AST-GIN: Attribute-Augmented Spatiotemporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting.

Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With accurate EV station availability prediction, suitable charging behaviors can be scheduled in advance to relieve range anxiety. Many existing deep learning methods have been proposed to address this issue; however, due to the complex road network structure and complex external factors, such as points of interest (POIs) and weather effects, many commonly used algorithms can only extract the historical usage information and do not consider the comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatiotemporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both the external and internal spatiotemporal dependence of relevant transportation data. The external factors are modeled as dynamic attributes by the attributeaugmented encoder for training. The AST-GIN model was tested on the data collected in Dundee City, and the experimental results showed the effectiveness of our model considering external factors' influence on various horizon settings compared with other baselines.

Clean technology for biochar and organic waste recycling, and utilization in apple orchard.

Present study evaluated the utilization of clean technology for biochar combined with organic fertilizer in apple orchard aspect of soil organic carbon fractions and microbial community. Four treatments were performed with control (CK), rice husk biochar alone (B), bagasse fermented organic fertilizer alone (O) and biochar combined with organic fertilizer (BO). The results demonstrated that utilization of organic fertilizer integrated with biochar were obviously enhanced the total and active fractions organic carbon in the top-soil (0-20 cm), enriched the bacterial community diversity and the richest abundance presented in BO treatment with 4253 operational taxonomic unit. The visualization illustrated the superior bacterial community was affiliated with Proteobacteria (35.14%), Actinobacteria (21.34%), Acidobacteria (16.82%) and Firmicutes (14.70%). Additionally, redundancy analysis suggested the strong interaction between microorganisms and organic carbon fractions. Overall, the application of biochar combine with organic fertilizer was favorable approach in apple orchard management, attributed to the influence of essential factors by improve organic carbon and bacterial diversity especially conductive to the profitable strain proliferation.