In this paper, motivated by [Peletier & Rottschafer, 2004; Peletier & Williams, 2007], we study the dynamical bifurcation of the modified Swift？Hohenberg equation endowed with an evenly periodic condition on the interval [？λ,λ].

Quantitative structure-activity relationship (QSAR) regression models are mathematical ones which relate the structural properties of chemicals to the potencies of the biological activities of the chemicals.

In 2020, the coronavirus disease 2019 (COVID-19) respiratory infection is spreading in Korea. In order to prevent the spread of an infectious disease, infected people must be quickly identified and isolated, and contact with the infected must be blocked early. This study attempted to verify the intervention effects on the spread of an infectious disease by using these measures in a mathematical model.

The direct sampling method (DSM) in limited-aperture inverse scattering problems for transverse electric (TE) polarization is considered. Based on the asymptotic expansion formula in the presence of small targets, we demonstrate that the indicator function of DSM can be represented by an infinite series of Bessel functions of integer order and correspondingly examine various properties of DSM.

To describe and evaluate epidemiological investigation results and containment measures implemented in Busan, where 108 cases were confirmed with coronavirus disease 2019 (COVID-19) between February 21, 2020 and March 24, 2020.

The outbreak of the novel coronavirus disease 2019 (COVID-19) occurred all over the world between 2019 and 2020.

For mitigation strategies of an influenza outbreak, it can be helpful to understand the characteristics of regional and age-group-specific spread. In South Korea, however, there has been no official statistic related to it. In this study, we extract the time series of influenza incidence from National Health Insurance Service claims database, which consists of all medical and preion drug-claim records for all South Korean population.

The aims of this study were to determine the predictive value of decision support analysis for the shock wave lithotripsy (SWL) success rate and to analyze the data obtained from patients who underwent SWL to assess the factors influencing the outcome by using machine learning methods.