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31 results on '"Lv, Siying"'

1. Research progress on intelligent optimization techniques for energy-efficient design of ship hull forms

Author

Zhu, Shuwei, Lv, Siying, Chen, Kaifeng, Fang, Wei, and Cao, Leilei

Subjects
Computer Science - Computational Engineering, Finance, and Science, 41C99, J.6, I.2.8
Abstract

The design optimization of ship hull form based on hydrodynamics theory and simulation-based design (SBD) technologies generally considers ship performance and energy efficiency performance as the design objective, which plays an important role in smart design and manufacturing of green ship. An optimal design of sustainable energy system requires multidisciplinary tools to build ships with the least resistance and energy consumption. Through a systematic approach, this paper presents the research progress of energy-efficient design of ship hull forms based on intelligent optimization techniques. We discuss different methods involved in the optimization procedure, especially the latest developments of intelligent optimization algorithms and surrogate models. Moreover, current development trends and technical challenges of multidisciplinary design optimization and surrogate-assisted evolutionary algorithms for ship design are further analyzed. We explore the gaps and potential future directions, so as to paving the way towards the design of the next generation of more energy-efficient ship hull form., Comment: 30 pages, 8 figures

Published
2024

12. Corrigendum to “Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study” [Bone 140, November 2020, 115561]

Author

Zhang, Bin, Yu, Keyan, Ning, Zhenyuan, Wang, Ke, Dong, Yuhao, Liu, Xian, Liu, Shuxue, Wang, Jian, Zhu, Cuiling, Yu, Qinqin, Duan, Yuwen, Lv, Siying, Zhang, Xintao, Chen, Yanjun, Wang, Xiaojia, Shen, Jie, Peng, Jia, Chen, Qiuying, Zhang, Yu, Zhang, Xiaodong, and Zhang, Shuixing

Published
2021
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26. Short-term predicting model for water bloom based on Elman neural network

Author

Cui Lifeng, Wang Xiaoyi, Liu Zaiwen, and Lv Siying

Subjects
Chemical technology, Artificial neural network, business.industry, Artificial intelligence, business, Machine learning, computer.software_genre, Bloom, computer, Electronic mail, Mathematics, Term (time)
Abstract

This paper addresses the problem of predicting water bloom in short-term period. Important factors of water bloom are studied. A short-term predicting model of Elman neural network is presented according to the characteristic of time accumulation. The algorithm of Elman is first improved, and then the predicting model is trained, tested and compared with BP model. Experimental results show that: The short-term change of chlorophyll could be predicted better by Elman predicting model, which is accurate and extensive. This model is proven to be useful to predict water bloom in short-term period.

Published
2008

30. [The correlation between obstructive sleep apnea and olfaction function].

Author

Jia Y, Lv S, and Bai S

Subjects
Humans, Incidence, Polysomnography, Smell, Olfaction Disorders, Sleep Apnea, Obstructive complications
Abstract

Objective: To investigate the olfactory function of patients with obstructive sleep apnea（OSA） without any treatment, and to providereference for clinical olfactory research. Methods: One hundred and thirty-one participants underwent polysomnography（PSG） overnight, and were divided into OSA group （observation group） and non-OSA group （control group） according to the results. The two groups were examined by the Korean version of the olfactory stick test Ⅱ（Korean Bersion） of Sniffin Sticks Test （KVSS Test Ⅱ）. SPSS 26.0 statistical software were used to analyze the data. Results: There was a significant difference in the incidence of olfactory disorders between the observation group and the control group（ χ ²=12.000, P =0.001）. The rate of olfactory disorders in patients with severe OSA was significantly higher than that in patients with mild OSA（ P <0.05）, and the proportion of patients with olfactory disorders increased with the increase of OSA severity（ χ ²=10.672, P =0.001）. There was no statistically significant difference between the observation group and the control group in the severity of olfactory disorders（hyposmia and anosmia）（ P =1.000）. KVSS between two groups of Test Ⅱ total score has no statistical difference（ t =1.166, P =0.249）, the sense of smell recognition scores also has no statistical difference（ t =1.598, P =0.116）, but the olfactory threshold score and olfactory cognition scores were statistically significant（ t =5.346, t =6.405, P <0.001）. Conclusion: OSA has a negative effect on the sense of smell, and the severity of OSA is positively correlated with the incidence of olfactory disorder. OSA olfactory disorder is mainly anosmia, and the main manifestation is the decrease of olfactory cognitive scores., Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose., (Copyright© by the Editorial Department of Journal of Clinical Otorhinolaryngology Head and Neck Surgery.)

Published
2021
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31. An ylide transformation of rhodium(I) carbene: enantioselective three-component reaction through trapping of rhodium(I)-associated ammonium ylides by β-nitroacrylates.

Author

Ma X, Jiang J, Lv S, Yao W, Yang Y, Liu S, Xia F, and Hu W

Subjects
Catalysis, Methane chemistry, Molecular Structure, Stereoisomerism, Acrylates chemistry, Ammonium Compounds chemistry, Methane analogs & derivatives, Rhodium chemistry
Abstract

The chiral Rh(I)-diene-catalyzed asymmetric three-component reaction of aryldiazoacetates, aromatic amines, and β-nitroacrylates was achieved to obtain γ-nitro-α-amino-succinates in good yields and with high diastereo- and enantioselectivity. This reaction is proposed to proceed through the enantioselective trapping of Rh(I)-associated ammonium ylides by nitroacrylates. This new transformation represents the first example of Rh(I)-carbene-induced ylide transformation., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Published
2014
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