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Abstract:The exposure level and distribution characteristics of airborne bacteria and fungi were assessed in the workers' activity areas (station office, bedroom, ticket office and driver's seat) and passengers' activity areas (station precinct, inside the passenger carriage, and platform) of the Seoul metropolitan subway. Among investigated areas, the levels of airborne bacteria and fungi in the workers' bedroom and station precincts were relatively high. No significant difference was found in the concentration of airborne bacteria and fungi between the underground and above ground activity areas of the subway. The genera identified in all subway activity areas with a 5% or greater detection rate were Staphylococcus, Micrococcus, Bacillus and Corynebacterium for airborne bacteria and Penicillium, Cladosporium, Chrysosporium, Aspergillus for airborne fungi. Staphylococcus and Micrococcus comprised over 50% of the total airborne bacteria and Penicillium and Cladosporium comprised over 60% of the total airborne fungi, thus these four genera are the predominant genera in the subway station.

Plant diseases pose a significant challenge for food production and safety. Therefore, it is indispensable to correctly identify plant diseases for timely intervention to protect crops from massive losses. The application of computer vision technology in phytopathology has increased exponentially due to automatic and accurate disease detection capability. However, a deep convolutional neural network (CNN) requires high computational resources, limiting its portability. In this study, a lightweight convolutional neural network was designed by incorporating different attention modules to improve the performance of the models. The models were trained, validated, and tested using tomato leaf disease datasets split into an 8:1:1 ratio. The efficacy of the various attention modules in plant disease classification was compared in terms of the performance and computational complexity of the models. The performance of the models was evaluated using the standard classification accuracy metrics (precision, recall, and F1 score). The results showed that CNN with attention mechanism improved the interclass precision and recall, thus increasing the overall accuracy (>1.1%). Moreover, the lightweight model significantly reduced network parameters (~16 times) and complexity (~23 times) compared to the standard ResNet50 model. However, amongst the proposed lightweight models, the model with attention mechanism nominally increased the network complexity and parameters compared to the model without attention modules, thereby producing better detection accuracy. Although all the attention modules enhanced the performance of CNN, the convolutional block attention module (CBAM) was the best (average accuracy 99.69%), followed by the self-attention (SA) mechanism (average accuracy 99.34%).