http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Sumin Heo,Soo Jin Yang 한국영양학회 2023 Journal of Nutrition and Health Vol.56 No.4
Purpose: Ketogenic diets (KDs) have anti-obesity effects that may be related to glucose control and the gut microbiota. This paper hypothesizes that KD reduces body weight and changes the insulin sensitivity and gut microbiota composition in a mouse model of dietinduced obesity. Methods: In this study, C57BL/6 male mice were assigned randomly to 3 groups. The assigned diets were provided to the control and high-fat (HF) diet groups for 14 weeks. The KD group was given a HF diet for 8 weeks to induce obesity, followed by feeding the KD for the next 6 weeks. Results: After the treatment period, the KD group exhibited a 35.82% decrease in body weight gain compared to the HF group. In addition, the KD group demonstrated enhanced glucose control, as shown by the lower levels of serum fasting glucose, serum fasting insulin, and the homeostatic model assessment of insulin resistance, compared to the HF group. An analysis of the gut microbiota using 16S ribosomal RNA sequencing revealed a significant decrease in the proportion of Firmicutes when the KD was administered. In addition, feeding the KD reduced the overall alpha-diversity measures and caused a notable separation of microbial composition compared to the HF diet group. The KD also led to a decrease in the relative abundance of specific species, such as Acetatifactor_muris, Ligilactobacillus_apodemi, and Muribaculum_intestinale, compared with the HF group. These species were positively correlated with the body weight, whereas the abundant species in the KD group (Kineothrix_alysoides and Saccharofermentans_acetigenes) showed a negative correlation with body weight. Conclusion: The current study presents supporting evidence that KD reduced the body weight and altered the insulin sensitivity and gut microbiota composition in a mouse model of diet-induced obesity.
중소병원 적정성 평가를 담당하는 간호사의 지식 및 교육 요구도
남소희(Nam, Sohee),전재희(Jeon, Jaehee),허연정(Heo, Yeon Jeong),조수민(Cho, Sumin) 대한근관절건강학회 2021 근관절건강학회지 Vol.28 No.1
Purpose: This study examined the knowledge and education needs of nurses in charge of adequacy evaluation in small and medium-sized hospitals. Methods: Study participants included 198 nurses in charge of adequacy evaluation in small and medium-sized hospitals. Data were collected from November 19 to 28, 2020 through an online survey. The data were analyzed by independent t-test and one way ANOVA analysis using the SPSS. Results: The knowledge score for the adequacy evaluation of small and medium-sized hospitals was 11.61±4.10 (total of 20 points), and the educational demand was 3.87±0.88 points on a 5-point scale. Regarding the necessity of education for adequacy evaluation of small and medium-sized hospitals, 80.3% of the participants claimed education as being necessary. “Online video lectures” were the most preferred (22.7%) education method, and “2 times” was the most frequent education (47.0%); “1 hour” was the usual duration of class (48.5%). Conclusion: It is necessary to develop and apply an educational program reflecting the measure of educational needs to improve the knowledge level of nurses in charge of adequacy evaluation in small and medium-sized hospitals.
역사 내 미세먼지 농도 조절을 위한 강화학습 기반의 공조설비 제어 에이전트 구축
권경빈(Kyung-bin Kwon),홍수민(Sumin Hong),허재행(Jae-Haeng Heo),정호성(Hosung Jung),박종영(Jong-young Park) 대한전기학회 2021 전기학회논문지 Vol.70 No.10
This study developed a reinforcement learning-based energy management agent that controls the concentration of fine dust by controlling the power consumption of energy facilities such as air conditioners and blowers in stations. To apply reinforcement learning, the problem was first defined based on the Markov decision-making process, and a model was developed to predict the concentration of fine dust in history using data correlated with fine dust. Based on the linear compensation function created based on this, the Deep Q-Network (DQN) method was applied to obtain the optimal policy based on the artificial neural network. In the case study, it was confirmed that convergence to the optimal policy was achieved through the learning process, and it was confirmed that the learned agent lowers the fine dust concentration by increasing the power consumption of the air conditioner when the fine dust concentration in the station rises above a certain level.