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| 1 | The complete mitochondrial genome of the grey bamboo shark (Chiloscyllium griseum) (Orectolobiformes: Hemiscylliidae): genomic characterization and phylogenetic application显示文摘The complete mitochondrial genome of the grey bamboo shark, Chiloscyllium griseum (Orectolobiformes: Hemiscylliidae) was obtained, with a total of 16 755 base pairs (bp). The mitogenome encoded 13 proteincoding genes, two ribosomal RNAs, 22 transfer RNAs and a noncoding control region, a gene arrangement identical to the most common type found in vertebrates. All tRNA sequences were folded into typical cloverleaf secondary structure with the exception of tRNA-Ser2, in which the DHU arm stem was replaced with 12 unpaired nucleotides and formed a simple loop. In the control region, the putative termination-associated sequences (TAS) with hairpin-loop structure and the conserved sequence blocks (CSB) Ⅰ-Ⅲ were identified and considered to be associated with the replication and transcription of mtDNA. No repeat motifs were found in the mitogenome of C. griseum. On the basis of the mitogenomic data available in the Class Chondrichthyes from both Genbank and this study, and the phylogenetic analyses using maximum likelihood (ML) and Bayesian inference (BI) methods, the results strongly support the basal division between batoids and sharks and the monophyly of the Superorder Galeomorphii, and confirm the phylogenetic position of C. griseum in the Subclass Elasmobranchii. This study suggests that the mitogenomic data are more robust for reconstructing phylogenetic relationships than individual genes in the Elasmobranchii. | CHEN Xiao AI Weiming YE Le WANG Xuehui LIN Chongwen YANG Shengyun | 2013 | Acta Oceanologica Sinica2013，32，4： | 0 |
| 2 | A graph autoencoder network to measure the geometric similarity of drainage networks in scaling transformation显示文摘Similarity measurement has been a prevailing research topic geographic information science.Geometric similarity measurement inin scaling transformation(GSM_ST)is critical to ensure spatial data quality while balancing detailed information with distinctive features.However,GSM_ST is an uncertain problem due to subjective spatial cognition,global and local concerns,and geometric complexity.Traditional rule-based methods considering multiple consistent conditions require subjective adjustments to characteristics and weights,leading to poor robustness in addressing GSM_ST.This study proposes an unsupervised representation learning framework for automated GSM_ST,using a Graph Autoencoder Network(GAE)and drainage networks as an example.The framework involves constructing a drainage graph,designing the GAE architecture for GSM_ST,and using Cosine similarity to measure similarity based on the GAE-derived drainage embeddings in different scales.We perform extensive experiments and compare methods across 71 drainage networks duringfive scaling transformations.The results show that the proposed GAE method outperforms other methods with a satisfaction ratio of around 88%and has strong robustness.Moreover,our proposed method also can be applied to other scenarios,such as measuring similarity between geographical entities at different times and data from different datasets. | Huafei Yu Tinghua Ai Min Yang Weiming Huang Lars Harrie | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
| 3 | Point cloud-based environment reconstruction and ray tracing simulations for railway tunnel channels显示文摘Radio propagation environment plays a critical role in the performance of wireless communication systems,and understanding channel characteristics is vital for ensuring reliable communication links and optimizing system performance.Ray tracing is an effective method to investigate propagation characteristics in a complex environment,and how to quickly and accurately obtain environmental information needs to be solved.This paper presents dynamic environment reconstruction and ray tracing simulation in railway tunnel environment based on Simultaneous Localization and Mapping(SLAM)algorithm and Poisson reconstruction algorithm.Accurate channel parameters are obtained and analyzed based on ray tracing simulation.Both straight and curved tunnels are considered and investigated,and the results show the channel characteristics in complex railway tunnel environments. | Ziyi Qi Ruisi He Mi Yang Xuejian Zhang Bo Ai Weiming Li Shaopeng Wang Wenpu Sun Lizhe Li | 2023 | High-Speed Railway2023，1，4： | 0 |
| 4 | Homogeneous intercalated graphene/manganic oxide hybrid fiber electrode assembly by non-liquid-crystal spinning for wearable energy storage显示文摘Reduced graphene oxide(rGO)-based fibers with high electrochemical performance have recently showed great potential in the field of flexible energy storage devices.However,they still suffer from low capacitance due to the severe stacking of graphene sheets.Hybrids with nanofillers are an efficient way to improve the electrochemical performance of rGO fibers.Nevertheless,controlling the distribution of nanoparticles in the matrix is still an enormous challenge due to the strong attraction among these nanoparticles which results into agglomeration.Here,we continually prepared rGO hybrid fibers via nonliquid-crystal spinning,accompanied by chemical reduction.Manganic oxide(Mn OX)nanoparticles remained well-dispersed in GO dispersion during the continuous spinning of rGO/Mn OXhybrid fibers.Results showed that rGO/Mn OX-20 hybrid fibers possessed the best capacitance of 123.3 F g^(-1)(87.6 F cm^(-3))and 97.1 F g^(-1)(68.9 F cm^(-3))at the current density of 0.2 A g^(-1),and 0.5 A g^(-1)respectively.Furthermore,a fiber-shaped all-solid-state supercapacitor assembly from the optimized hybrid fibers demonstrated an energy density of 2.67 m Wh cm^(-3)(3.76 m Wh g^(-1))at the power density of 24.76 m Wh cm^(-3)(34.89 m Wh g^(-1)).These fiber-based devices show great potential for application in the fields of wearable electronics and energy storage devices. | Guoyin Chen Weiming Wang Xin Lu Innocent Tendo Mugaanire Yang Zhang Yulu Ai Kai Hou Benjamin S.Hsiao Meifang Zhu | 2022 | Journal of Materials Science & Technology2022，，2： | 0 |
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