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340 results on '"Dai, Lingyun"'

5. Research on Real-Time Dynamic Prediction Algorithm of Expressway Operation Situation Facing Severe Weather

Author

Lu, Lixin, Wang, Haiyue, Dai, Lingyun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Wang, Wuhong, editor, Guo, Hongwei, editor, Jiang, Xiaobei, editor, Shi, Jian, editor, and Sun, Dongxian, editor

Published
2024
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6. Seizure Types Classification Based on Multi-branch Hybrid Deep Learning Network

Author

Jia, Qingwei, Liu, Jin-Xing, Shang, Junling, Dai, Lingyun, Wang, Yuxia, Hu, Wenrong, Yuan, Shasha, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Zhang, Chuanlei, editor, and Chen, Wei, editor

Published
2024
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10. Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization.

Author

Li, Man, Dai, Lingyun, and Hu, Yongjie

Abstract

Recent advances in machine learning (ML) have impacted research communities based on statistical perspectives and uncovered invisibles from conventional standpoints. Though the field is still in the early stage, this progress has driven the thermal science and engineering communities to apply such cutting-edge toolsets for analyzing complex data, unraveling abstruse patterns, and discovering non-intuitive principles. In this work, we present a holistic overview of the applications and future opportunities of ML methods on crucial topics in thermal energy research, from bottom-up materials discovery to top-down system design across atomistic levels to multi-scales. In particular, we focus on a spectrum of impressive ML endeavors investigating the state-of-the-art thermal transport modeling (density functional theory, molecular dynamics, and Boltzmann transport equation), different families of materials (semiconductors, polymers, alloys, and composites), assorted aspects of thermal properties (conductivity, emissivity, stability, and thermoelectricity), and engineering prediction and optimization (devices and systems). We discuss the promises and challenges of current ML approaches and provide perspectives for future directions and new algorithms that could make further impacts on thermal energy research.

Published
2022

14. Electron-Ion Collider in China

Author

Anderle, Daniele P., Bertone, Valerio, Cao, Xu, Chang, Lei, Chang, Ningbo, Chen, Gu, Chen, Xurong, Chen, Zhuojun, Cui, Zhufang, Dai, Lingyun, Deng, Weitian, Ding, Minghui, Feng, Xu, Gong, Chang, Gui, Longcheng, Guo, Feng-Kun, Han, Chengdong, He, Jun, Hou, Tie-Jiun, Huang, Hongxia, Huang, Yin, Kumerički, Krešimir, Kaptari, L. P., Li, Demin, Li, Hengne, Li, Minxiang, Li, Xueqian, Liang, Yutie, Liang, Zuotang, Liu, Chen, Liu, Chuan, Liu, Guoming, Liu, Jie, Liu, Liuming, Liu, Xiang, Liu, Tianbo, Luo, Xiaofeng, Lyu, Zhun, Ma, Boqiang, Ma, Fu, Ma, Jianping, Ma, Yugang, Mao, Lijun, Mezrag, Cédric, Moutarde, Hervé, Ping, Jialun, Qin, Sixue, Ren, Hang, Roberts, Craig D., Rojo, Juan, Shen, Guodong, Shi, Chao, Song, Qintao, Sun, Hao, Sznajder, Paweł, Wang, Enke, Wang, Fan, Wang, Qian, Wang, Rong, Wang, Ruiru, Wang, Taofeng, Wang, Wei, Wang, Xiaoyu, Wang, Xiaoyun, Wu, Jiajun, Wu, Xinggang, Xia, Lei, Xiao, Bowen, Xiao, Guoqing, Xie, Ju-Jun, Xie, Yaping, Xing, Hongxi, Xu, Hushan, Xu, Nu, Xu, Shusheng, Yan, Mengshi, Yan, Wenbiao, Yan, Wencheng, Yan, Xinhu, Yang, Jiancheng, Yang, Yi-Bo, Yang, Zhi, Yao, Deliang, Yin, Peilin, Yuan, C. -P., Zhan, Wenlong, Zhang, Jianhui, Zhang, Jinlong, Zhang, Pengming, Chang, Chao-Hsi, Zhang, Zhenyu, Zhao, Hongwei, Chao, Kuang-Ta, Zhao, Qiang, Zhao, Yuxiang, Zhao, Zhengguo, Zheng, Liang, Zhou, Jian, Zhou, Xiang, Zhou, Xiaorong, Zou, Bingsong, and Zou, Liping

Subjects
Nuclear Experiment, High Energy Physics - Experiment, High Energy Physics - Phenomenology, Nuclear Theory
Abstract

Lepton scattering is an established ideal tool for studying inner structure of small particles such as nucleons as well as nuclei. As a future high energy nuclear physics project, an Electron-ion collider in China (EicC) has been proposed. It will be constructed based on an upgraded heavy-ion accelerator, High Intensity heavy-ion Accelerator Facility (HIAF) which is currently under construction, together with a new electron ring. The proposed collider will provide highly polarized electrons (with a polarization of $\sim$80%) and protons (with a polarization of $\sim$70%) with variable center of mass energies from 15 to 20 GeV and the luminosity of (2-3) $\times$ 10$^{33}$ cm$^{-2}$ s$^{-1}$. Polarized deuterons and Helium-3, as well as unpolarized ion beams from Carbon to Uranium, will be also available at the EicC. The main foci of the EicC will be precision measurements of the structure of the nucleon in the sea quark region, including 3D tomography of nucleon; the partonic structure of nuclei and the parton interaction with the nuclear environment; the exotic states, especially those with heavy flavor quark contents. In addition, issues fundamental to understanding the origin of mass could be addressed by measurements of heavy quarkonia near-threshold production at the EicC. In order to achieve the above-mentioned physics goals, a hermetical detector system will be constructed with cutting-edge technologies. This document is the result of collective contributions and valuable inputs from experts across the globe. The EicC physics program complements the ongoing scientific programs at the Jefferson Laboratory and the future EIC project in the United States. The success of this project will also advance both nuclear and particle physics as well as accelerator and detector technology in China., Comment: EicC white paper, written by the whole EicC working group

Published
2021
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17. Epileptic Seizure Detection Based on Feature Extraction and CNN-BiGRU Network with Attention Mechanism

Author

Xu, Jie, Wang, Juan, Liu, Jin-Xing, Shang, Junliang, Dai, Lingyun, Yan, Kuiting, Yuan, Shasha, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Premaratne, Prashan, editor, Jin, Baohua, editor, Qu, Boyang, editor, Jo, Kang-Hyun, editor, and Hussain, Abir, editor

Published
2023
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44. Photoaffinity probe‐based antimalarial target identification of artemisinin in the intraerythrocytic developmental cycle of Plasmodium falciparum

Author

Gao, Peng, primary, Wang, Jianyou, additional, Qiu, Chong, additional, Zhang, Huimin, additional, Wang, Chen, additional, Zhang, Ying, additional, Sun, Peng, additional, Chen, Honglin, additional, Wong, Yin Kwan, additional, Chen, Jiayun, additional, Zhang, Junzhe, additional, Tang, Huan, additional, Shi, Qiaoli, additional, Zhu, Yongping, additional, Shen, Shengnan, additional, Han, Guang, additional, Xu, Chengchao, additional, Dai, Lingyun, additional, and Wang, Jigang, additional

Published
2024
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46. Spatially resolved multi-omics unravels region-specific responses, microenvironment remodeling and metabolic reprogramming in aristolochic acid nephropathy

Author

Chen, Jiayun, primary, Li, Huiying, additional, Bai, Yunmeng, additional, Luo, Piao, additional, Cheng, Guangqing, additional, Ding, Zihe, additional, Xu, Zhenyu, additional, Gu, Liwei, additional, Wong, Yin Kwan, additional, Pang, Huanhuan, additional, Wang, Chen, additional, Wu, Jiangpeng, additional, Zhang, Qian, additional, Chen, Ruixing, additional, Xiao, Kefeng, additional, Guo, Jinan, additional, Dai, Lingyun, additional, Zhang, Xinzhou, additional, and Wang, Jigang, additional

Published
2024
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47. A feature extraction framework for discovering pan‐cancer driver genes based on multi‐omics data.

Author

Xue, Xiaomeng, Li, Feng, Shang, Junliang, Dai, Lingyun, Ge, Daohui, and Ren, Qianqian

Subjects
CANCER diagnosis, DNA methylation, GENE expression, PROTEIN-protein interactions, CANCER genes
Abstract

The identification of tumor driver genes facilitates accurate cancer diagnosis and treatment, playing a key role in precision oncology, along with gene signaling, regulation, and their interaction with protein complexes. To tackle the challenge of distinguishing driver genes from a large number of genomic data, we construct a feature extraction framework for discovering pan‐cancer driver genes based on multi‐omics data (mutations, gene expression, copy number variants, and DNA methylation) combined with protein–protein interaction (PPI) networks. Using a network propagation algorithm, we mine functional information among nodes in the PPI network, focusing on genes with weak node information to represent specific cancer information. From these functional features, we extract distribution features of pan‐cancer data, pan‐cancer TOPSIS features of functional features using the ideal solution method, and SetExpan features of pan‐cancer data from the gene functional features, a method to rank pan‐cancer data based on the average inverse rank. These features represent the common message of pan‐cancer. Finally, we use the lightGBM classification algorithm for gene prediction. Experimental results show that our method outperforms existing methods in terms of the area under the check precision‐recall curve (AUPRC) and demonstrates better performance across different PPI networks. This indicates our framework's effectiveness in predicting potential cancer genes, offering valuable insights for the diagnosis and treatment of tumors. [ABSTRACT FROM AUTHOR]

Published
2024
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