No abstract

Emphysema is a debilitating disease that remodels the lung leading to reduced tissue stiffness. Thus, understanding emphysema progression requires assessing lung stiffness at both the tissue and alveolar scales. Here, we introduce an approach to determine multiscale tissue stiffness and apply it to precision-cut lung slices (PCLS). First, we established a framework for measuring stiffness of thin, disk-like samples. We then designed a device to verify this concept and validated its measuring capabilities using known samples. Next, we compared healthy and emphysematous human PCLS and found that the latter was 50% softer. Through computational network modeling, we discovered that this reduced macroscopic tissue stiffness was due to both microscopic septal wall remodeling and structural deterioration. Lastly, through protein expression profiling, we identified a wide spectrum of enzymes that can drive septal wall remodeling, which, together with mechanical forces, lead to rupture and structural deterioration of the emphysematous lung parenchyma.

Due to the advanced spatial data collection technologies, the locations of vehicles on roads are now being collected nationwide, so there is a demand for applying a micro-level emission calculation methods to estimate regional and national emissions. However, it is difficult to apply this method due to the low data collection rate and the complicated calculation procedure. To solve these problems, this study proposes a vehicle trajectory extraction method for estimating micro-level vehicle emissions using massive GPS data. We extracted vehicle trajectories from the GPS data to estimate the emission factors for each link at a specific time period. Vehicle trajectory data was divided into several groups through a k-means clustering method, in which the ratios of each operating mode were used as variables for clustering similar vehicle trajectories. The results showed that the proposed method has an acceptable accuracy in estimating emissions. Furthermore, it was also confirmed that the estimated emission factors appropriately reflected the driving characteristics of links. If the proposed method were utilized to update the link-based micro-level emission factors using continuously accumulated trajectory data for the road network, it would be possible to efficiently calculate the regional- or national-level emissions only using traffic volume.

Background: Factors related to the wellness of taxi drivers are important for identifying high-risk drivers based on human factors. The purpose of this study is to predict high-risk taxi drivers based on a deep learning method by identifying the wellness of a driver, which reflects the personal characteristics of the driver. Methods: In-depth interviews with taxi drivers are conducted to collect wellness data. The priorities of factors affecting the severity of accidents are derived through a random forest model. In addition, based on the derived priority of variables, various combinations of inputs are set as scenarios and optimal artificial neural network models are derived for each scenario. Finally, the model with the best performance for predicting high-risk taxi drivers is selected based on three criteria. Results: A model with variables up to the 16th priority as inputs is selected as the best model; this has a classification accuracy of 86% and an F1-score of 0.77. Conclusions: The wellness-based model for predicting high-risk taxi drivers presented in this study can be used for developing a taxi driver management system. In addition, it is expected to be useful when establishing customized traffic safety improvement measures for commercial vehicle drivers.

No abstract