Machine Learning-Guided Cycle Life Prediction for Electrochromic Devices Based on Deuterium and Water Mixing Solvent.

Electrochromic devices have demonstrated considerable potential in a range of applications, including smart windows and automotive rearview mirrors. However, traditional cycle life testing methods are time-consuming and require significant resources to process a substantial amount of generated data, which presents a significant challenge and remains an urgent issue to be addressed. To address this challenge, we proposed the use of Long Short-Term Memory (LSTM) networks to construct a prediction model of the cycle life of electrochromic devices and introduced an interpretable analysis method to further analyze the model's predictive capabilities.

Machine Learning-Guided Performance Evaluation of an All-Liquid Electrochromic Device.

Electrochromic devices, capable of modulating light transmittance under the influence of an electric field, have garnered significant interest in the field of smart windows and car rearview mirrors. However, the development of high-performance electrochromic devices via large-scale explorations under miscellaneous experimental settings remains challenging and is still an urgent problem to be solved. In this study, we employed a two-step machine learning approach, combining machine learning algorithms such as KNN and XGBoost with the reality of electrochromic devices, to construct a comprehensive evaluation system for electrochromic materials.

[Role of p38MAPK signaling pathway in autophagy of Henle-407 cells induced by spvB of Salmonella typhimurium].

Objective: To investigate the role of p38MAPK signaling pathway in autophagy of intestinal epithelial cells induced by spvB of S.typhimurium.

Methods: Henle-407 cells in exponential growth were infected with wild-type S.