• 제목/요약/키워드: Nuclear generation

검색결과 1,176건 처리시간 0.041초

Fucodiphlorethol G Purified from Ecklonia cava Suppresses Ultraviolet B Radiation-Induced Oxidative Stress and Cellular Damage

  • Kim, Ki Cheon;Piao, Mei Jing;Zheng, Jian;Yao, Cheng Wen;Cha, Ji Won;Kumara, Madduma Hewage Susara Ruwan;Han, Xia;Kang, Hee Kyoung;Lee, Nam Ho;Hyun, Jin Won
    • Biomolecules & Therapeutics
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    • 제22권4호
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    • pp.301-307
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    • 2014

  • Fucodiphlorethol G (6'-[2,4-dihydroxy-6-(2,4,6-trihydroxyphenoxy)phenoxy]biphenyl-2,2',4,4',6-pentol) is a compound purified from Ecklonia cava, a brown alga that is widely distributed offshore of Jeju Island. This study investigated the protective effects of fucodiphlorethol G against oxidative damage-mediated apoptosis induced by ultraviolet B (UVB) irradiation. Fucodiphlorethol G attenuated the generation of 2, 2-diphenyl-1-picrylhydrazyl radicals and intracellular reactive oxygen species in response to UVB irradiation. Fucodiphlorethol G suppressed the inhibition of human keratinocyte growth by UVB irradiation. Additionally, the wavelength of light absorbed by fucodiphlorethol G was close to the UVB spectrum. Fucodiphlorethol G reduced UVB radiation-induced 8-isoprostane generation and DNA fragmentation in human keratinocytes. Moreover, fucodiphlorethol G reduced UVB radiation-induced loss of mitochondrial membrane potential, generation of apoptotic cells, and active caspase-9 expression. Taken together, fucodiphlorethol G protected human keratinocytes against UVB radiation-induced cell damage and apoptosis by absorbing UVB radiation and scavenging reactive oxygen species.

  • https://doi.org/10.4062/biomolther.2014.044 인용 PDF KSCI KPUBS

Homogenized cross-section generation for pebble-bed type high-temperature gas-cooled reactor using NECP-MCX

  • Shuai Qin;Yunzhao Li;Qingming He;Liangzhi Cao;Yongping Wang;Yuxuan Wu;Hongchun Wu
    • Nuclear Engineering and Technology
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    • 제55권9호
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    • pp.3450-3463
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    • 2023

  • In the two-step analysis of Pebble-Bed type High-Temperature Gas-Cooled Reactor (PB-HTGR), the lattice physics calculation for the generation of homogenized cross-sections is based on the fuel pebble. However, the randomly-dispersed fuel particles in the fuel pebble introduce double heterogeneity and randomness. Compared to the deterministic method, the Monte Carlo method which is flexible in geometry modeling provides a high-fidelity treatment. Therefore, the Monte Carlo code NECP-MCX is extended in this study to perform the lattice physics calculation of the PB-HTGR. Firstly, the capability for the simulation of randomly-dispersed media, using the explicit modeling approach, is developed in NECP-MCX. Secondly, the capability for the generation of the homogenized cross-section is also developed in NECP-MCX. Finally, simplified PB-HTGR problems are calculated by a two-step neutronics analysis tool based on Monte Carlo homogenization. For the pebble beds mixed by fuel pebble and graphite pebble, the bias is less than 100 pcm when compared to the high-fidelity model, and the bias is increased to 269 pcm for pebble bed mixed by depleted fuel pebble. Numerical results show that the Monte Carlo lattice physics calculation for the two-step analysis of PB-HTGR is feasible.

  • https://doi.org/10.1016/j.net.2023.05.029 인용 PDF

발전용 천연가스 일일수요 예측 모형 연구-평일수요를 중심으로

  • 정희엽;박호정
    • 한국태양광발전학회지
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    • 제4권2호
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    • pp.45-53
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    • 2018

  • Natural gas demand for power generation continued to increase until 2013 due to the expansion of large-scale LNG power plants after the black-out of 2011. However, natural gas demand for power generation has decreased sharply due to the increase of nuclear power and coal power generation. But demand for power generation has increased again as energy policies have changed, such as reducing nuclear power and coal power plants, and abnormal high temperatures and cold waves have occurred. If the gas pipeline pressure can be properly maintained by predicting these fluctuations, it can contribute to enhancement of operation efficiency by minimizing the operation time of facilities required for production and supply. In this study, we have developed a regression model with daily power demand and base power generation capacity as explanatory variables considering characteristics by day of week. The model was constructed using data from January 2013 to December 2016, and it was confirmed that the error rate was 4.12% and the error rate in the 90th percentile was below 8.85%.

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