Formation and transformation of metastable LPSO building blocks clusters in Mg-Gd-Y-Zn-Zr alloys by spinodal decomposition and heterogeneous nucleation

To study the formation and transformation mechanism of long-period stacked ordered(LPSO)structures,a systematic atomic scale analysis was conducted for the structural evolution of long-period stacked ordered(LPSO)structures in the Mg-Gd-Y-Zn-Zr alloy annealed at 300℃~500℃.Various types of metastable LPSO building block clusters were found to exist in alloy structures at different temperatures,which precipitate during the solidification and homogenization process.The stability of Zn/Y clusters is explained by the first principles of density functional theory.The LPSO structure is distinguished by the arrangement of its different Zn/Y enriched LPSO structural units,which comprises local fcc stacking sequences upon a tightly packed plane.The presence of solute atoms causes local lattice distortion,thereby enabling the rearrangement of Mg atoms in the different configurations in the local lattice,and local HCP-FCC transitions occur between Mg and Zn atoms occupying the nearest neighbor positions.This finding indicates that LPSO structures can generate necessary Schockley partial dislocations on specific slip surfaces,providing direct evidence of the transition from 18R to 14H.Growth of the LPSO,devoid of any defects and non-coherent interfaces,was observed separately from other precipitated phases.As a result,the precipitation sequence of LPSO in the solidification stage was as follows:Zn/Ycluster+Mg layers→various metastable LPSO building block clusters→18R/24R LPSO;whereas the precipitation sequence of LPSO during homogenization treatment was observed to be as follows:18R LPSO→various metastable LPSO building block clusters→14H LPSO.Of these,14H LPSO was found to be the most thermodynamically stable structure.

Unsaturated bi-heterometal clusters in metal-vacancy sites of 2D MoS2 for efficient hydrogen evolution

The valence states and coordination structures of doped heterometal atoms in two-dimensional(2D)nanomaterials lack predictable regulation strategies.Hence,a robust method is proposed to form unsaturated heteroatom clusters via the metal-vacancy restraint mechanism,which can precisely regulate the bonding and valence state of heterometal atoms doped in 2D molybdenum disulfide.The unsaturated valence state of heterometal Pt and Ru cluster atoms form a spatial coordination structure with Pt–S and Ru–O–S as catalytically active sites.Among them,the strong binding energy of negatively charged suspended S and O sites for H+,as well as the weak adsorption of positively charged unsaturated heterometal atoms for H*,reduces the energy barrier of the hydrogen evolution reaction proved by theoretical calculation.Whereupon,the electrocatalytic hydrogen evolution performance is markedly improved by the ensemble effect of unsaturated heterometal atoms and highlighted with an overpotential of 84 mV and Tafel slope of 68.5 mV dec^(−1).In brief,this metal vacancy-induced valence state regulation of heterometal can manipulate the coordination structure and catalytic activity of heterometal atoms doped in the 2D atomic lattice but not limited to 2D nanomaterials.

Research of caged dynamics of clusters center atoms in Pd_(82)Si_(18) amorphous alloy

To date,there is still a lack of a comprehensive explanation for caged dynamics which is regarded as one of the intricate dynamic behaviors in amorphous alloys.This study focuses on Pd_(82)Si_(18)as the research object to further elucidate the underlying mechanism of caged dynamics from multiple perspectives,including the cage's lifetime,atomic local environment,and atomic potential energy.The results reveal that Si atoms exhibit a pronounced cage effect due to the hindrance of Pd atoms,resulting in an anomalous peak in the non-Gaussian parameters.An in-depth investigation was conducted on the caged dynamics differences between fast and slow Si atoms.In comparison to fast Si atoms,slow Si atoms were surrounded by more Pd atoms and occupied lower potential energy states,resulting in smaller diffusion displacements for the slow Si atoms.Concurrently,slow Si atoms tend to be in the centers of smaller clusters with coordination numbers of 9 and 10.During the isothermal relaxation process,clusters with coordination numbers 9 and 10 have longer lifetimes,suggesting that the escape of slow Si atoms from their cages is more challenging.The findings mentioned above hold significant implications for understanding the caged dynamics.

Geometries and electronic structures of Zr_(n)Cu(n=2–12) clusters: A joint machine-learning potential density functional theory investigation

Zr-based amorphous alloys have attracted extensive attention because of their large glassy formation ability, wide supercooled liquid region, high elasticity, and unique mechanical strength induced by their icosahedral local structures.To determine the microstructures of Zr–Cu clusters, the stable and metastable geometry of Zr_(n)Cu(n=2–12) clusters are screened out via the CALYPSO method using machine-learning potentials, and then the electronic structures are investigated using density functional theory. The results show that the Zr_(n)Cu(n ≥ 3) clusters possess three-dimensional geometries, Zr_(n)Cu(n≥9) possess cage-like geometries, and the Zr_(12)Cu cluster has icosahedral geometry. The binding energy per atom gradually gets enlarged with the increase in the size of the clusters, and Zr_(n)Cu(n=5,7,9,12) have relatively better stability than their neighbors. The magnetic moment of most Zr_(n)Cu clusters is just 1μB, and the main components of the highest occupied molecular orbitals(HOMOs) in the Zr_(12)Cu cluster come from the Zr-d state. There are hardly any localized two-center bonds, and there are about 20 σ-type delocalized three-center bonds.

Nomogram prediction of vessels encapsulating tumor clusters in small hepatocellular carcinoma≤3 cm based on enhanced magnetic resonance imaging

BACKGROUND Vessels encapsulating tumor clusters(VETC)represent a recently discovered vascular pattern associated with novel metastasis mechanisms in hepatocellular carcinoma(HCC).However,it seems that no one have focused on predicting VETC status in small HCC(sHCC).This study aimed to develop a new nomogram for predicting VETC positivity using preoperative clinical data and image features in sHCC(≤3 cm)patients.AIM To construct a nomogram that combines preoperative clinical parameters and image features to predict patterns of VETC and evaluate the prognosis of sHCC patients.METHODS A total of 309 patients with sHCC,who underwent segmental resection and had their VETC status confirmed,were included in the study.These patients were recruited from three different hospitals:Hospital 1 contributed 177 patients for the training set,Hospital 2 provided 78 patients for the test set,and Hospital 3 provided 54 patients for the validation set.Independent predictors of VETC were identified through univariate and multivariate logistic analyses.These independent predictors were then used to construct a VETC prediction model for sHCC.The model’s performance was evaluated using the area under the curve(AUC),calibration curve,and clinical decision curve.Additionally,Kaplan-Meier survival analysis was performed to confirm whether the predicted VETC status by the model is associated with early recurrence,just as it is with the actual VETC status and early recurrence.RESULTS Alpha-fetoprotein_lg10,carbohydrate antigen 199,irregular shape,non-smooth margin,and arterial peritumoral enhancement were identified as independent predictors of VETC.The model incorporating these predictors demonstrated strong predictive performance.The AUC was 0.811 for the training set,0.800 for the test set,and 0.791 for the validation set.The calibration curve indicated that the predicted probability was consistent with the actual VETC status in all three sets.Furthermore,the decision curve analysis demonstrated the clinical benefits of our model for patients with sHCC.Finally,early recurrence was more likely to occur in the VETC-positive group compared to the VETC-negative group,regardless of whether considering the actual or predicted VETC status.CONCLUSION Our novel prediction model demonstrates strong performance in predicting VETC positivity in sHCC(≤3 cm)patients,and it holds potential for predicting early recurrence.This model equips clinicians with valuable information to make informed clinical treatment decisions.

Preoperatively predicting vessels encapsulating tumor clusters in hepatocellular carcinoma:Machine learning model based on contrast-enhanced computed tomography

BACKGROUND Recently,vessels encapsulating tumor clusters(VETC)was considered as a distinct pattern of tumor vascularization which can primarily facilitate the entry of the whole tumor cluster into the bloodstream in an invasion independent manner,and was regarded as an independent risk factor for poor prognosis in hepatocellular carcinoma(HCC).AIM To develop and validate a preoperative nomogram using contrast-enhanced computed tomography(CECT)to predict the presence of VETC+in HCC.METHODS We retrospectively evaluated 190 patients with pathologically confirmed HCC who underwent CECT scanning and immunochemical staining for cluster of differentiation 34 at two medical centers.Radiomics analysis was conducted on intratumoral and peritumoral regions in the portal vein phase.Radiomics features,essential for identifying VETC+HCC,were extracted and utilized to develop a radiomics model using machine learning algorithms in the training set.The model’s performance was validated on two separate test sets.Receiver operating characteristic(ROC)analysis was employed to compare the identified performance of three models in predicting the VETC status of HCC on both training and test sets.The most predictive model was then used to constructed a radiomics nomogram that integrated the independent clinical-radiological features.ROC and decision curve analysis were used to assess the performance characteristics of the clinical-radiological features,the radiomics features and the radiomics nomogram.RESULTS The study included 190 individuals from two independent centers,with the majority being male(81%)and a median age of 57 years(interquartile range:51-66).The area under the curve(AUC)for the combined radiomics features selected from the intratumoral and peritumoral areas were 0.825,0.788,and 0.680 in the training set and the two test sets.A total of 13 features were selected to construct the Rad-score.The nomogram,combining clinicalradiological and combined radiomics features could accurately predict VETC+in all three sets,with AUC values of 0.859,0.848 and 0.757.Decision curve analysis revealed that the radiomics nomogram was more clinically useful than both the clinical-radiological feature and the combined radiomics models.CONCLUSION This study demonstrates the potential utility of a CECT-based radiomics nomogram,incorporating clinicalradiological features and combined radiomics features,in the identification of VETC+HCC.

Optimization of jamming formation of USV offboard active decoy clusters based on an improved PSO algorithm

Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.

Optimal operation of Internet Data Center with PV and energy storage type of UPS clusters

With the development of green data centers,a large number of Uninterruptible Power Supply(UPS)resources in Internet Data Center(IDC)are becoming idle assets owing to their low utilization rate.The revitalization of these idle UPS resources is an urgent problem that must be addressed.Based on the energy storage type of the UPS(EUPS)and using renewable sources,a solution for IDCs is proposed in this study.Subsequently,an EUPS cluster classification method based on the concept of shared mechanism niche(CSMN)was proposed to effectively solve the EUPS control problem.Accordingly,the classified EUPS aggregation unit was used to determine the optimal operation of the IDC.An IDC cost minimization optimization model was established,and the Quantum Particle Swarm Optimization(QPSO)algorithm was adopted.Finally,the economy and effectiveness of the three-tier optimization framework and model were verified through three case studies.

Adaptive Density-Based Spatial Clustering of Applications with Noise(ADBSCAN)for Clusters of Different Densities

Finding clusters based on density represents a significant class of clustering algorithms.These methods can discover clusters of various shapes and sizes.The most studied algorithm in this class is theDensity-Based Spatial Clustering of Applications with Noise(DBSCAN).It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects.It requires two input parameters:epsilon(fixed neighborhood radius)and MinPts(the lowest number of objects in epsilon).However,it can’t handle clusters of various densities since it uses a global value for epsilon.This article proposes an adaptation of the DBSCAN method so it can discover clusters of varied densities besides reducing the required number of input parameters to only one.Only user input in the proposed method is the MinPts.Epsilon on the other hand,is computed automatically based on statistical information of the dataset.The proposed method finds the core distance for each object in the dataset,takes the average of these distances as the first value of epsilon,and finds the clusters satisfying this density level.The remaining unclustered objects will be clustered using a new value of epsilon that equals the average core distances of unclustered objects.This process continues until all objects have been clustered or the remaining unclustered objects are less than 0.006 of the dataset’s size.The proposed method requires MinPts only as an input parameter because epsilon is computed from data.Benchmark datasets were used to evaluate the effectiveness of the proposed method that produced promising results.Practical experiments demonstrate that the outstanding ability of the proposed method to detect clusters of different densities even if there is no separation between them.The accuracy of the method ranges from 92%to 100%for the experimented datasets.

Intellectual Property Protection and High-quality Development of Grape Industrial Clusters in Xinjiang

This paper introduced the grape industry in Xinjiang and its agricultural intellectual property resources,and analyzed six major problems such as the ineffective leading role of intellectual property rights in high-quality development.Finally,it proposed eight strategies to build a national-level regional fine-grain breeding base for grapes and develop“agricultural chips”in the context of the strategy of strengthening the country with intellectual property and innovation-driven development strategy.

In silico curation of QTL-rich clusters and candidate gene identification for plant height of bread wheat

Many genetic loci for wheat plant height(PH) have been reported, and 26 dwarfing genes have been catalogued. To identify major and stable genetic loci for PH, here we thoroughly summarized these functionally or genetic verified dwarfing loci from QTL linkage analysis and genome-wide association study published from 2003 to 2022. A total of 332 QTL, 270 GWAS loci and 83 genes for PH were integrated onto chromosomes according to their locations in the IWGSC RefSeq v2.1 and 65 QTL-rich clusters(QRC) were defined. Candidate genes in each QRC were predicted based on IWGSC Annotation v2.1 and the information on functional validation of homologous genes in other species. A total of 38 candidate genes were predicted for 65 QRC including three GA2ox genes in QRC-4B-IV, QRC-5A-VIII and QRC-6A-II(Rht24) as well as GA 20-oxidase 2(TaSD1-3A) in QRC-3A-IV. These outcomes lay concrete foundations for mapbased cloning of wheat dwarfing genes and application in breeding.

LISC Catalog of Open ClustersⅢ.83 Newly Found Galactic Disk Open Clusters Using Gaia EDR3

As groups of coeval stars born from the same molecular cloud,an open cluster(OC)is an ideal laboratory for studying the structure and dynamical evolution of the Milky Way.The release of high-precision Gaia Early Data Release 3(Gaia EDR3)and modern machine-learning methods offer unprecedented opportunities to identify OCs.In this study,we extended conventional HDBSCAN(e-HDBSCAN)for searching for new OCs in Gaia EDR3.A pipeline was developed based on the parallel computing technique to blindly search for OCs from Gaia EDR3within Galactic latitudes∣b∣<25°.As a result,we obtained 3787 star clusters,of which 83 new OCs were reported after cross-match and visual inspection.At the same time,the main star cluster parameters are estimated by color-magnitude diagram fitting.The study significantly increases the sample size and physical parameters of OCs in the catalog of OCs.It shows the incompleteness of the census of OCs across our Galaxy.

Polyhedral silver clusters as single molecule ammonia sensor based on charge transfer-induced plasmon enhancement

The unique plasmon resonance characteristics of nanostructures based on metal clusters have always been the focus of various plasmon devices and different applications. In this work, the plasmon resonance phenomena of polyhedral silver clusters under the adsorption of NH_(3) , N_(2), H_(2), and CH_(4) molecules are studied by using time-dependent density functional theory. Under the adsorption of NH_(3) , the tunneling current of silver clusters changes significantly due to the charge transfer from NH_(3) to silver clusters. However, the effects of N_(2), H_(2), and CH_(4) adsorption on the tunneling current of silver clusters are negligible. Our results indicate that these silver clusters exhibit excellent selectivities and sensitivities for NH_(3) detection. These findings confirm that the silver cluster is a promising NH_(3) sensor and provide a new method for designing high-performance sensors in the future.

Classifying Globular Clusters and Applying them to Estimate the mass of the Milky Way

We combine the kinematics of 159 globular clusters(GCs) provided by the Gaia Early Data Release 3(EDR3) with other observational data to classify the GCs,and to estimate the mass of the Milky Way(MW).We use the agemetallicity relation,integrals of motion,action space and the GC orbits to identify the GCs as either formed in situ(Bulge and Disk) or ex situ(via accretion).We find that 45.3% have formed in situ,while 38.4% may be related to known merger events:Gaia-Sausage-Enceladus,the Sagittarius dwarf galaxy,the Helmi streams,the Sequoia galaxy and the Kraken galaxy.We also further identify three new sub-structures associated with the Gaia-Sausage-Enceladus.The remaining 16.3% of GCs are unrelated to the known mergers and thought to be from small accretion events.We select 46 GCs which have radii 8.0 <r<37.3 kpc and obtain the anisotropy parameter β=0.315_(-0.049)^(+0.055),which is lower than the recent result using the sample of GCs in Gaia Data Release 2,but still in agreement with it by considering the error bar.By using the same sample,we obtain the MW mass inside the outermost GC as M(<37.3 kpc)=0.423_(-0.02)^(+0.02)×10^(12)M_(⊙),and the corresponding M_(200)=1.11_(-0.18)^(+0.25)×10^(12)M_(⊙).The estimated mass is consistent with the results in many recent studies.We also find that the estimated β and mass depend on the selected sample of GCs.However,it is difficult to determine whether a GC fully traces the potential of the MW.

Optical Spectroscopy of Classical Be Stars in Old Open Clusters

We performed the optical spectroscopy of 16 classical Be stars in 11 open clusters older than 100 Myr.Ours is the first spectroscopic study of classical Be stars in open clusters older than 100 Myr.We found that the Hαemission strength of most of the stars is less than 40?,in agreement with previous studies.Our analysis further suggests that one of the stars,[KW97]35–12,might be a weak Hαemitter in nature,showing Hαequivalent width of-0.5?.Interestingly,we also found that the newly detected classical Be star LSⅢ+4737b might be a component of the possible visual binary system LSⅢ+4737,where the other companion is also a classical Be star.Hence,the present study indicates the possible detection of a binary Be system.Moreover,it is observed that all 16 stars exhibit a lesser number of emission lines compared to classical Be stars younger than 100 Myr.Furthermore,the spectral type distribution analysis of B-type and classical Be stars for the selected clusters points out that the existence of CBe stars can depend on the spectral type distribution of B-type stars present in these clusters.

Optical pulling force on nanoparticle clusters with gain due to Fano-like resonance

We demonstrate that,in a simple linearly-polarized plane wave,the optical pulling forces on nanoparticle clusters with gain can be induced by the Fano-like resonance.The numerical results based on the full-wave calculation show that the optical pulling forces can be attributed to the recoil forces for the nanoparticle clusters composed of dipolar nanoparticles with three different configurations.Interestingly,the recoil forces giving rise to optical pulling forces are exactly dominated by the coupling term between the electric and magnetic dipoles excited in the nanoparticle clusters,while other higherorder terms have a negligible contribution.In addition,the optical pulling force can be tailored by modulating the Fano-like resonance via either the particle size or the gain magnitude,offering an alternative freedom degree for optical manipulations of particle clusters.

Fractions of Compact Object Binaries in Star Clusters:Theoretical Predictions

The binary population in field stars and star clusters contributes to the formation of gravitational wave(GW)sources.However,the fraction of compact-object binaries(CBs),which is an important feature parameter of binary populations,is still difficult to measure and very uncertain.This paper predicts the fractions of important CBs and semi-compact object binaries(SCBs) making use of an advanced stellar population synthesis technique.A comparison with the result of N-body simulation is also presented.It is found that most CBs are formed within about 500 Myr after the starburst.The fractions of CBs and SCBs are demonstrated to correlate with stellar metallicity.The higher the metallicity becomes,the smaller the fraction of black hole binaries(BHBs),neutron star binaries(NSBs) and SCBs.This suggests that the GW sources of BHBs and NSBs are more likely to form in metal-poor environments.However,the fraction of black hole-neutron star binaries is shown to be larger for metalrich populations on average.

Highly dispersed 1 nm Pt Pd bimetallic clusters for formic acid electrooxidation through a CO-free mechanism

Direct formic acid fuel cell(DFAFC) is an important research project in clean energy field.However,commercialization of DFAFC is still largely limited by the available catalysts with unsatisfied activity,durability and cost for formic acid electrooxidation(FAEO).Using Pt-and Pd-based nanoclusters as electrocatalysts is a particularly promising strategy to solve the above problem,but two attendant problems need to be solved firstly.(Ⅰ) The controllable synthesis of practicable and stable sub-2 nm clusters remains challenging.(Ⅱ) The catalyzing mechanism of sub-2 nm metal clusters for FAEO has not yet completely understood.Herein,different from traditional solution synthesis,by designing a novel supporting material containing electron-rich and electron-deficient functional groups,size-and dispersioncontrollable synthesis of ~1 nm PtPd nanoclusters is realized by an electrochemical process.The electrocatalytic properties and reaction mechanism of the PtPd nanoclusters for the FAEO were studied by different electrochemical techniques,in-situ fourier transform infrared(FTIR) spectra and density functional theory(DFT) calculations.The tiny PtPd nanoclusters have much higher catalytic activity and durability than commercial Pt/C,Pd/C and 3.5 nm PtPd nanoparticles.The present study shows that the metalreactant interaction plays a decisive role in determining the catalytic activity and cluster-support interaction plays a decisive role in enhancing the durability of electrocatalyst.The ratio and arrangement of Pt and Pd atoms on the surface of 1 nm PtPd cluster as well as the overall valence state,d-band center and specific surface area make them exhibit different catalytic performance and reaction mechanism from nanoparticle catalysts.In addition,in situ FTIR and DFT calculations showed that on the surface of PtPd clusters,the generation of CO_(2)through trans-COOH intermediate is the most optimal reaction pathway for the FAEO.

Constraints on Dark Energy from the CSST Galaxy Clusters

We study the potential of the galaxy cluster sample expected from the Chinese Space Station Telescope(CSST)survey to constrain dark energy properties.By modeling the distribution of observed cluster mass for a given true mass to be log-normal and adopting a selection threshold in the observed mass M_(200m)≥0.836×10^(14)h^(-1)M_(⊙),we find about 4.1×10^(5)clusters in the redshift range 0≤z≤1.5 can be detected by the CSST.We construct the Fisher matrix for the cluster number counts from CSST,and forecast constraints on dark energy parameters for models with constant(w_(0)CDM)and time dependent(w_(0)w_(a)CDM)equation of state.In the self-calibration scheme,the dark energy equation of state parameter w_(0)of the w_(0)CDM model can be constrained toΔw_(0)=0.036.If w_(a)is added as a free parameter,we obtainΔw_(0)=0.077 andΔw_(a)=0.39 for the w_(0)w_(a)CDM model,with a Figure of Merit for(w_(0),w_(a))of 68.99.Should we have perfect knowledge of the observable-mass scaling relation("known SR"scheme),we would obtainΔw_(0)=0.012 for the w_(0)CDM model,andΔw_(0)=0.062 andΔw_(a)=0.24 for the w_(0)w_(a)CDM model.The dark energy Figure of Merit of(w_(0),w_(a))increases to 343.25.This indicates again the importance of calibrating the observable-mass scaling relation for optically selected galaxy clusters.By extending the_(max)imum redshift of the clusters from z_(max)~1.5 to Z_(max)~2,the dark energy Figure of Merit for(w_(0),w_(a))increases to 89.72(self-calibration scheme)and 610.97("known SR"scheme),improved by a factor of~1.30 and~1.78,respectively.We find that the impact of clusters’redshift uncertainty on the dark energy constraints is negligible as long as the redshift error of clusters is smaller than 0.01,achievable by CSST.We also find that the bias in logarithm mass must be calibrated to be 0.30 or better to avoid significant dark energy parameter bias.

Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is increasing.One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical data.In this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN algorithm.The existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the algorithm.To solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each keyword.In order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in groups.We conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force method.The experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 times.GKM-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB).