High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-co...High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-corrected transmission electron microscopy constrain resolution.A machine learning algorithm is developed to determine the aberration parameters with higher precision from small,lattice-periodic crystal images.The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide(MoS_(2))monolayer with 25 variables(14 aberrations)resolved in wide ranges.Using these measured parameters,the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS_(2)monolayers.The images were acquired due to the unexpected movement of the specimen holder.Four-dimensional data extraction reveals time-varying atomic structures and ripple.In particular,the atomic evolution of the sulfur-vacancy point and line defects,as well as the edge structure near the amorphous,is visualized as the resolution has been improved from about 1.75?to 0.9 A.This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.展开更多
While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used imag...While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.展开更多
Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-...Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-inflammatory,and immunomodulatory activities.However,its active constituents and mechanisms of action against diabetic foot remain to be elucidated.Methods:In this study,the chemical constituents of Sanhuang Oil were identified using UPLC-QE-Orbitrap-MS.Subsequently,the mechanism by which Sanhuang Oil promotes diabetic foot ulcer healing was predicted by integrating network pharmacology and molecular docking.Additionally,diabetic mouse model was established in ICR mice using a combination of a high-fat diet(HFD)and streptozotocin(STZ)chemical induction.A full-thickness skin defect was created on the dorsum of the mice.Wound healing and the healing rate were observed following Sanhuang Oil intervention.The mechanism underlying Sanhuang Oil’s promotion of diabetic ulcer healing was further investigated using transcriptomics and histopathological examination(H&E staining).Results:A total of 97 active ingredients were identified from Sanhuang Oil.Network pharmacology analysis predicted 543 common targets,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis identified 203 relevant pathways.Molecular docking further confirmed high binding affinity(binding energy≤−5.0 kcal/mol)between specific active components in Sanhuang Oil(e.g.,coptisine,phellodendrine,baicalein)and key targets associated with diabetic foot ulcers(e.g.,EGFR,AKT1,STAT3).In vivo experimental results demonstrated that the wound healing rate was significantly higher in Sanhuang Oil-treated groups compared to the model group(P<0.001).HE staining revealed that the high-dose Sanhuang Oil group exhibited more pronounced epithelial tissue coverage over the wound,reduced inflammatory cell infiltration,and increased collagen deposition and fibroblast proliferation.transcriptomic analysis identified Pdk4,Ttn,Csrp3,Actn2,Myoz2,Tnnc2,Myod1,Myog,Myot,and Myf6 as key regulatory proteins involved in promoting wound healing.Conclusion:Sanhuang Oil promotes wound healing in diabetic ulcer mice,potentially by mitigating inflammation and regulating key targets such as Pdk4 to enhance fibroblast function.These findings provide novel insights into the multi-target,multi-pathway mechanism of Sanhuang Oil for treating diabetic foot ulcers.展开更多
BACKGROUND Vertebral artery dissection(VAD)is a rare but life-threatening condition characterized by tearing of the intimal layer of the vertebral artery,leading to stenosis,occlusion or rupture.The clinical presentat...BACKGROUND Vertebral artery dissection(VAD)is a rare but life-threatening condition characterized by tearing of the intimal layer of the vertebral artery,leading to stenosis,occlusion or rupture.The clinical presentation of VAD can be heterogeneous,with common symptoms including headache,dizziness and balance problems.Timely diagnosis and treatment are crucial for favorable outcomes;however,VAD is often missed due to its variable clinical presentation and lack of robust diagnostic guidelines.High-resolution magnetic resonance imaging(HRMRI)has emerged as a reliable diagnostic tool for VAD,providing detailed visualization of vessel wall abnormalities.CASE SUMMARY A young male patient presented with an acute onset of severe headache,vomiting,and seizures,followed by altered consciousness.Imaging studies revealed bilateral VAD,basilar artery thrombosis,multiple brainstem and cerebellar infarcts,and subarachnoid hemorrhage.Digital subtraction angiography(DSA)revealed vertebral artery stenosis but failed to detect the dissection,potentially because intramural thrombosis obscured the VAD.In contrast,HRMRI confirmed the diagnosis by revealing specific signs of dissection.The patient was managed conservatively with antiplatelet therapy and other supportive measures,such as blood pressure control and pain management.After 5 mo of rehabilitation,the patient showed significant improvement in swallowing and limb strength.CONCLUSION HR-MRI can provide precise evidence for the identification of VAD.展开更多
The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on met...The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of the garment, addressing challenges such as the loss of patterns, colors, and other essential details commonly observed in virtual try-on images produced by existing methods. During the image generation stage, with the aim of maximizing the utilization of the information proffered by the input image, the input features undergo double sampling within the normalization procedure, thereby enhancing the detail fidelity and clothing alignment efficacy of the output image. Experimental evaluations conducted on high-resolution datasets validate the effectiveness of the proposed method. Results demonstrate significant improvements in preserving garment details, reducing artifacts, and achieving superior alignment between the clothing and body compared to baseline methods, establishing its advantage in generating realistic and high-quality virtual try-on images.展开更多
Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to pro...Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations.This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model(CHM)data to solve the ITDC problem.The new approach is studied in six urban scenes:farmland,woodland,park,industrial land,road and residential areas.First,it identifies tree canopy regions using a deep learning network from high-resolution imagery.It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing.Finally,it calculates and describes the number of individual trees and tree canopies.The proposed approach is experimented with the data from Shanghai,China.Our results show that the individual tree detection method had an average overall accuracy of 0.953,with a precision of 0.987 for woodland scene.Meanwhile,the R^(2) value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size,respectively.These results confirm that the proposed method is robust enough for urban tree planning and management.展开更多
In oceanic and atmospheric science,finer resolutions have become a prevailing trend in all aspects of development.For high-resolution fluid flow simulations,the computational costs of widely used numerical models incr...In oceanic and atmospheric science,finer resolutions have become a prevailing trend in all aspects of development.For high-resolution fluid flow simulations,the computational costs of widely used numerical models increase significantly with the resolution.Artificial intelligence methods have attracted increasing attention because of their high precision and fast computing speeds compared with traditional numerical model methods.The resolution-independent Fourier neural operator(FNO)presents a promising solution to the still challenging problem of high-resolution fluid flow simulations based on low-resolution data.Accordingly,we assess the potential of FNO for high-resolution fluid flow simulations using the vorticity equation as an example.We assess and compare the performance of FNO in multiple high-resolution tests varying the amounts of data and the evolution durations.When assessed with finer resolution data(even up to number of grid points with 1280×1280),the FNO model,trained at low resolution(number of grid points with 64×64)and with limited data,exhibits a stable overall error and good accuracy.Additionally,our work demonstrates that the FNO model takes less time than the traditional numerical method for high-resolution simulations.This suggests that FNO has the prospect of becoming a cost-effective and highly precise model for high-resolution simulations in the future.Moreover,FNO can make longer high-resolution predictions while training with less data by superimposing vorticity fields from previous time steps as input.A suitable initial learning rate can be set according to the frequency principle,and the time intervals of the dataset need to be adjusted according to the spatial resolution of the input when training the FNO model.Our findings can help optimize FNO for future fluid flow simulations.展开更多
Understanding vegetation water availability can be important for managing vegetation and combating climate change.Changes in vegetation water availability throughout China remains poorly understood,especially at a hig...Understanding vegetation water availability can be important for managing vegetation and combating climate change.Changes in vegetation water availability throughout China remains poorly understood,especially at a high spatial resolution.Standardized Precipitation Evapotranspiration Index(SPEI)is an ideal water availability index for assessing the spatiotemporal characteristics of drought and investigating the vegetation-water availability relationship.However,no high-resolution and long-term SPEI datasets over China are available.To fill this gap,we developed a new model based on machine learning to obtain high-resolution(1 km)SPEI data by combining climate variables with topographical and geographical features.Here,we analyzed the long-term drought over the past century(1901–2020)and vegetation-water availability relationship in the past two decades(2000–2020).The century-long drought trend analyses indicated an overall drying trend across China with increasing drought frequency,duration,and severity during the past century.We found that drought events in 1901–1961 showed a larger increase than that in 1961–2020,with the Qinghai-Xizang Plateau showing a significant drying trend during 1901–1960 but a wetting trend during 1961–2020.There were 13.90%and 28.21%of vegetation in China showing water deficit and water surplus respectively during 2000–2020.The water deficit area significantly shrank from 2000 to 2020 across China,which is dominated by the significant decrease in water deficit areas in South China.Among temperature,precipitation,and vegetation abundance,temperature is the most important factor for the vegetation-water availability dynamics in China over the past two decades,with high temperature contributing to water deficit.Our findings are important for water and vegetation management under a warming climate.展开更多
During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resol...During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resolution seismic data processing technologies and methods tailored for drilling scenarios.The high-resolution processing of seismic data is divided into three stages:pre-drilling processing,post-drilling correction,and while-drilling updating.By integrating seismic data from different stages,spatial ranges,and frequencies,together with information from drilled wells and while-drilling data,and applying artificial intelligence modeling techniques,a progressive high-resolution processing technology of seismic data based on multi-source information fusion is developed,which performs simple and efficient seismic information updates during drilling.Case studies show that,with the gradual integration of multi-source information,the resolution and accuracy of seismic data are significantly improved,and thin-bed weak reflections are more clearly imaged.The updated seismic information while-drilling demonstrates high value in predicting geological bodies ahead of the drill bit.Validation using logging,mud logging,and drilling engineering data ensures the fidelity of the processing results of high-resolution seismic data.This provides clearer and more accurate stratigraphic information for drilling operations,enhancing both drilling safety and efficiency.展开更多
BACKGROUND No studies have yet been conducted on changes in microcirculatory hemody-namics of colorectal adenomas in vivo under endoscopy.The microcirculation of the colorectal adenoma could be observed in vivo by a n...BACKGROUND No studies have yet been conducted on changes in microcirculatory hemody-namics of colorectal adenomas in vivo under endoscopy.The microcirculation of the colorectal adenoma could be observed in vivo by a novel high-resolution magnification endoscopy with blue laser imaging(BLI),thus providing a new insight into the microcirculation of early colon tumors.AIM To observe the superficial microcirculation of colorectal adenomas using the novel magnifying colonoscope with BLI and quantitatively analyzed the changes in hemodynamic parameters.METHODS From October 2019 to January 2020,11 patients were screened for colon adenomas with the novel high-resolution magnification endoscope with BLI.Video images were recorded and processed with Adobe Premiere,Adobe Photoshop and Image-pro Plus software.Four microcirculation parameters:Microcirculation vessel density(MVD),mean vessel width(MVW)with width standard deviation(WSD),and blood flow velocity(BFV),were calculated for adenomas and the surrounding normal mucosa.RESULTS A total of 16 adenomas were identified.Compared with the normal surrounding mucosa,the superficial vessel density in the adenomas was decreased(MVD:0.95±0.18 vs 1.17±0.28μm/μm2,P<0.05).MVW(5.11±1.19 vs 4.16±0.76μm,P<0.05)and WSD(11.94±3.44 vs 9.04±3.74,P<0.05)were both increased.BFV slowed in the adenomas(709.74±213.28 vs 1256.51±383.31μm/s,P<0.05).CONCLUSION The novel high-resolution magnification endoscope with BLI can be used for in vivo study of adenoma superficial microcirculation.Superficial vessel density was decreased,more irregular,with slower blood flow.展开更多
Severe acute respiratory syndrome (SARS) is an acute respiratory infectious disease caused by a novel coronavirus, firstly broke out in November 2002 in Guangdong and prevailed quickly in Beijing, Hong Kong, Taiwan an...Severe acute respiratory syndrome (SARS) is an acute respiratory infectious disease caused by a novel coronavirus, firstly broke out in November 2002 in Guangdong and prevailed quickly in Beijing, Hong Kong, Taiwan and other regions of China. It was one of the most potential pandemic diseases and had affected more than 20 other countries.^(1,2) There have been a lot of resear-ches^(2-7) in terms of its etiology, epidemiology, pathogenesis, clinical characteristics, diagnostics, treatment and prevention, vaccines and so on.Along with control of the epidemic situation, a great number of SARS patients were in the recovery phase, therefore, we undertook a half-year follow-up investigation on their clinical, laboratory and image situations.展开更多
基金financial support from the National Natural Science Foundation of China(Grant No.61971201)。
文摘High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-corrected transmission electron microscopy constrain resolution.A machine learning algorithm is developed to determine the aberration parameters with higher precision from small,lattice-periodic crystal images.The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide(MoS_(2))monolayer with 25 variables(14 aberrations)resolved in wide ranges.Using these measured parameters,the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS_(2)monolayers.The images were acquired due to the unexpected movement of the specimen holder.Four-dimensional data extraction reveals time-varying atomic structures and ripple.In particular,the atomic evolution of the sulfur-vacancy point and line defects,as well as the edge structure near the amorphous,is visualized as the resolution has been improved from about 1.75?to 0.9 A.This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.
文摘While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.
基金supported by the Natural Science Foundation of Hubei Provincial Department of Education(D20232101)Shandong Second Medical University 2024 Affiliated Hospital(Teaching Hospital)Scientific Research Development Fund Project(2024FYQ026)+3 种基金the innovative Research Programme of Xiangyang No.1 People’s Hospital(XYY2023ZY01)Faculty Development Grants of Xiangyang No.1 People’s Hospital Affiliated to Hubei University of Medicine(XYY2023D05)Joint supported by Hubei Provincial Natural Science Foundation and Xiangyang of China(2025AFD091)Traditional Chinese Medicine Scientific Research Project of Hubei Provincial Administration of Traditional Chinese Medicine(ZY2025D019).
文摘Background:Diabetic foot,a severe complication of diabetes,is characterized by chronic refractory wounds.Sanhuang Oil,a topical herbal formula,demonstrates significant therapeutic effects including antibacterial,anti-inflammatory,and immunomodulatory activities.However,its active constituents and mechanisms of action against diabetic foot remain to be elucidated.Methods:In this study,the chemical constituents of Sanhuang Oil were identified using UPLC-QE-Orbitrap-MS.Subsequently,the mechanism by which Sanhuang Oil promotes diabetic foot ulcer healing was predicted by integrating network pharmacology and molecular docking.Additionally,diabetic mouse model was established in ICR mice using a combination of a high-fat diet(HFD)and streptozotocin(STZ)chemical induction.A full-thickness skin defect was created on the dorsum of the mice.Wound healing and the healing rate were observed following Sanhuang Oil intervention.The mechanism underlying Sanhuang Oil’s promotion of diabetic ulcer healing was further investigated using transcriptomics and histopathological examination(H&E staining).Results:A total of 97 active ingredients were identified from Sanhuang Oil.Network pharmacology analysis predicted 543 common targets,and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis identified 203 relevant pathways.Molecular docking further confirmed high binding affinity(binding energy≤−5.0 kcal/mol)between specific active components in Sanhuang Oil(e.g.,coptisine,phellodendrine,baicalein)and key targets associated with diabetic foot ulcers(e.g.,EGFR,AKT1,STAT3).In vivo experimental results demonstrated that the wound healing rate was significantly higher in Sanhuang Oil-treated groups compared to the model group(P<0.001).HE staining revealed that the high-dose Sanhuang Oil group exhibited more pronounced epithelial tissue coverage over the wound,reduced inflammatory cell infiltration,and increased collagen deposition and fibroblast proliferation.transcriptomic analysis identified Pdk4,Ttn,Csrp3,Actn2,Myoz2,Tnnc2,Myod1,Myog,Myot,and Myf6 as key regulatory proteins involved in promoting wound healing.Conclusion:Sanhuang Oil promotes wound healing in diabetic ulcer mice,potentially by mitigating inflammation and regulating key targets such as Pdk4 to enhance fibroblast function.These findings provide novel insights into the multi-target,multi-pathway mechanism of Sanhuang Oil for treating diabetic foot ulcers.
基金Supported by The Clinical Innovation Guidance Program of Hunan Provincial Science and Technology Department,China,No.2021SK51714The Hunan Nature Science Foundation,China,No.2023JJ30531.
文摘BACKGROUND Vertebral artery dissection(VAD)is a rare but life-threatening condition characterized by tearing of the intimal layer of the vertebral artery,leading to stenosis,occlusion or rupture.The clinical presentation of VAD can be heterogeneous,with common symptoms including headache,dizziness and balance problems.Timely diagnosis and treatment are crucial for favorable outcomes;however,VAD is often missed due to its variable clinical presentation and lack of robust diagnostic guidelines.High-resolution magnetic resonance imaging(HRMRI)has emerged as a reliable diagnostic tool for VAD,providing detailed visualization of vessel wall abnormalities.CASE SUMMARY A young male patient presented with an acute onset of severe headache,vomiting,and seizures,followed by altered consciousness.Imaging studies revealed bilateral VAD,basilar artery thrombosis,multiple brainstem and cerebellar infarcts,and subarachnoid hemorrhage.Digital subtraction angiography(DSA)revealed vertebral artery stenosis but failed to detect the dissection,potentially because intramural thrombosis obscured the VAD.In contrast,HRMRI confirmed the diagnosis by revealing specific signs of dissection.The patient was managed conservatively with antiplatelet therapy and other supportive measures,such as blood pressure control and pain management.After 5 mo of rehabilitation,the patient showed significant improvement in swallowing and limb strength.CONCLUSION HR-MRI can provide precise evidence for the identification of VAD.
基金supported by the National Natural Science Foundation of China(61772179)Hunan Provincial Natural Science Foundation of China(2022JJ50016,2023JJ50095)+1 种基金the Science and Technology Plan Project of Hunan Province(2016TP1020)Double First-Class University Project of Hunan Province(Xiangjiaotong[2018]469,[2020]248).
文摘The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of the garment, addressing challenges such as the loss of patterns, colors, and other essential details commonly observed in virtual try-on images produced by existing methods. During the image generation stage, with the aim of maximizing the utilization of the information proffered by the input image, the input features undergo double sampling within the normalization procedure, thereby enhancing the detail fidelity and clothing alignment efficacy of the output image. Experimental evaluations conducted on high-resolution datasets validate the effectiveness of the proposed method. Results demonstrate significant improvements in preserving garment details, reducing artifacts, and achieving superior alignment between the clothing and body compared to baseline methods, establishing its advantage in generating realistic and high-quality virtual try-on images.
基金supported by the project funded by International Research Center of Big Data for Sustainable 740 Development Goals[Grant Number CBAS2022GSP07]Fundamental Research Funds for the Central Universities,Chongqing Natural Science Foundation[Grant Number CSTB2022NSCQMSX 2069]Ministry of Education of China[Grant Number 19JZD023].
文摘Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this direction.Despite their many advantages,still,the proposed methods are inadequate to provide robust results because they mostly rely on the direct field investigations.This paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model(CHM)data to solve the ITDC problem.The new approach is studied in six urban scenes:farmland,woodland,park,industrial land,road and residential areas.First,it identifies tree canopy regions using a deep learning network from high-resolution imagery.It then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region growing.Finally,it calculates and describes the number of individual trees and tree canopies.The proposed approach is experimented with the data from Shanghai,China.Our results show that the individual tree detection method had an average overall accuracy of 0.953,with a precision of 0.987 for woodland scene.Meanwhile,the R^(2) value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size,respectively.These results confirm that the proposed method is robust enough for urban tree planning and management.
基金The National Natural Science Foundation of China under contract No.42425606the Basic Scientific Fund for the National Public Research Institute of China(Shu-Xingbei Young Talent Program)under contract No.2023S01+1 种基金the Ocean Decade International Cooperation Center Scientific and Technological Cooperation Project under contract No.GHKJ2024005China-Korea Joint Ocean Research Center Project under contract Nos PI-20240101(China)and 20220407(Korea).
文摘In oceanic and atmospheric science,finer resolutions have become a prevailing trend in all aspects of development.For high-resolution fluid flow simulations,the computational costs of widely used numerical models increase significantly with the resolution.Artificial intelligence methods have attracted increasing attention because of their high precision and fast computing speeds compared with traditional numerical model methods.The resolution-independent Fourier neural operator(FNO)presents a promising solution to the still challenging problem of high-resolution fluid flow simulations based on low-resolution data.Accordingly,we assess the potential of FNO for high-resolution fluid flow simulations using the vorticity equation as an example.We assess and compare the performance of FNO in multiple high-resolution tests varying the amounts of data and the evolution durations.When assessed with finer resolution data(even up to number of grid points with 1280×1280),the FNO model,trained at low resolution(number of grid points with 64×64)and with limited data,exhibits a stable overall error and good accuracy.Additionally,our work demonstrates that the FNO model takes less time than the traditional numerical method for high-resolution simulations.This suggests that FNO has the prospect of becoming a cost-effective and highly precise model for high-resolution simulations in the future.Moreover,FNO can make longer high-resolution predictions while training with less data by superimposing vorticity fields from previous time steps as input.A suitable initial learning rate can be set according to the frequency principle,and the time intervals of the dataset need to be adjusted according to the spatial resolution of the input when training the FNO model.Our findings can help optimize FNO for future fluid flow simulations.
基金funded by the General Program of National Natural Science Foundation of China(Grant No.42377467).
文摘Understanding vegetation water availability can be important for managing vegetation and combating climate change.Changes in vegetation water availability throughout China remains poorly understood,especially at a high spatial resolution.Standardized Precipitation Evapotranspiration Index(SPEI)is an ideal water availability index for assessing the spatiotemporal characteristics of drought and investigating the vegetation-water availability relationship.However,no high-resolution and long-term SPEI datasets over China are available.To fill this gap,we developed a new model based on machine learning to obtain high-resolution(1 km)SPEI data by combining climate variables with topographical and geographical features.Here,we analyzed the long-term drought over the past century(1901–2020)and vegetation-water availability relationship in the past two decades(2000–2020).The century-long drought trend analyses indicated an overall drying trend across China with increasing drought frequency,duration,and severity during the past century.We found that drought events in 1901–1961 showed a larger increase than that in 1961–2020,with the Qinghai-Xizang Plateau showing a significant drying trend during 1901–1960 but a wetting trend during 1961–2020.There were 13.90%and 28.21%of vegetation in China showing water deficit and water surplus respectively during 2000–2020.The water deficit area significantly shrank from 2000 to 2020 across China,which is dominated by the significant decrease in water deficit areas in South China.Among temperature,precipitation,and vegetation abundance,temperature is the most important factor for the vegetation-water availability dynamics in China over the past two decades,with high temperature contributing to water deficit.Our findings are important for water and vegetation management under a warming climate.
基金Supported by the National Natural Science Foundation of China(U24B2031)National Key Research and Development Project(2018YFA0702504)"14th Five-Year Plan"Science and Technology Project of CNOOC(KJGG2022-0201)。
文摘During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resolution seismic data processing technologies and methods tailored for drilling scenarios.The high-resolution processing of seismic data is divided into three stages:pre-drilling processing,post-drilling correction,and while-drilling updating.By integrating seismic data from different stages,spatial ranges,and frequencies,together with information from drilled wells and while-drilling data,and applying artificial intelligence modeling techniques,a progressive high-resolution processing technology of seismic data based on multi-source information fusion is developed,which performs simple and efficient seismic information updates during drilling.Case studies show that,with the gradual integration of multi-source information,the resolution and accuracy of seismic data are significantly improved,and thin-bed weak reflections are more clearly imaged.The updated seismic information while-drilling demonstrates high value in predicting geological bodies ahead of the drill bit.Validation using logging,mud logging,and drilling engineering data ensures the fidelity of the processing results of high-resolution seismic data.This provides clearer and more accurate stratigraphic information for drilling operations,enhancing both drilling safety and efficiency.
基金This study was approved by the Medical Ethics Committee of Beijing Tsinghua Changgung Hospital(20002-0-02).
文摘BACKGROUND No studies have yet been conducted on changes in microcirculatory hemody-namics of colorectal adenomas in vivo under endoscopy.The microcirculation of the colorectal adenoma could be observed in vivo by a novel high-resolution magnification endoscopy with blue laser imaging(BLI),thus providing a new insight into the microcirculation of early colon tumors.AIM To observe the superficial microcirculation of colorectal adenomas using the novel magnifying colonoscope with BLI and quantitatively analyzed the changes in hemodynamic parameters.METHODS From October 2019 to January 2020,11 patients were screened for colon adenomas with the novel high-resolution magnification endoscope with BLI.Video images were recorded and processed with Adobe Premiere,Adobe Photoshop and Image-pro Plus software.Four microcirculation parameters:Microcirculation vessel density(MVD),mean vessel width(MVW)with width standard deviation(WSD),and blood flow velocity(BFV),were calculated for adenomas and the surrounding normal mucosa.RESULTS A total of 16 adenomas were identified.Compared with the normal surrounding mucosa,the superficial vessel density in the adenomas was decreased(MVD:0.95±0.18 vs 1.17±0.28μm/μm2,P<0.05).MVW(5.11±1.19 vs 4.16±0.76μm,P<0.05)and WSD(11.94±3.44 vs 9.04±3.74,P<0.05)were both increased.BFV slowed in the adenomas(709.74±213.28 vs 1256.51±383.31μm/s,P<0.05).CONCLUSION The novel high-resolution magnification endoscope with BLI can be used for in vivo study of adenoma superficial microcirculation.Superficial vessel density was decreased,more irregular,with slower blood flow.
文摘Severe acute respiratory syndrome (SARS) is an acute respiratory infectious disease caused by a novel coronavirus, firstly broke out in November 2002 in Guangdong and prevailed quickly in Beijing, Hong Kong, Taiwan and other regions of China. It was one of the most potential pandemic diseases and had affected more than 20 other countries.^(1,2) There have been a lot of resear-ches^(2-7) in terms of its etiology, epidemiology, pathogenesis, clinical characteristics, diagnostics, treatment and prevention, vaccines and so on.Along with control of the epidemic situation, a great number of SARS patients were in the recovery phase, therefore, we undertook a half-year follow-up investigation on their clinical, laboratory and image situations.
