The quality of surface water is rapidly changing due to climatic variations, natural processes, and anthropogenic activities. The objectives of this study were to classify and analyze the surface water quality of 12 m...The quality of surface water is rapidly changing due to climatic variations, natural processes, and anthropogenic activities. The objectives of this study were to classify and analyze the surface water quality of 12 major rivers of Alberta on the basis of 17 parameters during the period of five years (i.e., 2004-2008) using principal component analysis (PCA), total exceedance model and clustering technique. Seven major principal components (PCs) with variability of about 89% were identified. These PCs were the indicators of watershed geology, mineralization and anthropogenic activities related to land use/cover. The seven dominant parameters revealed from the seven PCs were total dissolved solids (TDS), true color (TC), pH, iron (Fe), fecal coliform (FC), dissolved oxygen (DO), and turbidity (TUR). The normalized data of dominant parameters were used to develop a model for obtaining total exceedance. The exceedance values acquired from the total exceedance model were used to determine the patterns for the development of five clusters. The performance of the clusters was compared with the classes obtained in Canadian Water Quality Index (CWQI). Cluster 1, cluster 2, cluster 3, cluster 4 and cluster 5 showed agreements of 85.71%, 83.54%, 90.22%, 80.74%, and 83.40% with their respective CWQI classes on the basis of the data for all rivers during 2004-2008. The water quality was deteriorated in growing season due to snow melting. This methodology could be applied to classify the raw surface water quality, analyze the spatio-temporal trends and study the impacts of the factors affecting the water quality anywhere in the world.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
Materials showing metallophilic interactions continue to attract considerable theoretical and experimental attention largely because of their unusual and unanticipated photophysical behavior as well as their unique st...Materials showing metallophilic interactions continue to attract considerable theoretical and experimental attention largely because of their unusual and unanticipated photophysical behavior as well as their unique stimuli-responsive behavior in an aggregate or solid state.Metallophilic interactions are mostly found between metals with either identical(d^(10)–d^(10))or different(s^(2)–d^(8),d^(8)–d^(10))configurations.Among various metallophilic interactions,aurophilic interactions(Au⋯Au)are well-known and widely reported.In this study,a new phosphorescent gold(I)complex,[(CF_(3)Ph)_(3)PAuC≡CPh](TPPGPA)was reported.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
Objective Sepsis patients exhibit diverse immune states,making it crucial to identify subtypes with distinct inflammatory profiles through Th1/Th2 cytokine data for personalized treatment and improved prognosis.Method...Objective Sepsis patients exhibit diverse immune states,making it crucial to identify subtypes with distinct inflammatory profiles through Th1/Th2 cytokine data for personalized treatment and improved prognosis.Methods We retrieved data from sepsis patients who underwent Th1/Th2 cytokine testing in Nanfang Hospital,Southern Medical University from June 1,2020,to February 1,2022.An unsupervised K-means clustering method classified participants based on Th1/Th2 cytokine levels,with the primary outcome being the 7-day mortality rate post-ICU admission.Cox proportional hazards and Restricted Mean Survival Time(RMST)analyses were utilized to explore survival outcomes.Results A total of 321 sepsis patients were included.IL-6(HR 1.69,95%CI:1.22,2.34)and IL-10(HR 1.81,95%CI:1.37,2.40)emerged as independent predictors of 7-day mortality.Unsupervised K-means clustering revealed 3 inflammatory/immune subgroups:Cluster 1(n=166,low inflammatory response),Cluster 2(n=99,moderate inflammatory response with immune suppression),and Cluster 3(n=56,strong inflammatory and immune suppression).Compared to Cluster 1,Clusters 2 and 3 had higher 7-day mortality risks(14.4%vs 23.2%,HR=4.30,95%CI:1.51-12.26;14.4%vs 35.7%,HR=7.32,95%CI:2.57-20.79).Conclusion Septic patients in a protective immune response state(Cluster 1)exhibit better short-term prognoses,suggesting the importance of understanding inflammatory/immune states for precise treatment and improved outcomes.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 study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.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
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.
In the Tianshan region,a complete textile industry chain has been established,covering the entire process from cotton cultivation and chemical fiber production,through spinning,weaving,dyeing,and finishing,and further...In the Tianshan region,a complete textile industry chain has been established,covering the entire process from cotton cultivation and chemical fiber production,through spinning,weaving,dyeing,and finishing,and further extending to apparel,home textiles,and industrial textiles.In November 2025,the first list of five characteristic textile and apparel industry clusters in Xinjiang was officially announced,marking a new stage in the clustering of Xinjiang's textile and apparel industry.Data shows that the total output value of Xinjiang's cotton and textile and apparel industry chain has exceeded 220 billion yuan.With the nation's largest cotton production,a complete industrial chain system,and strong synergistic effects,Xinjiang has become a leading and highly competitive textile industry hub in China.展开更多
Exploring high-performance electrocatalysts for the nitrate reduction reaction(NO_(3)RR)is crucial for environmental nitrate removal and ammonia synthesis.Single-atom collaboration with cluster can provide sufficient ...Exploring high-performance electrocatalysts for the nitrate reduction reaction(NO_(3)RR)is crucial for environmental nitrate removal and ammonia synthesis.Single-atom collaboration with cluster can provide sufficient active sites for catalysts to promote NO_(3)RR,yet the unclear synergistic effect between the two hinders their rational design.Herein,a series of Ir_(3)clusters and metal single atoms co-embedded in graphitic carbon nitride(g-CN)catalysts(Ir_(3)M1)were constructed,and the synergistic effects of Ir_(3)clusters and M1 single atoms on the NO_(3)RR catalytic mechanism and activity were systematically explored using density functional theory(DFT)calculations combined with machine learning.Comprehensive evaluations of structural stability and catalytic activity demonstrate that the synergy between single atoms and clusters effectively balances the adsorption energies of key intermediates,yielding exceptional catalytic performance(the limiting potential of Ir_(3)Ti_(1)can reach−0.22 V).Machine learning models further clarify the synergistic mechanism,where the geometric configurations of clusters serve as critical features for modulating the catalytic activity of single-atom sites,whereas the electronic structures of single atoms directly govern the reactivity of cluster sites.This DFT-machine learning approach provides theoretical guidelines for catalyst design and a predictive framework for efficient NO_(3)RR electrocatalysts.展开更多
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
Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental con...Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.展开更多
Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these netw...Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.展开更多
Established in 2019,the Shandong Port Group(SPG)comprises four port groups(Qingdao Port,Rizhao Port,Yantai Port,and Bohaiwan Port)and 12 business segments.SPG connects 3,345 kilometers of coastline within Shandong Pro...Established in 2019,the Shandong Port Group(SPG)comprises four port groups(Qingdao Port,Rizhao Port,Yantai Port,and Bohaiwan Port)and 12 business segments.SPG connects 3,345 kilometers of coastline within Shandong Province.Its cargo throughput has consistently ranked first globally for many years,and its container volume growth ranks second globally,forming a port cluster covering the entire industrial chain.展开更多
Traditional strategies for designing hyperhalogens,superatoms with exceptional electron-withdrawing capacity,rely on complex superhalogen assembly,posing significant experimental challenges.Here,we introduce a non-inv...Traditional strategies for designing hyperhalogens,superatoms with exceptional electron-withdrawing capacity,rely on complex superhalogen assembly,posing significant experimental challenges.Here,we introduce a non-invasive dual external field(DEF) approach combining solvent effects and an oriented external electric field(OEEF) to construct hyperhalogens,as demonstrated by density functional theory(DFT) calculations.Our DEF strategy proves versatile,successfully designing hyperhalogens not only in simplified Ag_n^(-) model systems but also in the experimentally synthesized Ag_(25) nanocluster.Using the 3D Ag_(19)^(-) structure as a model,we further reveal the DEF's pivotal role in O_(2) activation,where solvent-OEEF synergy induces tunable O-O bond elongation and charge transfer,proportional to field strength.Our findings establish a field-driven paradigm for hyperhalogen design that preserves native cluster composition,providing a theoretical foundation for tailoring high-performance catalysts through precise activesite modulation.展开更多
Deformation prediction for extra-high arch dams is highly important for ensuring their safe operation.To address the challenges of complex monitoring data,the uneven spatial distribution of deformation,and the constru...Deformation prediction for extra-high arch dams is highly important for ensuring their safe operation.To address the challenges of complex monitoring data,the uneven spatial distribution of deformation,and the construction and optimization of a prediction model for deformation prediction,a multipoint ultrahigh arch dam deformation prediction model,namely,the CEEMDAN-KPCA-GSWOA-KELM,which is based on a clustering partition,is pro-posed.First,the monitoring data are preprocessed via variational mode decomposition(VMD)and wavelet denoising(WT),which effectively filters out noise and improves the signal-to-noise ratio of the data,providing high-quality input data for subsequent prediction models.Second,scientific cluster partitioning is performed via the K-means++algorithm to precisely capture the spatial distribution characteristics of extra-high arch dams and ensure the consistency of deformation trends at measurement points within each partition.Finally,CEEMDAN is used to separate monitoring data,predict and analyze each component,combine the KPCA(Kernel Principal Component Analysis)and the KELM(Kernel Extreme Learning Machine)optimized by the GSWOA(Global Search Whale Optimization Algorithm),integrate the predictions of each component via reconstruction methods,and precisely predict the overall trend of ultrahigh arch dam deformation.An extra high arch dam project is taken as an example and validated via a comparative analysis of multiple models.The results show that the multipoint deformation prediction model in this paper can combine data from different measurement points,achieve a comprehensive,precise prediction of the deformation situation of extra high arch dams,and provide strong technical support for safe operation.展开更多
Transition metal selenides as sodium-ion hybrid capacitor(SIHC)anodes still suffer from amorphization difficulties and capacity degradation triggered by polyselenide dissolution.Herein,an atomistic amorphous strategy ...Transition metal selenides as sodium-ion hybrid capacitor(SIHC)anodes still suffer from amorphization difficulties and capacity degradation triggered by polyselenide dissolution.Herein,an atomistic amorphous strategy is proposed to construct adjacent Nb-Nb diatomic pairs with Se/O-coordination(Se4-Nb2-O2)in N-doped carbon-confined amorphous selenide clusters(a-Nb-Se/O@NC).Synergistic carbon confinement and hydrothermal oxygenation induce amorphization of Nb–Se bonds,eliminating crystalline rigidity while creating isotropic dual-ion transport channels and high-density active sites enriched with dangling bonds,thereby enhancing structural integrity and Na+storage capacity.The unique Se/O-coordinated Nb-Nb diatomic configuration establishes an electron-delocalized system,where the low electronegativity of Se counterbalances electron withdrawal from coordinated O at Nb centers.These strengthen d-p orbital hybridization,reduce Na+adsorption energy,and optimize charge transfer pathways and reaction kinetics in the amorphous clusters.Electrochemical tests reveal that the a-Nb-Se/O@NC anode delivers a high reversible capacity of 312.57 mAh g^(−1)and exceptional cyclic stability(103%capacity retention)after 5000 cycles at 10.0 A g^(−1).Assembled SIHCs achieve outstanding energy/power densities(207.1 Wh kg^(−1)/18966 W kg^(−1)),surpassing most amorphous and crystalline counterparts.This work provides methodological insights for the design of electrodes in high-power storage devices through atomic modulation and electronic optimization of amorphous selenides.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
We report first-principles predictions of a cage-like polymeric nitrogen phase(cage-N)composed of interlocked N10 clusters stabilized by mixed sp^(2)/sp^(3) hybridization.Under high pressure,cage-N exhibits exceptiona...We report first-principles predictions of a cage-like polymeric nitrogen phase(cage-N)composed of interlocked N10 clusters stabilized by mixed sp^(2)/sp^(3) hybridization.Under high pressure,cage-N exhibits exceptional mechanical performance,including an ideal compressive strength of 343 GPa at a pressure of 300 GPa,~33% higher than that of diamond.This ultrahigh strength arises from the synergistic interplay between its three-dimensional covalent framework and hybridized bonding topology,which enables isotropic stress accommodation and dynamic electronic rearrangement.These results establish cage-N as a promising non-carbon ultrahard material and provide a bonding-driven route toward designing superhard frameworks under extreme conditions.展开更多
文摘The quality of surface water is rapidly changing due to climatic variations, natural processes, and anthropogenic activities. The objectives of this study were to classify and analyze the surface water quality of 12 major rivers of Alberta on the basis of 17 parameters during the period of five years (i.e., 2004-2008) using principal component analysis (PCA), total exceedance model and clustering technique. Seven major principal components (PCs) with variability of about 89% were identified. These PCs were the indicators of watershed geology, mineralization and anthropogenic activities related to land use/cover. The seven dominant parameters revealed from the seven PCs were total dissolved solids (TDS), true color (TC), pH, iron (Fe), fecal coliform (FC), dissolved oxygen (DO), and turbidity (TUR). The normalized data of dominant parameters were used to develop a model for obtaining total exceedance. The exceedance values acquired from the total exceedance model were used to determine the patterns for the development of five clusters. The performance of the clusters was compared with the classes obtained in Canadian Water Quality Index (CWQI). Cluster 1, cluster 2, cluster 3, cluster 4 and cluster 5 showed agreements of 85.71%, 83.54%, 90.22%, 80.74%, and 83.40% with their respective CWQI classes on the basis of the data for all rivers during 2004-2008. The water quality was deteriorated in growing season due to snow melting. This methodology could be applied to classify the raw surface water quality, analyze the spatio-temporal trends and study the impacts of the factors affecting the water quality anywhere in the world.
文摘Noncohesive particle clusters are identified and tracked in turbulent flows to determine the breakdown and time evolution of cluster statistics and their implications for interscale mass transfer,which has connections to the classical turbulent energy cascade and its mass cascade counterpart running in parallel.In particular,the formation and dynamics of sediment and larvae clusters are of interest to coral larvae settlement in coastal regions and particularly the resilience of green-gray coastal protection solutions.Analogous cluster behavior is relevant to cloud microphysics and precipitation initiation,radiation transport and light transmission through colloids and suspensions,heat and mass transfer in particle-laden flows,and viral and pollutant transmission.Following a comparison between various clustering techniques,we adopt a density-based cluster identification algorithm based on its simplicity and efficiency,where particles are clustered based on the number of neighboring particles in their individual spheres of influence.We establish parallels with lattice-based percolation theory,as evident in the power-law scaling of the cluster size distribution near the percolation threshold.The degree of discontinuity of the phase transition associated with this percolation threshold is observed to broaden with larger Stokes numbers and thereby large-scale clustering.The sensitivity of our findings to the employed clustering algorithm is discussed.A novel cluster tracking algorithm is deployed to determine the interscale transfer rate along the particle-number phase-space dimension via accounting of cluster breakup and merger events,extending previous work on the bubble breakup cascade beneath surface breaking waves.Our findings shed light on the interaction between particle clusters and their carrier turbulent flows,with an eye toward transport models incorporating cluster characteristics and dynamics.
基金supported by the National Natural Science Foundation of China(no.21788102)the Natural Science Foundation of Guangdong Province(nos.2019B121205002 and 2019B030301003)+1 种基金the Research Grants Council of Hong Kong(nos.16305618,16305518,16304819,C6009-17G,and C6014-20W)the Innovation and Technology Commission(no.ITC-CNERC14SC01).
文摘Materials showing metallophilic interactions continue to attract considerable theoretical and experimental attention largely because of their unusual and unanticipated photophysical behavior as well as their unique stimuli-responsive behavior in an aggregate or solid state.Metallophilic interactions are mostly found between metals with either identical(d^(10)–d^(10))or different(s^(2)–d^(8),d^(8)–d^(10))configurations.Among various metallophilic interactions,aurophilic interactions(Au⋯Au)are well-known and widely reported.In this study,a new phosphorescent gold(I)complex,[(CF_(3)Ph)_(3)PAuC≡CPh](TPPGPA)was reported.
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.
文摘Objective Sepsis patients exhibit diverse immune states,making it crucial to identify subtypes with distinct inflammatory profiles through Th1/Th2 cytokine data for personalized treatment and improved prognosis.Methods We retrieved data from sepsis patients who underwent Th1/Th2 cytokine testing in Nanfang Hospital,Southern Medical University from June 1,2020,to February 1,2022.An unsupervised K-means clustering method classified participants based on Th1/Th2 cytokine levels,with the primary outcome being the 7-day mortality rate post-ICU admission.Cox proportional hazards and Restricted Mean Survival Time(RMST)analyses were utilized to explore survival outcomes.Results A total of 321 sepsis patients were included.IL-6(HR 1.69,95%CI:1.22,2.34)and IL-10(HR 1.81,95%CI:1.37,2.40)emerged as independent predictors of 7-day mortality.Unsupervised K-means clustering revealed 3 inflammatory/immune subgroups:Cluster 1(n=166,low inflammatory response),Cluster 2(n=99,moderate inflammatory response with immune suppression),and Cluster 3(n=56,strong inflammatory and immune suppression).Compared to Cluster 1,Clusters 2 and 3 had higher 7-day mortality risks(14.4%vs 23.2%,HR=4.30,95%CI:1.51-12.26;14.4%vs 35.7%,HR=7.32,95%CI:2.57-20.79).Conclusion Septic patients in a protective immune response state(Cluster 1)exhibit better short-term prognoses,suggesting the importance of understanding inflammatory/immune states for precise treatment and improved outcomes.
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funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
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.
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.
文摘In the Tianshan region,a complete textile industry chain has been established,covering the entire process from cotton cultivation and chemical fiber production,through spinning,weaving,dyeing,and finishing,and further extending to apparel,home textiles,and industrial textiles.In November 2025,the first list of five characteristic textile and apparel industry clusters in Xinjiang was officially announced,marking a new stage in the clustering of Xinjiang's textile and apparel industry.Data shows that the total output value of Xinjiang's cotton and textile and apparel industry chain has exceeded 220 billion yuan.With the nation's largest cotton production,a complete industrial chain system,and strong synergistic effects,Xinjiang has become a leading and highly competitive textile industry hub in China.
基金the financial support from the Shandong Province colleges and universities youth innovation technology plan innovation team project(2022KJ285)the Natural Science Foundation of Shandong Province(ZR2022QE076)+1 种基金the National Natural Science Foundation of China(52202092)the Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China(2023KJ104).
文摘Exploring high-performance electrocatalysts for the nitrate reduction reaction(NO_(3)RR)is crucial for environmental nitrate removal and ammonia synthesis.Single-atom collaboration with cluster can provide sufficient active sites for catalysts to promote NO_(3)RR,yet the unclear synergistic effect between the two hinders their rational design.Herein,a series of Ir_(3)clusters and metal single atoms co-embedded in graphitic carbon nitride(g-CN)catalysts(Ir_(3)M1)were constructed,and the synergistic effects of Ir_(3)clusters and M1 single atoms on the NO_(3)RR catalytic mechanism and activity were systematically explored using density functional theory(DFT)calculations combined with machine learning.Comprehensive evaluations of structural stability and catalytic activity demonstrate that the synergy between single atoms and clusters effectively balances the adsorption energies of key intermediates,yielding exceptional catalytic performance(the limiting potential of Ir_(3)Ti_(1)can reach−0.22 V).Machine learning models further clarify the synergistic mechanism,where the geometric configurations of clusters serve as critical features for modulating the catalytic activity of single-atom sites,whereas the electronic structures of single atoms directly govern the reactivity of cluster sites.This DFT-machine learning approach provides theoretical guidelines for catalyst design and a predictive framework for efficient NO_(3)RR electrocatalysts.
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Processes supported by process-aware information systems are subject to continuous and often subtle changes due to evolving operational,organizational,or regulatory factors.These changes,referred to as incremental concept drift,gradually alter the behavior or structure of processes,making their detection and localization a challenging task.Traditional process mining techniques frequently assume process stationarity and are limited in their ability to detect such drift,particularly from a control-flow perspective.The objective of this research is to develop an interpretable and robust framework capable of detecting and localizing incremental concept drift in event logs,with a specific emphasis on the structural evolution of control-flow semantics in processes.We propose DriftXMiner,a control-flow-aware hybrid framework that combines statistical,machine learning,and process model analysis techniques.The approach comprises three key components:(1)Cumulative Drift Scanner that tracks directional statistical deviations to detect early drift signals;(2)a Temporal Clustering and Drift-Aware Forest Ensemble(DAFE)to capture distributional and classification-level changes in process behavior;and(3)Petri net-based process model reconstruction,which enables the precise localization of structural drift using transition deviation metrics and replay fitness scores.Experimental validation on the BPI Challenge 2017 event log demonstrates that DriftXMiner effectively identifies and localizes gradual and incremental process drift over time.The framework achieves a detection accuracy of 92.5%,a localization precision of 90.3%,and an F1-score of 0.91,outperforming competitive baselines such as CUSUM+Histograms and ADWIN+Alpha Miner.Visual analyses further confirm that identified drift points align with transitions in control-flow models and behavioral cluster structures.DriftXMiner offers a novel and interpretable solution for incremental concept drift detection and localization in dynamic,process-aware systems.By integrating statistical signal accumulation,temporal behavior profiling,and structural process mining,the framework enables finegrained drift explanation and supports adaptive process intelligence in evolving environments.Its modular architecture supports extension to streaming data and real-time monitoring contexts.
文摘Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.
文摘Established in 2019,the Shandong Port Group(SPG)comprises four port groups(Qingdao Port,Rizhao Port,Yantai Port,and Bohaiwan Port)and 12 business segments.SPG connects 3,345 kilometers of coastline within Shandong Province.Its cargo throughput has consistently ranked first globally for many years,and its container volume growth ranks second globally,forming a port cluster covering the entire industrial chain.
基金supported by the National Natural Science Foundation of China (NSFC,Nos.12474274,92161101)the Innovation Project of Jinan Science and Technology Bureau(No.2021GXRC032)the Natural Science Foundation of Shandong Province (No.ZR2024MA091)。
文摘Traditional strategies for designing hyperhalogens,superatoms with exceptional electron-withdrawing capacity,rely on complex superhalogen assembly,posing significant experimental challenges.Here,we introduce a non-invasive dual external field(DEF) approach combining solvent effects and an oriented external electric field(OEEF) to construct hyperhalogens,as demonstrated by density functional theory(DFT) calculations.Our DEF strategy proves versatile,successfully designing hyperhalogens not only in simplified Ag_n^(-) model systems but also in the experimentally synthesized Ag_(25) nanocluster.Using the 3D Ag_(19)^(-) structure as a model,we further reveal the DEF's pivotal role in O_(2) activation,where solvent-OEEF synergy induces tunable O-O bond elongation and charge transfer,proportional to field strength.Our findings establish a field-driven paradigm for hyperhalogen design that preserves native cluster composition,providing a theoretical foundation for tailoring high-performance catalysts through precise activesite modulation.
基金supported by the National Natural Science Foundation of China(Grant Nos.52069029,52369026)the Belt and Road Special Foundation of National Key Laboratory of Water Disaster Preven-tion(Grant No.2023490411)+2 种基金the Yunnan Agricultural Basic Research Joint Special General Project(Grant Nos.202501BD070001-060,202401BD070001-071)Construction Project of the Yunnan Key Laboratory of Water Security(No.20254916CE340051)the Youth Talent Project of“Xingdian Talent Support Plan”in Yunnan Province(Grant No.XDYC-QNRC-2023-0412).
文摘Deformation prediction for extra-high arch dams is highly important for ensuring their safe operation.To address the challenges of complex monitoring data,the uneven spatial distribution of deformation,and the construction and optimization of a prediction model for deformation prediction,a multipoint ultrahigh arch dam deformation prediction model,namely,the CEEMDAN-KPCA-GSWOA-KELM,which is based on a clustering partition,is pro-posed.First,the monitoring data are preprocessed via variational mode decomposition(VMD)and wavelet denoising(WT),which effectively filters out noise and improves the signal-to-noise ratio of the data,providing high-quality input data for subsequent prediction models.Second,scientific cluster partitioning is performed via the K-means++algorithm to precisely capture the spatial distribution characteristics of extra-high arch dams and ensure the consistency of deformation trends at measurement points within each partition.Finally,CEEMDAN is used to separate monitoring data,predict and analyze each component,combine the KPCA(Kernel Principal Component Analysis)and the KELM(Kernel Extreme Learning Machine)optimized by the GSWOA(Global Search Whale Optimization Algorithm),integrate the predictions of each component via reconstruction methods,and precisely predict the overall trend of ultrahigh arch dam deformation.An extra high arch dam project is taken as an example and validated via a comparative analysis of multiple models.The results show that the multipoint deformation prediction model in this paper can combine data from different measurement points,achieve a comprehensive,precise prediction of the deformation situation of extra high arch dams,and provide strong technical support for safe operation.
基金supported by the National Natural Science Foundation of China(Grant No.52573299)the Natural Science Foundation of Jiangxi province(No.20242BAB25223,20232BCJ23025,20232BCJ25040,20232BAB214024)the Special Funding Program for Graduate Student Innovation of Jiangxi Province(No.YC2024-S594).
文摘Transition metal selenides as sodium-ion hybrid capacitor(SIHC)anodes still suffer from amorphization difficulties and capacity degradation triggered by polyselenide dissolution.Herein,an atomistic amorphous strategy is proposed to construct adjacent Nb-Nb diatomic pairs with Se/O-coordination(Se4-Nb2-O2)in N-doped carbon-confined amorphous selenide clusters(a-Nb-Se/O@NC).Synergistic carbon confinement and hydrothermal oxygenation induce amorphization of Nb–Se bonds,eliminating crystalline rigidity while creating isotropic dual-ion transport channels and high-density active sites enriched with dangling bonds,thereby enhancing structural integrity and Na+storage capacity.The unique Se/O-coordinated Nb-Nb diatomic configuration establishes an electron-delocalized system,where the low electronegativity of Se counterbalances electron withdrawal from coordinated O at Nb centers.These strengthen d-p orbital hybridization,reduce Na+adsorption energy,and optimize charge transfer pathways and reaction kinetics in the amorphous clusters.Electrochemical tests reveal that the a-Nb-Se/O@NC anode delivers a high reversible capacity of 312.57 mAh g^(−1)and exceptional cyclic stability(103%capacity retention)after 5000 cycles at 10.0 A g^(−1).Assembled SIHCs achieve outstanding energy/power densities(207.1 Wh kg^(−1)/18966 W kg^(−1)),surpassing most amorphous and crystalline counterparts.This work provides methodological insights for the design of electrodes in high-power storage devices through atomic modulation and electronic optimization of amorphous selenides.
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supported by the Natural Science Foundation of China(Grant Nos.T2325013,52288102,52090024,12034009,12474004,and 12304036)the National Key R&D Program of China Grant No.2023YFA1610000+1 种基金the Fundamental Research Funds for the Central Universitiesthe Program for Jilin University and Sun Yat-sen University.
文摘We report first-principles predictions of a cage-like polymeric nitrogen phase(cage-N)composed of interlocked N10 clusters stabilized by mixed sp^(2)/sp^(3) hybridization.Under high pressure,cage-N exhibits exceptional mechanical performance,including an ideal compressive strength of 343 GPa at a pressure of 300 GPa,~33% higher than that of diamond.This ultrahigh strength arises from the synergistic interplay between its three-dimensional covalent framework and hybridized bonding topology,which enables isotropic stress accommodation and dynamic electronic rearrangement.These results establish cage-N as a promising non-carbon ultrahard material and provide a bonding-driven route toward designing superhard frameworks under extreme conditions.
