The morphological features of germling cells were examined to identify an unspecified resting cyst (described as Cochlodinium cf. polykrikoides-like resting cyst) in the Korean coastal area. LSU rRNA gene sequences were also obtained from a strain established from the germling cells. The resting cysts isolated from Korean coastal sediment were characterized as being brown in color, having a large dark-red body, and fibrous lobed ornaments. The germling cells were ellipsoidal with an irregular outline and had an open comma-shaped ASC (apical structure complex), a wide and deep cingulum, and a deep sulcus. These morphological features were consistent with those of previously described harmful dinoflagellate Pseudocochlodinium profundisulcus. The molecular phylogeny revealed that the germling cells and P. profundisulcus were conspecific. Based on these morphological and phylogenetic data, this study documents the occurrence of P. profundisulcus in a Korean coastal area for the first time.

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.

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.

Toxicities of 10 insecticides were examined against late third instars of Culex tritaeniorhynchus using the direct-contact mortality bioassay. Six geospatially distant field mosquitoes were collected from Chuncheon-si (designated CC-CT), Hwaseong (HS-CT), Seosan (SS-CT) Jeonju (JJ-CT), Daegu (DG-CT), and Busan (BS-CT) in the Republic of Korea (ROK). Marked regional variations of insecticide susceptibility were observed. Field populations of SS-CT, JJ-CT and DG-CT from agricultural areas showed higher to extremely higher insecticide susceptibility to pyrethroids than those of CC-CT, HS-CT and BS-CT strains from none agricultural areas. Extremely high to low levels of susceptibility were measured: bifenthrin, susceptible ratio (SR) = 2.7–896.3; β-cyfluthrin, SR = 1.8–633.3; α-cypermethrin, SR = 1.2–1,051.9; deltamethrin, SR = 1.3–711.1; permethrin, SR = 1.5–1,053.4 etofenprox, SR = 2.2–29.3; chlorfenapyr, SR = 5.1–103.6; chlorpyrifos, SR = 2.3–337.0; fenitrothion, SR = 2.0–142.3 and fenthion, SR = 1.4–186.2. Culex tritaeniorhynchus populations from rice paddies had been under heavy selection pressure due to the agricultural insecticides and that’s why the mosquito species demonstrated high resistance to pyrethroids which were used for a long time to control agricultural pests in the localities. These results indicate that careful selection and rotational use of these insecticides mayresult in continued satisfactory control against field populations of Japanese encephalitis vector mosquitoes.