Researchers at QuTech, in collaboration with Fujitsu and Element Six, have demonstrated a complete set of quantum gates with error probabilities below 0.1%. While many challenges remain, being able to perform basic gate operations with errors occurring below this threshold, satisfies an important condition for future large-scale quantum computation. The research is published in Physical Review Applied on 21 March 2025.

Transfer learning, as a new machine learning methodology, may solve problems in related but different domains by using existing knowledge, and it is often applied to transfer training data from another domain for model training in the case of insufficient training data. In recent years, an increasing number of researchers who engage in brain-computer interface (BCI), have focused on using transfer learning to make most of the available electroencephalogram data from different subjects, effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the model. This paper surveys the development of transfer learning and reviews the transfer learning approaches in BCI. In addition, according to the “what to transfer” question in transfer learning, this review is organized into three contexts: instance-based transfer learning, parameter-based transfer learning, and feature-based transfer learning. Furthermore, the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods, datasets, evaluation performance, etc. At the end of the paper, the questions to be solved in future research are put forward, laying the foundation for the popularization and in-depth research of transfer learning in BCI.

Inspired by the concept of harmonious coexistence between humans and nature, a new concept, biogenic construction for civil engineering is proposed in this paper, as well as the definition, framework and four components.

Researchers at Saratov State University and their colleagues have developed an innovative approach to improving the visualization of subsurface blood vessels using laser speckle contrast imaging (LSCI) enhanced with principal component analysis (PCA).

This study explores the effects of Alpiniae oxyphyllae Fructus (AOF) on renal lipid deposition in diabetic kidney disease and elucidate its molecular mechanisms.