利用随机矩阵理论预测太阳紫外线辐射

Q3 Health Professions
Reza Malekzadeh, Masoud Seidi, Nikan Asadpour, Hadi Sabri
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引用次数: 0

摘要

目的:不同样本的相关性可以用随机矩阵理论等分析模型来描述。在这项研究中,我们试图描述不同类型的紫外线值在不同月份、星期和小时的相关性,以获得特殊时间的显著关系,人们需要获得足够的阳光强度或避免晒伤。 材料和方法:为此,我们重点测量了大不里士市区 2017-2018 年一整年的太阳辐射紫外线 A、B 和 C 强度的小时和日平均值。我们使用的紫外线值是在一天中的同一时刻测量的,以满足随机矩阵理论所需的相同对称性标准。这些数据按不同序列展开和分类,通过最大似然估计技术在近邻间距分布框架内进行分析。 结果:与其他类型的紫外线辐射相比,UVA 的日值具有很强的相关性。此外,我们还考虑了这三类紫外线的相关度与不同月份平均温度和湿度的关系。 结论结果表明,8 月份的 UVA 指数与平均气温和湿度的相关性更大,而 12 月份的 UVC 辐射与平均气温和湿度的相关性更大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Solar Ultraviolet Radiations Using Random Matrix Theory
Purpose: The correlation of different samples can be described by analytical models such as random matrix theory. In this study, we tried to describe the correlation of different types of ultraviolet values in different months, weeks, and hours to get a significant relationship of special times, which one needs to get enough intensity of the sun or avoid getting sunburn. Materials and Methods: To this aim, we focused on the hourly and daily mean amounts of ultraviolet A, B, and C intensities of solar radiation in Tabriz urban area were measured during a full year of 2017-2018. We used such ultraviolet values which are measured at the same hour of the day to satisfy the same symmetry criteria which are necessary in random matrix theory. These data are unfolded and classified in different sequences to analyze in the nearest neighbor spacing distribution framework via the maximum likelihood estimation technique. Results: Strong correlation is yielded for daily values of UVA in comparison with the other types of ultraviolet radiations. Also, we considered the dependence of correlation degrees of these three types of ultraviolet to average temperature and humidity at different months. Conclusion: The results propose more correlation of UVA indices in August while such correlation of UVC radiations are yielded in December.
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来源期刊
Frontiers in Biomedical Technologies
Frontiers in Biomedical Technologies Health Professions-Medical Laboratory Technology
CiteScore
0.80
自引率
0.00%
发文量
34
审稿时长
12 weeks
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