AI framework predicts network latency trends for safer telesurgery
Peer-Reviewed Publication
Updates every hour. Last Updated: 28-Oct-2026 11:16 ET (28-Oct-2026 15:16 GMT/UTC)
A research team from Botswana International University of Science and Technology has developed a deep reinforcement learning framework to estimate network latency in telesurgery systems. The method uses a deep Q-network agent to first predict whether latency will increase or decrease and then translate that directional signal into a quantitative delay forecast. Tested on simulated teleoperation latency data, the framework achieved 85.8% direction prediction accuracy, a 35.8% improvement over a random classifier, as well as 7.23% mean absolute percentage error and 92.55% value accuracy for latency estimation. By providing both direction and value information, the approach may help future telesurgical systems anticipate changing network conditions and improve resilience during remote surgical operation.
Recently, the research team led by Xiang Ma and Fan Gu from East China University of Science and Technology developed a luminescent soft material by doping coronene into an epoxy-based shape memory polymer, demonstrating both actuation capability and tunable ultralong afterglow property at high temperature. This strategy enabled real-time visual monitoring of dynamic material deformation at temperatures up to 200 °C, opening up new avenues for intelligent equipment monitoring, information encryption and circuit protection in extreme environments.
Hydrogel electrolytes have emerged as promising alternatives to conventional aqueous electrolytes in zinc-ion batteries owing to their high ionic conductivity and mechanical robustness. However, hydrogel electrolytes still suffer from stability challenges under wide-temperature conditions. At low temperatures, the freezing of water and the ordering of hydrogen-bond networks restrict ion transport and strengthen the hydrated structure of Zn2+. At high temperatures, water evaporation and intensified parasitic reactions can destroy the electrode–electrolyte interface and induce structural degradation. These coupled thermodynamic and kinetic effects indicate that achieving stable wide-temperature operation requires coordinated regulation of ion transport, solvation thermodynamics and interfacial dynamics. To address this challenge, the coordination–entropy (C-E) regulation framework is proposed as a unified design concept. Within this framework, coordination regulation reconstructs Zn2+ solvation environments and lowers desolvation barriers, while entropy regulation increases the diversity of accessible ionic configurations and transport states across multiple structural scales. Their intrinsic coupling enables stable ion transport and interfacial chemistry under temperature variation. This review systematically summarizes recent advances in polymer, salt, cosolvent and filler regulation strategies directed by the C-E framework, aiming to establish a unified physicochemical framework for wide-temperature hydrogel electrolytes and guide the development of durable, high-energy–density zinc-ion batteries.
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