BIG DATA ANALYSIS METHOD FOR ESTIMATING RISK OF AIR POLLUTANTS

• 1. 一种基于大数据的财务风险智能分析方法及系统 [P] . 中国专利： CN113988622A . 2022-01-28
• 2. 基于EPCM的工业大数据风险分析方法 [P] . 中国专利： CN113902257A . 2022-01-07
• 3. BIG DATA ANALYSIS METHOD FOR ESTIMATING RISK OF AIR POLLUTANTS [P] . KR20210129853A . 2021-10-29

  机译： 估算空气污染物风险的大数据分析方法

• 4. Method for treating pollutant based on A.I data analysis and apparatus for treating pollutant based on A.I data analysis [P] . 韩国专利： KR102101902B1 . 2020-04-20

  机译： 基于人工智能数据分析的污染物处理方法及基于人工智能数据分析的污染物处理装置

• 5. Method for treating pollutant based on A.I data analysis and apparatus for treating pollutant based on A.I data analysis [P] . 韩国专利： KR20200014048A . 2020-02-10

  机译： 基于人工智能数据分析的污染物处理方法及基于人工智能数据分析的污染物处理装置

• 1. An analysis of four methodologies for estimating highway capacity from ITS data [J] . Zhao Li, Rilett Laurence 现代交通学报（英文版） . 2015,第002期
• 2. An analysis of four methodologies for estimating highway capacity from ITS data [J] . Zhao Li, Rilett Laurence 现代交通学报：英文版 . 2015,第002期
• 3. Influence of seasonal adjustment in regression models on mortality risk estimates for multiple ambient air pollutants [C] . Kazuhiko Ito, Jennifer L. Peel, Jason Sacks Annual conference of the International Society of Exposure Science . 2014
• 4. Spatial and temporal variation in pollutant concentrations: Implications for exposure and risk assessment of airborne pollutants. [D] . Huang, Yu-Li. 1999
• 5. Rosiglitazone: can meta-analysis accurately estimate excess cardiovascular risk given the available data? Re-analysis of randomized trials using various methodologic approaches [O] . Jan O Friedrich, Joseph Beyene, Neill KJ Adhikari 2009
• 6. Methods for estimating air pollutant emissions. PART 1: Review and source of input data. [O] . Lopes-Aparicio Susana, Vogt Matthias, Vo Dam Thanh 2016
• 7. Study with Microbeam PIXE Technique Needed for the Interpretation of Data on Pollutants in Hair Obtained with NAA and Other Bulk Concentration Analysis. Part of a Coordinated Programme on Nuclear-Based Methods for Analysis of Poll [R] . Houtman, J. P. W. 1981

• 1. A Comparative Analysis of Generalized Estimating Equations Methods for Incomplete Longitudinal Ordinal Data with Ignorable Dropouts [J] . Kago Edwin Ditlhong ,Oscar Owino Ngesa ,Abdalla Yusuf Kombo . 统计学期刊（英文） . 2018,第5期
• 2. BACKGROUND： Vibrio vulnifi cus inside the body could activate the NF-!B signaling pathwayand initiate the inflammatory cascade. The lung is one of the earliest organs affected by sepsisassociated with acute lung injury. High mobility group protein B1 （HMGB1） is an important late-actingpro-infl ammatory cytokine involving in the pathophysiology of sepsis. It is also involved in the injuryprocess in the lung, liver and intestine. There has been no report on the involvement of HMGB1 inVibrio vulnifi cus sepsis-induced lung injury.METHODS： Sixty rats were randomly divided into a normal control group （group A, n=10） anda Vibrio vulnificus sepsis group （group B, n=50）. Sepsis was induced in the rats by subcutaneousinjection of Vibrio vulnificus （concentration 6×108 cfu/mL, volume 0.1 mL/100g）） into the left lowerlimbs. The rats in group B were sacrifi ced separately 1, 6, 12, 24, and 48 hours after the infection.Their lungs were stored as specimens, lung water content was measured, and lung pathology wasobserved under a light microscope. The expressions of the HMGB1 gene and protein in the lungswere detected by RT-PCR and Western blot. Data were analyzed with one-way analysis of variance（ANOVA） and the LSD method for pair-wise comparison between the two groups. P〈0.05 wasconsidered statistically signifi cant.RESULTS： Compared to group A （0.652±0.177）, HMGB1 mRNA expression in the lungs ofgroup B was signifi cantly higher at 0 hour （1.161±0.358, P=0.013）, 24 hours （1.679±0.235, P=0.000）,and 48 hours （1.258±0.274, P=0.004） （P〈0.05）, and peaked at 24 hours. Compared to group A（0.594±0.190）, HMGB1 protein expression at 6 hours （1.408±0.567, P=0.026） after infection wassignificantly increased （P〈0. 05）, and peaked at 24 hours （2.415±1.064, P=0.000） after infection.Compared to group A （0.699±0.054）, lung water content was significantly increased at 6 hours（0.759±0.030, P=0.001）,12 hours （0.767±0.023, P=0.000）, 24 hours （0.771±0.043, P=0.000） and 48hours （0.789±0.137, P=0.000） after infection （P〈0.05）. Compared to group A, pathological changesat 12 hours in group B indicate marked pulmonary vascular congestion, interstitial edema andinfl ammatory infi ltration. Alveolar cavity collapse and boundaries of the alveolar septum could not beclearly identifi ed.CONCLUSION： Vibrio vulnifi cus sepsis can lead to injury in rat lungs, and increased HMGB1expression in lung tissue may be one of the mechanisms for injury from Vibrio vulnifi cus sepsis. [J] . Qiao-meng Qiu ,Zhong-wang Li ,Lu-ming Tang . 世界急诊医学杂志(英文) . 2011,第4期
• 3. Statistical Comparison of Eight Alternative Methods for the Analysis of Paired Sample Data with Applications [J] . Godday Uwawunkonye Ebuh ,Ikewelugo Cyprian Anaene Oyeka . 统计学期刊（英文） . 2012,第3期
• 4. 建设工程事故风险路径、风险源分析与风险概率估算 [C] . 李惠强 ,涂晓敏 . 第十届全国结构工程学术会议 . 2001
• 5. 基于大数据的电力系统风险评估算法研究 [A] . 刘晟东 . 2018