• Korean Journal of Plant Resources
• /
• v.27 no.1
• /
• pp.1-10
• /
• 2014

Essential oils of various plants have been known for potential biological effects such as antibacterial, antifungal, spasmolytic, antiplasmodial activities and insect-repellent property. Recently, the essential oils have attracted considerable interest in oral disease therapy. This essential oil has been known as being effective on easing sick house syndrome, giving forest aroma therapy effect and acting as repellent against pest. The essential oil of Pinus koraiensi, a native plant from Hongcheon-gun, Gangwon-do, was obtained by hydrodistillation. In light of its medicinal importance, in this study its composition, antibacterial activity and the reducing effect of offensive odor have been analyzed. The composition of essential oil was determined by GC and GC-MS. We have identified 14 compounds, of which 1R-${\alpha}$-pinene (19.38 %), 3-carene (10.21 %), camphene (9.82 %), limonene (9.00 %), bicyclo[2,2,1] heptan-2-ol (8.76 %) and ${\beta}$-phellandrene (7.98 %) were the main components. Essential oils from P. koraiensis, Chamaecyparis obtusa, Abies holophylla and Pinus densiflora were compared in terms of alleviating effect of malodors caused from formaldehyde, ammonia, trimethylamine and methylmercaptan. P. koraiensis essential oil was found to decrease the amounts of ammonia and trimethylamine by 75.17 % and 77.36 %, respectively. Antibacterial activity against Streptococcus mutans and Streptococcus sobrinus, which were known as oral cavity inducer, was investigated using the paper disc agar diffusion method. The inhibition zone was observed against S. mutans (5.97 mm) and S. sobrinus (1.40 mm), respectively. P. koraiensis essential oil shown effective deodorization and inhibitory activity against oral cavity in this study might be potential material in oral sanitary industry.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.8 no.4
• /
• pp.392-400
• /
• 2003

We used an oxygen microelectrode to measure the vertical profiles of oxygen concentration in sediments located near point sources of organic matter. The measurements were carried out between 13th and 17th May, 2003, in semi-closed bay and coastal sediments in the central part of the South Sea. The measured oxygen penetration depths were extremely shallow and ranged from 1.30 to 3.80 mm. This suggested that the oxidation and reduction reactions in the early diagenesis should be studied at the mm depth scale. In order to estimate the oxygen consumption rate, we applied the one-dimension diffusion-reaction model to vertical profiles of oxygen near the sediment/water interface. Oxygen consumption rates were estimated to be between 10.8 and 27.6 mmol O$_2$ m$\^$-2/ day$\^$-1/(average: 19.1 mmol O$_2$ m$\^$-2/ day$\^$-1/). These rates showed a positive correlation with the organic carbon of the sediments. The corresponding benthic organic carbon oxidation rates calculated using an modified Redfield ratio (170/110) at the sediment/water interface were in the range of 89.5-228.1 mg C m$\^$-2/ day$\^$-1/(average: 158.0 mg C m$\^$-2/ day$\^$-1/). We suggest that these results are maximum values at the presents situation in the bay because the sampling sites were located near point sources of organic materials. This study will need to be carried out at many coastal sites and throughout the seasons to allow an understanding of the mechanisms of eutrophication e.g. the spatial distribution of oxygen consumption within the oxic zone and hypoxic conditions in the coastal sea.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.137-154
• /
• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.