To develop biocompatible antimicrobial agent, oyster shell wastes were thermally calcined at different temperatures ranging from 300 to 1000 °C. The chemical compositions and properties of oyster shells were characterized. As such, crystallographic analysis presented that oyster shells had a hexagonal crystalline shape, and calcination process reduced their crystalline size, volume (grain dimension), and bond length, which strongly affected antimicrobial efficacy. Results showed that the main components of uncalcined and calcined oyster shells were CaCO3 and CaO, by which CaO was found to be the main antimicrobial component. Notably, calcined oyster shells showed antimicrobial potency against both Gram‐negative (Escherichia coli) and Gram‐positive (Staphylococcus aureus). Furthermore, cytotoxicity analysis proved that calcined oyster shells had good cell viability and low cytotoxicity. Results highlighted that calcined oyster shells, particularly those treated at 750°C, could be a biocompatible alternative to synthetic biocidal and antimicrobial agents using in food packaging, biomedical, and cosmetic industries.

In many industries, prognostic health management (PHM) technology has become important as a key technology to increase reliability and operational efficiency. Recently, several methods using a deep learning architecture to estimate the remaining useful life (RUL) as a part of the PHM have been presented. However, the limitation of existing methods is that they do not explicitly capture the relationship among different time sequences, which reduces the accuracy of RUL estimation. This paper proposes a novel RUL estimation algorithm using the attention mechanism to solve this problem. The proposed method applies scaled dot product attention to the encoder and the decoder consisting of long short-term memory, convolutional neural network and fully connected layer. The encoder applies self-attention to extract the association between time sequences, and the decoder extracts the association between the target RUL value and the time sequences using the representative vector of the RUL. Therefore, the proposed model has better performance to capture the long-term dependency in the sequence data and outperforms other state-of-the-art models in the experimental results. In addition, the extracted attention map shows that our model has better interpretability for RUL estimation.

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