Chung-Ang University

About: Chung-Ang University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Population & Thin film. The organization has 13381 authors who have published 26978 publications receiving 416735 citations. The organization is also known as: CAU & Chung.

Two-dimensional materials as catalysts for solar fuels: hydrogen evolution reaction and CO2 reduction

Abstract: The issues of global warming and fossil fuel shortage have increased the demand for clean and renewable energy. Many researchers are investigating strategies to produce hydrogen and reduce CO2 by using solar power. Two-dimensional (2D) materials, such as graphene, graphene derivatives, and transition metal dichalcogenides (TMDs), have been extensively used owing to their extraordinary electronic and optical properties. In this review, we investigate the recent developments in 2D materials for photocatalytic applications involving the hydrogen evolution reaction and CO2 reduction. The synthesis methods and the photocatalytic properties of TMDs and graphene-based 2D materials are thoroughly discussed. Moreover, a summary of the recently developed 2D nanostructures and devices for solar hydrogen production and CO2 reduction is presented, and it is revealed that the use of 2D catalyst materials has great potential for commercialization in the near future to help overcome the energy crisis.

Mutual Information-based multi-label feature selection using interaction information

Abstract: We proposed a MI-based feature selection method without problem transformation.A score function measuring dependency between features and labels was derived.We derived theoretical bounds of score function.Based on theoretical bounds, a score function of variations from MI was chosen. Multi-label feature selection is regarded as one of the most promising techniques that can be used to maximize the efficacy and efficiency of multi-label classification. However, because multi-label feature selection algorithms must consider multiple labels concurrently, the task is more difficult than single-label feature selection tasks. In this paper, we propose the Mutual Information-based multi-label feature selection method using interaction information. This method is naturally able to measure dependencies among multiple variables. To develop an efficient multi-label feature selection method, we derive theoretical bounds for the interaction information. Empirical studies indicate that our proposed multi-label feature selection method discovers effective feature subsets for multi-label classification problems.

Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model

Abstract: Preventive maintenance is increasingly becoming an essential strategy in the bridge industry owing to its proactive advantage of maintaining the structural sustainability during its entire service ...

A Recombinant 10-kDa Protein of Taenia solium Metacestodes Specific to Active Neurocysticercosis

Abstract: Neurocysticercosis (NCC) is an important cause of neurological disease worldwide. A 10-kDa antigen of Taenia solium metacestodes (TsMs) has been shown to be specific for immunodiagnosis of NCC. Screening of a TsM complementary DNA (cDNA) library isolated a cDNA encoding this protein. The cloned cDNA contained a 258-bp complete open-reading frame that encodes an 86-amino acid polypeptide with a calculated molecular weight of 9582 Da. It showed 73% homology with a 10-kDa antigen of T. crassiceps. The recombinant protein was expressed bacterially as a fusion protein at a high level. In immunoblot with recombinant protein, 97% (184/190) of sera from patients with active NCC showed strong reactivity, whereas 14% (4/29) of those from patients with chronic calcified NCC reacted weakly. In 180 sera from other patients with parasitic infections and from normal controls, it showed 98% specificity. A single recombinant TsM antigen has a high potential for serological differentiation of active NCC.