Ceramifiable polyolefin materials have an excellent application prospect in high-temperature-resistant wires and cables because of their excellent fire safety performance via a ceramization process under fire conditions. During the ceramization process, the control of the crystalline phase plays a vital role in determining the final fire resistance and ceramifiable properties. In this work, ammonium polyphosphate/zinc borate (APP/ZB) was developed to achieve the highly efficient flame retardance and ceramization of the ethylene-vinyl acetate/mica powder/organo-modified montmorillonite (EVA/MP/OMMT) composite. In the combustion test, the EVA/MP/OMMT/APP/ZB system displayed obvious flame retardance feature, showing much lower total heat release and total smoke production than neat EVA. After treating at high temperatures, rigid ceramic products were formed for EVA/MP/OMMT/APP/ZB. The ceramic that was formed at 900 °C had a flexural strength of 10.3 MPa for EVA/MP/OMMT/APP/ZB containing 23 wt % of APP/ZB (9.9:13.1), increased by 2475.0, 635.7, and 586.7% compared to the corresponding values of EVA/MP/OMMT, EVA/MP/ OMMT/ZB, and EVA/MP/OMMT/APP. For the latter two systems, the content of ZB or APP was 23 wt %. APP/ZB showed a remarkable fluxing effect on the ceramization of the MP-based EVA composite. The fluxing mechanism of APP/ZB was revealed by different measurements. Both APP and ZB led to the formation of a glass melt containing α-Zn 3 (PO 4 ) 2 and orthophosphate by increasing the temperature. Successively, the melt crystalline structure cohered the OMMT and MP together, accompanied by the gradual disappearance of the mica phase and the generation of eutectic phenomenon. Finally, a ceramic with high flexural strength was formed, leading to the improved flame retardance and ceramifiable properties of EVAbased composites.
Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods. To tackle these challenges, we propose a novel Spatio-Temporal Self-Supervised Learning (ST-SSL) traffic prediction framework which enhances the traffic pattern representations to be reflective of both spatial and temporal heterogeneity, with auxiliary self-supervised learning paradigms. Specifically, our ST-SSL is built over an integrated module with temporal and spatial convolutions for encoding the information across space and time. To achieve the adaptive spatio-temporal self-supervised learning, our ST-SSL first performs the adaptive augmentation over the traffic flow graph data at both attribute- and structure-levels. On top of the augmented traffic graph, two SSL auxiliary tasks are constructed to supplement the main traffic prediction task with spatial and temporal heterogeneity-aware augmentation. Experiments on four benchmark datasets demonstrate that ST-SSL consistently outperforms various state-of-the-art baselines. Since spatio-temporal heterogeneity widely exists in practical datasets, the proposed framework may also cast light on other spatial-temporal applications. Model implementation is available at https://github.com/Echo-Ji/ST-SSL.
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