Results 261 to 270 of about 1,052,376 (334)

Deep reinforcement learning for scheduling semiconductor cluster tools in varying configurations. [PDF]

Sci Rep
Choi J, Kim SB.
europepmc   +1 more source

Microglia‐Targeted Biomimetic Tetrahedral Framework Nucleic Acid Nanovesicles for Synergistic Treatment of Sepsis‐Associated Encephalopathy

Advanced Science, EarlyView.
Sepsis‐associated encephalopathy (SAE) lacks effective therapies. We developed ME@FDsi, a biomimetic nanodrug using a tetrahedral framework nucleic acid to deliver disulfiram and siTNFα. It crosses the blood‐brain barrier, targets M1 microglia, inhibits pyroptosis and inflammation, and scavenges ROS.
Huimin Shi, Qiuxia Gao, Wenying Wang, Bin Li, Yukun Chen, Zhijun Yao, Yujie Li, Junrui Li, Na Li, Gong Gu, Zhimin Hou, Mengyuan Yang, Ruilin Zhang, Hongju Yang, Yuhui Liao, Hongyi Lei   +15 more
wiley   +1 more source

MMU-STCNN-BDQ: a deep reinforcement learning framework for secure and energy-efficient beamforming in 6G mMIMO networks. [PDF]

Sci Rep
Ramudu K, Medasani S, Addepalli T, Sharma M, Pandey A, Srinubabu M.   +5 more
europepmc   +1 more source

Endogenous Engineering Reprograms Extracellular Vesicles for Enhanced Therapeutic Function

Advanced Science, EarlyView.
This review explains how Extracellular vesicles‐producing cells can be endogenously engineered to load therapeutic proteins and nucleic acids. We summarize physiological and genetic strategies that harness native sorting pathways for selective cargo loading.
Jinghui Wang, Wenxuan Zhao, Xiaoming Hu, Yishu Wang, Peidong Yang, Diwei Zheng, Yugang Wang, Guanghui Ma, Min Shi, Wei Wei, Shuang Wang   +10 more
wiley   +1 more source

Competitive swarm reinforcement learning improves stability and performance of deep reinforcement learning. [PDF]

Sci Rep
Huang X, Luo W, Li L, Zhang H, Jiang H, Gao X.   +5 more
europepmc   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Deep reinforcement learning for network resource optimization in MIMO-NOMA networks to maximize utilization with minimal overhead. [PDF]

Sci Rep
Lahza HFM, Sreenivasa BR, Lahza H, Nandini G, Laxmi V, Shawly T, Alsheikhy AA.   +6 more
europepmc   +1 more source

Spontaneous Non‐Catalyzed Molecular Reactions and Interactions in the Human Body: Biomedical Implications

Advanced Science, EarlyView.
ABSTRACT The human body functions as a natural reactor for a vast network of chemical and biological reactions and physical interactions among small molecules, proteins, cells, and numerous other components. These reactions/interactions are essential for maintaining normal physiological functions.
Yuhao Cai, Chao Zhao
wiley   +1 more source

Deep Q-Managed: a new framework for multi-objective deep reinforcement learning. [PDF]

Front Artif Intell
Menezes R, Freire de Oliveira TH, de Souza Medeiros LP, Dória Neto AD.   +3 more
europepmc   +1 more source

Schooling Trajectories and the Development of Brain Dynamics: A Comparative Study of Montessori and Traditional Education

Advanced Science, EarlyView.
We investigate whether Montessori and traditional schooling systems shape the developmental trajectory of large‐scale brain dynamics in different ways. We quantify the arrow of time (“non‐reversibility”) in neural activity during resting state and movie‐watching, revealing distinct maturational patterns.
Elvira del Agua, Anira Escrichs, Yonatan Sanz Perl, Morten L. Kringelbach, Adele Diamond, Solange Denervaud, Gustavo Deco   +6 more
wiley   +1 more source