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678 results on '"Jiang, Lanlan"'

9. Construction of Rural Public Management and Social Service Platform Based on .NET Technology

Author

Jiang, Lanlan, Dou, Runliang, Editor-in-Chief, Liu, Jing, Editor-in-Chief, Khasawneh, Mohammad T., Editor-in-Chief, Balas, Valentina Emilia, Series Editor, Bhowmik, Debashish, Series Editor, Khan, Khalil, Series Editor, Masehian, Ellips, Series Editor, Mohammadi-Ivatloo, Behnam, Series Editor, Nayyar, Anand, Series Editor, Pamucar, Dragan, Series Editor, Shu, Dewu, Series Editor, Qiu, Daowen, editor, Jiao, Yusheng, editor, and Yeoh, William, editor

Published
2023
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18. Study on Seepage and Mass Transfer Characteristics During CO2 Storage in Saline Aquifer

Author

Zhang, Weixin, Wu, Bohao, Lv, Pengfei, Li, Shaohua, Jiang, Lanlan, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Sun, Baojiang, editor, Sun, Jinsheng, editor, Wang, Zhiyuan, editor, Chen, Litao, editor, and Chen, Meiping, editor

Published
2022
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22. Continuous-Time Dynamic Graph Networks Integrated with Knowledge Propagation for Social Media Rumor Detection.

Author

Li, Hui, Jiang, Lanlan, and Li, Jun

Subjects
*KNOWLEDGE graphs, *WIRELESS Internet, *RUMOR, *RESEARCH personnel, REPRODUCTIVE isolation
Abstract

The proliferation of the Internet and mobile devices has made it increasingly easy to propagate rumors on social media. Widespread rumors can incite public panic and have detrimental effects on individuals. In recent years, researchers have found that both the spatial structure of rumor diffusion and the temporal features of propagation can be effective in identifying rumors. However, existing methods tend to focus on either spatial structure or temporal information in isolation, and few models can effectively capture both types of information. Additionally, most existing methods treat continuously changing temporal information as static snapshots, neglecting the precise timing of propagation. Moreover, as users repost and comment, background knowledge associated with the posts also evolves dynamically, which is often ignored. To address these limitations, we propose CGNKP (Continuous-time Dynamic Graph Networks integrated with Knowledge Propagation), a model that jointly captures the spatial structure and continuous-time features of post propagation to fully understand the dynamics of background knowledge. Specifically, we introduce a novel method for encoding continuous-time dynamic graphs, modeling the propagation process through two dynamic graphs: a temporal propagation graph (for posts diffusion) and a temporal knowledge graph (for knowledge diffusion). Extensive experiments on real-world datasets demonstrate that CGNKP significantly outperforms multiple strong baselines, achieving accuracies of 0.861 on the Twitter15 dataset and 0.903 on the Twitter16 dataset. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Multimodal Seed Data Augmentation for Low-Resource Audio Latin Cuengh Language.

Author

Jiang, Lanlan, Qin, Xingguo, Zhang, Jingwei, and Li, Jun

Subjects
DATA augmentation, PROCESS capability, LATIN language, SPEECH, CHINESE language
Abstract

Latin Cuengh is a low-resource dialect that is prevalent in select ethnic minority regions in China. This language presents unique challenges for intelligent research and preservation efforts, primarily due to its oral tradition and the limited availability of textual resources. Prior research has sought to bolster intelligent processing capabilities with regard to Latin Cuengh through data augmentation techniques leveraging scarce textual data, with modest success. In this study, we introduce an innovative multimodal seed data augmentation model designed to significantly enhance the intelligent recognition and comprehension of this dialect. After supplementing the pre-trained model with extensive speech data, we fine-tune its performance with a modest corpus of multilingual textual seed data, employing both Latin Cuengh and Chinese texts as bilingual seed data to enrich its multilingual properties. We then refine its parameters through a variety of downstream tasks. The proposed model achieves a commendable performance across both multi-classification and binary classification tasks, with its average accuracy and F1 measure increasing by more than 3%. Moreover, the model's training efficiency is substantially ameliorated through strategic seed data augmentation. Our research provides insights into the informatization of low-resource languages and contributes to their dissemination and preservation. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Numerical Simulation of Heat Transfer Characteristics of CO2Gas Mixture Injection into Wellbores

Author

Zhang, Yi, Huang, Xin, Jiang, Lanlan, Chen, Cong, Zhao, Yuechao, and Song, Yongchen

Abstract

The flow behavior and heat transfer characteristics of CO2in wellbores are crucial for its safe and efficient storage. Based on the classic Ramey wellbore heat transfer model, the prediction models of the temperature field and pressure field in the CO2gas mixture injection wellbore were established in this paper. The model comprehensively considers the interactions between different gas molecules, Joule–Thomson effects, and heat generation due to wall friction and improves the energy and momentum equations. Methods for calculating the physical properties of the gas mixture under both high- and low-pressure (below 10 MPa) conditions are provided. The specific heat capacity at constant pressure, density, and Joule–Thomson coefficient of the gas mixture are calculated using the modified BWRS equation with mixing rules. The viscosity of the gas mixture is determined by using the Lucas correlation. The total heat transfer coefficient is iteratively calculated using a thermal resistance model. By using these physical parameters, the temperature and pressure fields of the wellbore can be calculated. Compared with the experimental data, the accuracy of the model is verified. The calculation error of temperature and pressure at each node is kept within 3%, indicating that the accuracy of the model is improved. Based on the improved model, the changes of the temperature field and pressure field in the wellbore under different working conditions are analyzed when different gas mixtures (CO2, N2, and CH4) are injected. The results indicate that a change in the composition of the gas mixture has a minor impact on the temperature field but significantly affects the pressure field; higher CO2composition leads to increased bottom-hole pressure. The increase of the injection temperature and pressure both result in higher bottom-hole temperatures but have little effect on the pressure field. The pressure field reaches equilibrium faster than the temperature field. The research results can provide a reference for the migration law of gas mixture in the wellbore and the optimization of wellhead injection parameters.

Published
2024
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