The lithium-ion battery has been utilized in various fields including energy storage system, portable electronic devices and electric vehicles due to their high energy and power densities, low self-discharge, and long cycle-life performances. However, despite of various research on electrode materials, there is a lack of research on developing of binder to replace conventional polymer-based binding materials. In this work, petroleum pitch (MP-50)/polymer (polyurethane, PU) composite binder for lithium-ion battery has fabricated not only to use as a binding material, but also to re-place conventional polymer-based binder. The MP-50/PU composite binder has also prepared to various ratios between petroleum pitch and polymer to optimize the physical and electro-chemical performance of the lithium-ion battery based on the MP-50/PU composite binder. The physical and electrochemical performances of the MP-50/PU composite binder-based lithium-ion battery were evaluated using a universal testing machine (UTM), charge/discharge test. As a result, lithium-ion battery based on the MP-50/PU composite (5:5, mass ratio) binder showed optimized performances with 1.53 gf mm− 1 of adhesion strength, 341 mAh g− 1 of specific discharge capacity and 99.5% of ICE value.

Nuclear power plants use ion exchange resins to purify liquid radioactive waste generated while operating nuclear power plants. In the case of PHWR, ion exchange resins are used in heavy water and dehydration systems, liquid waste treatment systems, and heavy water washing systems, and the used ion exchange resins are stored in waste resin storage tanks. The C-14 radioactivity concentration in the waste resin currently stored at the Wolseong Nuclear Power Plant is 4.6×106 Bq/g, exceeding the low-level limit, and if all is disposed of, it is 1.48×1015 Bq, exceeding the total limit of 3.04×1014 Bq of C-14 in the first stage disposal facility. Therefore, disposal is not possible at domestic low/medium-level disposal facilities. In addition, since the heavy water reactor waste resin mixture is stored at a ratio of about 20% activated carbon and zeolite mixture and about 80% waste resin, mixture extraction and separation technology and C-14 desorption and adsorption technology are required. Accordingly, research and development has been conducted domestically on methods to treat heavy water waste resin, but the waste resin mixture separation method is complex and inefficient, and there are limitations in applying it to the field due to the scale of the equipment being large compared to the field work space. Therefore, we would like to introduce a resin treatment technology that complements the problems of previous research. Previously, the waste resin mixture was extracted from the upper manhole and inspection hole of the storage tank, but in order to improve limitations such as worker safety, cost, and increased work time, the SRHS, which was planned at the time of nuclear power plant design, is utilized. In addition, by capturing high-purity 14CO2 in a liquid state in a high-pressure container, it ensures safety for long-term storage and is easy to handle when necessary, maximizing management efficiency. In addition, the modularization of the waste resin separation and withdrawal process from the storage tank, C-14 desorption and monitoring process, high-concentration 14CO2 capture and storage process, and 14CO2 adsorption process enables separation of each process, making it applicable to narrow work spaces. When this technology is used to treat waste resin mixtures in PHWR, it is expected to demonstrate its value as customized, high-efficiency equipment that can secure field applicability and safety and reflect the diverse needs of consumers according to changes in the working environment.

Emotion AI, a subset of AI that measures, understands, responds to, and elicits human emotions, is an emerging area that has great potential for advertising research and practice. Studies on the applicability of emotion AI in advertising and marketing have been growing in academic journals. This rapidly burgeoning scholarship creates a need for advertising scholars to comprehend the current status of the research on emotion AI in advertising as well as opportunities and challenges that this new technological development will bring to. Thus, this study aims to offer an overview of research on emotion AI in advertising to identify the scope of existing research, gaps in knowledge, and opportunities and challenges that lie ahead.

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.

Non-destructive estimation of leaf area is a more efficient and convenient method than leaf excision. Thus, several models predicting leaf area have been developed for various horticultural crops. However, there are limited studies on estimating the leaf area of strawberry plants. In this study, we predicted the leaf areas via nonlinear regression analysis using the leaf lengths and widths of three-compound leaves in five domestic strawberry cultivars (‘Arihyang’, ‘Jukhyang’, ‘Keumsil’, ‘Maehyang’, and ‘Seollhyang’). The coefficient of determination (R2) between the actual and estimated leaf areas varied from 0.923 to 0.973. The R2 value varied for each cultivar; thus, leaf area estimation models must be developed for each cultivar. The leaf areas of the three cultivars ‘Jukhyang’, ‘Seolhyang’, and ‘Maehyang’ could be non-destructively predicted using the model developed in this study, as they had R2 values over 0.96. The cultivars ‘Arihyang’ and ‘Geumsil’ had slightly low R2 values, 0.938 and 0.923, respectively. The leaf area estimation model for each cultivar was coded in Python and is provided in this manuscript. The estimation models developed in this study could be used extensively in other strawberry-related studies.

프리지아 ‘Sunny Gold’는 농촌진흥청 국립원예특작과학원 에서 2010년 노랑색 반겹꽃 프리지아 육성계통 ‘036010’을 모본으로 진노란색 홑꽃 ‘Golden Flame’을 부본으로 교배하여 획득한 종자로부터 2011년 진노란색 겹꽃의 향기가 강한 프리지아 계통을 선발하여 품종화 하였다. 2011년부터 2016년까지 개화 생육특성검을 수행하였으며 핵심수요자의 기호도 평가를 통해 선발되어 2017년 ‘Sunny Gold’ 로 명명되었다. ‘Sunny Gold’는 RHS color chart YO17B의 노란색 겹꽃 프리지아 품종으로 화폭은 6.7cm로 대조품종 ‘Golden Flame’ 6.1cm에 비해 크고, 분지수는 6.5로 다수확성 품종이다. 초장이 101.9cm로 초세가 강하다. ‘Sunny Gold’의 소화수 및 소화장은 각각 13.0개, 9.3cm이며 개화소요일수는 137.7일이다. 이 품종의 절화수명은 약 9일이며 자구번식력은 5.3배로 대조 품종 ‘Golden Flame’ 4.3배에 비해 우수하다. 전자코를 이용한 PCA분석결과 PC1과 PC2는 각각 99.3%와 0.6%로 전체 변이량의 99.9%를 반영하고 있다. Rader plot 분석결과 총 6개 센서에서 모두 ‘Sunny Gold’의 센서값이 향기가 강한 상용품종 ‘Yvonne’의 값에 비해 높게 나타나 ‘Sunny Gold’의 향기가 더 강한 것으로 나타났다.