Using EUROSTAT data and double randomization, the co-led study improves the Benefit of the Doubt model through a novel Ensemble-DEA framework that mitigates the curse of dimensionality in SDG indicators. Published in Expert Systems with Applications, the method offers more reliable EU performance rankings and benchmarking tools for evaluating sustainability policies across member states.

Researchers at Heriot-Watt University have unveiled a prototype quantum network that links two smaller networks into one reconfigurable, eight-user system capable of routing and even teleporting entanglement on demand. 

The demonstration, reported this week in Nature Photonics, sets a new benchmark for how large, flexible and capable quantum networks can become.

Researchers have demonstrated a new approach to building quantum convolutional neural networks (QCNNs) using photonic circuits, paving the way for more efficient quantum machine learning. The method, reported in Advanced Photonics, introduces an adaptive step called “state injection,” allowing the circuit to adjust its behavior based on real-time measurements. Using single photons and integrated quantum photonic processors, the team achieved over 92 percent classification accuracy on simple image patterns, closely matching theoretical predictions. This proof-of-concept shows that QCNNs can be implemented with existing photonic technology and highlights a path toward scalable quantum processors for future applications in AI and data processing.

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.