Atomfair Brainwave Hub: SciBase II / Artificial Intelligence and Machine Learning / AI-driven solutions for environmental and climate challenges

AI-driven solutions for environmental and climate challenges

Showing 25-36 of 176 articles

Bridging sonar technology with bat echolocation to improve underwater navigation systems

Resistive RAM architectures for energy-efficient in-memory computing systems

Considering 2100 sea level rise impacts on coastal aquifer salinization

Bridging current and next-gen AI with resistive RAM for in-memory computing

Employing soft robot control policies for underwater exploration in turbulent environments

Ocean iron fertilization impacts on diatom blooms monitored by autonomous glider fleets

For multi-generational studies, using blockchain for secure and transparent supply chains

Employing spectral analysis AI for real-time pollution monitoring in urban waterways

For stratospheric aerosol injection calibration using autonomous balloon networks

Employing soft robot control policies for adaptive underwater exploration in turbulent environments

Exploring circadian gene oscillations in shift workers and metabolic disorders

Planning post-2100 nuclear waste storage solutions using AI-driven geological stability assessments