A research paper by scientists at University of Rome Tor Vergata introduced a novel deep learning framework based on a graph deviation network (GDN) to analyze spiking activity from human forebrain organoids (hFOs) and predict network-level alterations associated with autism spectrum disorder (ASD) risk..

Space manipulators play an important role in the on-orbit services and planetary surface operation whose reliability is a key issue. In the extreme environment of space, space manipulators are susceptible to a variety of unknown disturbances. Since it is difficult for the manipulator to be repaired immediately, once it fails, it will mean that it cannot complete the mission as expected, which may cause serious losses and dangers. How to have a resilient guarantee, i.e., the manipulator’s ability to resiliently recover and continue to complete tasks, in failure or disturbance is the core capability of its future development. The motion planning unit is used as the computing terminal of the manipulator’s joint motion trajectory. Compared with traditional motion planning, learning-based motion planning has gradually become a hot spot in current research. However, no matter what kind of research ideas, the single robotic manipulator is studied as an independent agent, making it unable to provide sufficient flexibility under conditions such as external force disturbance, observation noise, and mechanical failure.

Promoting sustainable development in both urban and rural areas has become a globally shared concern. Urban–rural interactions can contribute to wealth creation, poverty alleviation, employment growth, and improved social equity. It has been widely recognized as a key strategy for addressing sustainability challenges, although it may also bring some negative impacts, such as labor outmigration and imbalanced capital allocation. However, the impacts of urban–rural interactions on rural development and how these impacts contribute to sustainable rural transformation require further in-depth investigation.