In a society dominated by tourism consumption,space changes occurring in rural areas can generally reflect their social changes.On the theoretical basis of flow,regeneration and adaptation of rural tourism space,this ...In a society dominated by tourism consumption,space changes occurring in rural areas can generally reflect their social changes.On the theoretical basis of flow,regeneration and adaptation of rural tourism space,this paper originally and creatively proposes that the spatial elements in a rural tourist area can be classified into three categories:Attractions(A),Towns(T)and Villages(V).By analyzing the spatial transformation characteristics of A,T and V,five types of rural spatial transition modes are found,the types of heritage,theme park,those serving as scenic spots,leisure industrial clusters and ecotourism areas.These different classes emerge due to their geographical differentiation.They show the same spatial evolution trend:The Attractions are distributed throughout the whole area and characterized by diversification;supporting services facilities gather in the Towns;and the Villages are landscape images.In this area the traditional rural benefit trends toward that of compound development.Mufu Town,Hubei province,is taken as a study case,and the changing characteristics of A,T and V from 2006 to 2016 are described.Problems in the process of establishing the new spatial order are considered.In order to realize the synergy between production space,living space and ecological space,the interactive development between Attractions,Towns and Villages is recommended.The perspective of Attraction-Town-Village(ATV)can lead to a better understanding of the situation of tourism space in rural areas and provide directions for thinking about the reconstruction path for the modernization of traditional societies.展开更多
Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited recepti...Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited receptive fields,hindering their ability to capture global features,while Transformers are constrained by high computational complexity.Recently,Mamba architecture,which is based on state space models(SSMs),has shown powerful global modeling capabilities while achieving linear computational complexity.Although some researchers have incorporated Mamba into CD tasks,the existing Mamba⁃based remote sensing CD methods struggle to effectively perceive the inherent locality of changed regions when flattening and scanning remote sensing images,leading to limitations in extracting change features.To address these issues,we propose a novel Mamba⁃based CD method termed difference feature fusion Mamba model(DFFMamba)by mitigating the loss of feature locality caused by traditional Mamba⁃style scanning.Specifically,two distinct difference feature extraction modules are designed:Difference Mamba(DMamba)and local difference Mamba(LDMamba),where DMamba extracts difference features by calculating the difference in coefficient matrices between the state⁃space equations of the bi⁃temporal features.Building upon DMamba,LDMamba combines a locally adaptive state⁃space scanning(LASS)strategy to enhance feature locality so as to accurately extract difference features.Additionally,a fusion Mamba(FMamba)module is proposed,which employs a spatial⁃channel token modeling SSM(SCTMS)unit to integrate multi⁃dimensional spatio⁃temporal interactions of change features,thereby capturing their dependencies across both spatial and channel dimensions.To verify the effectiveness of the proposed DFFMamba,extensive experiments are conducted on three datasets of WHU⁃CD,LEVIR⁃CD,and CLCD.The results demonstrate that DFFMamba significantly outperforms state⁃of⁃the⁃art CD methods,achieving intersection over union(IoU)scores of 90.67%,85.04%,and 66.56%on the three datasets,respectively.展开更多
The technological advances in Lithium-ion batteries have created many new applications, including electric vehicles. In this short note, we shall explain in simple terms the basic physics why and how it is possible to...The technological advances in Lithium-ion batteries have created many new applications, including electric vehicles. In this short note, we shall explain in simple terms the basic physics why and how it is possible to have high energy capacity in Lithium-ion batteries. However, heating has been a common problem and without appropriate design, they might give fire and explosion as reported.展开更多
基金The National Natural Science Foundation of China(41901180)The National Social Science Foundation of China(17ZDA165)。
文摘In a society dominated by tourism consumption,space changes occurring in rural areas can generally reflect their social changes.On the theoretical basis of flow,regeneration and adaptation of rural tourism space,this paper originally and creatively proposes that the spatial elements in a rural tourist area can be classified into three categories:Attractions(A),Towns(T)and Villages(V).By analyzing the spatial transformation characteristics of A,T and V,five types of rural spatial transition modes are found,the types of heritage,theme park,those serving as scenic spots,leisure industrial clusters and ecotourism areas.These different classes emerge due to their geographical differentiation.They show the same spatial evolution trend:The Attractions are distributed throughout the whole area and characterized by diversification;supporting services facilities gather in the Towns;and the Villages are landscape images.In this area the traditional rural benefit trends toward that of compound development.Mufu Town,Hubei province,is taken as a study case,and the changing characteristics of A,T and V from 2006 to 2016 are described.Problems in the process of establishing the new spatial order are considered.In order to realize the synergy between production space,living space and ecological space,the interactive development between Attractions,Towns and Villages is recommended.The perspective of Attraction-Town-Village(ATV)can lead to a better understanding of the situation of tourism space in rural areas and provide directions for thinking about the reconstruction path for the modernization of traditional societies.
基金supported by the National Natural Science Foundation of China(Nos.42371449,41801386).
文摘Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited receptive fields,hindering their ability to capture global features,while Transformers are constrained by high computational complexity.Recently,Mamba architecture,which is based on state space models(SSMs),has shown powerful global modeling capabilities while achieving linear computational complexity.Although some researchers have incorporated Mamba into CD tasks,the existing Mamba⁃based remote sensing CD methods struggle to effectively perceive the inherent locality of changed regions when flattening and scanning remote sensing images,leading to limitations in extracting change features.To address these issues,we propose a novel Mamba⁃based CD method termed difference feature fusion Mamba model(DFFMamba)by mitigating the loss of feature locality caused by traditional Mamba⁃style scanning.Specifically,two distinct difference feature extraction modules are designed:Difference Mamba(DMamba)and local difference Mamba(LDMamba),where DMamba extracts difference features by calculating the difference in coefficient matrices between the state⁃space equations of the bi⁃temporal features.Building upon DMamba,LDMamba combines a locally adaptive state⁃space scanning(LASS)strategy to enhance feature locality so as to accurately extract difference features.Additionally,a fusion Mamba(FMamba)module is proposed,which employs a spatial⁃channel token modeling SSM(SCTMS)unit to integrate multi⁃dimensional spatio⁃temporal interactions of change features,thereby capturing their dependencies across both spatial and channel dimensions.To verify the effectiveness of the proposed DFFMamba,extensive experiments are conducted on three datasets of WHU⁃CD,LEVIR⁃CD,and CLCD.The results demonstrate that DFFMamba significantly outperforms state⁃of⁃the⁃art CD methods,achieving intersection over union(IoU)scores of 90.67%,85.04%,and 66.56%on the three datasets,respectively.
文摘The technological advances in Lithium-ion batteries have created many new applications, including electric vehicles. In this short note, we shall explain in simple terms the basic physics why and how it is possible to have high energy capacity in Lithium-ion batteries. However, heating has been a common problem and without appropriate design, they might give fire and explosion as reported.
