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Forecasting epidemic progression is a complex task influenced by various factors, including human behaviour, pathogen dynamics and environmental conditions. Rodríguez, Kamarthi and colleagues provide a review of machine learning methods for epidemic forecasting from a data-centric computational perspective.
Machine learning approaches in micro- and nanorobotics promise to overcome challenges encountered by applying traditional control methods at the microscopic scale. Lidong Yang et al. review this emerging area in robotics and discuss machine learning developments in design, actuation, locomotion, planning, tracking and navigation of microrobots.
