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26 results on '"Hu, Yangyang"'

4. Babesia duncani multi-omics identifies virulence factors and drug targets

Author

Singh, Pallavi, primary, Lonardi, Stefano, additional, Liang, Qihua, additional, Vydyam, Pratap, additional, Khabirova, Eleonora, additional, Fang, Tiffany, additional, Gihaz, Shalev, additional, Thekkiniath, Jose, additional, Munshi, Muhammad, additional, Abel, Steven, additional, Ciampossin, Loic, additional, Batugedara, Gayani, additional, Gupta, Mohit, additional, Lu, Xueqing Maggie, additional, Lenz, Todd, additional, Chakravarty, Sakshar, additional, Cornillot, Emmanuel, additional, Hu, Yangyang, additional, Ma, Wenxiu, additional, Gonzalez, Luis Miguel, additional, Sánchez, Sergio, additional, Estrada, Karel, additional, Sánchez-Flores, Alejandro, additional, Montero, Estrella, additional, Harb, Omar S., additional, Le Roch, Karine G., additional, and Mamoun, Choukri Ben, additional

Published
2023
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7. The B-type response regulator GmRR11d mediates systemic inhibition of symbiotic nodulation

Author

Chen, Jiahuan, primary, Wang, Zhijuan, additional, Wang, Lixiang, additional, Hu, Yangyang, additional, Yan, Qiqi, additional, Lu, Jingjing, additional, Ren, Ziyin, additional, Hong, Yujie, additional, Ji, Hongtao, additional, Wang, Hui, additional, Wu, Xinying, additional, Lin, Yanru, additional, Su, Chao, additional, Ott, Thomas, additional, and Li, Xia, additional

Published
2022
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11. Microstructure and Texture Evolution of Fe-33Mn-3Si-3Al TWIP Steel on Strain

Author

Jun Li, Su Yu, Cui Haotian, Feng Xiaoxiao, and Hu Yangyang

Subjects
Materials science, Strain (chemistry), Stacking-fault energy, Scanning electron microscope, Twip, General Materials Science, Texture (crystalline), Plasticity, Composite material, Crystal twinning, Microstructure
Abstract

The microstructure and texture evolution of Fe-33Mn-3Si-3Al twinning induced plasticity (TWIP) steel were studied by the scanning electron microscope (SEM) and X-ray diffraction (XRD) at room temperature. After quasi-static tensile, the texture evolution of different strain was observed. It was shown that the Goss and Brass components increased within the strain range of less than 0.6. Whereas, the main components were decreased when the strain levels were greater than 0.6. This behavior was attributed to the low stacking fault energy (SFE) and was related to the strain energy of this high manganese steel. At high strain levels, the high strain energy may contribute to the Brass components transition to the A (rot-Brass) components.

Published
2019

17. Comparative experimental study of ethanol-air premixed laminar combustion characteristics by laser induced spark and electric spark ignition

Author

Xiaolu Li, Hu Yangyang, Zhou Xuan, Cangsu Xu, and Anhao Zhong

Subjects
Laminar flame speed, 020209 energy, General Chemical Engineering, Extrapolation, Analytical chemistry, Laminar flow, 02 engineering and technology, General Chemistry, Combustion, Laser, law.invention, Physics::Fluid Dynamics, Ignition system, 020401 chemical engineering, Physics::Plasma Physics, law, Spark (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Electric spark, Physics::Chemical Physics, 0204 chemical engineering
Abstract

An experimental study of laminar combustion characteristics of ethanol-air premixed mixtures was conducted with different ignition methods, including laser induced spark ignition (LISI) and electric spark ignition (SI) at an initial condition of 358 K temperature and 0.1MPa pressure. Flame propagation with the two different ignition methods was analyzed and discussed. The laminar flame speed was extrapolated with a nonlinear extrapolation method. Results indicate that the laminar speed of ethanol-air mixtures with LISI is faster than that with SI at lean mixtures, but slower at stoichiometric and rich mixtures. The peak values of the laminar burning velocity for SI and LISI with nonlinear extrapolation are 50.1 cm/s and 47.6 cm/s at the equivalence ratio of 1.1, respectively. Laser-induced spark ignition is able to ignite leaner ethanol-air mixtures.

Published
2017

20. A scalable and fast OPTICS for clustering trajectory big data

Author

Du Bo, Xiaohui Huang, Hu Yangyang, Ze Deng, and Mao Zhu

Subjects
DBSCAN, Clustering high-dimensional data, Computer Networks and Communications, Computer science, Correlation clustering, computer.software_genre, Data stream clustering, CURE data clustering algorithm, Consensus clustering, Metric (mathematics), Canopy clustering algorithm, Data mining, Cluster analysis, computer, Software
Abstract

Clustering trajectory data is an important way to mine hidden information behind moving object sampling data, such as understanding trends in movement patterns, gaining high popularity in geographic information and so on. In the era of `Big data', the current approaches for clustering trajectory data generally do not apply for excessive costs in both scalability and computing performance for trajectory big data. Aiming at these problems, this study first proposes a new clustering algorithm for trajectory big data, namely Tra-POPTICS by modifying a scalable clustering algorithm for point data (POPTICS). Tra-POPTICS has employed the spatiotemporal distance function and trajectory indexing to support trajectory data. Tra-POPTICS can process the trajectory big data in a distributed manner to meet a great scalability. Towards providing a fast solution to clustering trajectory big data, this study has explored the feasibility to utilize the contemporary general-purpose computing on the graphics processing unit (GPGPU). The GPGPU-aided clustering approach parallelized the Tra-POPTICS with the Hyper-Q feature of Kelper GPU and massive GPU threads. The experimental results indicate that (1) the Tra-POPTICS algorithm has a comparable clustering quality with T-OPTICS (the state of art work of clustering trajectories in a centralized fashion) and outperforms T-OPTICS by average four times in terms of scalability, and (2) the G-Tra-POPTICS has a comparable clustering quality with T-POPTICS as well and further gains about 30 speedup on average for clustering trajectories comparing to Tra-POPTICS with eight threads. The proposed algorithms exhibit great scalability and computing performance in clustering trajectory big data.

Published
2014

21. The MiR-135b–BMAL1–YY1 loop disturbs pancreatic clockwork to promote tumourigenesis and chemoresistance

Author

Jiang, Weiliang, primary, Zhao, Senlin, additional, Shen, Jia, additional, Guo, Lihong, additional, Sun, Yi, additional, Zhu, Yuntian, additional, Ma, Zhixiong, additional, Zhang, Xin, additional, Hu, Yangyang, additional, Xiao, Wenqin, additional, Li, Kai, additional, Li, Sisi, additional, Zhou, Li, additional, Huang, Li, additional, Lu, Zhanjun, additional, Feng, Yun, additional, Xiao, Junhua, additional, Zhang, Eric Erquan, additional, Yang, Lijuan, additional, and Wan, Rong, additional

Published
2018
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