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
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自动化研究所 [10]
西北高原生物研究所 [9]
合肥物质科学研究院 [5]
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成都山地灾害与环境研... [3]
广州地球化学研究所 [1]
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期刊论文 [35]
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2026 [1]
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2024 [3]
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Chain effects of landslide activity intensity decay on landslide sediment transfer and debris flow activity
期刊论文
OAI收割
CANADIAN GEOTECHNICAL JOURNAL, 2026, 卷号: 63, 页码: 24
作者:
Xiong, Jiang
;
Zeng, Lu
;
Tang, Chuan
  |  
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2026/03/05
driven mechanism
chain response
debris flow
landslides
GIS
Logjam controls the fluvial channel morphology: Interception and blockage characteristics of large wood and its influence on sedimentary dynamics
期刊论文
OAI收割
CATENA, 2025, 卷号: 260, 页码: 13
作者:
Chen, Jiangang
;
Li, Dandan
;
Fei, Gaogao
;
Zeng, Lu
;
Liu, Haitao
  |  
收藏
  |  
浏览/下载:1/0
  |  
提交时间:2025/11/06
Large wood
Transportation process
Sediment deposition
Logjam
Debris flow
Graphene-based hierarchical structure for significantly enhancing thermal conductivity of composites with high mechanical reinforcement
期刊论文
OAI收割
COMPOSITES SCIENCE AND TECHNOLOGY, 2024, 卷号: 258, 页码: 10
作者:
Sun, Xinyang
;
Wang, Han
;
Lu, Ni
;
Zhang, Jiangang
;
Ma, Chaoqun
  |  
收藏
  |  
浏览/下载:1/0
  |  
提交时间:2025/04/27
Graphene and other 2D-materials
Hybrid composites
Thermal properties
Mechanical properties
Synergism
Gold occurrence and mineralization in the Paleozoic Heijianshan Fe-Cu (-Au) deposit, Eastern Tianshan
期刊论文
OAI收割
ORE GEOLOGY REVIEWS, 2024, 卷号: 168, 页码: 14
作者:
Zhao, Liandang
;
Zhang, Shuanliang
;
Lu, Wanjian
;
Jiao, Jiangang
  |  
收藏
  |  
浏览/下载:41/0
  |  
提交时间:2024/10/10
Gold occurrence and mineralization
Incorporation mechanism of gold
Sulfide geochemistry
Ore-forming process
IOCG deposit
Synergistic effect of woven copper wires with graphene foams for high thermal conductivity of carbon fiber/epoxy composites
期刊论文
OAI收割
ADVANCED COMPOSITES AND HYBRID MATERIALS, 2024, 卷号: 7, 期号: 1, 页码: 11
作者:
Lu, Ni
;
Sun, Xinyang
;
Wang, Han
;
Zhang, Jiangang
;
Ma, Chaoqun
  |  
收藏
  |  
浏览/下载:1/0
  |  
提交时间:2025/04/27
Woven copper wire
Graphene foam
Thermal conductivity
Hybrid sandwich composites
Synergistic effect
Carbon fiber laminated composites
MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer
期刊论文
OAI收割
JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B, 2023, 卷号: 24, 期号: 8, 页码: 663-681
作者:
Zhu, Xuehua
;
Shao, Lizhi
;
Liu, Zhenyu
;
Liu, Zenan
;
He, Jide
  |  
收藏
  |  
浏览/下载:42/0
  |  
提交时间:2023/11/17
Magnetic resonance imaging (MRI)
Radiomics
Prostate cancer
Predictive model
Anisotropy in microstructural features and tensile performance of laser powder bed fusion NiTi alloys
期刊论文
OAI收割
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T, 2023, 卷号: 24, 页码: 8656-8668
作者:
  |  
收藏
  |  
浏览/下载:64/0
  |  
提交时间:2024/01/07
LPBF NiTi alloy
Processing window
Anisotropic behavior
Microstructural features
Tensile performances
Vertically-oriented graphene-boron nitride skeletons using graphene oxide as inorganic adhesives for high-efficiency thermal conduction of polymeric composites with electrical insulation and compressibility
期刊论文
OAI收割
COMPOSITES SCIENCE AND TECHNOLOGY, 2023, 卷号: 233, 页码: 8
作者:
Zhang, Jiangang
;
Wang, Han
;
Zhang, Tengxin
;
Sun, Xinyang
;
Meng, Yu
  |  
收藏
  |  
浏览/下载:38/0
  |  
提交时间:2023/05/09
Graphene and other 2D-materials
Hybrid composites
Thermal properties
Multifunctional properties
Thermal interface materials
Influences of a debris flow disaster chain on buildings in remote rural areas, Southwest China
期刊论文
OAI收割
GEOMATICS NATURAL HAZARDS & RISK, 2022, 卷号: 13, 期号: 1, 页码: 2777-2795
作者:
Zeng, Lu
;
Ge, Yonggang
;
Chen, Jiangang
;
Su, Fenghuan
;
Chen, Huayong
  |  
收藏
  |  
浏览/下载:71/0
  |  
提交时间:2022/12/28
Building blockage effect
debris flow
deposition morphology
disaster chain
outbreak flood
Patient-level grading prediction of prostate cancer from mp-MRI via GMINet
期刊论文
OAI收割
COMPUTERS IN BIOLOGY AND MEDICINE, 2022, 卷号: 150, 页码: 10
作者:
Shao, Lizhi
;
Liu, Zhenyu
;
Liu, Jiangang
;
Yan, Ye
;
Sun, Kai
  |  
收藏
  |  
浏览/下载:94/0
  |  
提交时间:2022/11/21
mp-MRI
Prostate cancer
Grade group
Patient-level prediction
Deep learning