This study addresses the persistent scarcity of systematic and comparable data on mountain tourism,with particular reference to Northern Italy,as highlighted by FAO/UNWTO reports and recent academic literature.It aims...This study addresses the persistent scarcity of systematic and comparable data on mountain tourism,with particular reference to Northern Italy,as highlighted by FAO/UNWTO reports and recent academic literature.It aims to contribute to this gap by analyzing tourist flows,socio-demographic characteristics,preferences,and behaviors of domestic visitors to the Italian Alps.Data were collected through a survey conducted between December 2023 and January 2024 among 1,218 residents of Northwest and Northeast Italy and Friuli Venezia Giulia,using a stratified sampling approach.Descriptive statistics and inferential analyses were employed to examine visitation patterns,while K-means clustering was applied to identify distinct segments of mountain tourists based on activity preferences and motivations.Overall,82.5%of respondents reported visiting Alpine areas.Chi-square tests revealed statistically significant differences in visitation behavior according to age,occupational status,and income.Notably,spiritual activities,such as pilgrimages,elicited levels of interest comparable to those of more traditional mountain sports.The cluster analysis identified three visitor profiles:Active Young Enthusiasts,characterized by high engagement in multiple outdoor activities and motivated by psychological well-being and cultural enrichment;Well-being-Oriented Walkers,preferring low-intensity activities primarily driven by psychological relaxation;and Hiking-Oriented Explorers,exhibiting a strong propensity for mountain excursions associated with high levels of psychophysical well-being.These findings enhance understanding of the heterogeneous structure of mountain tourism demand in Northern Italy and offer insights relevant to sustainable destination planning and management in Alpine regions.展开更多
Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rel...Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.展开更多
Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high c...Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high computational costs.Most existing contrastive methods adopt the data augmentation and then representation learning strategy,where representation learning with trainable graph convolution is coupled with complex and fixed data augmentation,inevitably limiting the efficiency and flexibility.The similarity metric between positive-negative sample pairs is complex and contrastive objective is partial,limiting the discriminability of representation learning.To solve these challenges,a novel wide graph clustering network(WGCN)adhering to representation and then augmentation framework is proposed,which mainly consists of multiorder filter fusion(MFF)and double-level contrastive learning(DCL)modules.Specifically,the MFF module integrates multiorder low-pass filters to extract smooth and multi-scale topological features,utilizing self-attention fusion to reduce redundancy and obtain comprehensive embedding representation.Further,the DCL module constructs two augmented views by the parallel parameter-unshared Siamese encoders rather than complex augmentations on graph.To achieve simple yet effective self-supervised learning,representation self-supervision and structural consistency oriented double-level contrastive loss is designed,where representation self-supervision maximizes the agreement between pairwise augmented embedding representations and structural consistency promotes the mutual information correlation between appending neighborhoods with similar semantics.Extensive experiments on six benchmark datasets demonstrate the superiority of the proposed WGCN,especially highlighting its time-saving characteristic.The code could be available in the https://github.com/Tianxiang Zhao0474/WGCN.展开更多
The pursuit of Ag-based alloys with both high strength and toughness has posed a longstanding chal-lenge.In this study,we investigated the cluster strengthening and grain refinement toughening mecha-nisms in fully oxi...The pursuit of Ag-based alloys with both high strength and toughness has posed a longstanding chal-lenge.In this study,we investigated the cluster strengthening and grain refinement toughening mecha-nisms in fully oxidized AgMgNi alloys,which were internally oxidized at 800℃ for 8 h under an oxy-gen atmosphere.We found that Mg-O clusters contributed to the hardening(138 HV)and strengthening(376.9 MPa)of the AgMg alloy through solid solution strengthening effects,albeit at the expense of duc-tility.To address this limitation,we introduced Ni nanoparticles into the AgMg alloy,resulting in signifi-cant grain refinement within its microstructure.Specifically,the grain size decreased from 67.2μm in the oxidized AgMg alloy to below 6.0μm in the oxidized AgMgNi alloy containing 0.3 wt%Ni.Consequently,the toughness increased significantly,rising from toughness value of 2177.9 MJ m^(-3) in the oxidized AgMg alloy to 6186.1 MJ m^(-3) in the oxidized AgMgNi alloy,representing a remarkable 2.8-fold enhancement.Furthermore,the internally oxidized AgMgNi alloy attained a strength of up to 387.6 MPa,comparable to that of the internally oxidized AgMg alloy,thereby demonstrating the successful realization of concurrent strengthening and toughening.These results collectively offer a novel approach for the design of high-performance alloys through the synergistic combination of cluster strengthening and grain refinement toughening.展开更多
Accurate description of noncova-lent interactions in large systems is challenging due to the require-ment of high-level electron corre-lation methods.The generalized energy-based fragmentation(GEBF)approach,in conjunc...Accurate description of noncova-lent interactions in large systems is challenging due to the require-ment of high-level electron corre-lation methods.The generalized energy-based fragmentation(GEBF)approach,in conjunc-tion with the domain-based local pair natural orbital(DLPNO)method,has been applied to assess the average binding energies(ABEs)of large benzene clus-ters,specifically(C6H6)13,at the coupled cluster singles and doubles with perturbative triples correction[CCSD(T)]level and the complete basis set(CBS)limit.Utilizing GEBF-DLPNO-CCSD(T)/CBS ABEs as benchmarks,various DFT functionals were evaluated.It was found that several functionals with empirical dispersion correction,including M06-2X-D3,B3LYP-D3(BJ),and PBE-D3(BJ),provide accurate descriptions of the ABEs for(C6H6)13 clusters.Additionally,the M06-2X-D3 functional was used to calculate the ABEs and relative stabili-ties of(C6H6)n clusters for n=11,12,13,14,and 15 revealing that the(C6H6)13 cluster ex-hibits the highest relative stability.These findings align with experimental evidence suggest-ing that n=13 is one of the magic numbers for benzene clusters(C6H6)n,with n≤30.展开更多
Noncohesive particle clusters are identified and tracked in turbulent flows to determine the breakdown and time evolution of cluster statistics and their implications for interscale mass transfer,which has connections...Noncohesive particle clusters are identified and tracked in turbulent flows to determine the breakdown and time evolution of cluster statistics and their implications for interscale mass transfer,which has connections to the classical turbulent energy cascade and its mass cascade counterpart running in parallel.In particular,the formation and dynamics of sediment and larvae clusters are of interest to coral larvae settlement in coastal regions and particularly the resilience of green-gray coastal protection solutions.Analogous cluster behavior is relevant to cloud microphysics and precipitation initiation,radiation transport and light transmission through colloids and suspensions,heat and mass transfer in particle-laden flows,and viral and pollutant transmission.Following a comparison between various clustering techniques,we adopt a density-based cluster identification algorithm based on its simplicity and efficiency,where particles are clustered based on the number of neighboring particles in their individual spheres of influence.We establish parallels with lattice-based percolation theory,as evident in the power-law scaling of the cluster size distribution near the percolation threshold.The degree of discontinuity of the phase transition associated with this percolation threshold is observed to broaden with larger Stokes numbers and thereby large-scale clustering.The sensitivity of our findings to the employed clustering algorithm is discussed.A novel cluster tracking algorithm is deployed to determine the interscale transfer rate along the particle-number phase-space dimension via accounting of cluster breakup and merger events,extending previous work on the bubble breakup cascade beneath surface breaking waves.Our findings shed light on the interaction between particle clusters and their carrier turbulent flows,with an eye toward transport models incorporating cluster characteristics and dynamics.展开更多
We propose a robust earthquake clustering method:the Bayesian Gaussian mixture model with nearest-neighbor distance(BGMM-NND)algorithm.Unlike the conventional nearest neighbor distance method,the BGMM-NND algorithm el...We propose a robust earthquake clustering method:the Bayesian Gaussian mixture model with nearest-neighbor distance(BGMM-NND)algorithm.Unlike the conventional nearest neighbor distance method,the BGMM-NND algorithm eliminates the need for hyperparameter tuning or reliance on fixed thresholds,offering enhanced flexibility for clustering across varied seismic scales.By integrating cumulative probability and BGMM with principal component analysis(PCA),the BGMM-NND algorithm effectively distinguishes between background and triggered earthquakes while maintaining the magnitude component and resolving the issue of excessively large spatial cluster domains.We apply the BGMM-NND algorithm to the Sichuan–Yunnan seismic catalog from 1971 to 2024,revealing notable variations in earthquake frequency,triggering characteristics,and recurrence patterns across different fault zones.Distinct clustering and triggering behaviors are identified along different segments of the Longmenshan Fault.Multiple seismic modes,namely,the short-distance mode,the medium-distance mode,the repeating-like mode,the uniform background mode,and the Wenchuan mode,are uncovered.The algorithm's flexibility and robust performance in earthquake clustering makes it a valuable tool for exploring seismicity characteristics,offering new insights into earthquake clustering and the spatiotemporal patterns of seismic activity.展开更多
基金funded by the European Union—Next Generation EU,in the framework of the consortium i NEST—Interconnected Nord-Est Innovation Ecosystem(PNRR,Missione 4 Componente 2,Investimento 1.5 D.D.105823 June 2022,ECS_00000043—Spoke1,RT2,CUP I43C22000250006)。
文摘This study addresses the persistent scarcity of systematic and comparable data on mountain tourism,with particular reference to Northern Italy,as highlighted by FAO/UNWTO reports and recent academic literature.It aims to contribute to this gap by analyzing tourist flows,socio-demographic characteristics,preferences,and behaviors of domestic visitors to the Italian Alps.Data were collected through a survey conducted between December 2023 and January 2024 among 1,218 residents of Northwest and Northeast Italy and Friuli Venezia Giulia,using a stratified sampling approach.Descriptive statistics and inferential analyses were employed to examine visitation patterns,while K-means clustering was applied to identify distinct segments of mountain tourists based on activity preferences and motivations.Overall,82.5%of respondents reported visiting Alpine areas.Chi-square tests revealed statistically significant differences in visitation behavior according to age,occupational status,and income.Notably,spiritual activities,such as pilgrimages,elicited levels of interest comparable to those of more traditional mountain sports.The cluster analysis identified three visitor profiles:Active Young Enthusiasts,characterized by high engagement in multiple outdoor activities and motivated by psychological well-being and cultural enrichment;Well-being-Oriented Walkers,preferring low-intensity activities primarily driven by psychological relaxation;and Hiking-Oriented Explorers,exhibiting a strong propensity for mountain excursions associated with high levels of psychophysical well-being.These findings enhance understanding of the heterogeneous structure of mountain tourism demand in Northern Italy and offer insights relevant to sustainable destination planning and management in Alpine regions.
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.
基金supported by the National Natural Science Foundation of China(62225303,62403043,62433004)the Beijing Natural Science Foundation(4244085)+1 种基金the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZC20230203)the China Postdoctoral Science Foundation(2023M740201)。
文摘Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high computational costs.Most existing contrastive methods adopt the data augmentation and then representation learning strategy,where representation learning with trainable graph convolution is coupled with complex and fixed data augmentation,inevitably limiting the efficiency and flexibility.The similarity metric between positive-negative sample pairs is complex and contrastive objective is partial,limiting the discriminability of representation learning.To solve these challenges,a novel wide graph clustering network(WGCN)adhering to representation and then augmentation framework is proposed,which mainly consists of multiorder filter fusion(MFF)and double-level contrastive learning(DCL)modules.Specifically,the MFF module integrates multiorder low-pass filters to extract smooth and multi-scale topological features,utilizing self-attention fusion to reduce redundancy and obtain comprehensive embedding representation.Further,the DCL module constructs two augmented views by the parallel parameter-unshared Siamese encoders rather than complex augmentations on graph.To achieve simple yet effective self-supervised learning,representation self-supervision and structural consistency oriented double-level contrastive loss is designed,where representation self-supervision maximizes the agreement between pairwise augmented embedding representations and structural consistency promotes the mutual information correlation between appending neighborhoods with similar semantics.Extensive experiments on six benchmark datasets demonstrate the superiority of the proposed WGCN,especially highlighting its time-saving characteristic.The code could be available in the https://github.com/Tianxiang Zhao0474/WGCN.
基金supported by the National Natural Science Foundation of China(Nos.51977027 and 51967008)the Scientific and Technological Project of Yunnan Precious Metals Lab-oratory(Nos.YPML-2023050250 and YPML-2022050206).
文摘The pursuit of Ag-based alloys with both high strength and toughness has posed a longstanding chal-lenge.In this study,we investigated the cluster strengthening and grain refinement toughening mecha-nisms in fully oxidized AgMgNi alloys,which were internally oxidized at 800℃ for 8 h under an oxy-gen atmosphere.We found that Mg-O clusters contributed to the hardening(138 HV)and strengthening(376.9 MPa)of the AgMg alloy through solid solution strengthening effects,albeit at the expense of duc-tility.To address this limitation,we introduced Ni nanoparticles into the AgMg alloy,resulting in signifi-cant grain refinement within its microstructure.Specifically,the grain size decreased from 67.2μm in the oxidized AgMg alloy to below 6.0μm in the oxidized AgMgNi alloy containing 0.3 wt%Ni.Consequently,the toughness increased significantly,rising from toughness value of 2177.9 MJ m^(-3) in the oxidized AgMg alloy to 6186.1 MJ m^(-3) in the oxidized AgMgNi alloy,representing a remarkable 2.8-fold enhancement.Furthermore,the internally oxidized AgMgNi alloy attained a strength of up to 387.6 MPa,comparable to that of the internally oxidized AgMg alloy,thereby demonstrating the successful realization of concurrent strengthening and toughening.These results collectively offer a novel approach for the design of high-performance alloys through the synergistic combination of cluster strengthening and grain refinement toughening.
基金supported by the National Key R&D Program of China(No.2023YFB3712504)the National Natural Science Foundation of China(Nos.22273038,22073043,and 22033004)。
文摘Accurate description of noncova-lent interactions in large systems is challenging due to the require-ment of high-level electron corre-lation methods.The generalized energy-based fragmentation(GEBF)approach,in conjunc-tion with the domain-based local pair natural orbital(DLPNO)method,has been applied to assess the average binding energies(ABEs)of large benzene clus-ters,specifically(C6H6)13,at the coupled cluster singles and doubles with perturbative triples correction[CCSD(T)]level and the complete basis set(CBS)limit.Utilizing GEBF-DLPNO-CCSD(T)/CBS ABEs as benchmarks,various DFT functionals were evaluated.It was found that several functionals with empirical dispersion correction,including M06-2X-D3,B3LYP-D3(BJ),and PBE-D3(BJ),provide accurate descriptions of the ABEs for(C6H6)13 clusters.Additionally,the M06-2X-D3 functional was used to calculate the ABEs and relative stabili-ties of(C6H6)n clusters for n=11,12,13,14,and 15 revealing that the(C6H6)13 cluster ex-hibits the highest relative stability.These findings align with experimental evidence suggest-ing that n=13 is one of the magic numbers for benzene clusters(C6H6)n,with n≤30.
文摘Noncohesive particle clusters are identified and tracked in turbulent flows to determine the breakdown and time evolution of cluster statistics and their implications for interscale mass transfer,which has connections to the classical turbulent energy cascade and its mass cascade counterpart running in parallel.In particular,the formation and dynamics of sediment and larvae clusters are of interest to coral larvae settlement in coastal regions and particularly the resilience of green-gray coastal protection solutions.Analogous cluster behavior is relevant to cloud microphysics and precipitation initiation,radiation transport and light transmission through colloids and suspensions,heat and mass transfer in particle-laden flows,and viral and pollutant transmission.Following a comparison between various clustering techniques,we adopt a density-based cluster identification algorithm based on its simplicity and efficiency,where particles are clustered based on the number of neighboring particles in their individual spheres of influence.We establish parallels with lattice-based percolation theory,as evident in the power-law scaling of the cluster size distribution near the percolation threshold.The degree of discontinuity of the phase transition associated with this percolation threshold is observed to broaden with larger Stokes numbers and thereby large-scale clustering.The sensitivity of our findings to the employed clustering algorithm is discussed.A novel cluster tracking algorithm is deployed to determine the interscale transfer rate along the particle-number phase-space dimension via accounting of cluster breakup and merger events,extending previous work on the bubble breakup cascade beneath surface breaking waves.Our findings shed light on the interaction between particle clusters and their carrier turbulent flows,with an eye toward transport models incorporating cluster characteristics and dynamics.
基金supported by the National Key Research and Development Program of China(Grant Nos.2021YFC3000705 and 2021YFC3000705-05)the National Natural Science Foundation of China(Grant No.42074049)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.2023471).
文摘We propose a robust earthquake clustering method:the Bayesian Gaussian mixture model with nearest-neighbor distance(BGMM-NND)algorithm.Unlike the conventional nearest neighbor distance method,the BGMM-NND algorithm eliminates the need for hyperparameter tuning or reliance on fixed thresholds,offering enhanced flexibility for clustering across varied seismic scales.By integrating cumulative probability and BGMM with principal component analysis(PCA),the BGMM-NND algorithm effectively distinguishes between background and triggered earthquakes while maintaining the magnitude component and resolving the issue of excessively large spatial cluster domains.We apply the BGMM-NND algorithm to the Sichuan–Yunnan seismic catalog from 1971 to 2024,revealing notable variations in earthquake frequency,triggering characteristics,and recurrence patterns across different fault zones.Distinct clustering and triggering behaviors are identified along different segments of the Longmenshan Fault.Multiple seismic modes,namely,the short-distance mode,the medium-distance mode,the repeating-like mode,the uniform background mode,and the Wenchuan mode,are uncovered.The algorithm's flexibility and robust performance in earthquake clustering makes it a valuable tool for exploring seismicity characteristics,offering new insights into earthquake clustering and the spatiotemporal patterns of seismic activity.
