Results 201 to 210 of about 338,520 (307)

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

open access: yesAdvanced 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   +8 more
wiley   +1 more source

The Role of the Institution of Navigation Education Regarding Safety Navigation

open access: yesProceedings of the International Conference on Science and Education and Technology (ISET 2019), 2020
openaire   +1 more source

Navigation System For Female Safety

open access: yesInternational Innovative Research Journal of Engineering and Technology, 2016
Kalyanasundaram C   +3 more
openaire   +1 more source

Smart Nanotechnologies for Multimodal Neuromodulation and Brain Interfacing

open access: yesAdvanced Science, EarlyView.
Recent advances in smart nanotechnologies are expanding the toolbox for brain interfacing, from wireless neuromodulation and high‐resolution sensing to targeted delivery within the central nervous system. By combining responsive nanomaterials with bioinspired design, these platforms enable multimodal interactions with neurons and glia, while also ...
Tommaso Curiale   +6 more
wiley   +1 more source

Magnetoelectric Nanoparticle‐Based Wireless Brain–Computer Interface: Underlying Physics and Projected Technology Pathway

open access: yesAdvanced Science, EarlyView.
Magnetoelectric nanoparticles (MENPs) enable fully wireless, minutely invasive neuromodulation, and potentially neural recording, by converting magnetic into electric and, conversely, electric into magnetic fields, respectively, at high spatiotemporal resolution.
Elric Zhang   +14 more
wiley   +1 more source

MGDP: Mastering a Generalized Depth Perception Model for Quadruped Locomotion

open access: yesAdvanced Science, EarlyView.
ABSTRACT Perception‐based Deep Reinforcement Learning (DRL) controllers demonstrate impressive performance on challenging terrains. However, existing controllers still face core limitations, struggling to achieve both terrain generality and platform transferability, and are constrained by high computational overhead and sensitivity to sensor noise.
Yinzhao Dong   +9 more
wiley   +1 more source

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