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In Vitro Antimicrobial Susceptibility of <i>Mycobacterium abscessus</i> in Korea
Park, Sunghoon,Kim, Shinok,Park, Eun Mi,Kim, Hojoong,Kwon, O Jung,Chang, Chulhun L.,Lew, Woo Jin,Park, Young Kil,Koh, Won-Jung The Korean Academy of Medical Sciences 2008 JOURNAL OF KOREAN MEDICAL SCIENCE Vol.23 No.1
<P><I>Mycobacterium abscessus</I> is the second most common etiology of pulmonary disease caused by nontuberculous mycobacteria in Korea. Although antimicrobial susceptibility tests are important for appropriate patient management in <I>M. abscessus</I> lung disease, the tests have never been investigated in Korea. Seventy-four isolates of <I>M. abscessus</I> recovered from patient respiratory samples were tested against eight antimicrobial agents following the guidelines set forth by the National Committee for Clinical Laboratory Standards. Of the parenteral antibiotics, amikacin (99%, 73/74) and cefoxitin (99%, 73/74) were active against most isolates. Imipenem (55%, 36/66) and tobramycin (36%, 27/74) had activity against moderate number of isolates. Of the oral antibiotics, clarithromycin (91%, 67/74) was active against the majority of isolates. Moxifloxacin (73%, 54/74) and ciprofloxacin (57%, 42/74) had activity against a moderate number of isolates. Doxycycline was the least active, inhibiting only 7% (5/74) of isolates. In conclusion, the variations in susceptibility within <I>M. abscessus</I> isolates to currently available antimicrobials suggest that the antimicrobial susceptibilities of any clinically significant <I>M. abscessus</I> isolate be needed individually.</P>
임진숙(Jinsook Lim),이도영(Doyoung Lee),강신옥(Shinok Kang),김인주(Injoo Kim),양석재(Sukjae Yang) 한국컴퓨터교육학회 2020 한국컴퓨터교육학회 학술발표대회논문집 Vol.24 No.2(A)
인공지능의 급속한 발전에 따라 학생들 역시 인공지능에 관한 소양과 역량을 갖추어야 하는 것이 시대적인 과제가 되었다. 이에 따라 전세계적으로 초·중등 교육에서의 인공지능 교육 연구가 활발하게 이루어지고 있다. 그러나 새롭게 등장한 인공지능의 개념과 내용이 초중등 학생의 수준에 맞게 해석되지 않았고, 인공지능 교육에 관한 체계적인 교육과정이나 내용체계도 아직 정립되어 있지 않아 교육현장에서는 많은 어려움을 겪고 있는 상태이다. 따라서 본 연구에서는 의사결정트리, 이미지 인식, 강화 학습, 규칙 기반 전문가 시스템, 튜링 테스트, 그리고 비지도 학습이라는 인공지능의 여섯 가지의 개념을 주제로 국내·외의 AI 언플러그드 내용을 비교·분석하고 개선 방향을 제시함으로써 학생들이 인공지능의 개념과 원리를 쉽게 이해할 수 있는 효과적인 인공지능 교육 방안을 모색하고자 한다.
Green Manufacturing Process for Helical Pinion Gear Using Cold Extrusion Process
Jeong, Myeong-Sik,Lee, Sang-Kon,Yun, Jeong-Hwan,Sung, Ji Hyun,Kim, Da Hye,Lee, Shinok,Choi, Tae-Hoon Korean Society for Precision Engineering 2013 International Journal of Precision Engineering and Vol.14 No.6
In this research, a cold extrusion process that can replace conventional machining process is developed for sustainable production. The cold extrusion method requires an analytical design process to assure desired dimensional accuracy of the final product. Therefore, the design must consider the material flow during the process in order to avoid product defects such as folding and underfilling. The forming load and material flow were analyzed using a commercial finite element code, DEFORM3D. To improve the dimensional accuracy, the shape of die and initial billet were designed using minimum distance analysis via FE simulation. Also the developed process included ejection for improving dimensional accuracy, and it was verified by a gear accuracy test. Isothermal annealing and spheroidizing were applied for increasing the formability of high strength workpiece. Finally a helical pinion gear was manufactured using the developed process and the product was compared to the simulation results. In conclusion, the developed process is a scrap reducing and energy saving method for manufacturing extrusion product with high dimensional accuracy.