Effects of silicon on mechanical properties of AM60 magnesium alloy

Silicon was added to improve the tensile, wear and creep behaviors of AM60 magnesium alloy in this study. The investigation has been undertaken by means of universal testing machine, HBE-3000A Brinell hardness tester, M-2000 friction-wear machine, DMA-Q800 creep machine, optical microscopy (OM) and scanning electron microscopy (SEM). The results indicate that the Chinese script type Mg2Si particles are formed by adding Si into the AM60 magnesium alloy. The ultimate tensile strength and hardness of the AM60 magnesium alloy increases with the Si addition, and the ultimate tensile strength and hardness of the AM60+1.0wt.%Si alloy are increased by 12% and 19.8%, respectively in comparison with that of the AM60 magnesium alloy. The wear property and the high temperature creep property of the AM60 magnesium alloy are also improved with Si addition. The wear mechanisms of the AM60 and AM60+1.0wt.%Si alloys are adhesive wear and abrasion wear, respectively. While, the elongation of the AM60 magnesium alloy decreases with the addition of Si. The optimum Si addition content is 1.0wt.%

2-[(2H-Tetra­zol-2-yl)meth­yl]benzonitrile

The title compound, C9H7N5, is non-planar with a dihedral angle between the substituted benzene and tetra­zole rings of 71.13 (9)°. Molecules are connected in centrosymmetric dimers by weak C—H⋯N inter­actions [C⋯N is 3.548 (5) Å]; these are the only interactions of significance in the crystal structure

Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency