Surfactants facilitate microbial growth on the surface of microplastics in biological wastewater treatment - Feiyuan Liu; Keyu Ma; Huan He; Xing Wan; Ziwei Guo; Jingye Cui; Wanqi Shi; Bin Huang; Xuejun Pan (Journal of Environmental Management, 2025)

Graphite-phase carbon nitride composites: Pioneering advancements and future prospects in capacitive desalination - Feifan Zhang; Hongying Lv; Yuying Pan; Yutao Chen; Keyu Hou; Zhiheng Wang; Jiuwen Sun; Hong Chen; Junli Pan; Shouqiang Huang (Journal of Environmental Management, 2025)

Synergy in the transformation of Bisphenol A by anaerobic microorganisms and manganese dioxide: The bridging role of extracellular polymeric substances - Feiyuan Liu; Zhicheng Liao; Huan He; Keyu Ma; Weiqing Ma; Weiwei Ren; Bin Huang; Hongwen Sun; Xuejun Pan (Bioresource Technology, 2025)

Degradation of Bisphenol A by manganese minerals: The impact of inorganic and organic ligands - Huan He; Feiyuan Liu; Zhicheng Liao; Min Shi; Keyu Ma; Shanshan Yang; Bin Huang; Xuejun Pan (Journal of Environmental Chemical Engineering, 2024)

Comparison analysis of microbial agent and different compost material on microbial community and nitrogen transformation genes dynamic changes during pig manure compost - Naiyuan Liu; Zhuangzhuang Liu; Keyu Wang; Jinfeng Zhao; Jun Fang; Gang Liu; Hao Yao; Junting Pan (Bioresource Technology, 2024)

Bufogarlides A-C, three new bufadienolides with Δ 14,15 double bond from the skins of Bufo bufo gargarizans - Lingjie Meng; Qinghong Kong; Jianxun Xie; Feng Pan; Keyu Lu; Sanhua Li; Yiling Li; Di Zhang; Yun Liu (Natural Product Research, 2024)

Selective efficient photocatalytic degradation of antibiotics and direct Z-type migration pathway for hierarchical core–shell TiO2/g-C3N4 composites - Hu Liu; Jianmei Pan; Keyu E; Yi Guan; Wenbo Gou; Peng Wang; Shahid Hussain; Ze Du; Chengfei Ma (Environmental Science and Pollution Research, 2023)

Associations between exposure to brominated flame retardants and metabolic syndrome and its components in U.S. adults - Zhiqiang Che; Huixun Jia; Renjie Chen; Keyu Pan; Zhe Fan; Chang Su; Zhenyu Wu; Tao Zhang (Science of The Total Environment, 2023)

Resistance of blended alkali-activated fly ash-OPC mortar to mild-concentration sulfuric and acetic acid attack - Keyu Chen; Dazhi Wu; Sijia Fei; Chonggen Pan; Xinyuan Shen; Chaoxia Zhang; Juntao Hu (Environmental Science and Pollution Research, 2022)

Towards Realizing Intelligent Coordinated Controllers for Multi-USV Systems Using Abstract Training Environments - Sulemana Nantogma; Keyu Pan; Weilong Song; Renwei Luo; Yang Xu (Journal of Marine Science and Engineering, 2021)
Unmanned autonomous vehicles for various civilian and military applications have become a particularly interesting research area. Despite their many potential applications, a related technological challenge is realizing realistic coordinated autonomous control and decision making in complex and multi-agent environments. Machine learning approaches have been largely employed in simplified simulations to acquire intelligent control systems in multi-agent settings. However, the complexity of the physical environment, unrealistic assumptions, and lack of abstract physical environments derail the process of transition from simulation to real systems. This work presents a modular framework for automated data acquisition, training, and the evaluation of multiple unmanned surface vehicles controllers that facilitate prior knowledge integration and human-guided learning in a closed-loop. To realize this, we first present a digital maritime environment of multiple unmanned surface vehicles that abstracts the real-world dynamics in our application domain. Then, a behavior-driven artificial immune-inspired fuzzy classifier systems approach that is capable of optimizing agents’ behaviors and action selection in a multi-agent environment is presented. Evaluation scenarios of different combat missions are presented to demonstrate the performance of the system. Simulation results show that the resulting controllers can achieved an average wining rate between 52% and 98% in all test cases, indicating the effectiveness of the proposed approach and its feasibility in realizing adaptive controllers for efficient multiple unmanned systems’ cooperative decision making. We believe that this system can facilitate the simulation, data acquisition, training, and evaluation of practical cooperative unmanned vehicles’ controllers in a closed-loop.