中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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CAS IR Grid
机构
西北高原生物研究所 [13]
寒区旱区环境与工程研... [8]
生态环境研究中心 [7]
成都山地灾害与环境研... [6]
苏州纳米技术与纳米仿... [4]
合肥物质科学研究院 [4]
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OAI收割 [89]
iSwitch采集 [2]
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期刊论文 [82]
专利 [7]
成果 [2]
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2025 [1]
2024 [9]
2023 [2]
2022 [1]
2021 [5]
2020 [9]
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天文学 [2]
G01C11/00,... [1]
Medicine, ... [1]
天文学::恒星与银河... [1]
环境科学::环境化学 [1]
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Probability analysis of shallow landslides in varying vegetation zones with random soil grain-size distribution
期刊论文
OAI收割
ENVIRONMENTAL MODELLING & SOFTWARE, 2025, 卷号: 183, 页码: 18
作者:
Jiang, Hu
;
Zou, Qiang
;
Li, Yong
;
Jiang, Yao
;
Cui, Junfang
  |  
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2024/12/23
landslide
Slope stability
Susceptibility prediction
Random soil strength parameter
Machine learning
Vegetated slopes
A novel framework for predicting glacial lake outburst debris flows in the Himalayas amidst climate change
期刊论文
OAI收割
SCIENCE OF THE TOTAL ENVIRONMENT, 2024, 卷号: 946, 页码: 13
作者:
Zhou, Bin
;
Zou, Qiang
;
Jiang, Hu
;
Yang, Tao
;
Zhou, Wentao
  |  
收藏
  |  
浏览/下载:13/0
  |  
提交时间:2024/08/19
Glacial lake outburst debris flow
Occurrence probability
Prediction
Climate change
Himalayas
GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model
期刊论文
OAI收割
CELL RESEARCH, 2024, 页码: 16
作者:
Yang, Xiaodong
;
Liu, Guole
;
Feng, Guihai
;
Bu, Dechao
;
Wang, Pengfei
  |  
收藏
  |  
浏览/下载:90/0
  |  
提交时间:2024/12/06
Discovery of Very High Energy Gamma-Ray Emissions from the Low-luminosity AGN NGC 4278 by LHAASO
期刊论文
OAI收割
The Astrophysical Journal Letters, 2024, 卷号: 971, 期号: 2
作者:
Cao,Zhen
;
Aharonian,F.
;
Axikegu,
;
Bai,Y. X.
;
Bao,Y. W.
  |  
收藏
  |  
浏览/下载:23/0
  |  
提交时间:2024/09/30
Deep learning prediction of rainfall-driven debris flows considering the similar critical thresholds within comparable background conditions
期刊论文
OAI收割
ENVIRONMENTAL MODELLING & SOFTWARE, 2024, 卷号: 179, 页码: 22
作者:
Jiang, Hu
;
Zou, Qiang
;
Zhu, Yunqiang
;
Li, Yong
;
Zhou, Bin
  |  
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2024/10/08
Spatiotemporal prediction
Rainfall-driven debris flow
Deep learning
Machine learning
Rainfall features
Deep learning prediction of rainfall-driven debris flows considering the similar critical thresholds within comparable background conditions
期刊论文
OAI收割
ENVIRONMENTAL MODELLING & SOFTWARE, 2024, 卷号: 179, 页码: 22
作者:
Jiang, Hu
;
Zou, Qiang
;
Zhu, Yunqiang
;
Li, Yong
;
Zhou, Bin
  |  
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2024/08/14
Spatiotemporal prediction
Rainfall-driven debris flow
Deep learning
Machine learning
Rainfall features
Tianyu—Search for the Second Solar System and Explore the Dynamic Universe
期刊论文
OAI收割
天文学报/Acta Astronomica Sinica, 2024, 卷号: 65, 期号: 04, 页码: 3-45
作者:
Feng FB(冯发波)
;
Rui YC(芮易成)
;
Du ZM(杜芝茂)
;
Lin Q(林清)
;
Zhang CC(张聪聪)
  |  
收藏
  |  
浏览/下载:78/0
  |  
提交时间:2024/08/20
techniques: photometric
planets and satellites: detection
planets and satellites: formation
stars: supernovae: general
stars: variables: general
minor planets
asteroids: general
stars: gamma-ray burst: general
AT 2023lli: A Tidal Disruption Event with Prominent Optical Early Bump and Delayed Episodic X-Ray Emission
期刊论文
OAI收割
ASTROPHYSICAL JOURNAL LETTERS, 2024, 卷号: 964, 期号: 2
作者:
Huang, Shifeng
;
Jiang, Ning
;
Zhu, Jiazheng
;
Wang, Yibo
;
Wang, Tinggui
  |  
收藏
  |  
浏览/下载:31/0
  |  
提交时间:2024/04/08
Development of an integrated model for assessing landslide susceptibility on vegetated slopes under random rainfall scenarios
期刊论文
OAI收割
ECOLOGICAL ENGINEERING, 2024, 卷号: 199, 页码: 18
作者:
Jiang, Hu
;
Zou, Qiang
;
Jiang, Yao
;
Zhou, Bin
;
Yao, Hongkun
  |  
收藏
  |  
浏览/下载:42/0
  |  
提交时间:2024/03/25
Landslides
Susceptibility
Slope stability
Infinite slope model
Qinghai -Tibet plateau
A GSD-driven approach to deriving stochastic soil strength parameters under hybrid machine learning models
期刊论文
OAI收割
EUROPEAN JOURNAL OF SOIL SCIENCE, 2024, 卷号: 75, 期号: 6, 页码: 18
作者:
Jiang, Hu
;
Li, Yong
;
Zou, Qiang
;
Zhang, Jun
;
Cui, Junfang
  |  
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2025/01/15
grain-size distribution
Hengduan mountain
hybrid machine learning models
regional physical modelling
stochastic soil strength parameters