doi.org/10.1080/01605682.2024.2436063

The adoption of new digital technologies in healthcare has surged over the past decade and significantly enhanced care delivery and accessibility. However, cyber-attacks have also sharply increased posing severe risks to hospital functionality and patient safety. Hence, Emilia Grass and colleagues from London have come up with new reliable tool for enhancing cybersecurity in healthcare presented in the Journal of the Operational Research Society. The two-stage stochastic model is designed to bolster the cyber resilience of healthcare providers by selecting optimal measures. Numerical tests demonstrate the model’s effectiveness, with the stochastic solution showing a 21% improvement over a deterministic approach. The robustness is further underscored by the model’s consistent performance across various scenarios, budget levels, and risk preferences.

doi.org/10.1016/j.mattod.2024.11.014

Ceramic materials are hard and brittle which renders them difficult to deform and prone to fracture. Improved plastic deformability and ductility would allow to harvest versatile mechanical and functional properties for engineering applications. In Materials Today, Xufei Fang and colleagues demonstrate a new facile approach to significantly improve the room-temperature plasticity of ceramics with a large plastic compressive strain beyond ∼30%. They use room-temperature mechanically seeded mobile dislocations to trigger profuse dislocation multiplication via cross slip and motion. This offers an avenue to suppress brittle fracture and harvest plasticity in ceramics without any additional high-temperature process. The scientists employ both in situ nano-/micromechanical deformation and ex situ bulk deformation. The work is mainly funded by Fang's ERC Starting Grant.

Kurt Fuchs, HI ERN

Perovskite solar cells might become a sustainable alternative to conventional silicon-based solar cells. In the journal Science, researchers around Pascal Friederich, KIT, and Christoph Brabec from the Helmholtz Institute Erlangen-Nürnberg (HI ERN) now demonstrate a closed-loop workflow that combines high-throughput synthesis of organic semiconductors with device characterization and Bayesian optimization to discover new hole-transporting materials with tailored properties for solar cell applications. The predictive models were based on molecular descriptors that allowed to link the structure of these materials directly to their performance in solar cell devices. A series of high-performance molecules, identified from minimal suggestions, achieved up to 26.2% (certified 25.9%) power conversion efficiency.