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N-Doped Activated Porous Carbons Derived from Biomass Lignin for Supercapacitors
김다영,마민정,고태훈,김병석 한국고분자학회 2021 한국고분자학회 학술대회 연구논문 초록집 Vol.46 No.2
Among various energy storage systems, supercapacitors have shown long cycle-lifetime, high power density, fast charge-discharge rate. Carbonaceous materials, carbon nanotubes, graphene, carbon aerogels and porous carbons (PCs) are mainly used as electrode materials for supercapacitors. PCs have attracted much attention because of their large specific surface areas, tunable pore sizes, superior stability, easy processability, and low cost. The recent development of biomass-derived PCs is of growing interest because it is sustainable, inexpensive and environmentally friendly. Lignin is the second most abundant organic polymer in nature. Herein, we introduce the preparation of lignin-derived PCs using a simple, energy-saving and environmentally friend hydrothermal carbonization (HTC) in the presence of H₂SO₄ catalysts and then activation with KOH and urea to improve specific surface area and electrochemical performance of hierarchically porous N-doped carbons.
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GEase-K: 부가 정보를 활용한 선형 및비선형 오토인코더 기반의 추천시스템
이태범,이승학,마민정,조윤호 한국지능정보시스템학회 2023 지능정보연구 Vol.29 No.3
In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.
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치과위생사의 국민건강보험 치과진료비 청구에 관한 연구: 업무 관심도, 필요도, 인지도, 성과 기여도 차이분석
정선영(Seon yeong Jeong),마민정(Min jeong Ma),김영경(Yeong gyeong Kim) 대한치과보험학회 2018 대한치과보험학회지 Vol.8 No.1
Objective: In recent years, the proportion of insured medical care has also increased in the dental medical field amid recent trends of policy that strengthens the coverage of health insurance. The expansion of such insured care requires accurate knowledge related to insurance-related reimbursement claims with regard to the implementation of duties and roles of dental hygienists. This study was intended to provide the data for education which would be necessary for making claims for dental care expenses under the national health insurance system by examining the awareness of dental hygienists towards the health insurance. Methods: The collected data were analyzed by SPSS 12.0 according to the study purpose and the following statistical analysis was performed based on the 95% confidence level. The statistical method used is Frequency analysis, Cross analysis, Independent T-test, one-way ANOVA. Results: 95.4% of dental hygienists surveyed in this study recognized the need for education on dental health insurance and 89.5% had the experience of receiving related education. The proportion of dental hygienists comprised 70.8%, the highest, among those who handled works associated with claims for insurance-related reimbursement. The proportion was found higher among those who received education for longer period of time, those who handled works related to patient counseling, and those who performed works associated with claims for insurance-related reimbursement (P<0.05). 50.6% of dental hygienists were found to have certificates related to health insurance. The interest in health insurance and the need for performance of works associated with claims for insurance-related reimbursement turned out to be higher when they had the experience of making claims for insurance-related reimbursement(P<0.05). The awareness towards the basis for estimation of reimbursement amount, the extent of education on insurance-related reimbursement, and the degree of contribution to hospital performance were higher among those who handled works associated with claims for insurance-related reimbursement(P<0.05). Conclusion: Most respondents were found to have greater need for health insurance-related education. Systematic and specialized education is needed as the professional knowledge of health insurance is necessary for dental hygienists to carry out works independently and take on their roles firmly.
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