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
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
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
A scheme is proposed to generate an N-qubit cluster-type entangled squeezed vacuum state （CTESVS） based on the two-photon interaction between a two-level atomand the cavity fields with the cavity QED system. The CTE...A scheme is proposed to generate an N-qubit cluster-type entangled squeezed vacuum state （CTESVS） based on the two-photon interaction between a two-level atomand the cavity fields with the cavity QED system. The CTESVS in N separate cavities can be effectively obtained after performing a simple one-qubit measurement on the atom. The influence of cavity decay on the CTESVS is also discussed.展开更多
We present a scheme to prepare cluster-type entangled squeezed vacuum states （CTESVS） by considering the two-photon interaction between a two-level atom and a high-quality cavity, driven by a strong classical field....We present a scheme to prepare cluster-type entangled squeezed vacuum states （CTESVS） by considering the two-photon interaction between a two-level atom and a high-quality cavity, driven by a strong classical field. After the realization of simple atomic measurements, the generation of CTESVS in four separate cavities is accomplished within the cavity decay time. In the case of large atom=cavity detuning, the scheme is immune to the effect of atomic spontaneous emission.展开更多
The surface density Σ-map and the convergence κ-map of Bullet Cluster 1E0657-558 show that the center of baryonic matters separates from the center of gravitational force,and the distribution of gravitational force ...The surface density Σ-map and the convergence κ-map of Bullet Cluster 1E0657-558 show that the center of baryonic matters separates from the center of gravitational force,and the distribution of gravitational force do not possess spherical symmetry.This hints that a modified gravity with difference to Newtonian inverse-square law at large scale,and less symmetry is worth investigating.In this paper,we study the dynamics in Randers-Finsler spacetime.The Newtonian limit and gravitational deflection of light in a Rindler-type potential is focused in particular.It is shown that the convergence in Finsler spacetime could account for the observations of Bullet Cluster.展开更多
In this paper, we propose an optical scheme to generate four-mode cluster-type entangled coherent states （ECSs） in free traveling optical fields by using two single-photon sources, coherent state sources, beam split...In this paper, we propose an optical scheme to generate four-mode cluster-type entangled coherent states （ECSs） in free traveling optical fields by using two single-photon sources, coherent state sources, beam splitters, pho- todetectors, cross-Kerr media, and phase shifters. And the success probability of the states preparation is calculated. At last we discuss the experimental feasibility of such proposal.展开更多
Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy cl...Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.展开更多
Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients a...Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.展开更多
Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subse...Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.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
Given customizable crystal structure and intriguing optical properties,lanthanide titanium-oxygen clusters(LTOCs)with atomic-level accuracy have gained a lot of interest.In this study,we prepared[Ln_(9)Ti_(2)(μ4-O)(...Given customizable crystal structure and intriguing optical properties,lanthanide titanium-oxygen clusters(LTOCs)with atomic-level accuracy have gained a lot of interest.In this study,we prepared[Ln_(9)Ti_(2)(μ4-O)(μ3-OH)_(14)(acac)_(17)(CH_(3)O)_(2)(CH_(3)OH)_(3)](Ln=Tb_(x)Eu_(9−x)(x=0,4,6,7,8,9),Hacac=acetylacetone),Tb^(3+)and Eu^(3+)co-doped LTOCs,to modify the optical properties for the luminescence thermometer.In detail,the serial LTOCs display dual characteristic emission peaks of ^(5)D_(4)→^(7)F_(5) for Tb^(3+)and^(5)D_(0)→^(7)F_(2) for Eu^(3+)at 548 and 616 nm,respectively,under 330 nm excitation.Effective energy transfer(ET)between Tb^(3+)ions and Eu^(3+)ions was revealed in terms of both emission spectra and luminescence lifetime.The ^(5)D_(0)→^(7)F_(2) emission intensity of Eu^(3+)ions at 616 nm is maximally enhanced(by a factor of 11.2)with a change in the relative molar ratio of Tb^(3+)to Eu^(3+),along with a change in the ET efficiency of Tb^(3+)→Eu^(3+).In addition,the luminescent color changes from red,orange,yellow,to green.This precise control of the ET process between rare-earth ions allows{Tb_(6)Eu_(3)Ti_(2)}to reach a maximum relative sensitivity of 1.241 K^(−1) at 355 K,which is an enhancement of up to 4.6-fold with respect to the previously reported homonuclear emission,holding great potential in the optical thermometers.展开更多
Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to p...Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to participate in this online questionnaire survey.The survey tools included a general information questionnaire and a self-made NMOSD symptoms scale,which included the prevalence,severity,and distress of 29 symptoms.Cluster analysis was used to identify symptom clusters,and network analysis was used to analyze the symptom network and node characteristics and central indicators including strength centrality(r_(s)),closeness centrality(r_(c))and betweeness centrality(r_(b))were used to identify core symptoms and symptom clusters.Results The most common symptom was pain(65.7%),followed by paraesthesia(65.0%),fatigue(65.0%),easy awakening(63.6%).Regarding the burden level of symptoms,pain was the most burdensome symptom,followed by paraesthesia,easy awakening,fatigue,and difficulty falling asleep.Six clusters were identified:somatosensory,motor,visual,and memory symptom clusters,bladder and rectum symptom clusters,sleep symptoms clusters,and neuropsychological symptom clusters.Fatigue(r_(s)=12.39,r_(b)=68.00,r_(c)=0.02)was the most central and prominent bridge symptom,and motor symptom cluster(r_(s)=2.68,r_(c)=0.10)was the most central symptom cluster among the six clusters.Conclusions Our study demonstrated the necessity of symptom management targeting fatigue,pain,and motor symptom cluster in patients with NMOSD.展开更多
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.
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.
基金Project supported by the International Research & Development Program of the National Research Foundation of Korea(NRF)funded by the Ministry of Education,Science and Technology(MEST)of Korea(Grant No.2011-0030864)the National Natural Science Foundation of China(Grant Nos.11264042and 61068001)+2 种基金the China Postdoctoral Science Foundation(Grant No.2012M520612)the Program for Chun Miao Excellent Talents of Jilin Provincial Department of Education(Grant No.201316)the Talent Program of Yanbian University of China(Grant No.950010001)
文摘A scheme is proposed to generate an N-qubit cluster-type entangled squeezed vacuum state （CTESVS） based on the two-photon interaction between a two-level atomand the cavity fields with the cavity QED system. The CTESVS in N separate cavities can be effectively obtained after performing a simple one-qubit measurement on the atom. The influence of cavity decay on the CTESVS is also discussed.
基金supported by the National Natural Science Foundation of China (Grant No 60667001)
文摘We present a scheme to prepare cluster-type entangled squeezed vacuum states （CTESVS） by considering the two-photon interaction between a two-level atom and a high-quality cavity, driven by a strong classical field. After the realization of simple atomic measurements, the generation of CTESVS in four separate cavities is accomplished within the cavity decay time. In the case of large atom=cavity detuning, the scheme is immune to the effect of atomic spontaneous emission.
基金Supported by the National Natural Science Foundation of China under Grant Nos. 10875129 and 11075166
文摘The surface density Σ-map and the convergence κ-map of Bullet Cluster 1E0657-558 show that the center of baryonic matters separates from the center of gravitational force,and the distribution of gravitational force do not possess spherical symmetry.This hints that a modified gravity with difference to Newtonian inverse-square law at large scale,and less symmetry is worth investigating.In this paper,we study the dynamics in Randers-Finsler spacetime.The Newtonian limit and gravitational deflection of light in a Rindler-type potential is focused in particular.It is shown that the convergence in Finsler spacetime could account for the observations of Bullet Cluster.
基金Supported by the National Natural Science Foundation of China under Grant Nos. 10774108 and 11074184
文摘In this paper, we propose an optical scheme to generate four-mode cluster-type entangled coherent states （ECSs） in free traveling optical fields by using two single-photon sources, coherent state sources, beam splitters, pho- todetectors, cross-Kerr media, and phase shifters. And the success probability of the states preparation is calculated. At last we discuss the experimental feasibility of such proposal.
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.
基金supported by the Foundation of President of Hebei University(XZJJ202303).
文摘Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.
基金supported in part by NIH grants R01NS39600,U01MH114829RF1MH128693(to GAA)。
文摘Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.
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 Natural Science Foundation of China(Nos.12174151 and 12304448)the Specific Research Fund of the Innovation Platform for Academicians of Hainan Province(No.YSPTZX202208)+3 种基金Hainan Province Clinical Medical Center(No.QWYH_(2)022341)the Key Laboratory of New Energy and Rare Earth Resource Utilization of the State People’s Committee of China(No.NERE202206)the Department of Science and Technology of Jilin Province(No.20220101059JC)the Key Laboratory of the Ministry of Education for First Aid and Trauma Research(No.KLET-202218).
文摘Given customizable crystal structure and intriguing optical properties,lanthanide titanium-oxygen clusters(LTOCs)with atomic-level accuracy have gained a lot of interest.In this study,we prepared[Ln_(9)Ti_(2)(μ4-O)(μ3-OH)_(14)(acac)_(17)(CH_(3)O)_(2)(CH_(3)OH)_(3)](Ln=Tb_(x)Eu_(9−x)(x=0,4,6,7,8,9),Hacac=acetylacetone),Tb^(3+)and Eu^(3+)co-doped LTOCs,to modify the optical properties for the luminescence thermometer.In detail,the serial LTOCs display dual characteristic emission peaks of ^(5)D_(4)→^(7)F_(5) for Tb^(3+)and^(5)D_(0)→^(7)F_(2) for Eu^(3+)at 548 and 616 nm,respectively,under 330 nm excitation.Effective energy transfer(ET)between Tb^(3+)ions and Eu^(3+)ions was revealed in terms of both emission spectra and luminescence lifetime.The ^(5)D_(0)→^(7)F_(2) emission intensity of Eu^(3+)ions at 616 nm is maximally enhanced(by a factor of 11.2)with a change in the relative molar ratio of Tb^(3+)to Eu^(3+),along with a change in the ET efficiency of Tb^(3+)→Eu^(3+).In addition,the luminescent color changes from red,orange,yellow,to green.This precise control of the ET process between rare-earth ions allows{Tb_(6)Eu_(3)Ti_(2)}to reach a maximum relative sensitivity of 1.241 K^(−1) at 355 K,which is an enhancement of up to 4.6-fold with respect to the previously reported homonuclear emission,holding great potential in the optical thermometers.
基金supported by the Specific Research Fund for Top-notch Talents of Guangdong Provincial Hospital of Chinese Medicine(No.2022KT1188).
文摘Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to participate in this online questionnaire survey.The survey tools included a general information questionnaire and a self-made NMOSD symptoms scale,which included the prevalence,severity,and distress of 29 symptoms.Cluster analysis was used to identify symptom clusters,and network analysis was used to analyze the symptom network and node characteristics and central indicators including strength centrality(r_(s)),closeness centrality(r_(c))and betweeness centrality(r_(b))were used to identify core symptoms and symptom clusters.Results The most common symptom was pain(65.7%),followed by paraesthesia(65.0%),fatigue(65.0%),easy awakening(63.6%).Regarding the burden level of symptoms,pain was the most burdensome symptom,followed by paraesthesia,easy awakening,fatigue,and difficulty falling asleep.Six clusters were identified:somatosensory,motor,visual,and memory symptom clusters,bladder and rectum symptom clusters,sleep symptoms clusters,and neuropsychological symptom clusters.Fatigue(r_(s)=12.39,r_(b)=68.00,r_(c)=0.02)was the most central and prominent bridge symptom,and motor symptom cluster(r_(s)=2.68,r_(c)=0.10)was the most central symptom cluster among the six clusters.Conclusions Our study demonstrated the necessity of symptom management targeting fatigue,pain,and motor symptom cluster in patients with NMOSD.
