project of the Ministry of Science and Technology of China(grant number 2023ZD0501202 to B.S.)+4 种基金institutional grants allocated to the National Institute of Biological Sciences,Beijing(NIBS)from the Chinese Ministry of Science and Technology,Beijing Municipal Commission of Science and Technology,and Tsinghua Universitythe support from China Pharmaceutical University(grant number 3150140001 to S.F.)National Natural Science Foundation of China(grant numbers 82203653 to S.F.,82371957 to L.W.,and 82371956 to X.C.)Beijing Municipal Public Welfare Development and Reform Pilot Project for Medical Research Institutes(grant number JYY2023-8 to X.C.)Research Grants Council of University Grants Committee Hong Kong(grant numbers 14113723,14108720,14121721,14202920,N_CUHK472/22,C7030-18G,T13-402/17-N,and AoE/M-402/20)。
文摘Tissue clearing combined with high-resolution confocal imaging is a cutting-edge approach for dissecting the three-dimensional(3D)architecture of tissues and deciphering cellular spatial interactions under physiological and pathological conditions.Deciphering the spatial interaction of leptin receptor-expressing(LepR^(+))stromal cells with other compartments in the bone marrow is crucial for a deeper understanding of the stem cell niche and the skeletal tissue.In this study,we introduce an optimized protocol for the 3D analysis of skeletal tissues,enabling the visualization of hematopoietic and stromal cells,especially LepR+stromal cells,within optically cleared bone hemisections.Our method preserves the 3D tissue architecture and is extendable to other hematopoietic sites such as calvaria and vertebrae.The protocol entails tissue fixation,decalcification,and cryosectioning to reveal the marrow cavity.Completed within approximately 12 days,this process yields highly transparent tissues that maintain genetically encoded or antibody-stained fluorescent signals.The bone hemisections are compatible with diverse antibody labeling strategies.Confocal microscopy of these transparent samples allows for qualitative and quantitative image analysis using Aivia or Bitplane Imaris software,assessing a spectrum of parameters.With proper storage,the fluorescent signal in the stained and cleared bone hemisections remains intact for at least 2–3 months.This protocol is robust,straightforward to implement,and highly reproducible,offering a valuable tool for tissue architecture and cellular interaction studies.
文摘The identification and characterization of rock types is a core activity in geology and related fields,including mining,petroleum,environmental science,industry,and construction.Traditionally,this task is performed by human specialists who analyze and describe the type,composition,texture,shape,and other properties of rock samples,whether collected in-situ or prepared in a laboratory.However,the process is subjective,dependent on the specialist’s experience,and time-consuming.This study proposes an artificial intelligence-based approach that combines computer vision and natural language processing to generate both textual and verbal descriptions from images of rock thin sections.A dataset of images and corresponding textual descriptions is used to train a hybrid deep learning model.Features extracted from the images using EfficientNetB7 are processed by a Transformer network to generate textual descriptions,which are then converted into verbal responses using a speech synthesis service.The experimental results show an accuracy of 0.892 and a BLEU score of 0.71.This model offers potential utility for research,professional,and academic applications and has been deployed as a web application for public use.
基金supported by the National Key Research and Development Program of China(No.2022YFA1602502)the National Natural Science Foundation of China(No.12127804).
文摘The direct and dissociative ionizations of oxygen molecule are investigated experimen-tally by electron collision with energies from 350 eV to 8000 eV.The absolute ionization cross sections for the product ions(O_(2)^(2+),O_(2)^(2+)O^(+),O^(2+),and their total)and two Coulomb explosion channels(O^(+)+O^(+)and O^(2+)+O^(+))are obtained by putting the data of O^(2+)on the scale of Ar+from O_(2)and Ar gases mixed with a fixed relative flow ratio of 1:1.The experimental errors are assessed by taking uncertainties of various factors into account.The present absolute cross sections are well consistent with the previous data in the overlapped energy range below 1000 eV.
基金supported by the R&D cooperation agreement be-tween Petrobras and CBPF(Contract No.0050.0121790.22.9)Brazilian Research Council(CNPq)for the scholarships for students.
文摘Convolutional neural networks have been widely used for analyzing image data in industry,especially in the oil and gas area.Brazil has an extensive hydrocarbon reserve on its coast and has also benefited from these neural network models.Image data from petrographic thin section can be essential to provide information about reservoir quality,highlighting important features such as carbonate lithology.However,the automatic identification of lithology in reservoir rocks is still a significant challenge,mainly due to the heterogeneity that is part of the lithologies of the Brazilian pre-salt.Within this context,this work presents an approach using one-class or specialist models to identify four classes of lithology present in reservoir rocks in the Brazilian pre-salt.The proposed methodology had the challenge of dealing with a small number of images for training the neural networks,in addition to the complexity involved in the analyzed data.An auto-machine learning tool called AutoKeras was used to define the hyperparameters of the implemented models.The results found were satisfactory and presented an accuracy greater than 70%for image samples belonging to other wells not seen during the model building,which increases the applicability of the implemented model.Finally,a comparison was made between the proposed methodology and multiple-class models,demonstrating the superiority of one-class models.
基金supported by the National Natural Science Foundation of China(Nos.92476207,22288201)the Chinese Academy of Sciences(No.XBD0970202)+2 种基金the Innovation Program for Quantum Science and Technology(No.2021ZD0303300)the Shenzhen Science and Technology Innovation Committee(No.20220814164755002)the Guangdong Innovative&Entrepreneurial Research Team Program(Nos.2019ZT08L455,2019JC01X091).
文摘Here,we report the spin-orbit state-resolved differential cross sections(DCSs)for the prototype barrierless reaction S(^(1)D)+HD.Both product channels,namely H+SD(^(2)Π_(3/2,1/2))and D+SH(^(2)Π_(3/2,1/2)),were measured by high-resolution crossed molecular beam experiments.The DCSs of the two product channels show an overall forward-backward symmetry,in accordance with statistical model predictions.However,the DCSs for different spin-orbit manifolds show different preferences in forward or backward scattering directions at the same collision energies.This study reveals that,even though the title reaction proceeds via the long-lived complex mechanism,the spin-orbit coupling effects in the product channels play an important role in the reaction process.
基金supported in part by the National Natural Science Foundation of China(Nos.12105327 and 11775108)the Hunan Provincial Innovation Foundation For Postgraduate(No.QL20220210)the Advanced Energy Science and Technology Guangdong Laboratory.
文摘Inelastic collisions are the dominant cause of energy loss in radiotherapy.In the energy range around the Bragg peak,single ionization(SI)and single-electron capture(SC)are the primary inelastic collisions that lead to energy loss.This study employs the classical trajectory Monte Carlo method to study the SI and SC processes of H_(2)O molecules using He^(2+) and C^(6+) projectiles in the energy range of 10 keV/u to 10 MeV/u.The total cross sections,single differential cross sections,impact parameter dependence of SI and SC,and fragmentation cross sections were investigated.Results illustrate that the cross section for SI is the highest when the projectile energy is close to the Bragg peak energy.When the projectile energy is below the Bragg peak energy,the ionized electrons in the forward direction dominate,and the removal of electrons can be associated with large impact parameters.As the projectile energy increases,the emission angle of the electrons gradually transitions from small angles(60°~120°)to large angles(60°~120°),and the removal of electrons is associated with small impact parameters.The energy distributions of the ionized electron are similar when the projectile energy is equal to,below or above the Bragg peak energy.The fragmentation cross sections after SI and SC in the energy range around the Bragg peak were also estimated.
基金supported by the National Natural Science Foundation of China(Nos.12465024,12365018,U2032146)Inner Mongolia National Science Foundation(Nos.2024ZD23,2024FX30,2023MS01005)+1 种基金Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region(NMGIRT2217)Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region(NJYT23109)。
文摘The neutron capture cross section for^(165)Ho was measured at the backstreaming white neutron beam line(Back-n)of the China Spallation Neutron Source(CSNS)using total energy detection systems,composed of a set of four C_(6)D_(6)scintillator detectors coupled with pulse height weighting techniques.The resonance parameters were extracted using the multilevel multichannel R-matrix code SAMMY to fit the measured capture yields of the^(165)Ho(n,γ)reaction in the neutron energy range below100 eV.Subsequently,the resonance region’s capture cross sections were reconstructed based on the obtained parameters.Furthermore,the unresolved resonance average cross section of the^(165)Ho(n,γ)reaction was determined relative to that of the standard sample^(197)Au within the neutron energy range of 2 keV to 1 MeV.The experimental data were compared with the recommended nuclear data from the ENDF/B-VIII.0 library,as well as with results of calculations performed using the TALYS-1.9 code.The comparison revealed agreement between the measured^(165)Ho(n,γ)cross sections and these data.The present results are crucial for evaluating the^(165)Ho neutron capture cross section and thus enhance the quality of evaluated nuclear data libraries.They provide valuable guidance for nuclear theoretical models and nuclear astrophysical studies.
