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Creators/Authors contains: "Zhang, Qiang"

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  1. ; ; ; ; ; ; ; (, IEEE Transactions on Medical Robotics and Bionics)
    Free, publicly-accessible full text available May 1, 2026
  2. ; ; ; (, IEEE Transactions on Biomedical Engineering)
    Free, publicly-accessible full text available December 1, 2025
  3. ; ; ; ; ; ; ; (, Chemistry of Materials)
    Free, publicly-accessible full text available March 11, 2026
  4. ; ; ; ; ; ; ; ; ; ; et al (, Journal of Lipid Research)
    Free, publicly-accessible full text available April 1, 2026
  5. ; ; ; (, SIAM Journal on Numerical Analysis)
    Free, publicly-accessible full text available October 31, 2025
  6. ; ; ; ; ; (, IEEE Transactions on Systems, Man, and Cybernetics: Systems)
    Free, publicly-accessible full text available December 1, 2025
  7. ; ; (, Journal of Scientific Computing)
    Full Text Available 
  8. ; ; ; ; ; ; (, IEEE)
    Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study rigorously evaluates DRL’s performance and limitations within actual operational contexts by utilizing detailed experiments across the IEEE 14-bus system, Illinois 200-bus system, and the ISO New England node-breaker model. Our analysis critically assesses DRL’s effectiveness for grid control from a system operator's perspective, identifying specific performance bottlenecks. The findings provide actionable insights that highlight the necessity of advancing AI technologies to effectively address the growing complexities of modern power systems. This research underscores the vital role of DRL in enhancing grid management and reliability. 
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  9. ; ; ; ; ; ; ; ; ; ; et al (, New Journal of Chemistry)
    Aluminum-doped porous silicon was produced by a molten salt reaction. 
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    Free, publicly-accessible full text available December 9, 2025
  10. ; ; (, Chemistry of Materials)
    Full Text Available