Visibility forecast in Jiangsu province based on the GCN-GRU model.

Low visibility weather easily leads to traffic accidents, posing threats to human life and property. To accurately forecast visibility, we conduct an empirical study focusing on Jiangsu Province. Firstly, we collect the monitoring data from meteorological stations and environmental stations for 2017-2018. Secondly, we analyze the changes in visibility from both spatial and temporal perspectives. Next, the maximum Relevance Minimum Redundancy (mRMR) algorithm is employed to select factors affecting visibility, finding that humidity and P M 2.5 concentrations are the primary factors. Finally, we propose GCN-GRU (Graph Convolutional Network and Gated Recurrent Unit) model for short-term visibility forecasting, which employs GCN to capture the interactions between stations and uses GRU to learn the interactions between times. Experimental results indicate that GCN-GRU outperforms the standalone GRU model and three machine learning models regarding 6-hour visibility forecasting. Compared to the best competitor, GCN-GRU achieves an average increase of 3.32% in Correlation Coefficient (CORR), a decrease of 17.52% in Root Mean Square Error (RMSE), a reduction of 26.62% in Mean Absolute Percentage Error (MAPE), and a decline of 16.53% in Mean Absolute Error (MAE).

Fiber Sedimentation and Layer-By-Layer Assembly Strategy for Designing Biomimetic Quasi-Ordered Mullite Fiber Aerogels as Extreme Conditions Thermal Insulators.

Ceramic fiber aerogels are attractive thermal insulating materials. In a thermomechanical coupling environment, however, they often show limited mechanical strength and considerably increased heat transfer which can lead to thermal runaway. In this paper, inspired by bird's nest and nacre, we demonstrate a sample strategy combining fiber sedimentation and layer-by-layer assembly to fabricate ultrastrong mullite fiber aerogels (MFAs) with quasi-ordered structures. The fibrous layers and fiber bridges are constructed in a fiber sedimentation self-assembly process. The fiber sedimentation technique optimizes the structure of the MFAs by regulating the fiber orientation. Owing to the quasi-ordered structure, the fabricated MFAs exhibit the integrated properties of high compression fatigue resistance, temperature-invariant compression resilience from -196 to 1300 °C, and low thermal conductivity (0.034 W·m-1·K-1). By deliberately pressing multilayer MFAs into a thin paper, we substantially enhance the load-bearing capacity of the MFAs and achieve large temperature differences (563 °C) between the cold and hot surfaces by using a thin layer of MFAs (3-5 mm) under the simulated high-temperature (685 °C) and high-pressure (0.9 MPa) environment test. The combination of compression resistance, mechanical flexibility, and excellent thermal insulation provides an appealing material for efficient thermal insulation in extreme environments.