Morphological and molecular data confirm a new species in genus Malcus from Xizang,China.Malcus zhengi sp.nov.is described and illustrated.Photos and illustrations of the adult,critical morphological characteristics a...Morphological and molecular data confirm a new species in genus Malcus from Xizang,China.Malcus zhengi sp.nov.is described and illustrated.Photos and illustrations of the adult,critical morphological characteristics and pygophore are provided.展开更多
The detection and parameterization of molecular clumps are the first step in studying them.We propose a method based on the Local Density Clustering algorithm while physical parameters of those clumps are measured usi...The detection and parameterization of molecular clumps are the first step in studying them.We propose a method based on the Local Density Clustering algorithm while physical parameters of those clumps are measured using the Multiple Gaussian Model algorithm.One advantage of applying the Local Density Clustering to the clump detection and segmentation,is the high accuracy under different signal-to-noise levels.The Multiple Gaussian Model is able to deal with overlapping clumps whose parameters can reliably be derived.Using simulation and synthetic data,we have verified that the proposed algorithm could accurately characterize the morphology and flux of molecular clumps.The total flux recovery rate in 13CO(J=1-0)line of M16 is measured as 90.2%.The detection rate and the completeness limit are 81.7%and 20 K km s-1 in 13CO(J=1-0)line of M16,respectively.展开更多
基金This study received financial support from the National Natural Science Foundation of China(31820103013,31430079)the Project of Ministry of Science and Technology of the People’s Republic of China(2015FY210300).
文摘Morphological and molecular data confirm a new species in genus Malcus from Xizang,China.Malcus zhengi sp.nov.is described and illustrated.Photos and illustrations of the adult,critical morphological characteristics and pygophore are provided.
基金Supported by the National Natural Science Foundation of China。
文摘The detection and parameterization of molecular clumps are the first step in studying them.We propose a method based on the Local Density Clustering algorithm while physical parameters of those clumps are measured using the Multiple Gaussian Model algorithm.One advantage of applying the Local Density Clustering to the clump detection and segmentation,is the high accuracy under different signal-to-noise levels.The Multiple Gaussian Model is able to deal with overlapping clumps whose parameters can reliably be derived.Using simulation and synthetic data,we have verified that the proposed algorithm could accurately characterize the morphology and flux of molecular clumps.The total flux recovery rate in 13CO(J=1-0)line of M16 is measured as 90.2%.The detection rate and the completeness limit are 81.7%and 20 K km s-1 in 13CO(J=1-0)line of M16,respectively.
