Use of big data analysis to investigate the relationship between natural radiation dose rates and cancer incidences in Republic of Korea

Joo, Han Young; Kim, Jae Wook; Moon, Joo Hyun

doi:10.1016/j.net.2020.01.015

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…Interpretation of correlation coefficients is not obvious and different practices are present in publications and in various fields. In the literature, it is possible to find various limits for levels of correlation (e.g., [69][70][71][72][73][74]). In principle, each coefficient can also have different limits.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…We assumed that |ρ| < 0.2 indicates no reasonable correlation (or a very weak correlation), and it was also applied by Itoh and Zheng [11,75] for various coefficients, by Freixa et al [74] for Pearson, and by Joo at al. for Spearman [71]. The assumed limit for low and moderate correlations was |ρ| < 0.4 and |ρ| < 0.7, respectively, and it was based on Joo [71] and Schober [72].…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…for Spearman [71]. The assumed limit for low and moderate correlations was |ρ| < 0.4 and |ρ| < 0.7, respectively, and it was based on Joo [71] and Schober [72]. In some special circumstances when the p-value was very low or similar correlation patterns were observed for PWR and FPT-1, we considered a very weak correlation for |ρ| > 0.1 and |ρ| < 0.2.…”

Section: Sensitivity Analysismentioning

confidence: 99%

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Uncertainty and Sensitivity Analysis of the In-Vessel Hydrogen Generation for Gen-III PWR and Phebus FPT-1 with MELCOR 2.2

2021

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In this study, uncertainty and sensitivity analyses were performed with MELCOR 2.2.18 to study the hydrogen generation (figure-of-merit (FoM)) during the in-vessel phase of a severe accident in a light water reactor. The focus of this work was laid on a large generation-III pressurized water reactor (PWR) and a double-ended hot leg (HL) large break loss of coolant accident (LB-LOCA) without a safety injection (SI). The FPT-1 Phebus integral experiment emulating LOCA was studied, where the experiment outcomes were applied for the plant scale modelling. The best estimate calculations were supplemented with an uncertainty analysis (UA) based on 400 input-decks and Latin hypercube sampling (LHS). Additionally, the sensitivity analysis (SA) utilizing the linear regression and linear and rank correlation coefficients was performed. The study was prepared with a new open-source MELCOR sensitivity and uncertainty tool (MelSUA), which was supplemented with this work. The FPT-1 best-estimate model results were within the 10% experimental uncertainty band for the final FoM. It was shown that the hydrogen generation uncertainties in PWR were similar to the FPT-1, with the 95% percentile being covered inside a ~50% band and the 50% percentile inside a ~25% band around the FoM median. Two different power profiles for PWR were compared, indicating its impact on the uncertainty but also on the sensitivity results. Despite a similar setup, different uncertainty parameters impacted FoM, showing the difference between scales but also a significant impact of boundary conditions on the sensitivity analysis.

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Section: Sensitivity Analysismentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%

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Uncertainty and Sensitivity Analysis of the In-Vessel Hydrogen Generation for Gen-III PWR and Phebus FPT-1 with MELCOR 2.2

2021

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“…Alexey v. bataev et al analyzed the application and main application direction of big data analysis technology in the financial field [4] . Han Youngjoo, Jae WookKim et al used big data analysis to study whether there is a significant relationship between the natural radiation dose rate and the incidence rate of cancer in South Korea [5] . LingjunSong et al conducted a study on the application of large data in assessing the health of vehicle-mounted lithium batteries [6] .…”

Section: Introductionmentioning

confidence: 99%

Research on the Data Driven Practice Teaching Mode: Take the Didi Data Set as Example

Liu

Zhou

Chen

et al. 2021

Materials, Computer Engineering and Education Technology

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The paper proposed a practice teaching mode by making analysis on Didi data set. There are more and more universities have provided the big data analysis courses with the rapid development and wide application of big data analysis technology. The theoretical knowledge of big data analysis is professional and hard to understand. That may reduce students' interest in learning and learning motivation. And the practice teaching plays an important role between theory learning and application. This paper first introduces the theoretical teaching part of the course, and the theoretical methods involved in the course. Then the practice teaching content of Didi data analysis case was briefly described. And the study selects the related evaluation index to evaluate the teaching effect through questionnaire survey and verify the effectiveness of teaching method. The results show that 78% of students think that practical teaching can greatly improve students' interest in learning, 89% of students think that practical teaching can help them learn theoretical knowledge, 89% of students have basically mastered the method of big data analysis technology introduced in the course, 90% of students think that the teaching method proposed in this paper can greatly improve students' practical ability. The teaching mode is effective, which can improve the learning effect and practical ability of students in data analysis, so as to improve the teaching effect.

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“…The first two statements imply the signs of range correlations, positive for removal efficiencies, and negative for sludge production and costs. As for the magnitude, a value of 0.9 is proposed, according to [56][57][58], as a strong correlation.…”

mentioning

confidence: 99%

Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks

Medina

Fonseca

Gallego-Alarcón

et al. 2022

Water

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Technical, economic, regulatory, environmental, and social and political interests make the process of selecting an appropriate wastewater treatment technology complex. Although this problem has already been addressed from the dimensioning approach, our proposal in this research, a model of decision making for conventional secondary treatment of municipal wastewater through continuous-discrete, non-parametric Bayesian networks was developed. The most suitable network was structured in unit processes, independent of each other. Validation, with data in a mostly Mexican context, provided a positive predictive power of 83.5%, an excellent kappa (0.77 > 0.75), and the criterion line was surpassed with the location of the model in a receiver operating characteristic (ROC) graph, so the model can be implemented in this region. The final configuration of the Bayesian network allows the methodology to be easily extended to other types of treatments, wastewater, and to other regions.

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Use of big data analysis to investigate the relationship between natural radiation dose rates and cancer incidences in Republic of Korea

Cited by 6 publications

References 6 publications

Uncertainty and Sensitivity Analysis of the In-Vessel Hydrogen Generation for Gen-III PWR and Phebus FPT-1 with MELCOR 2.2

Uncertainty and Sensitivity Analysis of the In-Vessel Hydrogen Generation for Gen-III PWR and Phebus FPT-1 with MELCOR 2.2

Research on the Data Driven Practice Teaching Mode: Take the Didi Data Set as Example

Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks

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