Tao He

Sichuan University

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Qiong Wu

Jiangnan University

Wenping He

Sun Yat-sen University

Shiquan Wan

Yangzhou Municipal Meteorological Bureau

Junlin Su

Southwest Petroleum University

Haifeng Sun

China Tobacco

Wenchang Yue

First Affiliated Hospital of Soochow University

Shuang Ma

Qiqihar Medical University

Dohoon Lee

Korea Institute of Industrial Technology

Guolin Feng

Yangzhou University

Zhaosheng Yu

Key Laboratory of Guangdong Province

Cooperative Institutions

Sichuan University

State Key Laboratory of Biotherapy

Jilin University

Chinese Academy of Sciences

South China University of Technology

Key Laboratory of Guangdong Province

China Meteorological Administration

University of Chinese Academy of Sciences

Qingdao Agricultural University

Yangzhou University

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Detecting abrupt change on the basis of skewness: numerical tests and applications

International Journal of Climatology (2012)

Wenping He Shiquan Wan Yundi Jiang Hongmei Jin Zhang Wen

ABSTRACT An abrupt change occasionally occurs when the dynamical system suddenly shifts from one stable state to a new state, which can take place in many complex systems, such as climate, ecosystem, social system, and so on. In order to detect abrupt change, this article presents a novel method – sliding transformation parameter ( STP ) on the basis of skewness change and the Box–Cox transformation. Tests on model time series and 1000 simulated daily precipitation data show the ability of the present method to identify and detect abrupt change of probability density function. The applications of STP in daily precipitation data show that there is an abrupt climate change between 1979 and 1980 in the selected observational stations, which is almost the same with the result obtained by approximate entropy ( ApEn ). Furthermore, it is found that the sample sizes of sliding windows have some influence on the Lambda parameter of the Box–Cox transformation, but it does not significantly affect the varying trend of the parameter and the identification of the change point in annual or interannual time scale. Comparing STP with the coefficient of skewness and kurtosis, ApEn , and some statistics approaches (e.g. percentiles and annual maxima), we find that the performance of the present method is much better than that of these methods.

Kurtosis

Power transform

Shape parameter

Scale parameter

10.1002/joc.3624

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Citations (35)

A new method for abrupt dynamic change detection of correlated time series

International Journal of Climatology (2011)

Wenping He Guolin Feng Qiong Wu Tao He Shiquan Wan

Abstract On the basis of detrended fluctuation analysis (DFA), a new method, moving cut data‐DFA (MC‐DFA), was presented to detect abrupt dynamic change in correlated time series. The numerical tests show the capability of the presented method to detect abrupt change time‐instants in model time series generated by Logistic map. Moving DFA (MDFA) and approximate entropy (ApEn) can provide some information such as a single time‐instant of abrupt dynamic change, but both of them cannot exactly detect all of the abrupt change regions. Some traditional methods, such as moving t ‐test, Cramer method, Mann–Kendall test and Yamamoto method, even cannot provide any information of abrupt dynamic change in these model time series. Meanwhile, results showed that windows sizes and strong noise have some less effect on the MC‐DFA results. In summary, MC‐DFA provides a reliable measure to detect the abrupt dynamic change in correlated time series, and perfectively make up the deficiencies of MDFA and ApEn. The applications in daily surface air pressure records further verify the validity of the present method. Copyright © 2011 Royal Meteorological Society

Approximate entropy

Moving average

10.1002/joc.2367

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Citations (59)

A glutathione-activatable nanoplatform for enhanced photodynamic therapy with simultaneous hypoxia relief and glutathione depletion

Chemical Engineering Journal (2020)

Furong Liu Songlin Gong Meiling Shen Tao He Xiuqi Liang

Hypoxia

10.1016/j.cej.2020.126305

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Citations (55)

Prediction of drilling leakage locations based on optimized neural networks and the standard random forest method

Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles (2021)

Junlin Su Yang Zhao Tao He Pingya Luo

Circulation loss is one of the most serious and complex hindrances for normal and safe drilling operations. Detecting the layer at which the circulation loss has occurred is important for formulating technical measures related to leakage prevention and plugging and reducing the wastage because of circulation loss as much as possible. Unfortunately, because of the lack of a general method for predicting the potential location of circulation loss during drilling, most current procedures depend on the plugging test. Therefore, the aim of this study was to use an Artificial Intelligence (AI)-based method to screen and process the historical data of 240 wells and 1029 original well loss cases in a localized area of southwestern China and to perform data mining. Using comparative analysis involving the Genetic Algorithm-Back Propagation (GA-BP) neural network and random forest optimization algorithms, we proposed an efficient real-time model for predicting leakage layer locations. For this purpose, data processing and correlation analysis were first performed using existing data to improve the effects of data mining. The well history data was then divided into training and testing sets in a 3:1 ratio. The parameter values of the BP were then corrected as per the network training error, resulting in the final output of a prediction value with a globally optimal solution. The standard random forest model is a particularly capable model that can deal with high-dimensional data without feature selection. To evaluate and confirm the generated model, the model is applied to eight oil wells in a well site in southwestern China. Empirical results demonstrate that the proposed method can satisfy the requirements of actual application to drilling and plugging operations and is able to accurately predict the locations of leakage layers.

Leakage (economics)

Backpropagation

10.2516/ogst/2021003

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Citations (9)

Corynoline protects lipopolysaccharide‐induced mastitis through regulating AKT/GSK3β/Nrf2 signaling pathway

Environmental Toxicology (2021)

Yunhao Wu Tao He Yunhe Fu Jie Chen

Inflammation has been known to be involved in the pathogenesis of mastitis. And anti-inflammatory agent is proposed to be a possible efficient therapeutic strategy for mastitis. Corynoline, a bioactive compound extracted from Corydalis bungeana Turcz., has been reported to have anti-inflammatory effect. However, whether corynoline has protective effect against mastitis remains unclear. The aim of this study was to evaluate the protective effect of corynoline on LPS-induced mastitis in mice. Inflammatory cytokine production was measured by ELISA. The proteins of signaling pathways were detected by western blot analysis. The results showed that treatment of corynoline at the doses of 15, 30, and 60 mg/kg significantly attenuated LPS-induced pathological damage of mammary tissues. Corynoline also ameliorated LPS-induced MPO activity, MDA content, and inflammatory cytokine TNF-α and IL-1β production in mammary tissues. LPS-induced NF-κB activation was inhibited by corynoline. Furthermore, our results showed corynoline significantly increased the expression of Nrf2 and the phosphorylation levels of AKT and GSK3β. In conclusion, our results indicated that corynoline protected against LPS-induced mastitis through regulating AKT/GSK3β/Nrf2 signaling pathway, which subsequently led to the inhibition of NF-κB and inflammatory response.

Proinflammatory cytokine

Pathogenesis

10.1002/tox.23362

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Citations (17)

Promoting mass transfer by tri-functional carriers for high performance direct electrochemical oxidation process

Separation and Purification Technology (2023)

Xinwei Dong Penghu Duan Dohoon Lee Tao He Dequan Wang

10.1016/j.seppur.2023.126206

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Citations (1)

NiO–Ca9Co12O28 bifunctional phase change catalysts for biomass pyrolysis to hydrogen-rich syngas