Global electricity use is increasing rapidly and must be addressed sustainably. Developing new materials could give us much more efficient solar cell materials than at present; materials so thin and flexible that they could encase anything from mobile phones or entire buildings. Using computer simulation and machine learning, researchers at Chalmers University of Technology in Sweden have now taken an important step towards understanding and handling halide perovskites, among the most promising but notoriously enigmatic materials.

Heriot-Watt University has launched its Professional Academy, a comprehensive online learning platform that will be offering over 50 university-credited courses designed specifically for busy professionals seeking to upskill rapidly in their current roles. The innovative platform addresses the growing demand for flexible, accessible professional development in an increasingly competitive job market. Users can start anytime, study anywhere and learn at their own pace.

The Shockley–Queisser (S-Q) model sets a theoretical limit on the power conversion efficiency (PCE) of single-junction solar cells at around 33%. Recently, a PCE of 50%-60% was achieved for the first time in n-type single-junction Si solar cells by inhibiting light conversion to heat at low temperatures. Understanding these new observations opens tremendous opportunities for designing solar cells with even higher PCE to provide efficient and powerful energy sources for cryogenic devices and outer and deep space explorations.

Using magnetic cameras, researchers at Linköping University, Sweden, have examined blood flow in an artificial heart in real time. The results make it possible to design the heart in a way to reduce the risk of blood clots and red blood cells breakdown, a common problem in today’s artificial hearts. The study, published in Scientific Reports, was done in collaboration with the company Scandinavian Real Heart AB, which is developing an artificial heart.

This paper proposes a deep learning framework F-GCN that integrates multiple wavelet bases, and extracts MI brain electrical features based on the functional topological relationships between electrodes. The average accuracy of the fused features reaches 92.44%, which is significantly higher than that of a single wavelet basis (coif4: 67.67%, db4: 82.93%, sym4: 73.10%). It also demonstrates good stability and individual convergence in the leave-one-out verification, proving the effectiveness of the method.

The rapid advancement of artificial intelligence (AI) presents significant opportunities for both societies and industries. At the same time, however, it raises growing concerns about the increasing frequency and sophistication of cyberattacks. These cyber risks can lead not only to substantial direct financial losses for firms, but also to indirect losses stemming from reputational damage and cascading effects within interconnected systems. To enhance resilience in the face of such events, it is essential for scholars across disciplines to engage in rigorous analysis and interdisciplinary dialogue on the assessment and management of cyber risks.