English | 繁體

Search in the results

UM

Browse/Search Results:  1-10 of 810 Help

Selected(0)Clear Items/Page:    Sort:
一种基于相遇模型的地铁网络密切接触群体快速定位方法 Patent
专利类型: 发明专利Invention,
Authors:  SUN LI;  ZHAO JUANJUAN;  YE KEJIANG;  XU CHENGZHONG
Favorite |  | Submit date：2022/08/29
Ride Plan  Travel Trajectories  Trajectory Matching  Contact Tracing  
Cyclododecane-based high-intactness and clean transfer method for fabricating suspended two-dimensional materials Journal article
Wang, Zhao, Liu, Wenlin, Shao, Jiaxin, Hao, He, Wang, Guorui, Zhao, Yixuan, Zhu, Yeshu, Jia, Kaicheng, Lu, Qi, Yang, Jiawei, Zhang, Yanfeng, Tong, Lianming, Song, Yuqing, Sun, Pengzhan, Mao, Boyang, Hu, Chenguo, Liu, Zhongfan, Lin, Li, Peng, Hailin. Cyclododecane-based high-intactness and clean transfer method for fabricating suspended two-dimensional materials[J]. Nature Communications, 2024, 15(1), 6957.
Authors:  Wang, Zhao;  Liu, Wenlin;  Shao, Jiaxin;  Hao, He;  Wang, Guorui; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:14.7/16.1 | Submit date：2024/09/03
Deep learning-driven evaluation and prediction of ion-doped NASICON materials for enhanced solid-state battery performance Journal article
Zhao, Zirui, Wang, Xiaoke, Wu, Si, Zhou, Pengfei, Zhao, Qian, Xu, Guanping, Sun, Kaitong, Li, Hai Feng. Deep learning-driven evaluation and prediction of ion-doped NASICON materials for enhanced solid-state battery performance[J]. AAPPS Bulletin, 2024, 34(1), 26.
Authors:  Zhao, Zirui;  Wang, Xiaoke;  Wu, Si;  Zhou, Pengfei;  Zhao, Qian; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0 | Submit date：2024/10/10
Deep Learning Model  Electrochemical Properties  Ion Doping  Nasicon  Solid-state Electrolyte  
Effect of fiber content and end geometry on the pullout behavior of straight and arc-shaped steel fibers embedded in SIFCON Journal article
Li, Yabiao, Liu, Fengshan, Li, Qingyu, Li, Hedong, Sun, Guoxing, Liang, Rui. Effect of fiber content and end geometry on the pullout behavior of straight and arc-shaped steel fibers embedded in SIFCON[J]. Construction and Building Materials, 2024, 451(138688), 138688.
Authors:  Li, Yabiao;  Liu, Fengshan;  Li, Qingyu;  Li, Hedong;  Sun, Guoxing; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:7.4/8.0 | Submit date：2024/11/05
Arc-shaped Steel Fiber  Sifcon  Pullout Behavior  End Geometry  Fiber Content  Interfacial Bonding Mechanism  
Exploring the role of SAP chemical composition in the internal curing of high-performance cementitious materials Journal article
Li, Yasen, Zhang, Cheng, Li, Tingzhong, Sun, Guoxing. Exploring the role of SAP chemical composition in the internal curing of high-performance cementitious materials[J]. Construction and Building Materials, 2024, 451, 138784.
Authors:  Li, Yasen;  Zhang, Cheng;  Li, Tingzhong;  Sun, Guoxing
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:7.4/8.0 | Submit date：2024/11/05
Chemical Structure  Sulfonic Acid Group  Superabsorbent Polymer (Sap)  High-performance Cementitious Materials  
Rigid polyurethane foam with durable hydrophobicity and flame retardancy endowed by novel functional surfactants Journal article
Wang, Ping, Li, Xinchao, Li, Jianfeng, Zhang, Jin, Wang, Ting, Fu, Zhicheng, An, Wenli, Sun, Guoxing, Chen, Mingjun, Deng, Jinni. Rigid polyurethane foam with durable hydrophobicity and flame retardancy endowed by novel functional surfactants[J]. Construction and Building Materials, 2024, 451, 138815.
Authors:  Wang, Ping;  Li, Xinchao;  Li, Jianfeng;  Zhang, Jin;  Wang, Ting; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:7.4/8.0 | Submit date：2024/11/05
Rigid Polyurethane Foam  Hydrophobicity  Flame Retardancy  Compressive Strength  Thermal Insulating Performance  Durability  
Understanding and Improving Low-Resource Neural Machine Translation with Shallow Features Conference paper
Yanming Sun, Xuebo Liu, Derek F. Wong, Yuchu Lin, Bei Li, Runzhe Zhan, Lidia S. Chao, Min Zhang. Understanding and Improving Low-Resource Neural Machine Translation with Shallow Features[C], 2024.
Authors:  Yanming Sun;  Xuebo Liu;  Derek F. Wong;  Yuchu Lin;  Bei Li; et al.
Favorite |  | Submit date：2024/08/30
A stabilized SAV difference scheme and its accelerated solver for spatial fractional Cahn–Hilliard equations Journal article
Huang, Xin, Lei, Siu Long, Li, Dongfang, Sun, Hai Wei. A stabilized SAV difference scheme and its accelerated solver for spatial fractional Cahn–Hilliard equations[J]. Mathematics and Computers in Simulation, 2024, 225, 232-249.
Authors:  Huang, Xin;  Lei, Siu Long;  Li, Dongfang;  Sun, Hai Wei
Favorite | TC[WOS]:1 TC[Scopus]:1  IF:4.4/3.6 | Submit date：2024/06/05
Convergence Analysis  Energy Stability  Preconditioning Technique  Sav Approach  Spatial Fractional Cahn–hilliard Equations  Stabilization Technique  
Tetramethylpyrazine nitrone exerts neuroprotection via activation of PGC-1α/Nrf2 pathway in Parkinson's disease models Journal article
Guo, Baojian, Zheng, Chengyou, Cao, Jie, Luo, Fangcheng, Li, Haitao, Hu, Shengquan, Mingyuan Lee, Simon, Yang, Xifei, Zhang, Gaoxiao, Zhang, Zaijun, Sun, Yewei, Wang, Yuqiang. Tetramethylpyrazine nitrone exerts neuroprotection via activation of PGC-1α/Nrf2 pathway in Parkinson's disease models[J]. Journal of Advanced Research, 2024, 64, 195-211.
Authors:  Guo, Baojian;  Zheng, Chengyou;  Cao, Jie;  Luo, Fangcheng;  Li, Haitao; et al.
Favorite | TC[WOS]:4 TC[Scopus]:4  IF:11.4/11.0 | Submit date：2024/02/22
Nuclear Factor Erythroid-2-related Factor 2  Parkinson's Disease  Peroxisome Proliferator-activated Receptor γ Co-activator 1α  Tetramethylpyrazine Nitrone  
A multi-stage stochastic dispatching method for electricity‑hydrogen integrated energy systems driven by model and data Journal article
Yang, Zhixue, Ren, Zhouyang, Li, Hui, Sun, Zhiyuan, Feng, Jianbing, Xia, Weiyi. A multi-stage stochastic dispatching method for electricity‑hydrogen integrated energy systems driven by model and data[J]. Applied Energy, 2024, 371, 123668.
Authors:  Yang, Zhixue;  Ren, Zhouyang;  Li, Hui;  Sun, Zhiyuan;  Feng, Jianbing; et al.
Favorite | TC[WOS]:3 TC[Scopus]:6  IF:10.1/10.4 | Submit date：2024/07/04
Chance-constrained  Electricity‑hydrogen Integrated Energy Systems  Hydrogen Energy  Multi-agent Deep Reinforcement Learning  Uncertainty