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
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
In this paper, a method and algorithm of skeleton extraction based on binary mathematical morphology is presented. Sequential structuring elements (SEs) is also studied, which is the key problem of skeleton extraction...In this paper, a method and algorithm of skeleton extraction based on binary mathematical morphology is presented. Sequential structuring elements (SEs) is also studied, which is the key problem of skeleton extraction. The examples of boiler flame image processing show that the detected skeletons can present the geometric shape of flame images well.展开更多
Action recognition has been recognized as an activity in which individuals’behaviour can be observed.Assembling profiles of regular activities such as activities of daily living can support identifying trends in the ...Action recognition has been recognized as an activity in which individuals’behaviour can be observed.Assembling profiles of regular activities such as activities of daily living can support identifying trends in the data during critical events.A skeleton representation of the human body has been proven to be effective for this task.The skeletons are presented in graphs form-like.However,the topology of a graph is not structured like Euclideanbased data.Therefore,a new set of methods to perform the convolution operation upon the skeleton graph is proposed.Our proposal is based on the Spatial Temporal-Graph Convolutional Network(ST-GCN)framework.In this study,we proposed an improved set of label mapping methods for the ST-GCN framework.We introduce three split techniques(full distance split,connection split,and index split)as an alternative approach for the convolution operation.The experiments presented in this study have been trained using two benchmark datasets:NTU-RGB+D and Kinetics to evaluate the performance.Our results indicate that our split techniques outperform the previous partition strategies and aremore stable during training without using the edge importance weighting additional training parameter.Therefore,our proposal can provide a more realistic solution for real-time applications centred on daily living recognition systems activities for indoor environments.展开更多
Two elongatoolithid dinosaur eggs from the Upper Cretaceous of Ganzhou, Jiangxi Province and the embryonic skeletons they bear are described. They represent the first oviraptorosaurian eggs with embryonic skeletons in...Two elongatoolithid dinosaur eggs from the Upper Cretaceous of Ganzhou, Jiangxi Province and the embryonic skeletons they bear are described. They represent the first oviraptorosaurian eggs with embryonic skeletons in China and provide the first example that an oospecies can be correlated to certain dinosaur taxon/taxa. The two eggs are the same as the pair of the eggs inside a female oviraptorosaurian pelvis from the same horizon of the same area in both macro- and micro-structures of the egg shells, and can he referred to the oospecies, Macroolithus yaotunensis Zhao, 1975. The morphology of the preserved part of the embryonic skeletons indicates that they may have been laid by an oviraptorid, Heyuannia huangi from Guangdong Province or a closely related oviraptorosaurian, which may have been lived in the Ganzhou area too in the Late Cretaceous. The embryonic skeletons of the two eggs are not in the same developing stage. In one of the eggs, the postzygapophysis of the preserved vertebrae are well ossified, indicating that it was just hatched.展开更多
Seven hundred and twenty one-day-old AA broiler chickens were randomly allocated into two groups （male and female for half）, and put into two identical closed houses with different lighting programs. The first house...Seven hundred and twenty one-day-old AA broiler chickens were randomly allocated into two groups （male and female for half）, and put into two identical closed houses with different lighting programs. The first house was illuminated by using common incandescence light, and the second one was added with ultraviolet radiation light from the second week onwards. The birds lived in a floor with litters and free access to feed and water. Temperature, humidity and immune programs in the two houses were similar. The results showed that under ultraviolet radiation, the growth speed of skeleton increased （the shank length was significantly increased in the third week, P〈0.05; the leg muscle weight was significantly improved by 3.87%, P〈 0.05）; the skeleton quality improved （the density of skeleton mineralization was significantly increased by 6.11%, P 〈 0.01; serum calcium, phosphorus, and alkaline phosphatase activity were all improved）; and the growth performance was improved （feed conversion ratio was improved by 1.4% averagely; the uniformity of body weight, the shank length, the inclined body length and body height were significantly improved） in broiler chicken.展开更多
A new specimen discovered from the Falang Formation in northeastern Yunnan represents the most complete skeleton of Triassic pistosauroids. The new specimen is referred to Yunguisaurus Cheng et al., 2006 on the basis ...A new specimen discovered from the Falang Formation in northeastern Yunnan represents the most complete skeleton of Triassic pistosauroids. The new specimen is referred to Yunguisaurus Cheng et al., 2006 on the basis of the skull features, such as the presence of a separated nasal entering the external naris, a large pineal foramen located at the frontal/parietal suture and an interpterygoid vacuity with a narrow anterior extension. The new specimen differs from the type species of Yunguisaurus liae Cheng et al., 2006 in some aspects. Most of these differences can be attributed to ontogenetic variations. The new specimen is provisionally considered as Yunguisaurus cf. liae although its relatively short snout of the skull and slenderer hyoid may not be explained ontogenetically. Whether or not the new specimen represents a different taxon has to wait for a detailed study of the whole skeleton.展开更多
Current studies have shown that the spatial-temporal graph convolutional network(STGCN)is effective for skeleton-based action recognition.However,for the existing STGCN-based methods,their temporal kernel size is usua...Current studies have shown that the spatial-temporal graph convolutional network(STGCN)is effective for skeleton-based action recognition.However,for the existing STGCN-based methods,their temporal kernel size is usually fixed over all layers,which makes them cannot fully exploit the temporal dependency between discontinuous frames and different sequence lengths.Besides,most of these methods use average pooling to obtain global graph feature from vertex features,resulting in losing much fine-grained information for action classification.To address these issues,in this work,the authors propose a novel spatial attentive and temporal dilated graph convolutional network(SATD-GCN).It contains two important components,that is,a spatial attention pooling module(SAP)and a temporal dilated graph convolution module(TDGC).Specifically,the SAP module can select the human body joints which are beneficial for action recognition by a self-attention mechanism and alleviates the influence of data redundancy and noise.The TDGC module can effectively extract the temporal features at different time scales,which is useful to improve the temporal perception field and enhance the robustness of the model to different motion speed and sequence length.Importantly,both the SAP module and the TDGC module can be easily integrated into the ST-GCN-based models,and significantly improve their performance.Extensive experiments on two large-scale benchmark datasets,that is,NTU-RGB+D and Kinetics-Skeleton,demonstrate that the authors’method achieves the state-of-the-art performance for skeleton-based action recognition.展开更多
Lithium(Li) metal is considered as the most promising anode material for the next-generation high performance Li batteries.However,the uncontrollable dendritic growth impedes its commercial application.Herein,we desig...Lithium(Li) metal is considered as the most promising anode material for the next-generation high performance Li batteries.However,the uncontrollable dendritic growth impedes its commercial application.Herein,we design a 3 D Si@carbon nanofibers(CNFs)@ZnO-ZnO-Cu skeleton(SCZ) for guiding the homogeneous bottom-growth of Li metal.The top LixSi@CNFs and bottom LiyZn@CNFs layers could form conductivity and overpotential gradient to avoid the "top-growth" of Li metal.Moreover,the top lithiophilic LixSi@CNFs layer could regulate the nucleation and deposition of Li-ions even if the lithium dendrites grow out of the skeleton under high capacity Li deposition(30 mAh cm^(-2)).As a result,the SCZ-Li||LiFePO_(4) full cell delivers a high capacity of ~104 mAh g^(-1)(~94.82% capacity retention) after 2000 cycles at 5 C, elucidating the potential application of the 3 D double-gradient Li metal composite anode.展开更多
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 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.
文摘In this paper, a method and algorithm of skeleton extraction based on binary mathematical morphology is presented. Sequential structuring elements (SEs) is also studied, which is the key problem of skeleton extraction. The examples of boiler flame image processing show that the detected skeletons can present the geometric shape of flame images well.
文摘Action recognition has been recognized as an activity in which individuals’behaviour can be observed.Assembling profiles of regular activities such as activities of daily living can support identifying trends in the data during critical events.A skeleton representation of the human body has been proven to be effective for this task.The skeletons are presented in graphs form-like.However,the topology of a graph is not structured like Euclideanbased data.Therefore,a new set of methods to perform the convolution operation upon the skeleton graph is proposed.Our proposal is based on the Spatial Temporal-Graph Convolutional Network(ST-GCN)framework.In this study,we proposed an improved set of label mapping methods for the ST-GCN framework.We introduce three split techniques(full distance split,connection split,and index split)as an alternative approach for the convolution operation.The experiments presented in this study have been trained using two benchmark datasets:NTU-RGB+D and Kinetics to evaluate the performance.Our results indicate that our split techniques outperform the previous partition strategies and aremore stable during training without using the edge importance weighting additional training parameter.Therefore,our proposal can provide a more realistic solution for real-time applications centred on daily living recognition systems activities for indoor environments.
基金supported by research grants from the NMNS and the National Science Council of RO China(NSC 96-2116-M-178-001) to Cheng Y.-N.the Ministry of Land and Resources,the Ministry of Science and Technology(973 Project,2006CB701405) and China Geological Survey for supportsupported by the NMNS for his sabbatical stay and grants from Canadian Museum of Nature,Canada
文摘Two elongatoolithid dinosaur eggs from the Upper Cretaceous of Ganzhou, Jiangxi Province and the embryonic skeletons they bear are described. They represent the first oviraptorosaurian eggs with embryonic skeletons in China and provide the first example that an oospecies can be correlated to certain dinosaur taxon/taxa. The two eggs are the same as the pair of the eggs inside a female oviraptorosaurian pelvis from the same horizon of the same area in both macro- and micro-structures of the egg shells, and can he referred to the oospecies, Macroolithus yaotunensis Zhao, 1975. The morphology of the preserved part of the embryonic skeletons indicates that they may have been laid by an oviraptorid, Heyuannia huangi from Guangdong Province or a closely related oviraptorosaurian, which may have been lived in the Ganzhou area too in the Late Cretaceous. The embryonic skeletons of the two eggs are not in the same developing stage. In one of the eggs, the postzygapophysis of the preserved vertebrae are well ossified, indicating that it was just hatched.
文摘Seven hundred and twenty one-day-old AA broiler chickens were randomly allocated into two groups （male and female for half）, and put into two identical closed houses with different lighting programs. The first house was illuminated by using common incandescence light, and the second one was added with ultraviolet radiation light from the second week onwards. The birds lived in a floor with litters and free access to feed and water. Temperature, humidity and immune programs in the two houses were similar. The results showed that under ultraviolet radiation, the growth speed of skeleton increased （the shank length was significantly increased in the third week, P〈0.05; the leg muscle weight was significantly improved by 3.87%, P〈 0.05）; the skeleton quality improved （the density of skeleton mineralization was significantly increased by 6.11%, P 〈 0.01; serum calcium, phosphorus, and alkaline phosphatase activity were all improved）; and the growth performance was improved （feed conversion ratio was improved by 1.4% averagely; the uniformity of body weight, the shank length, the inclined body length and body height were significantly improved） in broiler chicken.
文摘A new specimen discovered from the Falang Formation in northeastern Yunnan represents the most complete skeleton of Triassic pistosauroids. The new specimen is referred to Yunguisaurus Cheng et al., 2006 on the basis of the skull features, such as the presence of a separated nasal entering the external naris, a large pineal foramen located at the frontal/parietal suture and an interpterygoid vacuity with a narrow anterior extension. The new specimen differs from the type species of Yunguisaurus liae Cheng et al., 2006 in some aspects. Most of these differences can be attributed to ontogenetic variations. The new specimen is provisionally considered as Yunguisaurus cf. liae although its relatively short snout of the skull and slenderer hyoid may not be explained ontogenetically. Whether or not the new specimen represents a different taxon has to wait for a detailed study of the whole skeleton.
基金National Key Research and Development Program of China,Grant/Award Number:2018YFB1600600。
文摘Current studies have shown that the spatial-temporal graph convolutional network(STGCN)is effective for skeleton-based action recognition.However,for the existing STGCN-based methods,their temporal kernel size is usually fixed over all layers,which makes them cannot fully exploit the temporal dependency between discontinuous frames and different sequence lengths.Besides,most of these methods use average pooling to obtain global graph feature from vertex features,resulting in losing much fine-grained information for action classification.To address these issues,in this work,the authors propose a novel spatial attentive and temporal dilated graph convolutional network(SATD-GCN).It contains two important components,that is,a spatial attention pooling module(SAP)and a temporal dilated graph convolution module(TDGC).Specifically,the SAP module can select the human body joints which are beneficial for action recognition by a self-attention mechanism and alleviates the influence of data redundancy and noise.The TDGC module can effectively extract the temporal features at different time scales,which is useful to improve the temporal perception field and enhance the robustness of the model to different motion speed and sequence length.Importantly,both the SAP module and the TDGC module can be easily integrated into the ST-GCN-based models,and significantly improve their performance.Extensive experiments on two large-scale benchmark datasets,that is,NTU-RGB+D and Kinetics-Skeleton,demonstrate that the authors’method achieves the state-of-the-art performance for skeleton-based action recognition.
基金financial support from the National Natural Science Foundation of China(Grant Nos.51701169,51871188 and 51931006)the National Key R&D Program of China(Grant No.2016YFA0202602)+1 种基金the Natural Science Foundation of Fujian Province of China(No.2019J06003)the "Double-First Class" Foundation of Materials and Intelligent Manufacturing Discipline of Xiamen University。
文摘Lithium(Li) metal is considered as the most promising anode material for the next-generation high performance Li batteries.However,the uncontrollable dendritic growth impedes its commercial application.Herein,we design a 3 D Si@carbon nanofibers(CNFs)@ZnO-ZnO-Cu skeleton(SCZ) for guiding the homogeneous bottom-growth of Li metal.The top LixSi@CNFs and bottom LiyZn@CNFs layers could form conductivity and overpotential gradient to avoid the "top-growth" of Li metal.Moreover,the top lithiophilic LixSi@CNFs layer could regulate the nucleation and deposition of Li-ions even if the lithium dendrites grow out of the skeleton under high capacity Li deposition(30 mAh cm^(-2)).As a result,the SCZ-Li||LiFePO_(4) full cell delivers a high capacity of ~104 mAh g^(-1)(~94.82% capacity retention) after 2000 cycles at 5 C, elucidating the potential application of the 3 D double-gradient Li metal composite anode.
