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- 저자 : Yifan Fei (검색결과 3 건)
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Effects of carboxylesterase gene silence on wheat aphid Sitobion avenae (Fabricius)
Yifan Zhang,Fei Deng,Yongliang Fan,Zhangwu Zhao 한국응용곤충학회 2016 Journal of Asia-Pacific Entomology Vol.19 No.2
Multifunctional carboxylesterase (CarE) has been found in all animals, plants and microbes, and belongs to a superfamily enzyme of serine hydrolase involved in detoxification, allelochemical tolerance and some specific hormone or pheromone metabolism. Insects usually utilize carboxylesterases to detoxify xenobiotics, and positively correlated with insect resistance to some insecticides. Despite the importance of CarEs in insects, carboxylesterases and their functions in wheat aphid Sitobion avenae (Fabricius) have not been clear. In this study, a sequence that encodes a carboxylesterase protein from S. avenae (SaCarE) was sequenced and cloned. After aligning the encoded amino acid sequence of the SaCarE gene with other known CarEs of insects, we found that the CarE gene was highly conserved in insects. The SaCarE mRNA levels at different developmental stages of S. avenae were gradually increased from the first instar of nymphs to adult stage. RNAi was employed to further explore its functions, in which oral ingestion of SaCarE double-stranded RNA from the third instar nymph significantly knocked down SaCarE expression, and significantly decreased ecdysis index in S. avenae. These results indicate that SaCarE is functional in S. avenae and could serve one of the potential target genes for management of S. avenae.
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Xu Yongjia,Lu Xinzheng,Fei Yifan,Huang Yuli 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.5
There are numerous advantages of deep neural network surrogate modeling for response time-history prediction. However, due to the high cost of refined numerical simulations and actual experiments, the lack of data has become an unavoidable bottleneck in practical applications. An iterative self-transfer learning method for training neural networks based on small datasets is proposed in this study. A new mapping-based transfer learning network, named as deep adaptation network with three branches for regression (DAN-TR), is proposed. A general iterative network training strategy is developed by coupling DAN-TR and the pseudo-label strategy, and the establishment of corresponding datasets is also discussed. Finally, a complex component is selected as a case study. The results show that the proposed method can improve the model performance by near an order of magnitude on small datasets without the need of external labeled samples, well behaved pre-trained models, additional artificial labeling, and complex physical/mathematical analysis.
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Generative Artificial Intelligence for Structural Design of Tall Buildings
Wenjie Liao,Xinzheng Lu,Yifan Fei Council on Tall Building and Urban Habitat Korea 2023 International journal of high-rise buildings Vol.12 No.3
The implementation of artificial intelligence (AI) design for tall building structures is an essential solution for addressing critical challenges in the current structural design industry. Generative AI technology is a crucial technical aid because it can acquire knowledge of design principles from multiple sources, such as architectural and structural design data, empirical knowledge, and mechanical principles. This paper presents a set of AI design techniques for building structures based on two types of generative AI: generative adversarial networks and graph neural networks. Specifically, these techniques effectively master the design of vertical and horizontal component layouts as well as the cross-sectional size of components in reinforced concrete shear walls and frame structures of tall buildings. Consequently, these approaches enable the development of high-quality and high-efficiency AI designs for building structures.
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