The ferret is commonly used as an animal model for studying human respiratory diseases, but the validation is lacking. The particle deposition patterns in ferret airways was investigated and compared to those in humans. A computational fluid dynamics method was used to simulate particle deposition in the tracheobronchial airway by using the truncated single-path models. The deposition characteristics of particles with diameters of 1, 3, and 5 μm were investigated under various respiratory rates at different activity conditions (i.e., sedentary, light, moderate, and intense activities). For both human and ferret models, deposition increased with both generation and particle size but decreased with respiratory rate. Particles of 1–5 μm deposit more but transport upper in ferrets than in humans, which suggests that ferrets are more likely to be infected in the proximal airways. The results show that the trend of particle deposition in the ferret airways is similar to that in human airways but with different deposition rates and sites. Our findings indicate that ferret for studying human respiratory diseases is suitable for the upper respiratory diseases, such as human influenza, but may not be suitable for studying the lower respiratory diseases, such as pneumonia.
The precision cylindrical traverse grinding process of slender bar is very complex for the strongly time dependent properties of the wheel. Therefore, it is very difficult for operators to properly judge the grinding state using naked eyes and ears. This calls for automatic monitoring technology that can monitor the process in precision cylindrical traverse grinding to guarantee machining quality and productivity as well as reduction in cost. This study developed an automatic monitoring system for precision cylindrical traverse grinding of slender bar using Acoustic emission (AE) technology. Grinding tests on molybdenum were conducted under traverse conditions in a conventional cylindrical grinder. It was found that larger radial material removal depth results in larger root mean square value of Acoustic emission signals (AERMS). Based on this, the AERMS was analyzed and used to determine the finishing of spark-out process and the pre-processing of tool alignment. The variation tendency of AERMS in one spark-out process was applied to determine when a wheel wears out and has to be dressed. The experimental results showed that the AE system was effective to monitor the pre-processing of tool alignment, spark-out and wheel wear in precision cylindrical traverse grinding of slender bar.