Background 3D botanical tree reconstruction from a single image plays a vital role in the field of computer graphics.However,accurately capturing the intricate branching patterns and detailed morphologies of trees rem...Background 3D botanical tree reconstruction from a single image plays a vital role in the field of computer graphics.However,accurately capturing the intricate branching patterns and detailed morphologies of trees remains a challenge.Methods In this study,we proposed a novel approach for single-image tree reconstruction using a conditional generative adversarial network to infer the 3D skeleton of a tree in the form of a 2D skeleton depth map.Based on the 2D skeleton depth map,a corresponding branching structure(3D skeleton)that inherits the tree shape in the input image and leaves can be generated using a procedural modeling technique.Result Experimental results show that the proposed method accurately reconstructs diverse tree structures across species.Both quantitative and qualitative evaluations demonstrate improved skeleton completeness,branching accuracy,and visual realism over baseline methods,while requiring no user input.Conclusions Our proposed approach for generating lifelike 3D tree models from a single image with no user input shows its proficiency in achieving efficient and reliable reconstruction.These results showcase the capability of the proposed model to recreate complex tree architectures while capturing their visual authenticity.展开更多
Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combini...Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications.展开更多
The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for he...The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.展开更多
Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton s...Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused.Moreover,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes shared.Existing Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations efficiently.To solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different views.To make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the graph.Based on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation network.Hourglass-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features efficiently.Experimental results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimation accuracy.展开更多
Atractylodimers A-D(1-4),sesquiterpenoid dimers(SDs)featuring a unique cage-like structure,were isolated from the rhizomes of Atractylodes macrocephala.The most distinctive characteristic of these isolates was the hig...Atractylodimers A-D(1-4),sesquiterpenoid dimers(SDs)featuring a unique cage-like structure,were isolated from the rhizomes of Atractylodes macrocephala.The most distinctive characteristic of these isolates was the highly twisted“cap”structure based on highly twisted five-membered oxygen heterocyclic rings.Notably,compound 1 contained a furo[2,3b]furan ring,a caged 3,10-oxa-tricyclo[5.2.1.04,9]decane moiety,and 6/6/5/5/5/5/6/6 octocyclic skeleton.Compounds 2-3 exhibited a spiro-tetrahydrofuran ring,while compound 4 incorporated a caged spiro-2,5,9-oxa-tricyclo[5.2.1.0^(4,10)]decane scaffold for unit linkage.Their structures were definitively established through spectroscopic methods and X-ray diffraction experiments.Plausible biosynthetic pathways of compounds 1-4 were proposed.Compounds 1 and 2 demonstrated significant neuroprotective effects against serum deprivationinduced PC12 cell damage.展开更多
The first-ever synthesis of the unknown furo[2,3:4,5]pyrimido[1,2-b]indazole skeleton was demonstrated based on the undiscovered tetra-functionalization of enaminones,with simple substrates and reaction conditions.The...The first-ever synthesis of the unknown furo[2,3:4,5]pyrimido[1,2-b]indazole skeleton was demonstrated based on the undiscovered tetra-functionalization of enaminones,with simple substrates and reaction conditions.The key to realizing this process lies in the multiple trapping of the in situ generated ketenimine cation by the 3-aminoindazole,which results in the formation of four new chemical bonds and two new rings in one pot.Moreover,the products of this new reaction were found to exhibit aggregationinduced emission(AIE)without modification.展开更多
Layered ammonium vanadate has become a promising cathode material for aqueous zinc ion batteries(ZIBs)due to its small mass and large ionic radius of ammonium ions as well as the consequent large layer spacing and hig...Layered ammonium vanadate has become a promising cathode material for aqueous zinc ion batteries(ZIBs)due to its small mass and large ionic radius of ammonium ions as well as the consequent large layer spacing and high specific capacity.However,the irreversible de-ammoniation caused by N·H···O bonds damaged would impair cycle life of ZIBs and the strong electrostatic interaction between Zn^(2+)and V-O frame could slower the mobility of Zn^(2+).Furthermore,the thermal instability of ammonium vanadate also limits the use of common carbon coating modification method to solve the problem.Herein,V_(2)CT_(X)MXene was innovatively selected as a bifunctional source to in-situ derivatized(NH_(4))_(2)V_(8)O_(20)·x H_(2)O with amorphous carbon-coated(NHVO@C)via one-step hydrothermal method in relatively moderate temperature.The amorphous carbon shell derived from the V_(2)CT_(X)MXene as a conductive framework to effectively improve the diffusion kinetics of Zn^(2+)and the robust carbon skeleton could alleviate the ammonium dissolution during long-term cycling.As a result,zinc ion batteries using NHVO@C as cathode exhibit superior electrochemical performance.Moreover,the assembled foldable or high loading(10.2 mg/cm^(2))soft-packed ZIBs further demonstrates its practical application.This study provided new insights into the development of the carbon cladding process for thermally unstable materials in moderate temperatures.展开更多
SnO_(2)is regarded as a promising lithium storage material due to the advantage of sequential conversion-alloying reaction mechanism.Unfortunately,large volume expansion and undesirable reaction reversibility are iden...SnO_(2)is regarded as a promising lithium storage material due to the advantage of sequential conversion-alloying reaction mechanism.Unfortunately,large volume expansion and undesirable reaction reversibility are identified as two fatal drawbacks.Herein,SnO_(2)nanoparticles encapsulated in graphene oxide-coated porous biochar skeleton(SnO_(2)/PB@GO)are skillfully constructed via an efficient one-step hydrothermal process to be employed as composite anode materials,in which the PB skeleton extracted from waste tea-seed shells possesses enough space to buffer drastic volume variation and the GO coating acts as robust physical matrix to prevent structural degradation.Moreover,double-carbon components successfully anchor SnO_(2)nanoparticles to promote contact and reaction between Sn and Li_(2)O to guarantee high reaction reversibility and structural integration of SnO_(2)/PB@GO electrode.As expected,SnO_(2)/PB@GO-based cell achieves high reversible specific capacity of 783.5 mAh·g^(-1)after 100 cycles at0.1 A.g^(-1)and delivers desirable cycling stability with capacity retention ratio of 81.62%after 300 cycles at1.0 A.g^(-1).Therefore,this work may provide new perspectives on the modification of conversion or alloying typeanodes for lithium-ion batteries and present a feasible strategy to take full advantage of the waste biomass.展开更多
Studying various aurora morphology helps us understand space's physical processes and the mechanisms behind these patterns.Auroral arcs are the brightest and most prominent auroral patterns.Due to the difficulty i...Studying various aurora morphology helps us understand space's physical processes and the mechanisms behind these patterns.Auroral arcs are the brightest and most prominent auroral patterns.Due to the difficulty in precisely defining auroral shape edges,auroral arc skeleton extraction is expected as an alternative representation for studying auroral morphology,resorting skeletons extract key morphological features from complex auroral shapes.Transformer models provide a better understanding of the relationship between the overall morphology and the details when processing image data,so we proposed a Transformer-based method for auroral arc skeleton extraction.Combined with ridge-guided annotation on all-sky images,a Transformer-based skeleton extractor is trained and used to estimate the number of auroral arcs.Experiments demonstrate that the Transformer-based model can more effectively capture structural information and local details of auroral arcs,which is suitable for complex auroral morphologies.展开更多
The next-generation lithium(Li)metal batteries suffer severe low-temperature capacity degradation,appealing for expeditions on solutions.Herein,the feasibility of copper-based skeletons(i.e.,2D Cu foil,3D Cu mesh,and ...The next-generation lithium(Li)metal batteries suffer severe low-temperature capacity degradation,appealing for expeditions on solutions.Herein,the feasibility of copper-based skeletons(i.e.,2D Cu foil,3D Cu mesh,and CuZn mesh)frequently adopted in the stabilization of Li are evaluated at low temperatures.Li growth patterns and stripping behaviors on different skeletons and at different temperatures uncover the dendrite-free and dead-Li-less Li deposition/dissolution on CuZn mesh.Three-electrode impedance indicates the dynamic advantages of CuZn mesh,driving fast Li^(+)crossing through solidelectrolyte-interphase and charge transfer process.Notably,CuZn mesh enables the stable operation and fast charging(1.8 mA cm^(-2))of Li||LiFePO_(4)cells for over 120 cycles at-10℃ with a superior capacity retention of 88%.The success of CuZn mesh can be translated into lower temperature(-20℃)and 1.0-Ah-level pouch cells.This work provides fundamentals on improving low-temperature battery performances by skeletons with regulated spatial structure and lithiophilicity.展开更多
By substituting rock skeleton modulus expressions into Gassmann approximate fluid equation, we obtain a seismic porosity inversion equation. However, conventional rock skeleton models and their expressions are quite d...By substituting rock skeleton modulus expressions into Gassmann approximate fluid equation, we obtain a seismic porosity inversion equation. However, conventional rock skeleton models and their expressions are quite different from each other, resuling in different seismic porosity inversion equations, potentially leading to difficulties in correctly applying them and evaluating their results. In response to this, a uniform relation with two adjusting parameters suitable for all rock skeleton models is established from an analysis and comparison of various conventional rock skeleton models and their expressions including the Eshelby-Walsh, Pride, Geertsma, Nur, Keys-Xu, and Krief models. By giving the two adjusting parameters specific values, different rock skeleton models with specific physical characteristics can be generated. This allows us to select the most appropriate rock skeleton model based on geological and geophysical conditions, and to develop more wise seismic porosity inversion. As an example of using this method for hydrocarbon prediction and fluid identification, we apply this improved porosity inversion, associated with rock physical data and well log data, to the ZJ basin. Research shows that the existence of an abundant hydrocarbon reservoir is dependent on a moderate porosity range, which means we can use the results of seismic porosity inversion to identify oil reservoirs and dry or water-saturated reservoirs. The seismic inversion results are closely correspond to well log porosity curves in the ZJ area, indicating that the uniform relations and inversion methods proposed in this paper are reliable and effective.展开更多
文摘Background 3D botanical tree reconstruction from a single image plays a vital role in the field of computer graphics.However,accurately capturing the intricate branching patterns and detailed morphologies of trees remains a challenge.Methods In this study,we proposed a novel approach for single-image tree reconstruction using a conditional generative adversarial network to infer the 3D skeleton of a tree in the form of a 2D skeleton depth map.Based on the 2D skeleton depth map,a corresponding branching structure(3D skeleton)that inherits the tree shape in the input image and leaves can be generated using a procedural modeling technique.Result Experimental results show that the proposed method accurately reconstructs diverse tree structures across species.Both quantitative and qualitative evaluations demonstrate improved skeleton completeness,branching accuracy,and visual realism over baseline methods,while requiring no user input.Conclusions Our proposed approach for generating lifelike 3D tree models from a single image with no user input shows its proficiency in achieving efficient and reliable reconstruction.These results showcase the capability of the proposed model to recreate complex tree architectures while capturing their visual authenticity.
基金funded by the Natural Science Foundation of Chongqing Municipality,grant number CSTB2022NSCQ-MSX0503.
文摘Gait recognition is a key biometric for long-distance identification,yet its performance is severely degraded by real-world challenges such as varying clothing,carrying conditions,and changing viewpoints.While combining silhouette and skeleton data is a promising direction,effectively fusing these heterogeneous modalities and adaptively weighting their contributions in response to diverse conditions remains a central problem.This paper introduces GaitMAFF,a novelMulti-modal Adaptive Feature Fusion Network,to address this challenge.Our approach first transforms discrete skeleton joints into a dense SkeletonMap representation to align with silhouettes,then employs an attention-based module to dynamically learn the fusion weights between the two modalities.These fused features are processed by a powerful spatio-temporal backbone withWeighted Global-Local Feature FusionModules(WFFM)to learn a discriminative representation.Extensive experiments on the challenging CCPG and Gait3D datasets show that GaitMAFF achieves state-of-the-art performance,with an average Rank-1 accuracy of 84.6%on CCPG and 58.7%on Gait3D.These results demonstrate that our adaptive fusion strategy effectively integrates complementary multimodal information,significantly enhancing gait recognition robustness and accuracy in complex scenes and providing a practical solution for real-world applications.
基金funded by the ICT Division of theMinistry of Posts,Telecommunications,and Information Technology of Bangladesh under Grant Number 56.00.0000.052.33.005.21-7(Tracking No.22FS15306)support from the University of Rajshahi.
文摘The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.
基金supported in part by the National Natural Science Foundation of China under Grants 61973065,U20A20197,61973063.
文摘Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused.Moreover,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes shared.Existing Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations efficiently.To solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different views.To make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the graph.Based on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation network.Hourglass-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features efficiently.Experimental results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimation accuracy.
基金supported by the CAMS Innovation Fund for Medical Science(No.2023-I2M-006)the National Key R&D Program of China(No.2019YFC1711000)。
文摘Atractylodimers A-D(1-4),sesquiterpenoid dimers(SDs)featuring a unique cage-like structure,were isolated from the rhizomes of Atractylodes macrocephala.The most distinctive characteristic of these isolates was the highly twisted“cap”structure based on highly twisted five-membered oxygen heterocyclic rings.Notably,compound 1 contained a furo[2,3b]furan ring,a caged 3,10-oxa-tricyclo[5.2.1.04,9]decane moiety,and 6/6/5/5/5/5/6/6 octocyclic skeleton.Compounds 2-3 exhibited a spiro-tetrahydrofuran ring,while compound 4 incorporated a caged spiro-2,5,9-oxa-tricyclo[5.2.1.0^(4,10)]decane scaffold for unit linkage.Their structures were definitively established through spectroscopic methods and X-ray diffraction experiments.Plausible biosynthetic pathways of compounds 1-4 were proposed.Compounds 1 and 2 demonstrated significant neuroprotective effects against serum deprivationinduced PC12 cell damage.
基金supported by the National Natural Science Foundation of China(Nos.21971080,22171098)supported by Chengdu Guibao Science&Technology Co.,Ltd.This work was also supported by the 111 Project(No.B17019)。
文摘The first-ever synthesis of the unknown furo[2,3:4,5]pyrimido[1,2-b]indazole skeleton was demonstrated based on the undiscovered tetra-functionalization of enaminones,with simple substrates and reaction conditions.The key to realizing this process lies in the multiple trapping of the in situ generated ketenimine cation by the 3-aminoindazole,which results in the formation of four new chemical bonds and two new rings in one pot.Moreover,the products of this new reaction were found to exhibit aggregationinduced emission(AIE)without modification.
基金financially supported by the National Natural Science Foundation of China(Nos.52402271,22005167 and52302273)the Youth Innovation Team Project for Talent Introduction and Cultivation in Universities of Shandong Province(No.2024KJH129)+2 种基金the Taishan Scholar Project of Shandong Provinceof China(Nos.tsqn202211160,tsqn202312199)Shandong Provincial Natural Science Foundation of China(Nos.ZR2022QE003 and ZR2023QE176)China Postdoctoral Science Foundation(No.2023M741810)。
文摘Layered ammonium vanadate has become a promising cathode material for aqueous zinc ion batteries(ZIBs)due to its small mass and large ionic radius of ammonium ions as well as the consequent large layer spacing and high specific capacity.However,the irreversible de-ammoniation caused by N·H···O bonds damaged would impair cycle life of ZIBs and the strong electrostatic interaction between Zn^(2+)and V-O frame could slower the mobility of Zn^(2+).Furthermore,the thermal instability of ammonium vanadate also limits the use of common carbon coating modification method to solve the problem.Herein,V_(2)CT_(X)MXene was innovatively selected as a bifunctional source to in-situ derivatized(NH_(4))_(2)V_(8)O_(20)·x H_(2)O with amorphous carbon-coated(NHVO@C)via one-step hydrothermal method in relatively moderate temperature.The amorphous carbon shell derived from the V_(2)CT_(X)MXene as a conductive framework to effectively improve the diffusion kinetics of Zn^(2+)and the robust carbon skeleton could alleviate the ammonium dissolution during long-term cycling.As a result,zinc ion batteries using NHVO@C as cathode exhibit superior electrochemical performance.Moreover,the assembled foldable or high loading(10.2 mg/cm^(2))soft-packed ZIBs further demonstrates its practical application.This study provided new insights into the development of the carbon cladding process for thermally unstable materials in moderate temperatures.
基金financially supported by the National Natural Science Foundation of China(Nos.52274292 and 51874046)the Outstanding Youth Foundation of Hubei Province(No.2020CFA090)+1 种基金the Project of Scientific Research of Jingzhou(No.2023EC37)the Young Top-notch Talent Cultivation Program of Hubei Province
文摘SnO_(2)is regarded as a promising lithium storage material due to the advantage of sequential conversion-alloying reaction mechanism.Unfortunately,large volume expansion and undesirable reaction reversibility are identified as two fatal drawbacks.Herein,SnO_(2)nanoparticles encapsulated in graphene oxide-coated porous biochar skeleton(SnO_(2)/PB@GO)are skillfully constructed via an efficient one-step hydrothermal process to be employed as composite anode materials,in which the PB skeleton extracted from waste tea-seed shells possesses enough space to buffer drastic volume variation and the GO coating acts as robust physical matrix to prevent structural degradation.Moreover,double-carbon components successfully anchor SnO_(2)nanoparticles to promote contact and reaction between Sn and Li_(2)O to guarantee high reaction reversibility and structural integration of SnO_(2)/PB@GO electrode.As expected,SnO_(2)/PB@GO-based cell achieves high reversible specific capacity of 783.5 mAh·g^(-1)after 100 cycles at0.1 A.g^(-1)and delivers desirable cycling stability with capacity retention ratio of 81.62%after 300 cycles at1.0 A.g^(-1).Therefore,this work may provide new perspectives on the modification of conversion or alloying typeanodes for lithium-ion batteries and present a feasible strategy to take full advantage of the waste biomass.
基金supported by the National Natural Science Foundation of China(Grant no.41874173)。
文摘Studying various aurora morphology helps us understand space's physical processes and the mechanisms behind these patterns.Auroral arcs are the brightest and most prominent auroral patterns.Due to the difficulty in precisely defining auroral shape edges,auroral arc skeleton extraction is expected as an alternative representation for studying auroral morphology,resorting skeletons extract key morphological features from complex auroral shapes.Transformer models provide a better understanding of the relationship between the overall morphology and the details when processing image data,so we proposed a Transformer-based method for auroral arc skeleton extraction.Combined with ridge-guided annotation on all-sky images,a Transformer-based skeleton extractor is trained and used to estimate the number of auroral arcs.Experiments demonstrate that the Transformer-based model can more effectively capture structural information and local details of auroral arcs,which is suitable for complex auroral morphologies.
基金the funding support of the National Natural Science Foundation of China(52103342,22209032 and 22479134)Natural Science Foundation of Zhejiang Province(LY24B030008)+1 种基金China Jiliang University Research Fund Program for Young Scholars(221040)the funding support of the Zhejiang Provincial College Students’Scientific Research and Innovation Activity(Xinmiao Talent)Program(2023R409A045)。
文摘The next-generation lithium(Li)metal batteries suffer severe low-temperature capacity degradation,appealing for expeditions on solutions.Herein,the feasibility of copper-based skeletons(i.e.,2D Cu foil,3D Cu mesh,and CuZn mesh)frequently adopted in the stabilization of Li are evaluated at low temperatures.Li growth patterns and stripping behaviors on different skeletons and at different temperatures uncover the dendrite-free and dead-Li-less Li deposition/dissolution on CuZn mesh.Three-electrode impedance indicates the dynamic advantages of CuZn mesh,driving fast Li^(+)crossing through solidelectrolyte-interphase and charge transfer process.Notably,CuZn mesh enables the stable operation and fast charging(1.8 mA cm^(-2))of Li||LiFePO_(4)cells for over 120 cycles at-10℃ with a superior capacity retention of 88%.The success of CuZn mesh can be translated into lower temperature(-20℃)and 1.0-Ah-level pouch cells.This work provides fundamentals on improving low-temperature battery performances by skeletons with regulated spatial structure and lithiophilicity.
基金supported by the National Nature Science Foundation of China(Grant No.41174114)Important National Science and Technology Specific Projects(Grant No.2011ZX05025-005-010)
文摘By substituting rock skeleton modulus expressions into Gassmann approximate fluid equation, we obtain a seismic porosity inversion equation. However, conventional rock skeleton models and their expressions are quite different from each other, resuling in different seismic porosity inversion equations, potentially leading to difficulties in correctly applying them and evaluating their results. In response to this, a uniform relation with two adjusting parameters suitable for all rock skeleton models is established from an analysis and comparison of various conventional rock skeleton models and their expressions including the Eshelby-Walsh, Pride, Geertsma, Nur, Keys-Xu, and Krief models. By giving the two adjusting parameters specific values, different rock skeleton models with specific physical characteristics can be generated. This allows us to select the most appropriate rock skeleton model based on geological and geophysical conditions, and to develop more wise seismic porosity inversion. As an example of using this method for hydrocarbon prediction and fluid identification, we apply this improved porosity inversion, associated with rock physical data and well log data, to the ZJ basin. Research shows that the existence of an abundant hydrocarbon reservoir is dependent on a moderate porosity range, which means we can use the results of seismic porosity inversion to identify oil reservoirs and dry or water-saturated reservoirs. The seismic inversion results are closely correspond to well log porosity curves in the ZJ area, indicating that the uniform relations and inversion methods proposed in this paper are reliable and effective.
