We analyzed data to determine whether there are distinguishing characteristics depending on the success or failure of control for coronavirus disease 2019 (COVID-19) by country in the trend of the daily number of confirmed cases and the number of tests.

In recent years, wild boar (Sus scrofa) populations have increased rapidly, causing serious problems such as crop damage and disease transmission. In the present study, we built an individual-based model to explore how heterogeneous landscapes and the interactions of wild boars and hunters affect wild boar population control.

This paper proposes a data driven approach to calculate a new aging parameter for Lithiumion batteries by combining the ideas of incremental capacity analysis and voltage drops in discrete time.

To predict survival time of Korean hepatocellular carcinoma (HCC) patients using multi-center data as a foundation for the development of a predictive artificial intelligence model according to treatment methods based on machine learning.

Cone-beam computed tomography (CBCT) is widely used in dental and maxillofacial imaging applications. However, CBCT suffers from shading artifacts owing to several factors, including photon scattering and data truncation. This paper presents a deep-learning-based method for eliminating the shading artifacts that interfere with the diagnostic and treatment processes.

We provide an observation method for gravitational waves using a pulsar timing array to extend the observational frequency range up to the rotational frequency of pulsars. For this purpose, we perform an analysis of a perturbed electromagnetic wave in perturbed spacetime from the field perspective.

We study a threshold phenomenon of rumor outbreak on the SIR rumor spreading model with a variable trust rate depending on the populations of ignorants and spreaders.

To evaluate standard dose-like computed tomography (CT) images generated by a deep learning method, trained using unpaired low-dose CT (LDCT) and standard-dose CT (SDCT) images.