• Korean Journal of Environment and Ecology
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• v.37 no.5
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• pp.367-391
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• 2023

This study was conducted for 10 years from 2012, which is a year before the forest road construction in Minjujisan, to 2022 to analyze annual changes in flora and vegetation before and after the forest road construction and to provide strategies for management. The plant communities in the research sites along the forest road showed the differentiation between slopes with Quercus mongolica community on the northwestern slope and Quercus variabilis and Larix kaempferi communities on the southwestern slope. A total of 212 taxa have increased for number 7 between before and after the construction from a total of 66 taxa (44 families, 59 genera, 51 species, 13 varieties, and 2 forma) in 2012 and 207 taxa (71 families, 153 genera, 176 species, 27 varieties, and 4 forma) in 2015 to 278 taxa (78 families, 172 genera, 242 species, 1 subspecies, 31 varieties, and 4 forma) in 2022. It is noteworthy that the vegetation cover and the introduction of new taxa had been expanded in the sites adjacent to the construction, which is likely caused by the significantly increased amount of light and the introduction of annual herbaceous and naturalized plants after the construction. The results of 10 years of current study reveal that the vegetation cover and the number of new taxa had rapidly increased in earlier years after the construction, slowly decreased later on, and finally formed a stable forest with the increase in the ratio of dominant species. The vegetation cover of the herbaceous layer immediately increased on the slopes along the forest road for a few years after the construction although it had continuously decreased while that of the shrub layer quickly increased. It was shown that on the hillslope the vegetation cover of tall- and low-tree layers increased whereas that of herbaceous and shrub layers rapidly decreased.

• Pediatric Infection and Vaccine
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• v.8 no.2
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• pp.213-221
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• 2001

Propose : Tsutsugamushi fever is a acute febrile disease, which is caused by O. tsutsugamushi. Recently, this disease is increasingly reported in children. This study was undertaken to investigate clinical features of tsutsugamushi fever in children. Methods : This study involved 17 children with tsutsugamushi fever who were admitted to Masan Samsung hospital between September 1997 and December 2000. We investigated the age, sex ratio, clinical manifestations, laboratory findings, response of therapy and prognosis. Results : The age of patients was $6.9{\pm}3.6$ years, ranging from 6 months to 12 years and male predilection(58.8%) was noted and all cases of patients occured in October or November. The most common symptoms were fever in all cases and headache in 8(47.1%). The most common signs were skin rash in all cases, eschar in 14(82.4%) and lymphadenopathy 8(47.1%). Locations of the eschars were back and inguinal area in each 3 cases, neck and chest in each 2, popliteal area in 2, scalp and thigh in each 1. Laboratory findings included anemia in 1 case, leukopenia and thrombocytopenia in each 5, hematuria and proteinuria in each 1, ESR elevation in 2 and positive CRP in 12, AST elevation in 9 and ALT elevation in 7. Serologic diagnosis was made by passive hemagglutination assay(PHA) in 8 cases(47%) on admission, 4 cases in initial negative group were performed follow-up test at 2nd or 3rd weeks of illness and then all cases of 4 were converted to positive reaction. Clinical improvement was noticed in all cases after treatment to chloramhenicol or doxycycline. Mean duration for defervescence after treatment was $1.4{\pm}0.8$ days. Complications were interstitial pneumonia in 1 case and aseptic meningitis in 3, but all cases of patients were recovered without sequelae or recurrence. Conclusions : Tsutsugamushi fever in children was similiar to adult in the clinical features except male predilection. Early diagnosis and empirical treatment based on clinical manifestations such as fever, skin rash, eschar, lymphadenopathy is important and serologic diagnosis need to perform follow-up test at 2nd or 3rd weeks of illness.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.