Highly Stable Photo-Assisted Zinc-Ion Batteries via Regulated Photo-Induced Proton Transfer.

Photo-assisted ion batteries utilize light to boost capacity but face cycling instability due to complex charge/ion transfer under illumination. This study identified photo-induced proton transfer (photo-induced PT) as a significant process in photo-(dis)charging of widely-used V2O5-based zinc-ion batteries, contributing to enhanced capacity under illumination but jeopardizing photo-stability. Photo-induced PT occurs at 100 ps after photo-excitation, inducing rapid proton extraction into V2O5 photoelectrode. This process creates a proton-deficient microenvironment on surface, leading to repetitive cathode dissolution and anode corrosion in each cycle. Enabling the intercalated protons from photo-induced PT to be reversibly employed in charge-discharge processes via the anode-alloying strategy achieves high photo-stability for the battery. Consequently, a ~54 % capacity enhancement was achieved in a V2O5-based zinc-ion battery under illumination, with ~90 % capacity retention after 4000 cycles. This extends the photo-stability record by 10 times. This study offers promising advancements in energy storage by addressing instability issues in photo-assisted ion batteries.

Research on the Recognition and Tracking of Group-Housed Pigs' Posture Based on Edge Computing.

The existing algorithms for identifying and tracking pigs in barns generally have a large number of parameters, relatively complex networks and a high demand for computational resources, which are not suitable for deployment in embedded-edge nodes on farms. A lightweight multi-objective identification and tracking algorithm based on improved YOLOv5s and DeepSort was developed for group-housed pigs in this study. The identification algorithm was optimized by: (i) using a dilated convolution in the YOLOv5s backbone network to reduce the number of model parameters and computational power requirements; (ii) adding a coordinate attention mechanism to improve the model precision; and (iii) pruning the BN layers to reduce the computational requirements. The optimized identification model was combined with DeepSort to form the final Tracking by Detecting algorithm and ported to a Jetson AGX Xavier edge computing node. The algorithm reduced the model size by 65.3% compared to the original YOLOv5s. The algorithm achieved a recognition precision of 96.6%; a tracking time of 46 ms; and a tracking frame rate of 21.7 FPS, and the precision of the tracking statistics was greater than 90%. The model size and performance met the requirements for stable real-time operation in embedded-edge computing nodes for monitoring group-housed pigs.

Two new triterpenoid glycosides from leaves of Cyclocarya paliurus.

Two dammarane glycosides (1-2) were isolated from the leaves of Cyclocarya paliurus. The structures of new compounds were established by application of spectroscopic methods, including one-dimensional and two-dimensional NMR, HRESIMS, and chemical hydrolysis. When evaluated against seven human cancer cell lines, the two compounds exhibited selective cytotoxicity to MCF-7 cells.[Formula: see text].