A research paper by scientists from Beijing University of Posts and Telecommunications designed a multimodal controller termed the tactile, kinesthetic, and electromyography (EMG) bionic gripping controller (TKE-BGC).

The new research paper, published on May. 13 in the journal Cyborg and Bionic Systems, developed a bioinspired control framework that dramatically improves prosthetic hand stability during such dynamic tasks.

Researchers developed DGMoE, a mixture-of-experts framework that better handles individual differences in EEG signals and improves emotion recognition across unseen users. Tested on three public datasets, it achieved state-of-the-art accuracies of 79.5%, 59.1%, and 57.9%, showing a more practical path toward robust affective brain-computer interfaces.

Quantum particles in imperfect or quasiperiodic environments may spread, become localized, or enter critical states with fractal wave functions. These behaviors can combine into seven fundamental localization phases, but a unified framework for all of them has been missing. A team led by Prof. Xiong-Jun Liu at Peking University has now developed a generic spin-1/2 quasiperiodic system that captures all the fundamental phases, proves three theorems, constructs new exactly solvable models, and proposes the implementation with ultracold atoms in optical Raman lattices.

By adapting an algorithm from the 1980s to the modern context of mathematical objects called tensor networks, researchers at the Flatiron Institute extended the reach of classical computation and tackled a class of problems previously claimed to be solvable only by quantum computers.