Pusan National University

About: Pusan National University is a education organization based out in Busan, South Korea. It is known for research contribution in the topics: Population & Catalysis. The organization has 24124 authors who have published 45054 publications receiving 819356 citations. The organization is also known as: Busan National University & Pusan University.

An intuitive review of supercapacitors with recent progress and novel device applications

Abstract: Supercapacitors (SCs) possess paramount importance and are a promising solution among the class of energy storage devices since they have appreciable features like higher specific power, longer life span, and eco-friendly nature. They replace the difference of energy/power between the high-power traditional capacitors and high energy fuel cells/ batteries. In the current review, comprehensive research on the recent literature of SCs investigates and elucidates with great consideration. The global market analysis, manufacturing firms, challenges, and recent advances present the fundamental, as well as application perspective of SCs. An elaborate analysis of the classifications of SCs, fabrication of different electrode materials from the very beginning of their evolution gives the lucid idea. The evaluation of SC nanocomposites with different parameters such as specific capacitance, energy density, power density, cycling performance, and capacity retention depicts the performance. Detail description of the selection of electrolytes and various synthesis methods of the SCs explains comprehensively. In the end, future scope and challenges mention briefly and consider for the next-generation SC's design and applications. This review provides a reference point on the SCs for researchers and designers working in the field of energy storage devices and applications.

Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction

Abstract: We propose geometric mean based boosting algorithm (GMBoost).We propose GMBoost to resolve data imbalance problem.GMBoost considers geometric mean of error rates of majority and minority classes.We experiment GMBoost, AdaBoost and cost-sensitive boosting on bankruptcy prediction.The comparative results shows GMBoost outperforms in imbalanced and balanced data. In classification or prediction tasks, data imbalance problem is frequently observed when most of instances belong to one majority class. Data imbalance problem has received considerable attention in machine learning community because it is one of the main causes that degrade the performance of classifiers or predictors. In this paper, we propose geometric mean based boosting algorithm (GMBoost) to resolve data imbalance problem. GMBoost enables learning with consideration of both majority and minority classes because it uses the geometric mean of both classes in error rate and accuracy calculation. To evaluate the performance of GMBoost, we have applied GMBoost to bankruptcy prediction task. The results and their comparative analysis with AdaBoost and cost-sensitive boosting indicate that GMBoost has the advantages of high prediction power and robust learning capability in imbalanced data as well as balanced data distribution.

WHO Treatment Guidelines for Drug-Resistant Tuberculosis, 2016 Update: Applicability in South Korea.

Abstract: Despite progress made in tuberculosis control worldwide, the disease burden and treatment outcome of multidrug-resistant tuberculosis (MDR-TB) patients have remained virtually unchanged. In 2016, the World Health Organization released new guidelines for the management of MDR-TB. The guidelines are intended to improve detection rate and treatment outcome for MDR-TB through novel, rapid molecular testing and shorter treatment regimens. Key changes include the introduction of a new, shorter MDR-TB treatment regimen, a new classification of medicines and updated recommendations for the conventional MDR-TB regimen. This paper will review these key changes and discuss the potential issues with regard to the implementation of these guidelines in South Korea.

Adiabatic shear band formation during dynamic torsional deformation of an HY-100 steel

Abstract: Dynamic torsion experiments have been conducted on thin-walled tubular specimens of a tempered martensitic HY-100 steel, causing adiabatic shear bands to form. The strain rates imposed were ∼ 10 3 s −1 and local temperature increases up to 600°C within the shear bands were measured. The shear band microstructure was examined by transmission electron microscopy (TEM), revealing two distinct microstructures. In some regions, highly elongated narrow subgrains extended in the shear direction, while in other regions, fine equiaxed cells were characteristic. The proportions of the two microstructures varied for different specimens, and the observations were interpreted to indicate that a process of dynamic recovery accompanying large deformation and a high temperature rise occurred within the shear band. Although thermal effects were apparent, there was no evidence to support a phase transformation to austenite followed by martensite formation. On the basis of present findings, it appears that the thermodynamic stability of the original microstructure can influence the tendency toward shear localization under dynamic loading conditions.