Zihao Jiang
Biographic Data
| ID | 7565005 |
|---|---|
| NAME | Zihao Jiang |
| GIVEN NAMES | Zihao |
| FAMILY NAME | Jiang |
| SIGNATURE | JIANG Z |
| AFFILIATIONS | University of Science and Technology of China |
| ORCID | 0000-0003-2689-6522 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Industrial policy mix, substantive technological innovation, and sustainable growth of renewable energy enterprises in China
Renewable energy is widely prioritized because of its profitability and cleanliness. To encourage and guide the development of the renewable energy industry, the Chinese government has formulated a series of industrial policies, but the impact of different categories of industrial policies and their mix on substantive technological innovation and subsequent sustainable growth is still underdeveloped. In addition, renewable energy enterprises in e…
Temporal trends and machine learning prediction of depressive symptoms among Chinese middle-aged and elderly individuals
The prevalence of depressive symptoms in China's middle-aged and elderly population is high, and the influencing factors are complex. When predicting depressive symptoms, the model should be selected based on the prediction needs, and random undersampling with XGB is suitable for long-term risk prediction in large-scale populations. For high-risk groups, accurate prediction strategies can be used to reduce the risk of depressive symptoms
Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations
Gender, age, ethnicity, and region are significant factors influencing CVD incidence. Although the LGB model demonstrates good overall performance, its low F1 score and recall rate reveal limitations in identifying high-risk cardiovascular disease patients
Willingness to pay a premium price for green products
Policies and exploitative and exploratory innovations of the wind power industry in China
No prominent works on this page.
Policies and exploitative and exploratory innovations of the wind power industry in China
Willingness to pay a premium price for green products
Industrial policy mix, substantive technological innovation, and sustainable growth of renewable energy enterprises in China
Renewable energy is widely prioritized because of its profitability and cleanliness. To encourage and guide the development of the renewable energy industry, the Chinese government has formulated a series of industrial policies, but the impact of different categories of industrial policies and their mix on substantive technological innovation and subsequent sustainable growth is still underdeveloped. In addition, renewable energy enterprises in e…
Temporal trends and machine learning prediction of depressive symptoms among Chinese middle-aged and elderly individuals
The prevalence of depressive symptoms in China's middle-aged and elderly population is high, and the influencing factors are complex. When predicting depressive symptoms, the model should be selected based on the prediction needs, and random undersampling with XGB is suitable for long-term risk prediction in large-scale populations. For high-risk groups, accurate prediction strategies can be used to reduce the risk of depressive symptoms
Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations
Gender, age, ethnicity, and region are significant factors influencing CVD incidence. Although the LGB model demonstrates good overall performance, its low F1 score and recall rate reveal limitations in identifying high-risk cardiovascular disease patients
Biostatistics (2 works) · China (2 works) · Economics (2 works) · Energy, Environment, Economic Growth (2 works) · Epidemiology (2 works) · Health disparities and outcomes (2 works) · Microeconomics (2 works) · Public health (2 works) · Sustainable development (2 works) · Biology (1 work)