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Liqiang Wang,Yong Wang,Wangxiu Danzhen,Daihai Ma 한국지질과학협의회 2019 Geosciences Journal Vol.23 No.2
The Kenbale Cu mineralization occurrence related to the diorite is a newly discovered Cu mineralization event in the Bangong-Nujiang metallogenic belt, Tibet, China. The Cu mineralization is hosted in the contact between the monzogranite or biotite quartz diorite and the fine-grained diorite, which is the mineralization related intrusion. In order to constrain the Kenbale mineralization age, petrogenesis and tectonic setting, we conducted LA-ICP-MS zircon U-Pb dating and Hf isotopic analyses of the biotite quartz diorite and fine-grained diorite and also the whole-rock geochemical study of the biotite quartz diorite. Zircon UPb dating show that weighted mean 206Pb/238U ages of the biotite quartz diorite and fine-grained diorite are 123.5 ± 1.9 Ma (MSWD = 2.3, n = 16) and 118.9 ± 1.3 Ma (MSWD = 2.5, n = 18), respectively. The biotite quartz diorite is a high-K calc-alkaline I-type magma rock and was controlled mainly by partial melting process during the magma formation and evolution. This intrusion is characterized by positive εHf(t) values (2.6 to 5.8) and old Hf crustal model ages (813 to 1016 Ma), indicating that the magma was sourced from partial melting of the Mesoproterozoic to Neoproterozoic juvenile crust of the northern Lhasa Terrane. Compared with the biotite quartz diorite, the mineralization associated fine-grained diorite has much higher zircon εHf(t) values (8.2 to 11.4) and younger Hf crustal model ages (450 to 650 Ma). These characteristics are similar with those of the coeval magmatic rocks induced by slab break-off of the southward subducted Bangong-Nujiang Ocean. The geochronology and geochemical results show that the Kenbale Cu mineralization was controlled by the slab break-off of the southward subducted Bangong-Nujiang Ocean.
Liqiang Zhong,Minghua Wang,Daming Li,Shengkai Tang,Tongqing Zhang,Wenji Bian,Xiaohui Chen 한국유전학회 2018 Genes & Genomics Vol.40 No.11
Freshwater gobies Rhinogobius cliffordpopei and R. giurinus are invasive species with particular concern because they have become dominant and were fierce competitors in the invaded areas in Yunnan-Guizhou Plateau (southwest of China). Information about genetic characteristics of R. giurinus have been published, but there were still no relevant reports about R. cliffordpopei. In present study, the complete mitochondrial genome of R. cliffordpopei was determined, which was 16,511 bp in length with A + T content of 51.1%, consisting of 13 protein-coding genes, 22 tRNAs, 2 ribosomal RNAs, and a control region. The gene composition and the structural arrangement of the R. cliffordpopei complete mtDNA were identical to most of other teleosts. Phylogenetic analyses placed R. cliffordpopei in a well-supported monophyletic cluster with other Rhinogobius fish. But the phylogenetic relationship between genus Rhinogobius and Tridentiger remained to be resolved.
Lingxu Wang,Huiying Liu,Liqiang Liu,Fengqing Zhang 한양대학교 세라믹연구소 2021 Journal of Ceramic Processing Research Vol.22 No.1
In order to studying the effect of the annealing atmospheres (nitrogen and air) on the films, two kinds of Bi4Ti3O12 (BIT) samples were prepared by the sol-gel and layer-by-layer thermal annealing method. Various factors, including lesser VO••, more V'B'i and greater grain size in the air-annealed samples, interacted on the ferroelectric and light absorption properties. However, more (V'B'i)-(VO••) reinforced the imbalance of electron capture in the ITO/BIT/Au capacitors, resulting in stronger imprinting. And the arrangement of (V'B'i)-(VO••) and the domain pinning in the air-annealed films resulted in more serious aging effect.
Multiple butterfly recognition based on deep residual learning and image analysis
Xi Tianyu,Wang Jiangning,Han Yan,Lin Congtian,Ji Liqiang 한국곤충학회 2022 Entomological Research Vol.52 No.1
Insect recognition is crucial for taxonomy. It helps researchers to process tremendous and various ecology data. Most studies focus on fine-tuning the deep learning network or altering the algorithm to enhance the identification accuracy, and some useful tools have been generated with these methods. This study focuses on the influence of image data on the recognition model. The single data set source of the existing automated identification tools is relatively simple, and the competition-based data set released only focuses on evaluating the model at present. For the first time, this article integrates butterfly image data sets from multiple sources, covered illustrated books, and popular butterfly science websites. The image types include standard specimen images, illustrated book scan images and camera shots. In addition, these images included not only fixed poses, but also various other images of butterflies in natural poses. The size of these images is also various. The testing data set is new data that does not belong to the training set, which also verifies the generalizability of the model, indicating that in practical applications this model can identify new images. This testing method is a breakthrough compared to the previous work. We designed different data sets using the ResNet18 network to train a classifier, which achieves a validation accuracy of 86% in the end of the analysis. By adjusting the data sets, the accuracy changes as well. This study provides a method to recognize hundreds of butterfly species and analyzes the testing progress from the point of view of data. It is the first to combine butterflies from multiple countries in a single data set, with a recognition accuracy that outperforms previous experiments, to the best of our knowledge. We further analyze the testing results of butterfly recognition at the family and genus level. We perform two more experiments to demonstrate the model in the case of similar species or genus.