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It is common for people to use N95 filtering facepiece respirators (FFRs) in daily life, especially in locations where particulate matter (PM2.5) concentration is rising. Wearing N95 FFRs is helpful to reduce inhalation of PM2.5. Although N95 FFRs block at least 95% of particles from the atmosphere, the deadspace of N95 FFRs could be a warm, wet environment that may be a perfect breeding ground for bacterial growth. This work studies the micro-climate features including the temperature distribution and water vapor condensation in the deadspace of an N95 FFR using the computational fluid dynamics (CFD) method. Then, the temperature and relative humidity inside the same type of N95 FFR are experimentally measured. There is a good agreement between the simulation and experimental results. Moreover, an experiment is conducted to study the distribution of bacteria sampled from the inner surface of an N95 FFR after donning.

In this paper, we investigate the effect of different hyperparameters as well as different combinations of hyperparameters settings on the performance of the Attention-Gated Convolutional Neural Networks (AGCNNs), e.g., the kernel window size, the number of feature maps, the keep rate of the dropout layer, and the activation function. We draw practical advice from a wide range of empirical results. Through the sensitivity analysis, we further improve the hyperparameters settings of AGCNNs. Experiments show that our proposals could achieve an average of 0.81% and 0.67% improvements on AGCNN-NLReLU-rand and AGCNN-SELU-rand, respectively; and an average of 0.47% and 0.45% improvements on AGCNN-NLReLU-static and AGCNN-SELU-static, respectively.

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