Recent research from Professor Maryam Mehrnezhad at the Information Security Department, Royal Holloway University of London and a team of researchers reveals widespread privacy, security and regulatory failings in female-oriented health technologies (also known as FemTech). The researchers’ comprehensive analysis demonstrates how current practices leave sensitive health information vulnerable, while highlighting an urgent need for reform across technical, legal and social dimensions of digital healthcare.

In order to operate safely and efficiently, lithium-ion batteries rely on battery management systems to monitor their state and to control their operation. An essential part of this process is modelling battery behaviour under different conditions to predict performance and prevent failures. To do this efficiently, it is crucial to simplify the underlying physical processes, while sacrificing as little accuracy as possible. Through their research, Dr. Luc Raijmakers and colleagues at the Jülich Research Centre, Germany, compare various different approaches to simplifying simulations. Their results could make it easier for battery operators to decide which approach is best suited to their requirements for accuracy and computational efficiency.

Neuromorphic computing is a powerful tool for identifying time-varying patterns, but is often less effective than some AI-based techniques for more complex tasks. Researchers at the iCAS Lab directed by Ramtin Zand at the University of South Carolina, work on an NSF CAREER project to show how the capabilities of neuromorphic systems could be improved by blending them with specialized machine learning systems, without sacrificing their impressive energy efficiency. Using their approach, the team aims to show how the gestures of American Sign Language could be instantly translated into written and spoken language.