129 research outputs found
FIGURE 3 in Molecular phylogenetics and diversity of the Himalayan shrew (Soriculus nigrescens Gray, 1842) (Eulipotyphla, Soricidae) in Southwest China
No full textFIGURE 3: A: Plot showing JK values for different K values tested. The K with the highest JK value is most likely to represent the true number of clusters; B: The linear relationship between LnP(D) and the number of clusters. C: Bayesian clustering results at K = 3 from the structure analysis.Published as part of Jiang, Haijun, Fu, Changkun, Tang, Keyi, Li, Fengjun, Faiz, Abu Ul Hassan, Guo, Keji, Liu, Shaoying & Chen, Shunde, 2023, Molecular phylogenetics and diversity of the Himalayan shrew (Soriculus nigrescens Gray, 1842) (Eulipotyphla, Soricidae) in Southwest China, pp. 61-78 in Zootaxa 5263 (1) on page 66, DOI: 10.11646/zootaxa.5263.1.3, http://zenodo.org/record/779780
FIGURE 1 in Molecular phylogenetics and diversity of the Himalayan shrew (Soriculus nigrescens Gray, 1842) (Eulipotyphla, Soricidae) in Southwest China
No full textFIGURE 1: Map of Southwest China showing the sampling localities of S. nigrescens included in this study. Locality numbers are presented in Table 1, and the lineages have been labeled with different colors: blue for Clade A, red for Clade B, and purple for Clade YN. The sample of Nepal represents the collection site of the sequence downloaded from Genbank. Shaded area represents the distribution map of S. nigrescens from the Handbook Mammals of the World and Global Biodiversity Information Facility (GBIF: https://www.gbif.org/).Published as part of Jiang, Haijun, Fu, Changkun, Tang, Keyi, Li, Fengjun, Faiz, Abu Ul Hassan, Guo, Keji, Liu, Shaoying & Chen, Shunde, 2023, Molecular phylogenetics and diversity of the Himalayan shrew (Soriculus nigrescens Gray, 1842) (Eulipotyphla, Soricidae) in Southwest China, pp. 61-78 in Zootaxa 5263 (1) on page 64, DOI: 10.11646/zootaxa.5263.1.3, http://zenodo.org/record/779780
FIGURE 2 in Molecular phylogenetics and diversity of the Himalayan shrew (Soriculus nigrescens Gray, 1842) (Eulipotyphla, Soricidae) in Southwest China
No full textFIGURE 2: Maximum Likelihood and Bayesian phylogenetic trees based on the mitochondrial Cyt-B (A) and nuclear (APOB, BRCA-1, and RAG-2) (B) sequences. Branch numbers refer to BEAST posterior probabilities (Left: PP), ML posterior probabilities (Middle: PP), and ML bootstrap support values (Right: BS). The mtDNA and nuDNA lineages have been labeled with different colors: blue for Clade A, red for Clade B, and purple for Clade YN. The abbreviations represent the sampling sites (DR: Dingri; NLM: Nielamu; YD: Yadong; MT-L: Motuo low altitude; MT-H: Motuo high altitude; BM: Bomi; BY: Bayi; ML: Milin; GBJD: Gongbujiangda; LX: Langxian).Published as part of Jiang, Haijun, Fu, Changkun, Tang, Keyi, Li, Fengjun, Faiz, Abu Ul Hassan, Guo, Keji, Liu, Shaoying & Chen, Shunde, 2023, Molecular phylogenetics and diversity of the Himalayan shrew (Soriculus nigrescens Gray, 1842) (Eulipotyphla, Soricidae) in Southwest China, pp. 61-78 in Zootaxa 5263 (1) on page 65, DOI: 10.11646/zootaxa.5263.1.3, http://zenodo.org/record/779780
FIGURE 4 in Molecular phylogenetics and diversity of the Himalayan shrew (Soriculus nigrescens Gray, 1842) (Eulipotyphla, Soricidae) in Southwest China
No full textFIGURE 4: A: Bayesian species tree results for S. nigrescens in Southwestern China assuming three species based on mtDNA + nuDNA. The mtDNA and nuDNA lineages have been labeled with different colors: blue for Clade A, red for Clade B, and purple for Clade YN; B: Divergence time estimation of the S. nigrescens derived from BEAST by using Cyt-B dataset. Numbers above the clades and in the brackets represent estimated divergence dates (Left) and the posterior probabilities (Right) of each node. The lineages have been labeled with different colors: blue for Clade A, red for Clade B, and purple for Clade YN. The abbreviations represent the sampling sites (DR: Dingri; NLM: Nielamu; YD: Yadong; MT-L: Motuo low altitude; MT-H: Motuo high altitude; BM: Bomi; BY: Bayi; ML: Milin; GBJD: Gongbujiangda; LX: Langxian).Published as part of Jiang, Haijun, Fu, Changkun, Tang, Keyi, Li, Fengjun, Faiz, Abu Ul Hassan, Guo, Keji, Liu, Shaoying & Chen, Shunde, 2023, Molecular phylogenetics and diversity of the Himalayan shrew (Soriculus nigrescens Gray, 1842) (Eulipotyphla, Soricidae) in Southwest China, pp. 61-78 in Zootaxa 5263 (1) on page 67, DOI: 10.11646/zootaxa.5263.1.3, http://zenodo.org/record/779780
Remarks on the Pressure Regularity Criterion of the Micropolar Fluid Equations in Multiplier Spaces
No full textThis study is devoted to investigating the regularity criterion of weak solutions of the micropolar fluid equations in . The weak solution of micropolar fluid equations is proved to be smooth on when the pressure satisfies the following growth condition in the multiplier spaces , . The previous results on Lorentz spaces and Morrey spaces are obviously improved
Analysis of the Volatile Components in the Leaves of Cinnamomum camphora by Static Headspace Gas Chromatography Mass Spectrometry Combined with Accurate Weight Measurement
No full textTractor and Semitrailer Scheduling with Time Windows in Highway Ports with Unbalanced Demand Under Network Conditions
No full textTo address the challenges of unbalanced demand and high operational costs in highway port logistics, this study investigates the scheduling of tractors and semitrailers under time window constraints in a networked environment, where geographically distributed ports are interconnected by fixed routes, and tractors dynamically transport semitrailers between ports to balance asymmetric demands. A mathematical optimization model is developed, incorporating multiple car yards, diverse transport demands, and temporal constraints. To solve the model efficiently, an Adaptive Large Neighborhood Search (ALNS) algorithm is proposed and benchmarked against an improved Ant Colony System (IACS). Simulation results show that, compared to traditional scheduling methods, the proposed approach reduces the number of required tractors by up to 61% and operational costs by up to 21%, depending on tractor working hours. The tractor-to-semitrailer ratio improves from 1.00:1.10 to 1.00:2.59, demonstrating the enhanced resource utilization enabled by the ALNS algorithm. These findings offer practical guidance for optimizing tractor and semitrailer configurations in highway port operations under varying conditions
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