Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their sim...Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their simplicity and efficiency.This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering performance.The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)algorithm.Firstly,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population diversity.Secondly,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence speed.Finally,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation effectively.The performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic algorithms.To further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven datasets.Experimental results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering approaches.This study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.展开更多
Sorbus pohuashanensis(Hance) Hedl. is a potential horticulture and medicinal plant, but its genomic and genetic backgrounds remain unknown. Here, we sequence and assemble the S. pohuashanensis reference genome using P...Sorbus pohuashanensis(Hance) Hedl. is a potential horticulture and medicinal plant, but its genomic and genetic backgrounds remain unknown. Here, we sequence and assemble the S. pohuashanensis reference genome using Pac Bio long reads. Based on the new reference genome, we resequence a core collection of22 Sorbus spp. samples, which are divided into 2 groups(G1 and G2) based on phylogenetic and PCA analyses. These phylogenetic clusters are highly consistent with their classification based on leaf shape.Natural hybridization between the G1 and G2 groups is evidenced by a sample(R21) with a highly heterozygous genotype. Nucleotide diversity(π) analysis shows that G1 has a higher diversity than G2 and that G2 originated from G1. During the evolution process, the gene families involved in photosynthesis pathways expanded and the gene families involved in energy consumption contracted. RNA-seq data suggests that flavonoid biosynthesis and heat-shock protein(HSP)-heat-shock factor(HSF) pathways play important roles in protection against sunburn. This study provides new insights into the evolution of Sorbus spp.genomes. In addition, the genomic resources, and the identified genetic variations, especially those related to stress resistance, will help future efforts to produce and breed Sorbus spp.展开更多
We have collected nearly all the available observed data of the elements from Ba to Dy in halo and disk stars in the metallicity range -4.0 <[Fe/H]< 0.5. Based on the observed data of Ba and Eu, we evaluated the...We have collected nearly all the available observed data of the elements from Ba to Dy in halo and disk stars in the metallicity range -4.0 <[Fe/H]< 0.5. Based on the observed data of Ba and Eu, we evaluated the least-squares regressions of [Ba/Fe] on [Fe/H], and [Eu/H] on [Ba/H]. Assuming that the heavy elements (heavier than Ba) are produced by a combination of the main components of s- and r-processes in metal-poor stars, and choosing Ba and Eu as respective representative elements of the main s- and the main r-processes, a statistical model for predicting the Galactic chemical evolution of the heavy elements is presented. With this model, we calculate the mean abundance trends of the heavy elements La, Ce, Pr, Nd, Sm, and Dy with the metallicity. We compare our results with the observed data at various metallicities, showing that the predicted trends are in good agreement with the observed trends, at least for the metallicity range [Fe/H]≥ -2.5. Finally, we discuss our results and deduce some important information about the Galactic chemical evolution.展开更多
文摘Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their simplicity and efficiency.This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering performance.The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)algorithm.Firstly,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population diversity.Secondly,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence speed.Finally,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation effectively.The performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic algorithms.To further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven datasets.Experimental results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering approaches.This study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.
基金supported by the Fund of the National Natural Science Foundation of China (grant number 31770369) to J. Z。
文摘Sorbus pohuashanensis(Hance) Hedl. is a potential horticulture and medicinal plant, but its genomic and genetic backgrounds remain unknown. Here, we sequence and assemble the S. pohuashanensis reference genome using Pac Bio long reads. Based on the new reference genome, we resequence a core collection of22 Sorbus spp. samples, which are divided into 2 groups(G1 and G2) based on phylogenetic and PCA analyses. These phylogenetic clusters are highly consistent with their classification based on leaf shape.Natural hybridization between the G1 and G2 groups is evidenced by a sample(R21) with a highly heterozygous genotype. Nucleotide diversity(π) analysis shows that G1 has a higher diversity than G2 and that G2 originated from G1. During the evolution process, the gene families involved in photosynthesis pathways expanded and the gene families involved in energy consumption contracted. RNA-seq data suggests that flavonoid biosynthesis and heat-shock protein(HSP)-heat-shock factor(HSF) pathways play important roles in protection against sunburn. This study provides new insights into the evolution of Sorbus spp.genomes. In addition, the genomic resources, and the identified genetic variations, especially those related to stress resistance, will help future efforts to produce and breed Sorbus spp.
基金This research has been supported by the National Natural Science Foundation of China through grant No.19973002 Chinese Academy of Sciences-Peking University Joint Beijing Astrophysical Center.
文摘We have collected nearly all the available observed data of the elements from Ba to Dy in halo and disk stars in the metallicity range -4.0 <[Fe/H]< 0.5. Based on the observed data of Ba and Eu, we evaluated the least-squares regressions of [Ba/Fe] on [Fe/H], and [Eu/H] on [Ba/H]. Assuming that the heavy elements (heavier than Ba) are produced by a combination of the main components of s- and r-processes in metal-poor stars, and choosing Ba and Eu as respective representative elements of the main s- and the main r-processes, a statistical model for predicting the Galactic chemical evolution of the heavy elements is presented. With this model, we calculate the mean abundance trends of the heavy elements La, Ce, Pr, Nd, Sm, and Dy with the metallicity. We compare our results with the observed data at various metallicities, showing that the predicted trends are in good agreement with the observed trends, at least for the metallicity range [Fe/H]≥ -2.5. Finally, we discuss our results and deduce some important information about the Galactic chemical evolution.
