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.

碳笼负载镍基磁性催化剂Ni@Cage-C的制备与性能研究

以自然结晶法制备的ZIF-67为前驱体,采用包裹-刻蚀-碳化策略,得到大小均匀的纳米碳笼(Cage-C),再于液相条件下以碳笼为载体负载上活性金属镍(Ni),成功制备了非贵金属磁性催化剂Ni@Cage-C,并应用于对硝基苯酚催化还原反应以考察其多相催化性能。结果表明:优化条件下制备的Ni@Cage-C催化剂为碳笼包裹单质镍结构,其平均颗粒大小为550 nm;将Ni@Cage-C用于对硝基苯酚催化还原反应时,催化性能明显优于参照催化剂雷尼镍(Raney-Ni)。质量反应速率常数kM为6.11 mg^(-1)·min^(-1),催化效率达到98.87%,循环反应十圈后活性仍高于初始活性的85%。

基于Blending-Clustering集成学习的大坝变形预测模型

【目的】变形是反映大坝结构性态最直观的效应量,构建科学合理的变形预测模型是保障大坝安全健康运行的重要手段。针对传统大坝变形预测模型预测精度低、误报率高等问题导致的错误报警现象,【方法】选取不同预测模型和聚类算法集成,构建了一种Blending-Clustering集成学习的大坝变形预测模型,该模型以Blending对单一预测模型集成提升预测精度为核心,并通过Clustering聚类优选预测值改善模型稳定性。以新疆某面板堆石坝变形监测数据为实例分析,通过多模型预测性能比较,对所提出模型的预测精度和稳定性进行全面评估。【结果】结果显示:Blending-Clustering模型将预测模型和聚类算法集成,均方根误差(RMSE)和归一化平均百分比误差(nMAPE)明显降低,模型的预测精度得到显著提高;回归相关系数(R~2)得到提升,模型具备更强的拟合能力;在面板堆石坝上22个测点变形数据集上的预测评价指标波动范围更小,模型的泛化性和稳定性得到有效增强。【结论】结果表明:Blending-Clustering集成预测模型对于预测精度、泛化性和稳定性均有明显提升,在实际工程具有一定的应用价值。

基于MRI椎体和终板骨质量评分对经椎间孔椎间融合术后cage沉降的预测价值

目的探讨基于MRI的椎体骨质量评分(vertebral bone quality score,VBQ)和终板骨质量评分(endplate bone quality score,EBQ)在经椎间孔腰椎椎间融合(transforaminal lumbar interbody fusion,TLIF)术后cage沉降中的预测价值。方法因腰椎退行性疾病在我院行TLIF手术的226例患者,根据术后有无cage沉降将患者分为沉降组和非沉降组,比较两组患者VBQ和EBQ评分。通过多元回归分析cage沉降的危险因素,并根据受试者工作特征曲线下面积(AUC)评估VBQ和EBQ预测TLIF术后cage沉降的能力。结果226例患者中30例出现术后cage沉降。沉降组VBQ(3.8±0.4)分,EBQ(5.1±0.7)分,明显高于非沉降组(3.1±0.6)分和(4.2±1.0)分,差异有统计学意义(P<0.001)。多元回归分析显示VBQ(OR=4.258,95%CI:1.983~9.142,P<0.001)和EBQ(OR=1.971,95%CI:1.212~3.203,P=0.006)评分越高,发生cage沉降风险也越大。受试者工作特征曲线结果显示VBQ的AUC为0.843,EBQ的AUC是0.864。VBQ和EBQ预测cage沉降的最佳阈值分别为3.480(敏感性90%;特异性75.5%)和4.620(敏感性96.7%;特异性74.5%)。结论术前VBQ或EBQ评分越高,TLIF术后发生cage沉降风险越大。其中EBQ可能是一个更好的预测融合术后cage沉降的指标。

后路寰枢椎侧块关节cage植骨融合内固定术治疗难复性寰枢椎脱位疗效初探

目的:探讨后路寰枢椎侧块关节cage植骨融合内固定术治疗难复性寰枢椎脱位的临床疗效,并与经口咽松解后路复位固定融合术进行疗效对比。方法:回顾性分析2018年1月~2022年8月我科采用后路寰枢椎侧块关节cage植骨融合内固定术(23例,cage组)与经口咽松解后路复位固定融合术(25例,对照组)治疗的难复性寰枢椎脱位患者的临床资料,cage组男8例,女15例,年龄9~79岁(48.35±14.38岁);对照组男6例,女19例,年龄21~69岁(47.84±13.51岁)。记录两组患者手术时间、术中出血量、住院时间及并发症情况,术前、术后及末次随访时使用JOA评分评估患者神经功能状态,测量术前、术后及末次随访时的寰齿间距(atlantodental interval,ADI)、齿状突顶点距离Chamberlain线的垂直距离(vertical distance from odon to idprocess to Chamberlain′s line,DOCL)、延髓颈髓角(cervicomedullary angle,CMA)、斜坡枢椎角(clivus-axial angle,CAA),评估寰枢椎复位情况。评估侧块关节cage及后方植骨融合情况。结果:所有患者内固定位置良好,减压充分复位满意,症状均明显缓解,未出现椎动脉损伤和脊髓损伤加重。cage组手术时间133.04±34.04min、术中出血量119.13±54.77mL、住院时间14.74±6.10d,均明显短于或少于对照组(253.20±53.98min、181.20±45.40mL、23.96±5.47d)。cage组术前JOA、ADI、DOCL、CMA、CAA分别为6.33±1.13分、7.31±3.05mm、9.47±3.32mm、122.89°±12.58°、122.02°±12.50°,术后分别为13.04±2.17分、2.18±0.67mm、0.89±1.00mm、148.81°±5.43°、146.70°±9.32°,末次随访时分别为14.89±1.17分、2.09±0.69mm、0.83±0.86mm、149.10°±5.11°、146.89°±8.95°;对照组术前JOA、ADI、DOCL、CMA、CAA分别为6.76±1.21分、7.70±0.97mm、10.56±1.99mm、121.53°±4.87°、123.77°±8.95°,术后分别为13.26±1.32分、1.89±0.50mm、1.13±1.08mm、151.40°±6.15°、149.86°±5.58°,末次随访时分别为15.02±0.88分、1.87±0.44mm、0.87±1.39mm、149.48°±4.06°、149.94°±6.61°,两组术后及末次随访JOA、ADI、DOCL、CMA及CAA均较术前明显改善(P<0.05),术后JOA评分与末次随访相比存在统计学差异(P<0.05),但ADI、DOCL、CMA及CAA无统计学差异(P>0.05)。cage组仅1例切口感染;对照组3例切口感染(口咽2例,后路1例),1例脑脊液漏。两组随访期间内固定在位稳定,末次随访植骨均达到骨性融合,cage组关节间隙高度无丢失。结论:难复性寰枢椎脱位采用后路寰枢椎侧块关节cage植骨融合内固定术与经口咽松解后路复位固定融合术相比疗效相当,但增加了植骨融合位点,能更有效融合,避免了经口手术,减少了手术时间、术中出血量、住院时间及并发症的发生。

Clustered张拉整体结构的动力学建模

针对clustered张拉整体结构,提出了一种基于任意拉格朗日-欧拉(arbitrary Lagrangian-Eulerian, ALE)法的多体动力学模型。相较于传统的拉格朗日法模型,该研究中的模型具有更为简单的运动学约束。首先,引入了一种ALE时变长度索单元,其网格节点与物质点可以独立运动,提供了一种自然的方式描述结构中运动的滑轮和滑动绳索;其次,利用达朗贝尔原理,推导了该单元的广义力向量并计算了相应的雅可比矩阵;然后,建立了张拉整体系统的动力学方程,并利用广义α算法对其进行求解,选取节点的全局位置坐标和物质坐标作为广义坐标,其中全局位置坐标可以被不同物体共享,以减少动力学方程的自由度数和消除物体间的约束;最后,展示了一个数值算例,10层可折叠张拉整体塔架,对其折叠过程进行了准静态和动力学仿真,验证了模型的有效性。所提出的模型和算法可为clustered张拉整体结构的设计提供理论指导,具有工程意义。

Harmonics in the Squirrel Cage Induction Motor Analytic Calculation Part III: Influence on the Torque-speed Characteristic

The harmonics that appear in the squirrel cage asynchronous machine have been discussed in great detail in the literature for a long time. However, the systematization of the phenomenon is still pending, so we made an attempt to fill this gap in the previous parts of our study by elaborating formulas for calculation of parasitic torques. It was a general demand among those who work in this field towards the author to verify his formulas with measurements. In the literature, it seems,only one detailed, purposeful series of measurements has been published so far, the purpose of which was to investigate the effect of the number of rotor slots on the torque-speed characteristic curve of the machine. The main goal of this study is to verify the correctness of the formulas by comparing them with the referred series of measurements. Relying on this, the expected synchronous parasitic torques were developed for the frequently used rotor slot numbers-as a design guide for the engineer.Thus, together with our complete table for radial magnetic pull published in our previous work, the designer has all the principles, data and formulas available for the right number of rotor slots for his given machine and for the drive system. This brings this series of papers to an end.

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.

基于Windows Server Failover Cluster的Apache故障转移集群应用实践

在当前互联网时代,Web应用程序的高可用性和可靠性是确保业务持续运行的关键因素。针对Apache Web服务器,介绍了利用Windows Server 2019 Failover Cluster构建Apache故障转移集群的实践方案。通过配置两个节点的故障转移集群,当某个节点发生故障时,Apache服务将自动在另一个节点上重新启动,从而最大程度地减少系统中断的时间,提高Web应用的可用性。详细阐述了集群搭建的具体步骤、配置要点、集群验证方法以及运行时的监控与维护方法,为构建高可用Apache Web服务器提供一种实用的解决方案。

Deep Learning and Tensor-Based Multiple Clustering Approaches for Cyber-Physical-Social Applications

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.

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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.

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.

Effects of interstitial cluster mobility on dislocation loops evolution under irradiation of austenitic steel

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.

Engineering of oxygen vacancy and bismuth cluster assisted ultrathin Bi_(12)O_(17)Cl_(2)nanosheets with efficient and selective photoreduction of CO_(2)to CO

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.

Weak Merging Scenario of CLASH Cluster A209

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.

A Shared Natural Neighbors Based-Hierarchical Clustering Algorithm for Discovering Arbitrary-Shaped Clusters

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.

Selective photosynthesis of Z-olefins through crystalline metal-organic cage-initiated expeditious cascade reactions

The semi-hydrogenation of alkyne to form Z-olefins with high conversion and high selectivity is still a huge challenge in the chemical industry.Moreover,flammable and explosive hydrogen as the common hydrogen source of this reaction increases the cost and danger of industrial production.Herein,we connect the photocatalytic hydrogen evolution reaction and the semihydrogenation reaction of alkynes in series and successfully realize the high selective production of Z-alkenes using low-cost,safe,and green water as the proton source.Before the cascade reaction,a series of isomorphic metal–organic cage catalysts(Co_(x)Zn_(8−x)L_(6),x=0,3,4,5,and 8)are designed and synthesized to improve the yield of the photocatalytic hydrogen production.Among them,Co_(5)Zn_(3)L_(6) shows the highest photocatalytic activity,with a H_(2) generation rate of 8.81 mmol g^(−1) h^(−1).Then,Co_(5)Zn_(3)L_(6) is further applied in the above tandem reaction to efficiently reduce alkynes to Z-alkenes under ambient conditions,which can reach high conversion of>98%and high selectivity of>99%,and maintain original catalytic activity after multiple cycles.This“one-pot”tandem reaction can achieve a highly selective and safe stepwise conversion from water into hydrogen into Z-olefins under mild reaction conditions.

Effects of anions on the structural regulation of Zn‑salen‑modified metal‑organic cage

By virtue of a 3∶1 complementary coordination strategy,a chiral heteroleptic metal-organic cage that con-tains divergent functional units,Pd‑R(Zn),was precisely constructed via self-assembly of monodentate variational Zn-salen ligands RZn and NADH(reduced nicotinamide adenine dinucleotide)mimic modified tridentate ligands with square-planar Pd ions.UV-Vis and luminescence spectra experiments reveal that different anions could selec-tively interact with different sites of Zn-salen modified metal-organic cages to achieve the structural regulation of cage compound,by using the differentiated host-guest electrostatic interactions of counter ions with metal-organic hosts.Compared to other anions,the presence of chloride ions caused the most significant fluorescence emission enhancement of Pd‑R(Zn),meanwhile,the UV-Vis absorption band attributed to the salen aromatic backbone showed an absorption decrease,and the metal-to-ligand induced peak displayed a blue shift effect.Circular dichro-ism and ^(1)H NMR spectra further demonstrate that the introduction of chloride anions is beneficial to keeping a more rigid scaffold.

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

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.

Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

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.