Scalable and Ultra-Sensitive Nanofibers Coaxial Yarn-Woven Triboelectric Nanogenerator Textile Sensors for Real-Time Gait Analysis.

Yarn-woven triboelectric nanogenerators (TENGs) have greatly advanced wearable sensor technology, but their limited sensitivity and stability hinder broad adoption. To address these limitations, Poly(VDF-TrFE) and P(olyadiohexylenediamine (PA66)-based nanofibers coaxial yarns (NCYs) combining coaxial conjugated electrospinning and online conductive adhesive coating are developed. The integration of these NCYs led to enhanced TENGs (NCY-TENGs), notable for their flexibility, stretchability, and improved sensitivity, which is ideal for capturing body motion signals. One significant application of this technology is the fabrication of smart insoles from NCY-TENG plain-woven fabrics. These insoles are highly sensitive and possess antibacterial, breathable, and washable properties, making them ideal for real-time gait monitoring in patients with diabetic foot conditions. The NCY-TENGs and their derivatives show immense potential for a variety of wearable electronic devices, representing a considerable advancement in the field of wearable sensors.

A noise-immune LSTM network for short-term traffic flow forecasting.

Accurate and timely short-term traffic flow forecasting plays a key role in intelligent transportation systems, especially for prospective traffic control. For the past decade, a series of methods have been developed for short-term traffic flow forecasting. However, due to the intrinsic stochastic and evolutionary trend, accurate forecasting remains challenging. In this paper, we propose a noise-immune long short-term memory (NiLSTM) network for short-term traffic flow forecasting, which embeds a noise-immune loss function deduced by maximum correntropy into the long short-term memory (LSTM) network. Different from the conventional LSTM network equipped with the mean square error loss, the maximum correntropy induced loss is a local similar metric, which is immunized to non-Gaussian noises. Extensive experiments on four benchmark datasets demonstrate the superior performance of our NiLSTM network by comparing it with the frequently used models and state-of-the-art models.