Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algor...Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.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
Effective control of gas-phase pollutants(volatile organic compounds(VOCs)and CO)is critical to human health and the ecological environment.Catalytic oxidation is one of the most promising technologies for achieving e...Effective control of gas-phase pollutants(volatile organic compounds(VOCs)and CO)is critical to human health and the ecological environment.Catalytic oxidation is one of the most promising technologies for achieving efficient volatile organic compounds and CO emission control.The subnano cluster catalyst can not only provide catalytic sites with multiple metal atoms,but also maintain full utilization efficiency.Almost all metal atoms in highly dispersed clusters can be used for adsorption and conversion of reactants.Recently,various types of sub-nano clusters,including subnano cluster oxides,have been developed and demonstrated excellent performance in low-temperature gas-phase pollutants combustion.In this mini review,we systematically summarize the structure,physicochemical properties,characterization,and applications of sub-nano cluster catalysts in catalytic oxidation of CO,methane,propane,propylene,toluene and its derivatives,formaldehyde and chlorinated volatile organic compounds.Finally,we have analyzed and discussed the problems and challenges faced by sub-nano cluster catalysts in both basic research and practical applications,providing a scientific basis for the design,synthesis,and application of efficient heterogeneous catalysts for CO and VOCs oxidation.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
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
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
The oxygen reduction reaction(ORR)critical for electrochemical energy conversion systems suffers from sluggish kinetics and high overpotentials that hinder the efficiency of these technologies.Herein,a curvature-domin...The oxygen reduction reaction(ORR)critical for electrochemical energy conversion systems suffers from sluggish kinetics and high overpotentials that hinder the efficiency of these technologies.Herein,a curvature-dominated microenvironment modulation strategy is demonstrated to enhance ORR performance via engineering a helical hollow carbon nanotube with embedded sub-nanometer tungsten nitride(W_(2)N)clusters.This architecture yields optimized electrostatic field distributions and reduced d-band center of W_(2)N,thereby promoting the enrichment of OH-,the adsorption of oxygen,and the desorption of oxygen intermediates(OH).The catalyst shows remarkable ORR activity with a high onset potential of 1.00 V and a half-wave potential of 0.89 V,outperforming both Pt/C and other W_(2)N-based catalysts.Theoretical calculations verify that the curved support enhances the electron delocalization within the W_(2)N clusters,regulating the interaction between the catalyst and reactants.Our findings establish a general design principle of curvature-induced microenvironment modulation and offer a new pathway toward designing efficient electrocatalysts for sustainable energy storage applications.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
In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper prese...In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.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
Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satell...Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.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
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
In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in spec...In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in specific genes,type ofβ-cell impairment,degree of insulin resistance,and clinical characteristics of metabolic profiles.Improved classification methods enable healthcare providers to formulate blood glucose management strategies more precisely.Applying these updated classification systems,will assist clinicians in further optimising treatment plans,including targeted drug therapies,personalized dietary advice,and specific exercise plans.Ultimately,this will facilitate stricter blood glucose control,minimize the risks of hypoglycaemia and hyperglycaemia,and reduce long-term complications associated with diabetes.展开更多
A multi-phase heterogeneous FeCoNi-based high-entropy alloy is developed to overcome the trade-off between strength and ductility.By alloying with a small amount of Cu and employing a rapid recrystalliza-tion process,...A multi-phase heterogeneous FeCoNi-based high-entropy alloy is developed to overcome the trade-off between strength and ductility.By alloying with a small amount of Cu and employing a rapid recrystalliza-tion process,it exhibits a good combination of yield strength(roughly 1300 MPa)and ductility(approach-ing 20%).Firstly,a multi-phase heterogeneous structure is tailored ranging from nano to micron.Cu is efficiently precipitated as nanoscale clusters(4.2 nm),high-density cuboidal L1_(2) particles(20-40 nm)and L2_(1) particles(500-800 nm)are found to be embedded in the matrix and a bimodal heterogeneous grain structure(1-40μm)is constructed.Secondly,the introduction of Cu effectively suppresses the pre-cipitation of coarse L21 phase at grain boundaries,reducing its volume fraction by 80%and replaced by smaller-scale continuous precipitations within the grains.Thirdly,the high mixing enthalpy gap of Cu and the matrix leads to the formation of local chemical fluctuation and the consequential rugged topog-raphy in the matrix,which result in retarded dislocation motion and promotes dislocation plugging and interlocking during strain,enhancing yield stress and work hardening rate.This study provides a valuable perspective to enhance strength and ductility via enlarged local chemical fluctuation-tailored multi-phase heterogeneous structures.展开更多
文摘Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.
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.
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supported by the National Natural Science Foundation of China(No.22506042)the Natural Science Foundation of Henan Province(Nos.252300421710 and 252300421552)the High level Talent Research Launch Fund of Henan University of Technology(No.2024BS061).
文摘Effective control of gas-phase pollutants(volatile organic compounds(VOCs)and CO)is critical to human health and the ecological environment.Catalytic oxidation is one of the most promising technologies for achieving efficient volatile organic compounds and CO emission control.The subnano cluster catalyst can not only provide catalytic sites with multiple metal atoms,but also maintain full utilization efficiency.Almost all metal atoms in highly dispersed clusters can be used for adsorption and conversion of reactants.Recently,various types of sub-nano clusters,including subnano cluster oxides,have been developed and demonstrated excellent performance in low-temperature gas-phase pollutants combustion.In this mini review,we systematically summarize the structure,physicochemical properties,characterization,and applications of sub-nano cluster catalysts in catalytic oxidation of CO,methane,propane,propylene,toluene and its derivatives,formaldehyde and chlorinated volatile organic compounds.Finally,we have analyzed and discussed the problems and challenges faced by sub-nano cluster catalysts in both basic research and practical applications,providing a scientific basis for the design,synthesis,and application of efficient heterogeneous catalysts for CO and VOCs oxidation.
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.
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the financial support provided by the National Natural Science Foundation of China(Nos.52302220 and 22405052)the China Postdoctoral Science Foundation(No.2024M750491).
文摘The oxygen reduction reaction(ORR)critical for electrochemical energy conversion systems suffers from sluggish kinetics and high overpotentials that hinder the efficiency of these technologies.Herein,a curvature-dominated microenvironment modulation strategy is demonstrated to enhance ORR performance via engineering a helical hollow carbon nanotube with embedded sub-nanometer tungsten nitride(W_(2)N)clusters.This architecture yields optimized electrostatic field distributions and reduced d-band center of W_(2)N,thereby promoting the enrichment of OH-,the adsorption of oxygen,and the desorption of oxygen intermediates(OH).The catalyst shows remarkable ORR activity with a high onset potential of 1.00 V and a half-wave potential of 0.89 V,outperforming both Pt/C and other W_(2)N-based catalysts.Theoretical calculations verify that the curved support enhances the electron delocalization within the W_(2)N clusters,regulating the interaction between the catalyst and reactants.Our findings establish a general design principle of curvature-induced microenvironment modulation and offer a new pathway toward designing efficient electrocatalysts for sustainable energy storage applications.
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the National Natural Science Foundation of China(No.62063006)to the Guangxi Natural Science Foundation under Grant(Nos.2023GXNSFAA026025,AA24010001)+3 种基金to the Innovation Fund of Chinese Universities Industry-University-Research(ID:2023RY018)to the Special Guangxi Industry and Information Technology Department,Textile and Pharmaceutical Division(ID:2021 No.231)to the Special Research Project of Hechi University(ID:2021GCC028)to the Key Laboratory of AI and Information Processing,Education Department of Guangxi Zhuang Autonomous Region(Hechi University),No.2024GXZDSY009。
文摘In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.
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.
文摘Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.
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 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.
文摘In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in specific genes,type ofβ-cell impairment,degree of insulin resistance,and clinical characteristics of metabolic profiles.Improved classification methods enable healthcare providers to formulate blood glucose management strategies more precisely.Applying these updated classification systems,will assist clinicians in further optimising treatment plans,including targeted drug therapies,personalized dietary advice,and specific exercise plans.Ultimately,this will facilitate stricter blood glucose control,minimize the risks of hypoglycaemia and hyperglycaemia,and reduce long-term complications associated with diabetes.
基金financial support from the National Natural Science Foundation of China(Nos.52104306,52274301,52334009)the Aeronautical Science Foundation of China(No.2023Z0530S6005)+3 种基金the National Key Research and Development Program of China(No.2023YFB3712401)the Science and Technology Commission of Shanghai Municipality(No.21DZ1208900)the Academician Workstation of Kunming University of Science and Technology(2024),the Ningbo Yongjiang Talent-Introduction Programme(No.2022A-023-C)the Zhejiang Phenomenological Materials Technology Co.,Ltd.,China.
文摘A multi-phase heterogeneous FeCoNi-based high-entropy alloy is developed to overcome the trade-off between strength and ductility.By alloying with a small amount of Cu and employing a rapid recrystalliza-tion process,it exhibits a good combination of yield strength(roughly 1300 MPa)and ductility(approach-ing 20%).Firstly,a multi-phase heterogeneous structure is tailored ranging from nano to micron.Cu is efficiently precipitated as nanoscale clusters(4.2 nm),high-density cuboidal L1_(2) particles(20-40 nm)and L2_(1) particles(500-800 nm)are found to be embedded in the matrix and a bimodal heterogeneous grain structure(1-40μm)is constructed.Secondly,the introduction of Cu effectively suppresses the pre-cipitation of coarse L21 phase at grain boundaries,reducing its volume fraction by 80%and replaced by smaller-scale continuous precipitations within the grains.Thirdly,the high mixing enthalpy gap of Cu and the matrix leads to the formation of local chemical fluctuation and the consequential rugged topog-raphy in the matrix,which result in retarded dislocation motion and promotes dislocation plugging and interlocking during strain,enhancing yield stress and work hardening rate.This study provides a valuable perspective to enhance strength and ductility via enlarged local chemical fluctuation-tailored multi-phase heterogeneous structures.
