A new study introduces a global probabilistic forecasting model that predicts when and where ionospheric disturbances—measured by the Rate of total electron content (TEC) Index (ROTI)—are likely to ...

This study presents a bio-inspired control framework for soft robots, enhancing tracking accuracy by over 44% under disturbances while maintaining stability.

The integration of deep reinforcement learning with PD control in humanoid robots enhances gait stability and patient comfort ...

With the introduction of adaptive deep brain stimulation (aDBS) for Parkinson's disease, new questions emerge regarding who, why, and how to treat. This paper outlines the pathophysiological rationale ...

No body, no dopamine, no problem. Scientists have successfully coached lab-grown brain tissue to solve a classic robotics challenge, proving that the will to learn is hardwired into our neurons.

Imagine balancing a ruler vertically in the palm of your hand: you have to constantly pay attention to the angle of the ruler and make many small adjustments to make sure it doesn't fall over. It ...

This study proposes a cross-species transcriptomic framework to predict vaccine reactogenicity, with implications for preclinical vaccine safety assessment. The findings show that mouse muscle ...

Department of Chemistry and Biochemistry, University of South Carolina, Columbia, United States ...

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...

Abstract: To address the issues of high missed detection rates for small objects and interference from complex backgrounds in brain tumor detection, this paper proposes an improved model named ...