Feral cats are widely considered to be leading the potential impacts on public health. This study aimed to provide estimates of vital data for feral cats relating Trap-Neuter-Return (TNR) to establish strategies effectively to manage feral cats in Pyeongtaek. Thus, this study focused on estimating feral cat population in Pyeongtaek and conducted a comparative analysis of the data for feral cats in Seoul (2013). The number of feral cats was estimated from 23,069 to 26,655 in Pyeongtaek, 2019. In relation to human population, when comparing the number of feral cats of Pyeongtaek and Seoul, it ranged from 4.57% to 5.28%, and from 1.97% to 2.55% respectively. This showed that Pyeongtaek was higher than Seoul. Fewer kittens were found in high-density areas, which the TNR project is believed to be generally effective in controlling the number of feral cats. In conclusion, in urban and rural complexes such as Pyeongtaek City, the number of feral cats compared to the population was higher than that of Seoul City, and the TNR program is believed to be somewhat effective in controlling the number of feral cats. When implementing TNR, it is necessary periodically to investigate the population and reflect them in policymaking.

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.