Imaging sonar devices generate sonar images by receiving echoes from objects,which are often accompanied by severe speckle noise,resulting in image distortion and information loss.Common optical denoising methods do n...Imaging sonar devices generate sonar images by receiving echoes from objects,which are often accompanied by severe speckle noise,resulting in image distortion and information loss.Common optical denoising methods do not work well in removing speckle noise from sonar images and may even reduce their visual quality.To address this issue,a sonar image denoising method based on fuzzy clustering and the undecimated dual-tree complex wavelet transform is proposed.This method provides a perfect translation invariance and an improved directional selectivity during image decomposition,leading to richer representation of noise and edges in high frequency coefficients.Fuzzy clustering can separate noise from useful information according to the amplitude characteristics of speckle noise,preserving the latter and achieving the goal of noise removal.Additionally,the low frequency coefficients are smoothed using bilateral filtering to improve the visual quality of the image.To verify the effectiveness of the algorithm,multiple groups of ablation experiments were conducted,and speckle sonar images with different variances were evaluated and compared with existing speckle removal methods in the transform domain.The experimental results show that the proposed method can effectively improve image quality,especially in cases of severe noise,where it still achieves a good denoising performance.展开更多
Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully construct...Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully constructed by coordinatively assembling the semi-rigid multidentate ligand 5-(1-carboxyethoxy)isophthalic acid(H₃CIA)with the Nheterocyclic ligands 1,4-di(4H-1,2,4-triazol-4-yl)benzene(1,4-dtb)and 1,4-di(1H-imidazol-1-yl)benzene(1,4-dib),respectively,around Co^(2+)ions.Single-crystal X-ray diffraction analysis revealed that in both complexes HU23 and HU24,the CIA^(3-)anions adopt aκ^(7)-coordination mode,bridging six Co^(2+)ions via their five carboxylate oxygen atoms and one ether oxygen atom.This linkage forms tetranuclear[Co4(μ3-OH)2]^(6+)units.These Co-oxo cluster units were interconnected by CIA^(3-)anions to assemble into 2D kgd-type structures featuring a 3,6-connected topology.The 2D layers were further connected by 1,4-dtb and 1,4-dib,resulting in 3D pillar-layered frameworks for HU23 and HU24.Notably,despite the similar configurations of 1,4-dtb and 1,4-dib,differences in their coordination spatial orientations lead to topological divergence in the 3D frameworks of HU23 and HU24.Topological analysis indicates that the frameworks of HU23 and HU24 can be simplified into a 3,10-connected net(point symbol:(4^(10).6^(3).8^(2))(4^(3))_(2))and a 3,8-connected tfz-d net(point symbol:(4^(3))_(2)((4^(6).6^(18).8^(4)))),respectively.This structural differentiation confirms the precise regulatory role of ligands on the topology of metal-organic frameworks.Moreover,the ultraviolet-visible absorption spectra confirmed that HU23 and HU24 have strong absorption capabilities for ultraviolet and visible light.According to the Kubelka-Munk method,their bandwidths were 2.15 and 2.08 eV,respectively,which are consistent with those of typical semiconductor materials.Variable-temperature magnetic susceptibility measurements(2-300 K)revealed significant antiferromagnetic coupling in both complexes,with their effective magnetic moments decreasing markedly as the temperature lowered.CCDC:2457554,HU23;2457553,HU24.展开更多
As large-scale astronomical surveys,such as the Sloan Digital Sky Survey(SDSS)and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST),generate increasingly complex datasets,clustering algorithms have...As large-scale astronomical surveys,such as the Sloan Digital Sky Survey(SDSS)and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST),generate increasingly complex datasets,clustering algorithms have become vital for identifying patterns and classifying celestial objects.This paper systematically investigates the application of five main categories of clustering techniques-partition-based,density-based,model-based,hierarchical,and“others”-across a range of astronomical research over the past decade.This review focuses on the six key application areas of stellar classification,galaxy structure analysis,detection of galactic and interstellar features,highenergy astrophysics,exoplanet studies,and anomaly detection.This paper provides an in-depth analysis of the performance and results of each method,considering their respective suitabilities for different data types.Additionally,it presents clustering algorithm selection strategies based on the characteristics of the spectroscopic data being analyzed.We highlight challenges such as handling large datasets,the need for more efficient computational tools,and the lack of labeled data.We also underscore the potential of unsupervised and semi-supervised clustering approaches to overcome these challenges,offering insight into their practical applications,performance,and results in astronomical research.展开更多
This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for ...This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.展开更多
Taking the Changli Institute of Pomology,Hebei Academy of Agriculture and Forestry Sciences(hereinafter referred to as the Changli Institute of Pomology)as a case study,this paper explores the practical pathways throu...Taking the Changli Institute of Pomology,Hebei Academy of Agriculture and Forestry Sciences(hereinafter referred to as the Changli Institute of Pomology)as a case study,this paper explores the practical pathways through which Party building leads to the high-quality development of fruit tree research from three dimensions:theoretical convergence points,current development status,and functional mechanisms.It proposes that Party building should focus on its core roles of steering political direction,enhancing team cohesion,and upholding ethical standards.Deep integration of Party building and scientific research should be achieved through concrete platforms,with the effectiveness measured by breakthroughs in critical"bottleneck"technologies and increased income for fruit growers.The study aims to provide a practical reference for integrating Party building with professional work in the agricultural research sector.展开更多
Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster...Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster(TAIC)and Wuhu automobile industry cluster(WAIC)as cases,using historical statistical data and field interview data from the 1980s to 2023,combined with qualitative research methods of thematic and diachronic analysis,and quantitative research methods of social network analysis,we compare both endogenous automobile clusters’evolutionary traits and driving mechanisms.The results confirm both clusters undergo multi-scale spatial reconfiguration,organizational complexification,and intelligent networking technological transformation,yet diverge fundamentally:TAIC evolves through market-driven progressive expansion,transitioning from single to dual-core structures via private enterprise networking,with innovation following market-integrated logic and institutional thickness built on demand-driven evolution.Conversely,WAIC follows planned expansion,maintaining state-led hierarchical single-core stability through policy-driven breakthrough innovation and supply-dominated institutional construction-though both ultimately require formal-informal system synergy.Their coevolution is driven by dynamic interactions of path dependence(weakening influence),learning-innovation(strengthening influence),and relationship selection(inverted U-shaped trajectory),with divergent development paths rooted in TAIC’s grassroots self-organization genes versus WAIC’s top-level design genes,amplified by core enterprises’strategic disparities.The research findings can not only provide decision-making support for China’s industrial upgrading,but also contribute China’s insights to global economic governance.展开更多
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl...This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.展开更多
Villager Pan Chunlin is witnessing a boom in his homestay business.More and more visitors are coming to his village,Yucun Village in Anji County,Huzhou City,Zhejiang Province.
The shift toward specialized and large-scale agricultural production has spurred the emergence of agricultural clusters as key forces of rural vitalization and sustainable development.This paper explored the formation...The shift toward specialized and large-scale agricultural production has spurred the emergence of agricultural clusters as key forces of rural vitalization and sustainable development.This paper explored the formation and evolution of Meizhou pomelo industry cluster in China,focusing on its role in restructuring rural socio-economic systems and integrating the whole value chains.Based on a case study employing qualitative methods such as in-depth interviews and participatory observation,the agricultural cluster evolution of Meizhou pomelo was categorized into three key phases of initial decentralization,self-organized scaling,and reorganized clustering.Geographical proximity and industrial agglomeration constitute the physical foundation,while vertical/horizontal linkages,technologic-al innovation,and policy support enhance competitiveness.Special mechanisms emerge through localized social networks,farmer co-operatives’activation,and cross-regional market expansion.The cluster’s impact is manifested in the shift from extensive to standard-ized and modernized production,diversified and flexible livelihood of farmers,and the integration of agriculture with industry and ser-vices.The development of the whole value chain based on agricultural cluster represents a critical pathway for achieving agricultural modernization,encompassing both internal and external value chain optimization.Through quality assurance systems,product diversi-fication strategies,operational efficiency improvements,and brand enhancement,these clusters amplify product value propositions and market competitiveness.This systemic approach facilitates supply-demand coordination,enables resource synergies,and optimizes eco-nomic returns across the horizontal and vertical value chain.This paper argues that agricultural clusters serve as strategic catalysts for sustainable rural development by reconstructing local production systems,fostering innovation ecosystems,and aligning agricultural modernization.It contributes to debates on rural vitalization by demonstrating how agricultural clustering can reconfigure rural areas as hubs of ecological modernization,rather than mere urban peripheries.展开更多
By encouraging collaboration,attracting young entrepreneurs,and setting up village-invested enterprises,rural communities in Zhejiang Province are growing their income streams.“Over the past years,we have seen more a...By encouraging collaboration,attracting young entrepreneurs,and setting up village-invested enterprises,rural communities in Zhejiang Province are growing their income streams.“Over the past years,we have seen more and more tourists coming to our village,and their stay here has grown longer.Many even said they don’t want to leave,”Pan Chunlin,a resident of Yucun Village in Anji County of Huzhou City,east China’s Zhejiang Province,told China Today.Pan is the owner of the Chunlin Lodge,a bed&breakfast(B&B)which received more than 70,000 visitors last year,generating over RMB 4 million in revenue.展开更多
Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic devel...Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.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
AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 to...AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 total deviation values(TDVs)from the first 10 VF tests of the training dataset,VF points were clustered into several regions using the hierarchical ordered partitioning and collapsing hybrid(HOPACH)and K-means clustering.Based on the clustering results,a linear regression analysis was applied to each clustered region of the testing dataset to predict the TDVs of the 10th VF test.Three to nine VF tests were used to predict the 10th VF test,and the prediction errors(root mean square error,RMSE)of each clustering method and pointwise linear regression(PLR)were compared.RESULTS:The training group consisted of 228 patients(mean age,54.20±14.38y;123 males and 105 females),and the testing group included 81 patients(mean age,54.88±15.22y;43 males and 38 females).All subjects were diagnosed with POAG.Fifty-two VF points were clustered into 11 and nine regions using HOPACH and K-means clustering,respectively.K-means clustering had a lower prediction error than PLR when n=1:3 and 1:4(both P≤0.003).The prediction errors of K-means clustering were lower than those of HOPACH in all sections(n=1:4 to 1:9;all P≤0.011),except for n=1:3(P=0.680).PLR outperformed K-means clustering only when n=1:8 and 1:9(both P≤0.020).CONCLUSION:K-means clustering can predict longterm VF test results more accurately in patients with POAG with limited VF data.展开更多
基金the National Natural Science Foundation of China(No.62065001)the Yunnan Young and Middle-aged Academic and Technical Leaders Reserve Talent Project(No.202205AC160001)+1 种基金the Science and Technology Programs of Yunnan Provincial Science and Technology Department(No.202101BA070001-054)the Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities Association(No.2019FH001(-066))。
文摘Imaging sonar devices generate sonar images by receiving echoes from objects,which are often accompanied by severe speckle noise,resulting in image distortion and information loss.Common optical denoising methods do not work well in removing speckle noise from sonar images and may even reduce their visual quality.To address this issue,a sonar image denoising method based on fuzzy clustering and the undecimated dual-tree complex wavelet transform is proposed.This method provides a perfect translation invariance and an improved directional selectivity during image decomposition,leading to richer representation of noise and edges in high frequency coefficients.Fuzzy clustering can separate noise from useful information according to the amplitude characteristics of speckle noise,preserving the latter and achieving the goal of noise removal.Additionally,the low frequency coefficients are smoothed using bilateral filtering to improve the visual quality of the image.To verify the effectiveness of the algorithm,multiple groups of ablation experiments were conducted,and speckle sonar images with different variances were evaluated and compared with existing speckle removal methods in the transform domain.The experimental results show that the proposed method can effectively improve image quality,especially in cases of severe noise,where it still achieves a good denoising performance.
文摘Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully constructed by coordinatively assembling the semi-rigid multidentate ligand 5-(1-carboxyethoxy)isophthalic acid(H₃CIA)with the Nheterocyclic ligands 1,4-di(4H-1,2,4-triazol-4-yl)benzene(1,4-dtb)and 1,4-di(1H-imidazol-1-yl)benzene(1,4-dib),respectively,around Co^(2+)ions.Single-crystal X-ray diffraction analysis revealed that in both complexes HU23 and HU24,the CIA^(3-)anions adopt aκ^(7)-coordination mode,bridging six Co^(2+)ions via their five carboxylate oxygen atoms and one ether oxygen atom.This linkage forms tetranuclear[Co4(μ3-OH)2]^(6+)units.These Co-oxo cluster units were interconnected by CIA^(3-)anions to assemble into 2D kgd-type structures featuring a 3,6-connected topology.The 2D layers were further connected by 1,4-dtb and 1,4-dib,resulting in 3D pillar-layered frameworks for HU23 and HU24.Notably,despite the similar configurations of 1,4-dtb and 1,4-dib,differences in their coordination spatial orientations lead to topological divergence in the 3D frameworks of HU23 and HU24.Topological analysis indicates that the frameworks of HU23 and HU24 can be simplified into a 3,10-connected net(point symbol:(4^(10).6^(3).8^(2))(4^(3))_(2))and a 3,8-connected tfz-d net(point symbol:(4^(3))_(2)((4^(6).6^(18).8^(4)))),respectively.This structural differentiation confirms the precise regulatory role of ligands on the topology of metal-organic frameworks.Moreover,the ultraviolet-visible absorption spectra confirmed that HU23 and HU24 have strong absorption capabilities for ultraviolet and visible light.According to the Kubelka-Munk method,their bandwidths were 2.15 and 2.08 eV,respectively,which are consistent with those of typical semiconductor materials.Variable-temperature magnetic susceptibility measurements(2-300 K)revealed significant antiferromagnetic coupling in both complexes,with their effective magnetic moments decreasing markedly as the temperature lowered.CCDC:2457554,HU23;2457553,HU24.
基金supported by the National Natural Science Foundation of China (12473105 and 12473106)the central government guides local funds for science and technology development (YDZJSX2024D049)the Graduate Student Practice and Innovation Program of Shanxi Province (2024SJ313)
文摘As large-scale astronomical surveys,such as the Sloan Digital Sky Survey(SDSS)and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST),generate increasingly complex datasets,clustering algorithms have become vital for identifying patterns and classifying celestial objects.This paper systematically investigates the application of five main categories of clustering techniques-partition-based,density-based,model-based,hierarchical,and“others”-across a range of astronomical research over the past decade.This review focuses on the six key application areas of stellar classification,galaxy structure analysis,detection of galactic and interstellar features,highenergy astrophysics,exoplanet studies,and anomaly detection.This paper provides an in-depth analysis of the performance and results of each method,considering their respective suitabilities for different data types.Additionally,it presents clustering algorithm selection strategies based on the characteristics of the spectroscopic data being analyzed.We highlight challenges such as handling large datasets,the need for more efficient computational tools,and the lack of labeled data.We also underscore the potential of unsupervised and semi-supervised clustering approaches to overcome these challenges,offering insight into their practical applications,performance,and results in astronomical research.
基金supported by Istanbul Technical University(Project No.45698)supported through the“Young Researchers’Career Development Project-training of doctoral students”of the Croatian Science Foundation.
文摘This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.
基金Supported by Qinhuangdao Social Sciences Development Research Project(2025LX378).
文摘Taking the Changli Institute of Pomology,Hebei Academy of Agriculture and Forestry Sciences(hereinafter referred to as the Changli Institute of Pomology)as a case study,this paper explores the practical pathways through which Party building leads to the high-quality development of fruit tree research from three dimensions:theoretical convergence points,current development status,and functional mechanisms.It proposes that Party building should focus on its core roles of steering political direction,enhancing team cohesion,and upholding ethical standards.Deep integration of Party building and scientific research should be achieved through concrete platforms,with the effectiveness measured by breakthroughs in critical"bottleneck"technologies and increased income for fruit growers.The study aims to provide a practical reference for integrating Party building with professional work in the agricultural research sector.
基金Under the auspices of National Natural Science Foundation of China(No.42571219)Key Project of Zhejiang Province Soft Science Research Plan(No.2023C25014)。
文摘Bottom-up and top-down endogenous automobile clusters exhibit distinct evolutionary traits and driving mechanisms,yet their comparative analysis remains understudied.Therefore,using Taizhou automobile industry cluster(TAIC)and Wuhu automobile industry cluster(WAIC)as cases,using historical statistical data and field interview data from the 1980s to 2023,combined with qualitative research methods of thematic and diachronic analysis,and quantitative research methods of social network analysis,we compare both endogenous automobile clusters’evolutionary traits and driving mechanisms.The results confirm both clusters undergo multi-scale spatial reconfiguration,organizational complexification,and intelligent networking technological transformation,yet diverge fundamentally:TAIC evolves through market-driven progressive expansion,transitioning from single to dual-core structures via private enterprise networking,with innovation following market-integrated logic and institutional thickness built on demand-driven evolution.Conversely,WAIC follows planned expansion,maintaining state-led hierarchical single-core stability through policy-driven breakthrough innovation and supply-dominated institutional construction-though both ultimately require formal-informal system synergy.Their coevolution is driven by dynamic interactions of path dependence(weakening influence),learning-innovation(strengthening influence),and relationship selection(inverted U-shaped trajectory),with divergent development paths rooted in TAIC’s grassroots self-organization genes versus WAIC’s top-level design genes,amplified by core enterprises’strategic disparities.The research findings can not only provide decision-making support for China’s industrial upgrading,but also contribute China’s insights to global economic governance.
基金supported by the Research Project of China Southern Power Grid(No.056200KK52222031).
文摘This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.
文摘Villager Pan Chunlin is witnessing a boom in his homestay business.More and more visitors are coming to his village,Yucun Village in Anji County,Huzhou City,Zhejiang Province.
基金Under the auspices of the Key Projects of Philosophy and Social Sciences Research,Ministry of Education of China(No.23JZD008)National Natural Science Foundation of China(No.42171193)+2 种基金Key Project of Guangdong Provincial Philosophy and Social Sciences Planning(No.GD24ES013,GD25ZX04)2025 Guangzhou Basic and Applied Basic Research Special Project(No.2025A04J7127)Fundamental Research Funds for the Central Universities,Sun Yat-sen University(No.24wkjc11)。
文摘The shift toward specialized and large-scale agricultural production has spurred the emergence of agricultural clusters as key forces of rural vitalization and sustainable development.This paper explored the formation and evolution of Meizhou pomelo industry cluster in China,focusing on its role in restructuring rural socio-economic systems and integrating the whole value chains.Based on a case study employing qualitative methods such as in-depth interviews and participatory observation,the agricultural cluster evolution of Meizhou pomelo was categorized into three key phases of initial decentralization,self-organized scaling,and reorganized clustering.Geographical proximity and industrial agglomeration constitute the physical foundation,while vertical/horizontal linkages,technologic-al innovation,and policy support enhance competitiveness.Special mechanisms emerge through localized social networks,farmer co-operatives’activation,and cross-regional market expansion.The cluster’s impact is manifested in the shift from extensive to standard-ized and modernized production,diversified and flexible livelihood of farmers,and the integration of agriculture with industry and ser-vices.The development of the whole value chain based on agricultural cluster represents a critical pathway for achieving agricultural modernization,encompassing both internal and external value chain optimization.Through quality assurance systems,product diversi-fication strategies,operational efficiency improvements,and brand enhancement,these clusters amplify product value propositions and market competitiveness.This systemic approach facilitates supply-demand coordination,enables resource synergies,and optimizes eco-nomic returns across the horizontal and vertical value chain.This paper argues that agricultural clusters serve as strategic catalysts for sustainable rural development by reconstructing local production systems,fostering innovation ecosystems,and aligning agricultural modernization.It contributes to debates on rural vitalization by demonstrating how agricultural clustering can reconfigure rural areas as hubs of ecological modernization,rather than mere urban peripheries.
文摘By encouraging collaboration,attracting young entrepreneurs,and setting up village-invested enterprises,rural communities in Zhejiang Province are growing their income streams.“Over the past years,we have seen more and more tourists coming to our village,and their stay here has grown longer.Many even said they don’t want to leave,”Pan Chunlin,a resident of Yucun Village in Anji County of Huzhou City,east China’s Zhejiang Province,told China Today.Pan is the owner of the Chunlin Lodge,a bed&breakfast(B&B)which received more than 70,000 visitors last year,generating over RMB 4 million in revenue.
基金Under the auspices of National Natural Science Foundation of China(No.42571300)。
文摘Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.
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 Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),the Ministry of Health&Welfare,Republic of Korea(No.RS-2020-KH088726)the Patient-Centered Clinical Research Coordinating Center(PACEN),the Ministry of Health and Welfare,Republic of Korea(No.HC19C0276)the National Research Foundation of Korea(NRF),the Korea Government(MSIT)(No.RS-2023-00247504).
文摘AIM:To evaluate long-term visual field(VF)prediction using K-means clustering in patients with primary open angle glaucoma(POAG).METHODS:Patients who underwent 24-2 VF tests≥10 were included in this study.Using 52 total deviation values(TDVs)from the first 10 VF tests of the training dataset,VF points were clustered into several regions using the hierarchical ordered partitioning and collapsing hybrid(HOPACH)and K-means clustering.Based on the clustering results,a linear regression analysis was applied to each clustered region of the testing dataset to predict the TDVs of the 10th VF test.Three to nine VF tests were used to predict the 10th VF test,and the prediction errors(root mean square error,RMSE)of each clustering method and pointwise linear regression(PLR)were compared.RESULTS:The training group consisted of 228 patients(mean age,54.20±14.38y;123 males and 105 females),and the testing group included 81 patients(mean age,54.88±15.22y;43 males and 38 females).All subjects were diagnosed with POAG.Fifty-two VF points were clustered into 11 and nine regions using HOPACH and K-means clustering,respectively.K-means clustering had a lower prediction error than PLR when n=1:3 and 1:4(both P≤0.003).The prediction errors of K-means clustering were lower than those of HOPACH in all sections(n=1:4 to 1:9;all P≤0.011),except for n=1:3(P=0.680).PLR outperformed K-means clustering only when n=1:8 and 1:9(both P≤0.020).CONCLUSION:K-means clustering can predict longterm VF test results more accurately in patients with POAG with limited VF data.
