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Nanjing Institute of Railway Technology | Researchclopedia

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Nanjing Institute of Railway Technology

Nanjingeducation

2,572

Works

22,979

Citations

66

h-index

Top Research Topics

Asphalt Pavement Performance Evaluation Infrastructure Maintenance and Monitoring Higher Education and Teaching Methods

Top Researchers

Kai Wang Kunming University of Science and Technology

7,378 works216,134 citationsh-index: 172

Recent Research

Characterization and functional analysis of the interferon regulatory factor 5 (IRF5) from the GIFT Oreochromis niloticus

Youchuan Wei,Honglian Tan,et al.

20260 citations

Geotechnical Engineering and Underground Structures

Concrete and Cement Materials Research

Transportation Planning and Optimization

Railway Engineering and Dynamics

Structural Health Monitoring Techniques

Railway Systems and Energy Efficiency

Power Systems and Technologies

Advanced Algorithms and Applications

Advanced Computational Techniques and Applications

Li Zhu Zhejiang Chinese Medical University

7,645 works194,441 citationsh-index: 175

Simin Liu Huazhong University of Science and Technology

1,375 works129,381 citationsh-index: 139

Lei Wang Minzu University of China

5,907 works108,139 citationsh-index: 137

Yong‐Guan Zhu Institute of Urban Environment

1,582 works100,248 citationsh-index: 159

Jinde Cao Ahlia University

2,570 works99,492 citationsh-index: 145

Microscopic damage mechanism of asphalt mixtures under freeze-thaw cycles: The role of initial void content

Xiaojin Lu,Gang Xu,et al.

20260 citations

Reduced irrigation and phosphorus redistribution optimizes common vetch–mediated soil metabolic and microbial shifts suppress potato late blight

Ting Che,Sun,et al.

20260 citations

An Interpretable Machine Learning Framework for Next-Day Frost Forecasting in Tea Plantations Using Multi-Source Meteorological Data

Zhongqiu Zhang,Pei Li,et al.

20260 citations

Pricing strategy for O2O delivery supply chain with anti-food waste regulation

Qian Wang,Xinlan Weng,et al.

20260 citations

Predicting and interpreting the uplift bearing capacity of squeezed branch piles utilizing machine learning with explainability

Kai Shi,Tingting Sun

20260 citations