One of the most important environmental cleanliness indicators is airborne microbe. However, the particularity of clean operating environment and controlled experimental environment often leads to the limitation of the airborne microbe research. This paper designed and implemented a microenvironment test chamber for airborne microbe research in normal test conditions. Numerical simulation by Fluent showed that airborne microbes were evenly dispersed in the upper part of test chamber, and had a bottom-up concentration growth distribution. According to the simulation results, the verification experiment was carried out by selecting 5 sampling points in different space positions in the test chamber. Experimental results showed that average particle concentrations of all sampling points reached 107 counts/m3 after 5 minutes’ distributing of Staphylococcus aureus, and all sampling points showed the accordant mapping of concentration distribution. The concentration of airborne microbe in the upper chamber was slightly higher than that in the middle chamber, and that was also slightly higher than that in the bottom chamber. It is consistent with the results of numerical simulation, and it proves that the system can be well used for airborne microbe research.
Detection and classification of malignant arrhythmia are key tasks of automated external defibrillators. In this paper, 21 metrics extracted from existing algorithms were studied by retrospective analysis. Based on these metrics, a back propagation neural network optimized by genetic algorithm was constructed. A total of 1,343 electrocardiogram samples were included in the analysis. The results of the experiments indicated that this network had a good performance in classification of sinus rhythm, ventricular fibrillation, ventricular tachycardia and asystole. The balanced accuracy on test dataset reached up to 99.06%. It illustrates that our proposed detection algorithm is obviously superior to existing algorithms. The application of the algorithm in the automated external defibrillators will further improve the reliability of rhythm analysis before defibrillation and ultimately improve the survival rate of cardiac arrest.