Zhaowei Wang
Biographic Data
| ID | 6838891 |
|---|---|
| NAME | Zhaowei Wang |
| GIVEN NAMES | Zhaowei |
| FAMILY NAME | Wang |
| SIGNATURE | WANG Z |
| AFFILIATIONS | China National Environmental Monitoring Center |
| ORCID | 0009-0004-8565-8326 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Advanced machine learning techniques reveal multidimensional EEG abnormalities in children with ADHD
We showed that EEG features was effectively extracted by machine learning methods, which could play a critical role in classification between pediatric ADHD and HC. These findings provide strong evidence for revealing the electrophysiological mechanisms through multidimensional EEG characteristics and move a step forward towards future automatic diagnosis of ADHD
Feasibility of Remediation of Heavy-Metal-Contaminated Marine Dredged Sediments by Active Capping with Enteromorpha Biochar
Enteromorpha biochar (BC) has been proposed as a potential absorbent in the marine environments. This study attempts to understand the process of active capping using Enteromorpha BC to prevent the release of heavy metals (Pb and Cd) from contaminated marine dredged sediments. The capping efficiency was assessed with a series of lab-scale column experiments. Results showed that the Enteromorpha BC exhibits rough pore structure and higher specific…
The Effect of Social Interaction on Rural Children’s Self-identity
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The Effect of Social Interaction on Rural Children’s Self-identity
Feasibility of Remediation of Heavy-Metal-Contaminated Marine Dredged Sediments by Active Capping with Enteromorpha Biochar
Enteromorpha biochar (BC) has been proposed as a potential absorbent in the marine environments. This study attempts to understand the process of active capping using Enteromorpha BC to prevent the release of heavy metals (Pb and Cd) from contaminated marine dredged sediments. The capping efficiency was assessed with a series of lab-scale column experiments. Results showed that the Enteromorpha BC exhibits rough pore structure and higher specific…
Advanced machine learning techniques reveal multidimensional EEG abnormalities in children with ADHD
We showed that EEG features was effectively extracted by machine learning methods, which could play a critical role in classification between pediatric ADHD and HC. These findings provide strong evidence for revealing the electrophysiological mechanisms through multidimensional EEG characteristics and move a step forward towards future automatic diagnosis of ADHD
Psychiatry (2 works) · Psychology (2 works) · Adsorption (1 work) · Adsorption and biosorption for pollutant removal (1 work) · Artificial Intelligence (1 work) · Attention Deficit Hyperactivity Disorder (1 work) · Biochar (1 work) · Chemistry (1 work) · Child Development and Digital Technology (1 work) · China (1 work)