The authors have adopted acupuncture at Jǐngjiājǐ（颈夹脊） combined with nape cluster needling in treatment of 100 patients with cervicogenic headache. Nape cluster needling was： Xiànǎohù（下脑户）（lo...The authors have adopted acupuncture at Jǐngjiājǐ（颈夹脊） combined with nape cluster needling in treatment of 100 patients with cervicogenic headache. Nape cluster needling was： Xiànǎohù（下脑户）（located in the median depression under occipital bone）, Fēngfǔ（风府 GV 16） and Yǎmén（哑门 GV 15） were selected longitudinally; horizontally, the part from GV 16 to Wángǔ（完骨 GB 12） was divided into six equal sections, one section was an acupoint, and there were 12 acupoints in total at the left and right sides. Bilateral Jǐngjiājǐ（颈夹脊） points on the second vertebra to the seventh vertebra were selected. The acupuncture was conducted once a day, five days were considered as one course of treatment, and two days were free from treatment between two courses. Four courses of treatment were needed. All the patients were cured clinically. It can be seen that acupuncture at Jǐngjiājǐ combined with nape cluster needling in treatment of cervicogenic headache has sound effect.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
BACKGROUND Cluster headache(CH)is a severe incapacitating headache disorder.By definition,its diagnosis must exclude possible underlying structural conditions.AIM To review available information on CLH caused by struc...BACKGROUND Cluster headache(CH)is a severe incapacitating headache disorder.By definition,its diagnosis must exclude possible underlying structural conditions.AIM To review available information on CLH caused by structural lesions and to provide better guides in the distinguishing process and to ensure that there is not a potentially treatable structural lesion.METHODS We conducted a systematic review of 77 published cases of symptomatic CH and cluster-like headache(CLH)in PubMed and Google Scholar databases.RESULTS Structural pathologies associated with CH were vascular(37.7%),tumoral(32.5%)and inflammatory(27.2%).Brain mass-like lesions(tumoural and inflammatory)were the most common diseases(28.6%),among which 77.3%lesions were at the suprasellar(pituitary)region.Cases of secondary CH related to sinusitis rose dramatically,occupying 19.5%.The third most common disease was internal carotid artery dissection,accounting for 14.3%.Atypical clinical features raise an early suspicion of a secondary cause:Late age at onset and eye and retroorbital pains were common conditions requiring careful evaluation and were present in at least one-third of cases.Abnormal neurological examination was the most significant red flag for impaired cranial nerves.CLH patients may be responsive to typical CH treatments;therefore,the treatment response is not specific.CLH can be triggered by contralateral structural pathologies.CLH associated with sinusitis and cerebral venous thrombosis required more attention.CONCLUSION Since secondary headache could perfectly mimick primary CH,neuroimaging should be conducted in patients in whom primary and secondary headaches are suspected.Cerebral magnetic resonance imaging scans is the diagnostic management of choice,and further examinations include vessel imaging with contrast agents and dedicated scans focusing on specific cerebral areas(sinuses,ocular and sellar regions).Neuroimaging is as necessary at follow-up visits as at the first observation.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
Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin s...Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighbor relationship between samples.To overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model.We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module.By this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one cluster.In addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering methods.The code is available at https://github.com/songzuolong/MNF-MDSC(accessed on 25 December 2024).展开更多
Symplectic symmetry approach to clustering(SSAC)in atomic nuclei,recently proposed,is modified and further developed in more detail.It is firstly applied to the light two-cluster^(20)Ne+αsystem of^(24)Mg,the latter e...Symplectic symmetry approach to clustering(SSAC)in atomic nuclei,recently proposed,is modified and further developed in more detail.It is firstly applied to the light two-cluster^(20)Ne+αsystem of^(24)Mg,the latter exhibiting well developed low-energy K^(π)=0_(1)^(+),k^(π)=2_(1)^(+) and π^(π)=0_(1)^(-) rotational bands in its spectrum.A simple algebraic Hamiltonian,consisting of dynamical symmetry,residual and vertical mixing parts is used to describe these three lowest rotational bands of positive and negative parity in^(24)Mg.A good description of the excitation energies is obtained by considering only the SU(3)cluster states restricted to the stretched many-particle Hilbert subspace,built on the leading Pauli allowed SU(3)multiplet for the positive-and negative-parity states,respectively.The coupling to the higher cluster-model configurations allows us to describe the known low-lying experimentally observed B(E2)transition probabilities within and between the cluster states of the three bands under consideration without the use of an effective charge.展开更多
Objective To analyze the prevalence and burden of headache disorders in China and its provinces from 1990 to 2021.Methods Using data from the Global Burden of Disease Study(GBD)2021,the number of prevalent cases,preva...Objective To analyze the prevalence and burden of headache disorders in China and its provinces from 1990 to 2021.Methods Using data from the Global Burden of Disease Study(GBD)2021,the number of prevalent cases,prevalence rate,disability-adjusted life years(DALYs),and age-standardized DALY rates were analyzed by sex,age group,and province for headache disorders and their subtypes(migraine and tension-type headache[TTH])between 1990 and 2021.Percentage changes during this period were also estimated.Results In 2021,approximately 426 million individuals in China were affected by headache disorders,with an age-standardized prevalence rate of 27,582.61/100,000.The age-standardized DALY rate for all headache disorders was 487.15/100,000.Between 1990 and 2021,the number of prevalent cases increased by 37.78%,while the prevalence of all headache disorders,migraine,and TTH increased by 6.92%,7.57%,and 7.86%,respectively.The highest prevalence was observed in the 30-34 age group(39,520.60/100,000).Migraine accounted for a larger proportion of DALYs attributable to headache disorders,whereas TTH has a greater impact on its prevalence.In 2021,the highest age-standardized DALY rates for headache disorders were observed in Heilongjiang(617.85/100,000)and Shanghai(542.86/100,000).Conclusion The prevalence of headache disorders is increasing in China.Effective health education,improve diagnosis and treatment are essential,particularly for middle-aged working populations and women of childbearing age.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
In modern distributed systems and cloud computing architectures,high availability and high scalability are core requirements to ensure the continuous and stable operation of services.As key technologies for achieving ...In modern distributed systems and cloud computing architectures,high availability and high scalability are core requirements to ensure the continuous and stable operation of services.As key technologies for achieving these two goals,high-availability clusters and load-balancing clusters have significant differences in their design concepts and application scenarios,while also maintaining close connections.This paper aims to conduct an in-depth analysis of the core objectives,working principles,technical advantages and disadvantages,and typical application cases of high-availability clusters and load-balancing clusters.By introducing an analogical model of a“restaurant kitchen,”the differences between the two are intuitively explained,and their technical characteristics are compared in detail.Additionally,a detailed practical case is included to specifically demonstrate the collaborative work of high-availability and load-balancing technologies through the construction process of Keepalived and HAProxy.Finally,taking the architecture of a typical e-commerce website as an example,this paper demonstrates the best practice of organically combining the two cluster technologies in a production environment to build a robust and high-performance distributed system.Research shows that understanding the differences between the two and implementing collaborative deployment is the cornerstone of designing modern IT infrastructure.展开更多
Numerous clustering algorithms are valuable in pattern recognition in forest vegetation,with new ones continually being proposed.While some are well-known,others are underutilized in vegetation science.This study comp...Numerous clustering algorithms are valuable in pattern recognition in forest vegetation,with new ones continually being proposed.While some are well-known,others are underutilized in vegetation science.This study compares the performance of practical iterative reallocation algorithms with model-based clustering algorithms.The data is from forest vegetation in Virginia(United States),the Hyrcanian Forest(Asia),and European beech forests.Practical iterative reallocation algorithms were applied as non-hierarchical methods and Finite Gaussian mixture modeling was used as a model-based clustering method.Due to limitations on dimensionality in model-based clustering,principal coordinates analysis was employed to reduce the dataset’s dimensions.A log transformation was applied to achieve a normal distribution for the pseudo-species data before calculating the Bray-Curtis dissimilarity.The findings indicate that the reallocation of misclassified objects based on silhouette width(OPTSIL)with Flexible-β(-0.25)had the highest mean among the tested clustering algorithms with Silhouette width 1(REMOS1)with Flexible-β(-0.25)second.However,model-based clustering performed poorly.Based on these results,it is recommended using OPTSIL with Flexible-β(-0.25)and REMOS1 with Flexible-β(-0.25)for forest vegetation classification instead of model-based clustering particularly for heterogeneous datasets common in forest vegetation community data.展开更多
Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integr...Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integrating both structural and attribute-based information.However,these models often emphasize node proximities without adequately balancing the efficiency of clustering based on both structural and attribute data.Furthermore,they tend to neglect the critical fuzzy information inherent in attributed graph clusters.To address these issues,we introduce a new framework,Markov lumpability optimization,for efficient clustering of large-scale attributed graphs.Specifically,we define a lumped Markov chain on an attribute-augmented graph and introduce a new metric,Markov lumpability,to quantify the differences between the original and lumped Markov transition probability matrices.To minimize this measure,we propose a conjugate gradient projectionbased approach that ensures the partitioning closely aligns with the intrinsic structure of fuzzy clusters through conditional optimization.Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed framework compared to existing clustering algorithms.This framework has many potential applications,including dynamic community analysis of social networks,user profiling in recommendation systems,functional module identification in biological molecular networks,and financial risk control,offering a new paradigm for mining complex patterns in high-dimensional attributed graph data.展开更多
As a cluster overlap amplitude,the reduced-width amplitude is an important physical quantity for analyzing clustering in the nucleus depending on specified channels and has been calculated and widely applied in nuclea...As a cluster overlap amplitude,the reduced-width amplitude is an important physical quantity for analyzing clustering in the nucleus depending on specified channels and has been calculated and widely applied in nuclear cluster physics.In this review,we briefly revisit the theoretical framework for calculating the reduced-width amplitude,as well as the outlines of cluster models to obtain microscopic or semi-microscopic cluster wave functions.We also introduce the recent progress related to cluster overlap amplitudes,including the implementation of cross-section estimation and extension to three-body clustering analysis.Comprehensive examples are provided to demonstrate the application of the reduced-width amplitude in analyzing clustering structures.展开更多
Accurate description of noncova-lent interactions in large systems is challenging due to the require-ment of high-level electron corre-lation methods.The generalized energy-based fragmentation(GEBF)approach,in conjunc...Accurate description of noncova-lent interactions in large systems is challenging due to the require-ment of high-level electron corre-lation methods.The generalized energy-based fragmentation(GEBF)approach,in conjunc-tion with the domain-based local pair natural orbital(DLPNO)method,has been applied to assess the average binding energies(ABEs)of large benzene clus-ters,specifically(C6H6)13,at the coupled cluster singles and doubles with perturbative triples correction[CCSD(T)]level and the complete basis set(CBS)limit.Utilizing GEBF-DLPNO-CCSD(T)/CBS ABEs as benchmarks,various DFT functionals were evaluated.It was found that several functionals with empirical dispersion correction,including M06-2X-D3,B3LYP-D3(BJ),and PBE-D3(BJ),provide accurate descriptions of the ABEs for(C6H6)13 clusters.Additionally,the M06-2X-D3 functional was used to calculate the ABEs and relative stabili-ties of(C6H6)n clusters for n=11,12,13,14,and 15 revealing that the(C6H6)13 cluster ex-hibits the highest relative stability.These findings align with experimental evidence suggest-ing that n=13 is one of the magic numbers for benzene clusters(C6H6)n,with n≤30.展开更多
Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflecti...Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflection point method”to analyze specific deviation trajectories,determine deviation thresholds,and identify commonly used deviation paths.By combining multiple similarity metrics,including Euclidean distance,Hausdorff distance,and sector edit distance,with the density-based spatial clustering of applications with noise(DBSCAN)algorithm,the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs.A case study of 23578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework.Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances,with higher silhouette coefficients and lower Davies⁃Bouldin indices,ensuring better intra-cluster compactness and inter-cluster separation.Based on clustering results,19 representative trajectory options are identified,covering both nominal and deviation paths,which significantly enhance route diversity and reflect actual flight practices.This provides a practical basis for optimizing flight paths and scheduling,enhancing the flexibility of route selection for flights between city pairs.展开更多
A novel model of fuzzy clustering, i.e. an allied fuzzy c means （AFCM） model is proposed based on the combination of advantages of fuzzy c means （FCM） and possibilistic c means （PCM） clustering. PCM is sensitive...A novel model of fuzzy clustering, i.e. an allied fuzzy c means （AFCM） model is proposed based on the combination of advantages of fuzzy c means （FCM） and possibilistic c means （PCM） clustering. PCM is sensitive to initializations and often generates coincident clusters. AFCM overcomes this shortcoming and it is an ex tension of PCM. Membership and typicality values can be simultaneously produced in AFCM. Experimental re- suits show that noise data can be well processed, coincident clusters are avoided and clustering accuracy is better.展开更多
Clustering data with varying densities and complicated structures is important,while many existing clustering algorithms face difficulties for this problem. The reason is that varying densities and complicated structu...Clustering data with varying densities and complicated structures is important,while many existing clustering algorithms face difficulties for this problem. The reason is that varying densities and complicated structure make single algorithms perform badly for different parts of data. More intensive parts are assumed to have more information probably,an algorithm clustering from high density part is proposed,which begins from a tiny distance to find the highest density-connected partition and form corresponding super cores,then distance is iteratively increased by a global heuristic method to cluster parts with different densities. Mean of silhouette coefficient indicates the cluster performance. Denoising function is implemented to eliminate influence of noise and outliers. Many challenging experiments indicate that the algorithm has good performance on data with widely varying densities and extremely complex structures. It decides the optimal number of clusters automatically.Background knowledge is not needed and parameters tuning is easy. It is robust against noise and outliers.展开更多
Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subse...Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.展开更多
The pursuit of Ag-based alloys with both high strength and toughness has posed a longstanding chal-lenge.In this study,we investigated the cluster strengthening and grain refinement toughening mecha-nisms in fully oxi...The pursuit of Ag-based alloys with both high strength and toughness has posed a longstanding chal-lenge.In this study,we investigated the cluster strengthening and grain refinement toughening mecha-nisms in fully oxidized AgMgNi alloys,which were internally oxidized at 800℃ for 8 h under an oxy-gen atmosphere.We found that Mg-O clusters contributed to the hardening(138 HV)and strengthening(376.9 MPa)of the AgMg alloy through solid solution strengthening effects,albeit at the expense of duc-tility.To address this limitation,we introduced Ni nanoparticles into the AgMg alloy,resulting in signifi-cant grain refinement within its microstructure.Specifically,the grain size decreased from 67.2μm in the oxidized AgMg alloy to below 6.0μm in the oxidized AgMgNi alloy containing 0.3 wt%Ni.Consequently,the toughness increased significantly,rising from toughness value of 2177.9 MJ m^(-3) in the oxidized AgMg alloy to 6186.1 MJ m^(-3) in the oxidized AgMgNi alloy,representing a remarkable 2.8-fold enhancement.Furthermore,the internally oxidized AgMgNi alloy attained a strength of up to 387.6 MPa,comparable to that of the internally oxidized AgMg alloy,thereby demonstrating the successful realization of concurrent strengthening and toughening.These results collectively offer a novel approach for the design of high-performance alloys through the synergistic combination of cluster strengthening and grain refinement toughening.展开更多
Given customizable crystal structure and intriguing optical properties,lanthanide titanium-oxygen clusters(LTOCs)with atomic-level accuracy have gained a lot of interest.In this study,we prepared[Ln_(9)Ti_(2)(μ4-O)(...Given customizable crystal structure and intriguing optical properties,lanthanide titanium-oxygen clusters(LTOCs)with atomic-level accuracy have gained a lot of interest.In this study,we prepared[Ln_(9)Ti_(2)(μ4-O)(μ3-OH)_(14)(acac)_(17)(CH_(3)O)_(2)(CH_(3)OH)_(3)](Ln=Tb_(x)Eu_(9−x)(x=0,4,6,7,8,9),Hacac=acetylacetone),Tb^(3+)and Eu^(3+)co-doped LTOCs,to modify the optical properties for the luminescence thermometer.In detail,the serial LTOCs display dual characteristic emission peaks of ^(5)D_(4)→^(7)F_(5) for Tb^(3+)and^(5)D_(0)→^(7)F_(2) for Eu^(3+)at 548 and 616 nm,respectively,under 330 nm excitation.Effective energy transfer(ET)between Tb^(3+)ions and Eu^(3+)ions was revealed in terms of both emission spectra and luminescence lifetime.The ^(5)D_(0)→^(7)F_(2) emission intensity of Eu^(3+)ions at 616 nm is maximally enhanced(by a factor of 11.2)with a change in the relative molar ratio of Tb^(3+)to Eu^(3+),along with a change in the ET efficiency of Tb^(3+)→Eu^(3+).In addition,the luminescent color changes from red,orange,yellow,to green.This precise control of the ET process between rare-earth ions allows{Tb_(6)Eu_(3)Ti_(2)}to reach a maximum relative sensitivity of 1.241 K^(−1) at 355 K,which is an enhancement of up to 4.6-fold with respect to the previously reported homonuclear emission,holding great potential in the optical thermometers.展开更多
文摘The authors have adopted acupuncture at Jǐngjiājǐ（颈夹脊） combined with nape cluster needling in treatment of 100 patients with cervicogenic headache. Nape cluster needling was： Xiànǎohù（下脑户）（located in the median depression under occipital bone）, Fēngfǔ（风府 GV 16） and Yǎmén（哑门 GV 15） were selected longitudinally; horizontally, the part from GV 16 to Wángǔ（完骨 GB 12） was divided into six equal sections, one section was an acupoint, and there were 12 acupoints in total at the left and right sides. Bilateral Jǐngjiājǐ（颈夹脊） points on the second vertebra to the seventh vertebra were selected. The acupuncture was conducted once a day, five days were considered as one course of treatment, and two days were free from treatment between two courses. Four courses of treatment were needed. All the patients were cured clinically. It can be seen that acupuncture at Jǐngjiājǐ combined with nape cluster needling in treatment of cervicogenic headache has sound effect.
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BACKGROUND Cluster headache(CH)is a severe incapacitating headache disorder.By definition,its diagnosis must exclude possible underlying structural conditions.AIM To review available information on CLH caused by structural lesions and to provide better guides in the distinguishing process and to ensure that there is not a potentially treatable structural lesion.METHODS We conducted a systematic review of 77 published cases of symptomatic CH and cluster-like headache(CLH)in PubMed and Google Scholar databases.RESULTS Structural pathologies associated with CH were vascular(37.7%),tumoral(32.5%)and inflammatory(27.2%).Brain mass-like lesions(tumoural and inflammatory)were the most common diseases(28.6%),among which 77.3%lesions were at the suprasellar(pituitary)region.Cases of secondary CH related to sinusitis rose dramatically,occupying 19.5%.The third most common disease was internal carotid artery dissection,accounting for 14.3%.Atypical clinical features raise an early suspicion of a secondary cause:Late age at onset and eye and retroorbital pains were common conditions requiring careful evaluation and were present in at least one-third of cases.Abnormal neurological examination was the most significant red flag for impaired cranial nerves.CLH patients may be responsive to typical CH treatments;therefore,the treatment response is not specific.CLH can be triggered by contralateral structural pathologies.CLH associated with sinusitis and cerebral venous thrombosis required more attention.CONCLUSION Since secondary headache could perfectly mimick primary CH,neuroimaging should be conducted in patients in whom primary and secondary headaches are suspected.Cerebral magnetic resonance imaging scans is the diagnostic management of choice,and further examinations include vessel imaging with contrast agents and dedicated scans focusing on specific cerebral areas(sinuses,ocular and sellar regions).Neuroimaging is as necessary at follow-up visits as at the first observation.
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.
基金supported by the National Key R&D Program of China(2023YFC3304600).
文摘Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighbor relationship between samples.To overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model.We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module.By this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one cluster.In addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering methods.The code is available at https://github.com/songzuolong/MNF-MDSC(accessed on 25 December 2024).
文摘Symplectic symmetry approach to clustering(SSAC)in atomic nuclei,recently proposed,is modified and further developed in more detail.It is firstly applied to the light two-cluster^(20)Ne+αsystem of^(24)Mg,the latter exhibiting well developed low-energy K^(π)=0_(1)^(+),k^(π)=2_(1)^(+) and π^(π)=0_(1)^(-) rotational bands in its spectrum.A simple algebraic Hamiltonian,consisting of dynamical symmetry,residual and vertical mixing parts is used to describe these three lowest rotational bands of positive and negative parity in^(24)Mg.A good description of the excitation energies is obtained by considering only the SU(3)cluster states restricted to the stretched many-particle Hilbert subspace,built on the leading Pauli allowed SU(3)multiplet for the positive-and negative-parity states,respectively.The coupling to the higher cluster-model configurations allows us to describe the known low-lying experimentally observed B(E2)transition probabilities within and between the cluster states of the three bands under consideration without the use of an effective charge.
基金supported by the National Key Research and Development Program of China(2018YFC1315301).
文摘Objective To analyze the prevalence and burden of headache disorders in China and its provinces from 1990 to 2021.Methods Using data from the Global Burden of Disease Study(GBD)2021,the number of prevalent cases,prevalence rate,disability-adjusted life years(DALYs),and age-standardized DALY rates were analyzed by sex,age group,and province for headache disorders and their subtypes(migraine and tension-type headache[TTH])between 1990 and 2021.Percentage changes during this period were also estimated.Results In 2021,approximately 426 million individuals in China were affected by headache disorders,with an age-standardized prevalence rate of 27,582.61/100,000.The age-standardized DALY rate for all headache disorders was 487.15/100,000.Between 1990 and 2021,the number of prevalent cases increased by 37.78%,while the prevalence of all headache disorders,migraine,and TTH increased by 6.92%,7.57%,and 7.86%,respectively.The highest prevalence was observed in the 30-34 age group(39,520.60/100,000).Migraine accounted for a larger proportion of DALYs attributable to headache disorders,whereas TTH has a greater impact on its prevalence.In 2021,the highest age-standardized DALY rates for headache disorders were observed in Heilongjiang(617.85/100,000)and Shanghai(542.86/100,000).Conclusion The prevalence of headache disorders is increasing in China.Effective health education,improve diagnosis and treatment are essential,particularly for middle-aged working populations and women of childbearing age.
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.
文摘In modern distributed systems and cloud computing architectures,high availability and high scalability are core requirements to ensure the continuous and stable operation of services.As key technologies for achieving these two goals,high-availability clusters and load-balancing clusters have significant differences in their design concepts and application scenarios,while also maintaining close connections.This paper aims to conduct an in-depth analysis of the core objectives,working principles,technical advantages and disadvantages,and typical application cases of high-availability clusters and load-balancing clusters.By introducing an analogical model of a“restaurant kitchen,”the differences between the two are intuitively explained,and their technical characteristics are compared in detail.Additionally,a detailed practical case is included to specifically demonstrate the collaborative work of high-availability and load-balancing technologies through the construction process of Keepalived and HAProxy.Finally,taking the architecture of a typical e-commerce website as an example,this paper demonstrates the best practice of organically combining the two cluster technologies in a production environment to build a robust and high-performance distributed system.Research shows that understanding the differences between the two and implementing collaborative deployment is the cornerstone of designing modern IT infrastructure.
基金financially supported by the vice chancellor for research and technology of Urmia University
文摘Numerous clustering algorithms are valuable in pattern recognition in forest vegetation,with new ones continually being proposed.While some are well-known,others are underutilized in vegetation science.This study compares the performance of practical iterative reallocation algorithms with model-based clustering algorithms.The data is from forest vegetation in Virginia(United States),the Hyrcanian Forest(Asia),and European beech forests.Practical iterative reallocation algorithms were applied as non-hierarchical methods and Finite Gaussian mixture modeling was used as a model-based clustering method.Due to limitations on dimensionality in model-based clustering,principal coordinates analysis was employed to reduce the dataset’s dimensions.A log transformation was applied to achieve a normal distribution for the pseudo-species data before calculating the Bray-Curtis dissimilarity.The findings indicate that the reallocation of misclassified objects based on silhouette width(OPTSIL)with Flexible-β(-0.25)had the highest mean among the tested clustering algorithms with Silhouette width 1(REMOS1)with Flexible-β(-0.25)second.However,model-based clustering performed poorly.Based on these results,it is recommended using OPTSIL with Flexible-β(-0.25)and REMOS1 with Flexible-β(-0.25)for forest vegetation classification instead of model-based clustering particularly for heterogeneous datasets common in forest vegetation community data.
基金supported by the National Natural Science Foundation of China(Grant No.72571150)Beijing Natural Science Foundation(Grant No.9182015)。
文摘Attributed graph clustering plays a vital role in uncovering hidden network structures,but it presents significant challenges.In recent years,various models have been proposed to identify meaningful clusters by integrating both structural and attribute-based information.However,these models often emphasize node proximities without adequately balancing the efficiency of clustering based on both structural and attribute data.Furthermore,they tend to neglect the critical fuzzy information inherent in attributed graph clusters.To address these issues,we introduce a new framework,Markov lumpability optimization,for efficient clustering of large-scale attributed graphs.Specifically,we define a lumped Markov chain on an attribute-augmented graph and introduce a new metric,Markov lumpability,to quantify the differences between the original and lumped Markov transition probability matrices.To minimize this measure,we propose a conjugate gradient projectionbased approach that ensures the partitioning closely aligns with the intrinsic structure of fuzzy clusters through conditional optimization.Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of the proposed framework compared to existing clustering algorithms.This framework has many potential applications,including dynamic community analysis of social networks,user profiling in recommendation systems,functional module identification in biological molecular networks,and financial risk control,offering a new paradigm for mining complex patterns in high-dimensional attributed graph data.
基金supported by the National Key R&D Program of China(No.2023YFA1606701)the National Natural Science Foundation of China(Nos.12175042 and 12147101)。
文摘As a cluster overlap amplitude,the reduced-width amplitude is an important physical quantity for analyzing clustering in the nucleus depending on specified channels and has been calculated and widely applied in nuclear cluster physics.In this review,we briefly revisit the theoretical framework for calculating the reduced-width amplitude,as well as the outlines of cluster models to obtain microscopic or semi-microscopic cluster wave functions.We also introduce the recent progress related to cluster overlap amplitudes,including the implementation of cross-section estimation and extension to three-body clustering analysis.Comprehensive examples are provided to demonstrate the application of the reduced-width amplitude in analyzing clustering structures.
基金supported by the National Key R&D Program of China(No.2023YFB3712504)the National Natural Science Foundation of China(Nos.22273038,22073043,and 22033004)。
文摘Accurate description of noncova-lent interactions in large systems is challenging due to the require-ment of high-level electron corre-lation methods.The generalized energy-based fragmentation(GEBF)approach,in conjunc-tion with the domain-based local pair natural orbital(DLPNO)method,has been applied to assess the average binding energies(ABEs)of large benzene clus-ters,specifically(C6H6)13,at the coupled cluster singles and doubles with perturbative triples correction[CCSD(T)]level and the complete basis set(CBS)limit.Utilizing GEBF-DLPNO-CCSD(T)/CBS ABEs as benchmarks,various DFT functionals were evaluated.It was found that several functionals with empirical dispersion correction,including M06-2X-D3,B3LYP-D3(BJ),and PBE-D3(BJ),provide accurate descriptions of the ABEs for(C6H6)13 clusters.Additionally,the M06-2X-D3 functional was used to calculate the ABEs and relative stabili-ties of(C6H6)n clusters for n=11,12,13,14,and 15 revealing that the(C6H6)13 cluster ex-hibits the highest relative stability.These findings align with experimental evidence suggest-ing that n=13 is one of the magic numbers for benzene clusters(C6H6)n,with n≤30.
基金supported in part by Boeing Company and Nanjing University of Aeronautics and Astronautics(NUAA)through the Research on Decision Support Technology of Air Traffic Operation Management in Convective Weather under Project 2022-GT-129in part by the Postgraduate Research and Practice Innovation Program of NUAA(No.xcxjh20240709)。
文摘Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflection point method”to analyze specific deviation trajectories,determine deviation thresholds,and identify commonly used deviation paths.By combining multiple similarity metrics,including Euclidean distance,Hausdorff distance,and sector edit distance,with the density-based spatial clustering of applications with noise(DBSCAN)algorithm,the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs.A case study of 23578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework.Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances,with higher silhouette coefficients and lower Davies⁃Bouldin indices,ensuring better intra-cluster compactness and inter-cluster separation.Based on clustering results,19 representative trajectory options are identified,covering both nominal and deviation paths,which significantly enhance route diversity and reflect actual flight practices.This provides a practical basis for optimizing flight paths and scheduling,enhancing the flexibility of route selection for flights between city pairs.
文摘A novel model of fuzzy clustering, i.e. an allied fuzzy c means （AFCM） model is proposed based on the combination of advantages of fuzzy c means （FCM） and possibilistic c means （PCM） clustering. PCM is sensitive to initializations and often generates coincident clusters. AFCM overcomes this shortcoming and it is an ex tension of PCM. Membership and typicality values can be simultaneously produced in AFCM. Experimental re- suits show that noise data can be well processed, coincident clusters are avoided and clustering accuracy is better.
基金Supported by the National Key Research and Development Program of China(No.2016YFB0201305)National Science and Technology Major Project(No.2013ZX0102-8001-001-001)National Natural Science Foundation of China(No.91430218,31327901,61472395,61272134,61432018)
文摘Clustering data with varying densities and complicated structures is important,while many existing clustering algorithms face difficulties for this problem. The reason is that varying densities and complicated structure make single algorithms perform badly for different parts of data. More intensive parts are assumed to have more information probably,an algorithm clustering from high density part is proposed,which begins from a tiny distance to find the highest density-connected partition and form corresponding super cores,then distance is iteratively increased by a global heuristic method to cluster parts with different densities. Mean of silhouette coefficient indicates the cluster performance. Denoising function is implemented to eliminate influence of noise and outliers. Many challenging experiments indicate that the algorithm has good performance on data with widely varying densities and extremely complex structures. It decides the optimal number of clusters automatically.Background knowledge is not needed and parameters tuning is easy. It is robust against noise and outliers.
基金supported in part by NIH grants R01NS39600,U01MH114829RF1MH128693(to GAA)。
文摘Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.
基金supported by the National Natural Science Foundation of China(Nos.51977027 and 51967008)the Scientific and Technological Project of Yunnan Precious Metals Lab-oratory(Nos.YPML-2023050250 and YPML-2022050206).
文摘The pursuit of Ag-based alloys with both high strength and toughness has posed a longstanding chal-lenge.In this study,we investigated the cluster strengthening and grain refinement toughening mecha-nisms in fully oxidized AgMgNi alloys,which were internally oxidized at 800℃ for 8 h under an oxy-gen atmosphere.We found that Mg-O clusters contributed to the hardening(138 HV)and strengthening(376.9 MPa)of the AgMg alloy through solid solution strengthening effects,albeit at the expense of duc-tility.To address this limitation,we introduced Ni nanoparticles into the AgMg alloy,resulting in signifi-cant grain refinement within its microstructure.Specifically,the grain size decreased from 67.2μm in the oxidized AgMg alloy to below 6.0μm in the oxidized AgMgNi alloy containing 0.3 wt%Ni.Consequently,the toughness increased significantly,rising from toughness value of 2177.9 MJ m^(-3) in the oxidized AgMg alloy to 6186.1 MJ m^(-3) in the oxidized AgMgNi alloy,representing a remarkable 2.8-fold enhancement.Furthermore,the internally oxidized AgMgNi alloy attained a strength of up to 387.6 MPa,comparable to that of the internally oxidized AgMg alloy,thereby demonstrating the successful realization of concurrent strengthening and toughening.These results collectively offer a novel approach for the design of high-performance alloys through the synergistic combination of cluster strengthening and grain refinement toughening.
基金supported by the National Natural Science Foundation of China(Nos.12174151 and 12304448)the Specific Research Fund of the Innovation Platform for Academicians of Hainan Province(No.YSPTZX202208)+3 种基金Hainan Province Clinical Medical Center(No.QWYH_(2)022341)the Key Laboratory of New Energy and Rare Earth Resource Utilization of the State People’s Committee of China(No.NERE202206)the Department of Science and Technology of Jilin Province(No.20220101059JC)the Key Laboratory of the Ministry of Education for First Aid and Trauma Research(No.KLET-202218).
文摘Given customizable crystal structure and intriguing optical properties,lanthanide titanium-oxygen clusters(LTOCs)with atomic-level accuracy have gained a lot of interest.In this study,we prepared[Ln_(9)Ti_(2)(μ4-O)(μ3-OH)_(14)(acac)_(17)(CH_(3)O)_(2)(CH_(3)OH)_(3)](Ln=Tb_(x)Eu_(9−x)(x=0,4,6,7,8,9),Hacac=acetylacetone),Tb^(3+)and Eu^(3+)co-doped LTOCs,to modify the optical properties for the luminescence thermometer.In detail,the serial LTOCs display dual characteristic emission peaks of ^(5)D_(4)→^(7)F_(5) for Tb^(3+)and^(5)D_(0)→^(7)F_(2) for Eu^(3+)at 548 and 616 nm,respectively,under 330 nm excitation.Effective energy transfer(ET)between Tb^(3+)ions and Eu^(3+)ions was revealed in terms of both emission spectra and luminescence lifetime.The ^(5)D_(0)→^(7)F_(2) emission intensity of Eu^(3+)ions at 616 nm is maximally enhanced(by a factor of 11.2)with a change in the relative molar ratio of Tb^(3+)to Eu^(3+),along with a change in the ET efficiency of Tb^(3+)→Eu^(3+).In addition,the luminescent color changes from red,orange,yellow,to green.This precise control of the ET process between rare-earth ions allows{Tb_(6)Eu_(3)Ti_(2)}to reach a maximum relative sensitivity of 1.241 K^(−1) at 355 K,which is an enhancement of up to 4.6-fold with respect to the previously reported homonuclear emission,holding great potential in the optical thermometers.
