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ABSTRACT An increase in atmospheric pO2 has been proposed as a trigger for the Cambrian Explosion at ∼539–514 Ma but the mechanistic linkage remains unclear. To gain insights into marine habitability for the Cambrian Explosion, we analysed excess Ba contents (Baexcess) and isotope compositions (δ138Baexcess) of ∼521-Myr-old metalliferous black shales in South China. The δ138Baexcess values vary within a large range and show a negative logarithmic correlation with Baexcess, suggesting a major (>99%) drawdown of oceanic Ba inventory via barite precipitation. Spatial variations in Baexcess and δ138Baexcess indicate that Ba removal was driven by sulfate availability that was ultimately derived from the upwelling of deep seawaters. Global oceanic oxygenation across the Ediacaran–Cambrian transition may have increased the sulfate reservoir via oxidation of sulfide and concurrently decreased the Ba reservoir by barite precipitation. The removal of both H2S and Ba that are deleterious to animals could have improved marine habitability for early animals. 

Abstract Part quality manufactured by the laser powder bed fusion process is significantly affected by porosity. Existing works of process–property relationships for porosity prediction require many experiments or computationally expensive simulations without considering environmental variations. While efforts that adopt real-time monitoring sensors can only detect porosity after its occurrence rather than predicting it ahead of time. In this study, a novel porosity detection-prediction framework is proposed based on deep learning that predicts porosity in the next layer based on thermal signatures of the previous layers. The proposed framework is validated in terms of its ability to accurately predict lack of fusion porosity using computerized tomography (CT) scans, which achieves a F1-score of 0.75. The framework presented in this work can be effectively applied to quality control in additive manufacturing. As a function of the predicted porosity positions, laser process parameters in the next layer can be adjusted to avoid more part porosity in the future or the existing porosity could be filled. If the predicted part porosity is not acceptable regardless of laser parameters, the building process can be stopped to minimize the loss.