Patient-Specific Deep Reinforcement Learning for Proton Beam Delivery Under Inter-Phase Variations. [PDF]
Ghislain M +6 more
europepmc +1 more source
A Critical Time Window of Neocortex Modulation for Remote Fear Memory in Rats and Humans
This study identifies a delayed consolidation window around 18 h after fear learning in the mPFC essential for remote fear memory. VTA dopaminergic input during this window sustains remote memory in rats, while non‐invasive stimulation of homologous human mPFC at the corresponding time selectively weakens remote but not recent fear memory, revealing a ...
Lu Cao +19 more
wiley +1 more source
Deep Reinforcement Learning-Based Automated Treatment Planning for a Rotating Gamma System. [PDF]
Li J, Guo P, Guo X.
europepmc +1 more source
An Integrated NLP‐ML Framework for Property Prediction and Design of Steels
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju +5 more
wiley +1 more source
Dynamic monitoring of railway bridges via coupling digital twins with deep reinforcement learning. [PDF]
Xu X, He G, Liu C, Ding X.
europepmc +1 more source
Longitudinal Morphomolecular Monitoring of Head and Neck Carcinogenesis
A miniaturized endoscopic probe that integrates Raman spectroscopy and optical coherence tomography, enabling simultaneous molecular and structural imaging of living tissue, was developed. Applied longitudinally in a mouse model of head and neck cancer, the system tracks disease progression from precancerous lesions to invasive cancer.
Jianrong Qiu +7 more
wiley +1 more source
Sustainable Materials Design With Multi‐Modal Artificial Intelligence
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 +8 more
wiley +1 more source
A deep reinforcement learning approach for dynamic transaction fee adjustment in Ethereum. [PDF]
Jang H, Shim J.
europepmc +1 more source
An integrated transfer learning framework integrates CALPHAD simulations, diffusion‐multiple experiments, and literature data to predict long‐term microstructural stability and short‐term mechanical properties of Ni‐based powder metallurgy superalloys. Based on these model predictions, a high‐performance, low‐density alloy, USTB‐PM750, is designed from
Zixin Li +8 more
wiley +1 more source
On the need for abstract, deep reinforcement learning models in neuroscience. [PDF]
Duarte S +3 more
europepmc +1 more source

