Vibrotactile stimulation at 40 Hz inhibits Aβ-induced changes in SH-SY5Y, BV2 cells, and pericytes.

Alzheimer's disease (AD) poses a major societal challenge, yet no definitive cure exists. Noninvasive brain stimulation methods, such as transcranial magnetic stimulation and transcranial direct current stimulation, have shown promise in alleviating cognitive symptoms associated with neurodegenerative disorders. This study investigated the effects of 40 Hz vibrotactile stimulation on AD-related cellular responses using SH-SY5Y neuroblastoma cells, primary human brain pericytes, and BV2 microglia.

Inverse design of Bézier curve-based mechanical metamaterials with programmable negative thermal expansion and negative Poisson's ratio a data augmented deep autoencoder.

Controlling stress and deformation induced by thermo-mechanical stimulation in high-precision mechanical systems can be achieved by mechanical metamaterials (MM) exhibiting negative thermal expansion (NTE) and negative Poisson's ratio (NPR). However, the inverse design of MM exhibiting a wide range of arbitrary target NTEs and NPRs is a challenging task due to the low design flexibility of analytical methods and parametric studies based on numerical simulation. In this study, we propose Bézier curve-based programmable chiral mechanical metamaterials (BPCMs) and a deep autoencoder-based inverse design model (DAIM) for the inverse design of BPCMs.

Sonic vibration ameliorates inflammatory diseases via the up-regulation of IL-10.

Sonic vibration (SV), or vibroacoustic therapy, is applied to enhance local and systemic blood circulation and alleviate pain using low-frequency sine wave vibrations. However, there is limited scientific data on the mechanisms through which the benefits are achieved. In this study, we investigated the impact of SV on inflammatory responses by assessing cytokine secretion in both and models.

Environmentally Robust Triboelectric Tire Monitoring System for Self-Powered Driving Information Recognition via Hybrid Deep Learning in Time-Frequency Representation.

Developing a robust artificial intelligence of things (AIoT) system with a self-powered triboelectric sensor for harsh environment is challenging because environmental fluctuations are reflected in triboelectric signals. This study presents an environmentally robust triboelectric tire monitoring system with deep learning to capture driving information in the triboelectric signals generated from tire-road friction. The optimization of the process and structure of a laser-induced graphene (LIG) electrode layer in the triboelectric tire is conducted, enabling the tire to detect universal driving information for vehicles/robotic mobility, including rotation speeds of 200-2000 rpm and contact fractions of line.