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Akademický článek

Cerebral venous thrombosis at high altitude: more severe symptoms and specific predisposing factors than plain areas

Autor: Yuhui Sha, Junyi Zhang, Yang Ci, Cidan zhuoga, Yuhua Zhao, Lixin Zhou, Jun Ni

Publikováno v: Thrombosis Journal, Vol 22, Iss 1, Pp 1-7 (2024)

Abstract Background Exposure to a high-altitude environment is a risk factor for cerebral venous thrombosis (CVT) probably due to hypercoagulability. The study aims to explore the unique characteristics of CVT patients in high-altitude areas of China

Externí odkaz: https://doaj.org/article/59b2de307c4c4a05805256fee6769955

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Effect of near-earth thunderstorm electric field on the flux of cosmic ray air showers in LHAASO-KM2A

Autor: Yang, Ci, Zhou, Xunxiu, He, Huihai, Huang, Daihui, Chen, Xuejian, Zhou, Tian, Guo, Kejun

The Large High Altitude Air Shower Observatory (LHAASO) is located at Haizi Mountain, Daocheng, Sichuan province, China. Due to its high-altitude location with frequent thunderstorm activities, the LHAASO is suited for studying the effects of near-ea

Externí odkaz: http://arxiv.org/abs/2410.07925

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Akademický článek

Detection of debris-flow events from seismic signals using Benford’s law

Autor: Zhou Qi, Tang Hui, Turowski Jens M., Braun Jean, Yang Ci-Jian, Dietze Michael, Lagarde Sophie, Abdelwahab Ahmed, Walter Fabian

Publikováno v: E3S Web of Conferences, Vol 415, p 03033 (2023)

The first step in building an early warning system using seismic signals is to automatically identify events of interest. Here, the first digit distribution of seismic signals generated by debris flows and other surface processes was calculated to va

Externí odkaz: https://doaj.org/article/f145966e20ec4e6fb8f493c66a419621

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Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs

Autor: Xu, Shaoxian, Shao, Zhiyuan, Yang, Ci, Liao, Xiaofei, Jin, Hai

Publikováno v: in IEEE Transactions on Parallel & Distributed Systems, vol. 33, no. 12, pp. 4891-4902, 2022

The emerging Graph Convolutional Network (GCN) has now been widely used in many domains, and it is challenging to improve the efficiencies of applications by accelerating the GCN trainings. For the sparsity nature and exploding scales of input real-w

Externí odkaz: http://arxiv.org/abs/2204.02662

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Akademický článek

Measuring Bedload Motion Time at Second Resolution Using Benford's Law on Acoustic Data.

Autor: Yang, Ci‐Jian¹ (AUTHOR) cijianyang@ntu.edu.tw, Turowski, Jens M.² (AUTHOR), Zhou, Qi^2,3 (AUTHOR), Nativ, Ron^2,4 (AUTHOR), Tang, Hui² (AUTHOR), Chang, Jui‐Ming⁵ (AUTHOR), Chen, Wen‐Sheng⁶ (AUTHOR)

Publikováno v: Earth & Space Science. Jul2024, Vol. 11 Issue 7, p1-10. 10p.

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