We study the structural and dynamical properties of A209 based on Chandra and XMM-Newton observations.We obtain detailed temperature,pressure,and entropy maps with the contour binning method,and find a hot region in t...We study the structural and dynamical properties of A209 based on Chandra and XMM-Newton observations.We obtain detailed temperature,pressure,and entropy maps with the contour binning method,and find a hot region in the NW direction.The X-ray brightness residual map and corresponding temperature profiles reveal a possible shock front in the NW direction and a cold front feature in the SE direction.Combined with the galaxy luminosity density map we propose a weak merger scenario.A young sub-cluster passing from the SE to NW direction could explain the optical subpeak,the intracluster medium temperature map,the X-ray surface brightness excess,and the X-ray peak offset together.展开更多
Clustering a social network is a process of grouping social actors into clusters where intra-cluster similarities among actors are higher than inter-cluster similarities. Clustering approaches, i.e. , k-medoids or hie...Clustering a social network is a process of grouping social actors into clusters where intra-cluster similarities among actors are higher than inter-cluster similarities. Clustering approaches, i.e. , k-medoids or hierarchical, use the distance function to measure the dissimilarities among actors. These distance functions need to fulfill various properties, including the triangle inequality (TI). However, in some cases, the triangle inequality might be violated, impacting the quality of the resulting clusters. With experiments, this paper explains how TI violates while performing traditional clustering techniques: k-medoids, hierarchical, DENGRAPH, and spectral clustering on social networks and how the violation of TI affects the quality of the resulting clusters.展开更多
The evolution of dislocation loops in austenitic steels irradiated with Fe^(+)is investigated using cluster dynamics(CD)simulations by developing a CD model.The CD predictions are compared with experimental results in...The evolution of dislocation loops in austenitic steels irradiated with Fe^(+)is investigated using cluster dynamics(CD)simulations by developing a CD model.The CD predictions are compared with experimental results in the literature.The number density and average diameter of the dislocation loops obtained from the CD simulations are in good agreement with the experimental data obtained from transmission electron microscopy(TEM)observations of Fe~+-irradiated Solution Annealed 304,Cold Worked 316,and HR3 austenitic steels in the literature.The CD simulation results demonstrate that the diffusion of in-cascade interstitial clusters plays a major role in the dislocation loop density and dislocation loop growth;in particular,for the HR3 austenitic steel,the CD model has verified the effect of temperature on the density and size of the dislocation loops.展开更多
Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric data.Recently,knowledge hints have ...Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric data.Recently,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge hints.However,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation indices.Moreover,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to knowledgemisguidance.To solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density points.First,a newdatadensitycalculation function is proposed.The Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge hints.Then,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data structure.Finally,the initial number of clusters is set to be greater than the true one based on the number of knowledge hints.Then,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination mechanism.Through experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.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
In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set f...In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.展开更多
In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world da...In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.展开更多
In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared...In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.展开更多
Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embe...Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.展开更多
Path-based clustering algorithms typically generate clusters by optimizing a benchmark function.Most optimiza-tion methods in clustering algorithms often offer solutions close to the general optimal value.This study a...Path-based clustering algorithms typically generate clusters by optimizing a benchmark function.Most optimiza-tion methods in clustering algorithms often offer solutions close to the general optimal value.This study achieves the global optimum value for the criterion function in a shorter time using the minimax distance,Maximum Spanning Tree“MST”,and meta-heuristic algorithms,including Genetic Algorithm“GA”and Particle Swarm Optimization“PSO”.The Fast Path-based Clustering“FPC”algorithm proposed in this paper can find cluster centers correctly in most datasets and quickly perform clustering operations.The FPC does this operation using MST,the minimax distance,and a new hybrid meta-heuristic algorithm in a few rounds of algorithm iterations.This algorithm can achieve the global optimal value,and the main clustering process of the algorithm has a computational complexity of O�k2×n�.However,due to the complexity of the minimum distance algorithm,the total computational complexity is O�n2�.Experimental results of FPC on synthetic datasets with arbitrary shapes demonstrate that the algorithm is resistant to noise and outliers and can correctly identify clusters of varying sizes and numbers.In addition,the FPC requires the number of clusters as the only parameter to perform the clustering process.A comparative analysis of FPC and other clustering algorithms in this domain indicates that FPC exhibits superior speed,stability,and performance.展开更多
We fit various color–magnitude diagrams(CMDs) of the high-latitude Galactic globular clusters NGC 5024(M53),NGC 5053,NGC 5272(M3),NGC 5466,and NGC 7099(M30) by isochrones from the Dartmouth Stellar Evolution Database...We fit various color–magnitude diagrams(CMDs) of the high-latitude Galactic globular clusters NGC 5024(M53),NGC 5053,NGC 5272(M3),NGC 5466,and NGC 7099(M30) by isochrones from the Dartmouth Stellar Evolution Database and Bag of Stellar Tracks and Isochrones for α–enrichment [α/Fe] = +0.4.For the CMDs,we use data sets from Hubble Space Telescope,Gaia,and other sources utilizing,at least,25 photometric filters for each cluster.We obtain the following characteristics with their statistical uncertainties for NGC 5024,NGC 5053,NGC 5272,NGC 5466,and NGC 7099,respectively:metallicities [Fe/H] =-1.93 ± 0.02,-2.08 ± 0.03,-1.60 ± 0.02,-1.95 ± 0.02,and-2.07 ± 0.04 dex with their systematic uncertainty 0.1 dex;ages 13.00 ± 0.11,12.70 ± 0.11,11.63 ± 0.07,12.15 ± 0.11,and 12.80 ± 0.17 Gyr with their systematic uncertainty 0.8 Gyr;distances(systematic uncertainty added) 18.22 ± 0.06 ± 0.60,16.99 ± 0.06 ± 0.56,10.08 ± 0.04 ± 0.33,15.59 ±0.03 ± 0.51,and 8.29 ± 0.03 ± 0.27 kpc;reddenings E(B-V) = 0.023 ± 0.004,0.017 ± 0.004,0.023 ± 0.004,0.023 ± 0.003,and 0.045 ± 0.002 mag with their systematic uncertainty 0.01 mag;extinctions AV= 0.08 ± 0.01,0.06 ± 0.01,0.08 ± 0.01,0.08 ± 0.01,and 0.16 ± 0.01 mag with their systematic uncertainty 0.03 mag,which suggest the total Galactic extinction AV= 0.08 across the whole Galactic dust to extragalactic objects at the North Galactic Pole.The horizontal branch morphology difference of these clusters is explained by their different metallicity,age,mass-loss efficiency,and loss of low-mass members in the evolution of the core-collapse cluster NGC 7099 and loose clusters NGC 5053 and NGC 5466.展开更多
Based on the analysis of the importance of professional cluster construction by ecological theory,with the change of social demand for talents,this paper explores the practice of environmental chemical professional cl...Based on the analysis of the importance of professional cluster construction by ecological theory,with the change of social demand for talents,this paper explores the practice of environmental chemical professional cluster construction in Pingdingshan University,including gradually perfecting teaching conditions and reforming teaching mode,breaking through the limitations of resources,integrating the boundaries of colleges and departments,integrating multiple resources,innovating systems and mechanisms,reconstructing professional clusters,decon-structing professional connotations,reorganizing curriculum systems,etc.,in order to better build the ecological chain network of education in application-oriented colleges and universities,realize the deep integration of industry and education,train future-oriented interdisciplinary applied talents of new engineering,and realize the construction of characteristic professional cluster in application-oriented colleges.展开更多
Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The signif...Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.展开更多
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)stru...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.展开更多
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 clu...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.展开更多
The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Inst...The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contributions and adverse consequences. The proposed methodologies offer novel approaches to address these dualities, providing a foundation for future research and practical applications. Ultimately, this study contributes to the ongoing discourse on optimizing the utility of network platforms while minimizing their negative impacts.展开更多
Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effect...Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.展开更多
The photocatalytic conversion of CO_(2)into solar‐powered fuels is viewed as a forward‐looking strategy to address energy scarcity and global warming.This work demonstrated the selective photoreduction of CO_(2)to C...The photocatalytic conversion of CO_(2)into solar‐powered fuels is viewed as a forward‐looking strategy to address energy scarcity and global warming.This work demonstrated the selective photoreduction of CO_(2)to CO using ultrathin Bi_(12)O_(17)Cl_(2)nanosheets decorated with hydrothermally synthesized bismuth clusters and oxygen vacancies(OVs).The characterizations revealed that the coexistences of OVs and Bi clusters generated in situ contributed to the high efficiency of CO_(2)–CO conversion(64.3μmol g^(−1)h^(−1))and perfect selectivity.The OVs on the facet(001)of the ultrathin Bi_(12)O_(17)Cl_(2)nanosheets serve as sites for CO_(2)adsorption and activation sites,capturing photoexcited electrons and prolonging light absorption due to defect states.In addition,the Bi‐cluster generated in situ offers the ability to trap holes and the surface plasmonic resonance effect.This study offers great potential for the construction of semiconductor hybrids as multiphotocatalysts,capable of being used for the elimination and conversion of CO_(2)in terms of energy and environment.展开更多
基金supported by the National Natural Science Foundation of China(grant Nos.U2038104 and 11703014)the Bureau of International Cooperation,Chinese Academy of Sciences(GJHZ1864)。
文摘We study the structural and dynamical properties of A209 based on Chandra and XMM-Newton observations.We obtain detailed temperature,pressure,and entropy maps with the contour binning method,and find a hot region in the NW direction.The X-ray brightness residual map and corresponding temperature profiles reveal a possible shock front in the NW direction and a cold front feature in the SE direction.Combined with the galaxy luminosity density map we propose a weak merger scenario.A young sub-cluster passing from the SE to NW direction could explain the optical subpeak,the intracluster medium temperature map,the X-ray surface brightness excess,and the X-ray peak offset together.
文摘Clustering a social network is a process of grouping social actors into clusters where intra-cluster similarities among actors are higher than inter-cluster similarities. Clustering approaches, i.e. , k-medoids or hierarchical, use the distance function to measure the dissimilarities among actors. These distance functions need to fulfill various properties, including the triangle inequality (TI). However, in some cases, the triangle inequality might be violated, impacting the quality of the resulting clusters. With experiments, this paper explains how TI violates while performing traditional clustering techniques: k-medoids, hierarchical, DENGRAPH, and spectral clustering on social networks and how the violation of TI affects the quality of the resulting clusters.
基金supported by the National Natural Science Foundation of China(No.U1967212)the Fundamental Research Funds for the Central Universities(No.2021MS032)the Nuclear Materials Innovation Foundation(No.WDZC-2023-AW-0305)。
文摘The evolution of dislocation loops in austenitic steels irradiated with Fe^(+)is investigated using cluster dynamics(CD)simulations by developing a CD model.The CD predictions are compared with experimental results in the literature.The number density and average diameter of the dislocation loops obtained from the CD simulations are in good agreement with the experimental data obtained from transmission electron microscopy(TEM)observations of Fe~+-irradiated Solution Annealed 304,Cold Worked 316,and HR3 austenitic steels in the literature.The CD simulation results demonstrate that the diffusion of in-cascade interstitial clusters plays a major role in the dislocation loop density and dislocation loop growth;in particular,for the HR3 austenitic steel,the CD model has verified the effect of temperature on the density and size of the dislocation loops.
基金supported by the National Key Research and Development Program of China(No.2022YFB3304400)the National Natural Science Foundation of China(Nos.6230311,62303111,62076060,61932007,and 62176083)the Key Research and Development Program of Jiangsu Province of China(No.BE2022157).
文摘Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric data.Recently,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge hints.However,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation indices.Moreover,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to knowledgemisguidance.To solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density points.First,a newdatadensitycalculation function is proposed.The Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge hints.Then,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data structure.Finally,the initial number of clusters is set to be greater than the true one based on the number of knowledge hints.Then,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination mechanism.Through experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.
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National Natural Science Foundation of China(U2133208,U20A20161)National Natural Science Foundation of China(No.62273244)Sichuan Science and Technology Program(No.2022YFG0180).
文摘In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.
基金supported in part by the National Natural Science Foundation of China under Grant 62171203in part by the Jiangsu Province“333 Project”High-Level Talent Cultivation Subsidized Project+2 种基金in part by the SuzhouKey Supporting Subjects for Health Informatics under Grant SZFCXK202147in part by the Changshu Science and Technology Program under Grants CS202015 and CS202246in part by Changshu Key Laboratory of Medical Artificial Intelligence and Big Data under Grants CYZ202301 and CS202314.
文摘In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.
基金This work was supported by Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-M202300502,KJQN201800539).
文摘In clustering algorithms,the selection of neighbors significantly affects the quality of the final clustering results.While various neighbor relationships exist,such as K-nearest neighbors,natural neighbors,and shared neighbors,most neighbor relationships can only handle single structural relationships,and the identification accuracy is low for datasets with multiple structures.In life,people’s first instinct for complex things is to divide them into multiple parts to complete.Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures.Taking inspiration from this,we propose a novel neighbor method:Shared Natural Neighbors(SNaN).To demonstrate the superiority of this neighbor method,we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters(HC-SNaN).Our algorithm excels in identifying both spherical clusters and manifold clusters.Tested on synthetic datasets and real-world datasets,HC-SNaN demonstrates significant advantages over existing clustering algorithms,particularly when dealing with datasets containing arbitrary shapes.
文摘Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.
文摘Path-based clustering algorithms typically generate clusters by optimizing a benchmark function.Most optimiza-tion methods in clustering algorithms often offer solutions close to the general optimal value.This study achieves the global optimum value for the criterion function in a shorter time using the minimax distance,Maximum Spanning Tree“MST”,and meta-heuristic algorithms,including Genetic Algorithm“GA”and Particle Swarm Optimization“PSO”.The Fast Path-based Clustering“FPC”algorithm proposed in this paper can find cluster centers correctly in most datasets and quickly perform clustering operations.The FPC does this operation using MST,the minimax distance,and a new hybrid meta-heuristic algorithm in a few rounds of algorithm iterations.This algorithm can achieve the global optimal value,and the main clustering process of the algorithm has a computational complexity of O�k2×n�.However,due to the complexity of the minimum distance algorithm,the total computational complexity is O�n2�.Experimental results of FPC on synthetic datasets with arbitrary shapes demonstrate that the algorithm is resistant to noise and outliers and can correctly identify clusters of varying sizes and numbers.In addition,the FPC requires the number of clusters as the only parameter to perform the clustering process.A comparative analysis of FPC and other clustering algorithms in this domain indicates that FPC exhibits superior speed,stability,and performance.
基金financial support from the Russian Science Foundation (grant No.20-72-10052)。
文摘We fit various color–magnitude diagrams(CMDs) of the high-latitude Galactic globular clusters NGC 5024(M53),NGC 5053,NGC 5272(M3),NGC 5466,and NGC 7099(M30) by isochrones from the Dartmouth Stellar Evolution Database and Bag of Stellar Tracks and Isochrones for α–enrichment [α/Fe] = +0.4.For the CMDs,we use data sets from Hubble Space Telescope,Gaia,and other sources utilizing,at least,25 photometric filters for each cluster.We obtain the following characteristics with their statistical uncertainties for NGC 5024,NGC 5053,NGC 5272,NGC 5466,and NGC 7099,respectively:metallicities [Fe/H] =-1.93 ± 0.02,-2.08 ± 0.03,-1.60 ± 0.02,-1.95 ± 0.02,and-2.07 ± 0.04 dex with their systematic uncertainty 0.1 dex;ages 13.00 ± 0.11,12.70 ± 0.11,11.63 ± 0.07,12.15 ± 0.11,and 12.80 ± 0.17 Gyr with their systematic uncertainty 0.8 Gyr;distances(systematic uncertainty added) 18.22 ± 0.06 ± 0.60,16.99 ± 0.06 ± 0.56,10.08 ± 0.04 ± 0.33,15.59 ±0.03 ± 0.51,and 8.29 ± 0.03 ± 0.27 kpc;reddenings E(B-V) = 0.023 ± 0.004,0.017 ± 0.004,0.023 ± 0.004,0.023 ± 0.003,and 0.045 ± 0.002 mag with their systematic uncertainty 0.01 mag;extinctions AV= 0.08 ± 0.01,0.06 ± 0.01,0.08 ± 0.01,0.08 ± 0.01,and 0.16 ± 0.01 mag with their systematic uncertainty 0.03 mag,which suggest the total Galactic extinction AV= 0.08 across the whole Galactic dust to extragalactic objects at the North Galactic Pole.The horizontal branch morphology difference of these clusters is explained by their different metallicity,age,mass-loss efficiency,and loss of low-mass members in the evolution of the core-collapse cluster NGC 7099 and loose clusters NGC 5053 and NGC 5466.
基金Supported by Education and Teaching Reform Research Project of Pingdingshan University(2021-JY55,2020-JY05)Key Scientifie Research Project of Col-leges and Universities in Henan Province(22B180011)+2 种基金Project of Henan Sci-ence and Technology Department(232102320262)Ideological and Political Theories Teaching in Key Demonstration Courses at School Level in Pingdings-han College in 2022-Comprehensive Experiment of Environmental BiologyIde-ological and Political Theories Teaching in Demonstration Courses at School Level in Pingdingshan College in 2023-Ecological Engineering.
文摘Based on the analysis of the importance of professional cluster construction by ecological theory,with the change of social demand for talents,this paper explores the practice of environmental chemical professional cluster construction in Pingdingshan University,including gradually perfecting teaching conditions and reforming teaching mode,breaking through the limitations of resources,integrating the boundaries of colleges and departments,integrating multiple resources,innovating systems and mechanisms,reconstructing professional clusters,decon-structing professional connotations,reorganizing curriculum systems,etc.,in order to better build the ecological chain network of education in application-oriented colleges and universities,realize the deep integration of industry and education,train future-oriented interdisciplinary applied talents of new engineering,and realize the construction of characteristic professional cluster in application-oriented colleges.
文摘Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.
基金financially funded by Natural Science Basic Research Program of Shaanxi(grant number 2022JM-239)Key Research and Development Project of Shaanxi Provincial(grant number 2021LLRH-05–08)。
文摘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.
基金supported by the National Natural Science Foundation of China(22205209,52202373 and U21A200972)China Postdoctoral Science Foundation(2022M722867)Key Research Project of Higher Education Institutions in Henan Province(23A530001)。
文摘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.
基金sponsored by the National Natural Science Foundation of P.R.China(Nos.62102194 and 62102196)Six Talent Peaks Project of Jiangsu Province(No.RJFW-111)Postgraduate Research and Practice Innovation Program of Jiangsu Province(Nos.KYCX23_1087 and KYCX22_1027).
文摘The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed at enhancing their positive aspects and mitigating their negative repercussions. To achieve this, the study introduces a weight learning method grounded in multi-linear attribute ranking. This approach serves to evaluate the significance of attribute combinations across all feature spaces. Additionally, a novel clustering method based on tensors is proposed to elevate the quality of clustering while effectively distinguishing selected features. The methodology incorporates a weighted average similarity matrix and optionally integrates weighted Euclidean distance, contributing to a more nuanced understanding of attribute importance. The analysis of the proposed methods yields significant findings. The weight learning method proves instrumental in discerning the importance of attribute combinations, shedding light on key aspects within feature spaces. Simultaneously, the clustering method based on tensors exhibits improved efficacy in enhancing clustering quality and feature distinction. This not only advances our understanding of attribute importance but also paves the way for more nuanced data analysis methodologies. In conclusion, this research underscores the pivotal role of network platforms in contemporary society, emphasizing their potential for both positive contributions and adverse consequences. The proposed methodologies offer novel approaches to address these dualities, providing a foundation for future research and practical applications. Ultimately, this study contributes to the ongoing discourse on optimizing the utility of network platforms while minimizing their negative impacts.
文摘Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.
基金Natural Science Foundation of Shandong Province,Grant/Award Number:ZR2022MB106national training program of innovation and entrepreneurship for undergraduates,Grant/Award Number:202210424099National Natural Science Foundation of China,Grant/Award Numbers:21601067,21701057,21905147。
文摘The photocatalytic conversion of CO_(2)into solar‐powered fuels is viewed as a forward‐looking strategy to address energy scarcity and global warming.This work demonstrated the selective photoreduction of CO_(2)to CO using ultrathin Bi_(12)O_(17)Cl_(2)nanosheets decorated with hydrothermally synthesized bismuth clusters and oxygen vacancies(OVs).The characterizations revealed that the coexistences of OVs and Bi clusters generated in situ contributed to the high efficiency of CO_(2)–CO conversion(64.3μmol g^(−1)h^(−1))and perfect selectivity.The OVs on the facet(001)of the ultrathin Bi_(12)O_(17)Cl_(2)nanosheets serve as sites for CO_(2)adsorption and activation sites,capturing photoexcited electrons and prolonging light absorption due to defect states.In addition,the Bi‐cluster generated in situ offers the ability to trap holes and the surface plasmonic resonance effect.This study offers great potential for the construction of semiconductor hybrids as multiphotocatalysts,capable of being used for the elimination and conversion of CO_(2)in terms of energy and environment.
