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위험물 안전관리·관계자 전계층의 역량 및 환경에 관한 Data Analytics 분석
신은지,박소현,신동일 한국화재감식학회 2020 한국화재감식학회 학회지 Vol.11 No.4
Increased use of hazardous materials increases overall number of safety accidents such as leaks, fires, and explosions. Also, it causes environmental damage such as water pollution and air pollution, exacerbating extensive and diverse damage. In this study, a wide range of surveys were conducted on the entire groups related to safety management of hazardous materials, including safety managers and carriers responsible for safety management at hazardous materials industrial sites, and safety management agents and tank performance test workers who are specialized of hazardous materials, and civil service personnel and inspectors at fire stations. Because the sample size was large for each group, a multi-faceted survey was analyzed using data analytics such as correlation analysis and regression. The results show the findings in the aspect of safety capabilities and institutional environment to be improved for the enhanced safety management of hazardous materials.
신은지,이재환,남상용 강원대학교 농업생명과학연구원 2023 강원 농업생명환경연구 Vol.35 No.3
Peperomia, the commonly cultivated house plants, are known for their superior shade tolerance, and suitability as ornamental indoor plants. Here, the effects of different color temperatures of white light-emitting diodes (LEDs) were experimentally investigated on Peperomia. Three white LEDs with different color temperatures of 3000, 4100, and 6500 K, respectively, were used in the cultivation of Peperomia species and cultivars namely: P. obtusifolia, P. caperata cv. Napoli Nights (‘Napoli Nights’), and P. caperata cv. Eden Rosso (‘Eden Rosso’) for experimental purposes. Results showed that the sizes of the plants P. obtusifolia and ‘Napoli Nights’ were optimal under 4100 and 6500 K white LEDs, whereas, ‘Eden Rosso’ exhibited optimal growth under 6500 K white LED. Compared to the other plants, P. obtusifolia exhibited superior biomass production under 4100 K white LED. Conversely, ‘Eden Rosso’ and ‘Napoli Nights’ had the highest biomass under 6500 and 3000 K white LEDs, respectively. Regarding the leaf color, L * and b * values demonstrated an inverse relationship with plant biomass, suggesting that leaves turn yellow when the growth of a plant is inhibited. Fv/Fm ranged from 0.77 to 0.81 across all treatments, and these values are generally acceptable. Compared to the other plants, P. obtusifolia and ‘Eden Rosso’ had higher ΦDo, ABS/RC, and DIo/RC under 6500 and 3000 K white LEDs, respectively, contradicting the results observed for plant sizes. In addition, PIABS values were higher for P. obtusifolia under 4100 and 6500 K white LEDs and the highest for ‘Eden Rosso’ under 6500 K white LED. In conclusion, P. obtusifolia can be cultivated under 4100-6500 K white LEDs, whereas, ‘Eden Rosso’ and ‘Napoli Nights’, under 6500 and 4100 K white LEDs, respectively.
무쾌감성 우울 및 정서자극에 대한 접근-회피 동기의 관계
신은지,허효선,권석만 한국임상심리학회 2022 한국심리학회지: 임상심리 연구와 실제 Vol.8 No.2
본 연구의 목적은 무쾌감증이 우울 장애를 불안 장애로부터 구분짓는 핵심적인 특성임을 규명하는 것이며, 실험 연구를 통해 무쾌감증과 정서 자극에 대한 접근-회피 동기의 관계를 검증하고자 했다. 이를 위해 한국판 불안-우울-고통 척도의 점수를 기준으로 무쾌감성 우울 집단(n=17)과 불안 집단(n=16) 및 통제 집단(n=19)을 모집하여 접근-회피 과제(Approach-Avoidance Task; AAT)를 실시했으며, 정서 자극의 정서가 및 각성가에 따른 개인의 접근-회피 경향성을 측정했다. 통계 분석 결과, 무쾌감성 우울 집단은 불안 집단 및 통제 집단에 비해 긍정 정서 자극에 대한 접근 동기가 유의미하게 감소되어 있었다. 또한 긍정 정서 자극의 각성가 변화에 따른 접근-회피 동기의 변화에 있어 무쾌감성 우울 집단은 불안 및 통제집단보다 덜 민감한 반응을 보였다. 본 연구의 결과는 긍정적 정서에 대한 접근 동기의 감소, 즉 무쾌감증이 우울 장애를 구성하는 핵심적인 특성임을 시사하며, AAT 과제를 통해 보다 암묵적인 접근-회피 동기를 수정 및 훈련할 수 있도록 개입에 대한 기초를 제공한다.
글루텐으로 자극한 피부각질형성세포에서 참모자반 열수 추출물의 염증 및산화적 손상 억제 효과
신은지,한의정,김 민 주,정재규,박준용,안긴내 한국키틴키토산학회 2019 한국키틴키토산학회지 Vol.24 No.2
In this study, we investigated the beneficial capacity of Sargassum Fulvellum (S. fulvellum, SF) against gluten-induced inflammation and oxidative damage in HaCaT cells, a human keratinocytes. First of all, we prepared the hot water extract from SF (SFH) and it was used for this study. The result showed SFH has no cytoxicities at the used all concentrations. Also, SFH improved the cell viability by reducing the generation of intracellular reactive oxygen species (ROS) and nitric oxide (NO), and the apoptotic body formation in gluten-stimulated HaCaT cells. In addition, SFH inhibited the expression levels of inflammatory cytokines such as interleukin (IL)-1β, IL-4, IL-5, IL-6, IL-8 and IL-13 compared to the only gluten-stimulated cells. Moreover, western blot analysis revealed that SFH suppressed the activation of nuclear factor (NF)-κB signaling by regulating the phosphorylation and degradation of IκB-α and cytoplasmic NF-κB p65 as well as the translocation of NF-κB p65 into the nucleus. Furthermore, SFH inhibited the activation of extracellular signal regulated kinase (ERK)/P38 signaling in the gluten-stimulated HaCaT cells. Therefore, these results suggest that SFH improved the inflammation and oxidative damage caused by caused by the exposure of gluten in HaCaT cells and it might be a natural material for the improvement of gluten-caused various diseases.
Data Analytics를 활용한 위험물 화재사고 분석
신은지,고문수,신동일 한국가스학회 2020 한국가스학회지 Vol.24 No.5
Hazardous materials accidents are not limited to the leakage of the material, butif the early response is not appropriate, it can lead to a fire or an explosion, which increasesthe scale of the damage. However, as the 4th industrial revolution and the rise of the big dataera are being discussed, systematic analysis of hazardous materials accidents based on newtechniques has not been attempted, but simple statistics are being collected. In this study, weperform the systematic analysis, using machine learning, on the fire accident data for the past11 years (2008 ~ 2018), accumulated by the National Fire Service. The analysis results are vi sualized and presented through text mining analysis, and the possibility of developing a dam age-scale prediction model is explored by applying the regression analysis method, using themain factors present in the hazardous materials fire accident data. 위험물 사고는 해당 물질의 누출에 그치지 않고, 초기대응이 부적합한 경우, 화재, 폭발로 이어져 그 피해규모가확대될 위험이 크다. 하지만 4차 산업혁명과 빅데이터 시대의 대두가 논의되고 있는 시점에서, 새로운 기법들에 바탕한 위험물 사고의 체계적인 분석은 시도되지 못하고, 단편적인 통계 수집에 그치고 있는 것이 아쉬운 실정이다. 본 연구에서는 지난 11년간(2008~2018) 축적된 소방청 위험물 화재사고 데이터를 대상으로 기계학습에 기반한 분석을 진행하였다. Text mining 분석을 통해 분석한 자료를 시각화하여 나타내었고, 아울러 위험물 화재사고 데이터에 존재하는 주요 인자를 이용해 피해규모 예측모델의 개발 가능성을 회귀분석 방법을 적용하여 탐색하였다.