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정가을 ( Ga Eul Jeong ),간밧다리마 ( Dariimaa Ganbat ),염유정 ( Yujeong Yeom ),최보경 ( Bo Gyeong Choi ),이한승 ( Han-seung Lee ),박미화 ( Mi-hwa Park ),신기선 ( Kee-sun Shin ),이용직 ( Yong-jik Lee ),이상재 ( Sang-jae Lee ) 한국산업식품공학회 2021 산업 식품공학 Vol.25 No.2
To identify the diversity of halophilic bacteria from fermented seafoods, 86 strains were isolated and a phylogenetic analysis was carried out based on the results of 16S rRNA gene sequencing. The isolated strains were divided into 3 phyla, 7 families, 9 genera, and 24 species. Bacilli class, the main phyletic group, comprised 84.9% with 4 families, 6 genera, and 19 species of Bacillaceae, Planococcaceae, Staphylococcaceae, and Enterococcaceae. The strains were tested for amylolytic, cellulolytic, lipolytic, and proteolytic activity and 55 strains showed at least one enzyme activity. Furthermore, auxin activity was determined in two strains. These results indicate that the isolated strains have the possibility for application in the food and feed industries and of being important genetic resources in Korea.
장영은(Young Eun Jang),안성미(Sung Mi Ahn),박성지(Sung Ji Park),권현지(Hyun Ji Kwon),고가연(Ga Yeon Ko) 한국건강간호융합학회 2024 한국건강간호융합학회지 Vol.1 No.1
Purpose : The purpose of this study was to analyze the trends in domestic nursing education research applying artificial intelligence (AI) in South Korea by examining recent research trends and suggesting future research directions. Method : This study conducted a literature review to analyze the trends in research on AI-applied domestic nursing education published in domestic academic journals. Results : A total of 55 keywords were identified based on the titles and major keywords from 12 literature sources, including multiple entries. The major keywords included AI (12, 21.9%), nursing students (10, 18.3%), nurses (4, 7.4%), AI remote medicine (2, 3.6%), general public (2, 3.6%), AI (2, 3.6%), nursing education (2, 3.6%), nursing process (2, 3.6%), AI application(2, 3.6%), program development (2, 3.6%), bioethics (2, 3.6%), eHealth (1, 1.8%), simulation training (1, 1.8%), AI generation (1, 1.8%), remote medicine (1, 1.8%), phenomenological study (1, 1.8%), systematic review(1, 1.8%), validation study (1, 1.8%), social network analysis (1, 1.8%), and newspaper articles (1, 1.8%). Conclusion : Through this study, it is evident that diverse research on AI-related nursing education is necessary for future nursing education research.