Jianlong Li
Dados Biográficos
| ID | 7366906 |
|---|---|
| NOME | Jianlong Li |
| PRENOMES | Jianlong |
| SOBRENOME | Li |
| ASSINATURA | LI J |
| AFILIAÇÕES | Nanjing University |
| ORCID | 0000-0002-0302-0061 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAÇÕES | 0 |
| TOTAL COMO AUTOR | 2 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2017 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2025 |
| ÍNDICE H | 0 |
Decomposition-Based Dynamic Inductive Graph Embedding Learning Method to Forecast Stock Trends
The stock market is a profit-oriented, chaotic, and nonlinear market game platform. Because price changes are directly related to investors’ returns, the accurate prediction of the short-term trend of stock prices has garnered significant attention. Recently, the application of a graph neural network (GNN) has become a research hotspot because of its ability to mine the momentum spillover effect between asset prices to achieve more effective fore…
Quantitative assessment of carbon sequestration reduction induced by disturbances in temperate Eurasian steppe
The temperate Eurasian steppe (TES) is a region where various environmental, social, and economic stresses converge. Multiple types of disturbance exist widely across the landscape, and heavily influence carbon cycling in this region. However, a current quantitative assessment of the impact of disturbances on carbon sequestration is largely lacking. In this study, we combined the boreal ecosystem productivity simulator (BEPS), the Shiyomi grazing…
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Quantitative assessment of carbon sequestration reduction induced by disturbances in temperate Eurasian steppe
The temperate Eurasian steppe (TES) is a region where various environmental, social, and economic stresses converge. Multiple types of disturbance exist widely across the landscape, and heavily influence carbon cycling in this region. However, a current quantitative assessment of the impact of disturbances on carbon sequestration is largely lacking. In this study, we combined the boreal ecosystem productivity simulator (BEPS), the Shiyomi grazing…
Decomposition-Based Dynamic Inductive Graph Embedding Learning Method to Forecast Stock Trends
The stock market is a profit-oriented, chaotic, and nonlinear market game platform. Because price changes are directly related to investors’ returns, the accurate prediction of the short-term trend of stock prices has garnered significant attention. Recently, the application of a graph neural network (GNN) has become a research hotspot because of its ability to mine the momentum spillover effect between asset prices to achieve more effective fore…
Agroforestry (1 obras) · Artificial Intelligence (1 obras) · Atmospheric sciences (1 obras) · Biology (1 obras) · Boreal (1 obras) · Carbon cycle (1 obras) · Carbon dioxide (1 obras) · Carbon fibers (1 obras) · Carbon sequestration (1 obras) · Chemistry (1 obras)