• Spatial Information Research
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• v.22 no.4
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• pp.77-87
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• 2014

Recently there are many disasters caused by volcanic activities such as the eruptions in Tungurahua, Ecuador(2014) and $Eyjafjallaj\ddot{o}kull$, Iceland(2010). Therefore, it is required to prepare countermeasures for the disasters. This study analyzes the Baekdu Mountain area, where is the risky area because it is active volcano, based on the observed data and scientific methods in order to assess a risk, produce a hazard map and analyze a degree of risk caused by the volcano. Firstly, it is reviewed for the research about the Baekdu mountain volcanic eruption in 1215(${\pm}15$ years) done by Liu Ruoxin. And the factors causing volcanic disaster, environmental effects, and vulnerability of Baekdu Mountain are assessed by the dataset, which includes the earthquake monitoring data, the volcanic deformation monitoring data, the volcanic fluid geochemical monitoring data, and the socio-economic statistics data. A hazard, especially caused by a volcano, distribution map for the Baekdu Mountain Area is produced by using the assessment results, and the map is used to establish the disaster risk index system which has the four phases. The first and second phases are very high risky area when the Baekdu Mountain erupts, and the third and fourth phases are less dangerous area. The map shows that the center of mountain has the first phase and the farther area from the center has the lower phase. Also, the western of Baekdu Mountain is more vulnerable to get the risk than the eastern when the factors causing volcanic disasters are equally applied. It seems to be caused by the lower stability of the environment and the higher vulnerability.

• Journal of Applied Biological Chemistry
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• v.63 no.1
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• pp.9-22
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• 2020

This study was designed to use the safety management data for residual pesticides in imported banana based on the investigation of pesticide residue detection of agricultural products with different origins in the Republic of Korea. From the USA, EU, UK, Japan and Korea from 2007 to 2018, the results of banana residue pesticides were summarized into detected pesticides, number of inspections, number of pesticide detection cases, and the amount of detected pesticide residue. A total of 109 pesticides were detected for the pesticide residue and pesticide detection rate was 4.58% in 206,894 cases. The detection rate was ranged within 10.62-24.62% for chlorpyrifos, imazalil, methyl-bromide, azoxystrobin, carbendazim, pretilachlor and thiabendazole. Among them, chlorpyrifos was detected most often followed by imazalil, azoxystrobin, thiabendazole, bifenthrin and carbendazim. According to the results of monitoring data for bananas in EU, Japan, USA, UK and Korea, the kinds of detected pesticides were 85, 57, 23, 18 and 8, respectively. Azoxystrobin, bifenthrin and chloropyrifos were found in monitoring data of all countries. Fourteen and twelve pesticides were detected in bananas from Costa Rica and Ecuador, respectively. Imazalil and thiabendazole were detected in 16 and 11 origins, respectively. Myclobutanil and iprodione were detected in four and two countries, respectively. In bananas from Costa Rica, azoxystrobin and bifenthrin were detected 11.8 and 9.8%, respectively, and the detection rate of azoxystrobin was 19% in bananas from Colombia. Chlorpyrifos was detected 22.7, 13.3 and 10.8% in bananas from Belize, Colombia and Costa Rica respectively. Myclobutanil was detected in bananas from Colombia and Costa Rica with the rate of 17.9 and 10.4%, respectively.

• Korean Journal of Microbiology
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• v.55 no.3
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• pp.206-212
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• 2019

Propionibacterium acnes (P. acnes) lives in the hair follicles and pores, and it uses cell debris, sebum and metabolic byproducts of surrounding skin tissues as energy and nutrients. Increased production of sebum due to sebaceous hyperplasia or blockage of the follicle can cause growth and proliferation of P. acnes. The rapid growth of P. acnes in follicles produces cell damage, metabolic byproducts and bacterial chips, which can cause inflammation. In this study, we examined the possibility of Senecio iscoensis Hieron. (S. iscoensis) extract to regulate P. acnes-induced inflammatory signaling pathways. We observed that S. iscoensis extract effectively inhibited P. acnes-induced pro-inflammatory cytokine expressions such as IL-$1{\beta}$, TNF-${\alpha}$, and iNOS in mouse macrophage cell line Raw 264.7. The inhibitory effect of S. iscoensis in pro-inflammatory cytokine levels was accompanied by the inhibition of the transcription factors NF-${\kappa}B$ and NF-AT. However, S. iscoensis did not alter the P. acnes-induced MAPK signaling pathways. This study first suggests the potential of using S. iscoensis extract as an alternative agent for the treatment of acne.

• The Journal of Bigdata
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• v.7 no.2
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• pp.1-9
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• 2022

In the global era, the importance of imported food safety management is increasing. Address information of overseas food companies is key information for imported food safety management, and must be verified for prompt response and follow-up management in the event of a food risk. However, because each country's address system is different, one verification system cannot verify the addresses of all countries. Also, the purpose of address verification may be different depending on the field used. In this paper, we deal with the problem of classifying a given overseas food business address into the administrative district level of the country. This is because, in the event of harm to imported food, it is necessary to find the administrative district level from the address of the relevant company, and based on this trace the food distribution route or take measures to ban imports. However, in some countries the administrative district level name is omitted from the address, and the same place name is used repeatedly in several administrative district levels, so it is not easy to accurately classify the administrative district level from the address. In this study we propose a deep learning-based administrative district level classification model suitable for this case, and verify the actual address data of overseas food companies. Specifically, a method of training using a label powerset in a multi-label classification model is used. To verify the proposed method, the accuracy was verified for the addresses of overseas manufacturing companies in Ecuador and Vietnam registered with the Ministry of Food and Drug Safety, and the accuracy was improved by 28.1% and 13%, respectively, compared to the existing classification model.