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Hu F, Hu Y, Ge Y, Dai R, Tian Z, Cui E, Wu H, Zhang Y. BiPLS-RF: A hybrid wavelength selection strategy for laser induced fluorescence spectroscopy of power transformer oil. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 321:124693. [PMID: 38909555 DOI: 10.1016/j.saa.2024.124693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 06/18/2024] [Accepted: 06/18/2024] [Indexed: 06/25/2024]
Abstract
In this paper, a method for indirect diagnosis of transformer faults based on the fluorescence spectrum and characteristic wavelength screening of transformer oil has been proposed. Specifically, a hybrid strategy (BiPLS-RF) for establishing the fluorescence spectrum feature screening of transformer oil using backward interval partial least squares (BiPLS) and random forest (RF) has been proposed. Aiming at the problem of transformer fault diagnosis, the laser induced fluorescence (LIF) spectroscopy of transformer oil in different states was first collected, and it is found that the fluorescence spectrum intensity of normal transformer oil was stronger than that of faulty transformer oil. Then the characteristic bands of the original fluorescence spectra were screened by BiPLS. It is found that when the original fluorescence spectra were divided into 15 sub-intervals, the minimum root mean squares error of cross-validation can be obtained by selecting 3 sub-intervals (including 411 wavelengths). On this basis, RF was employed to further screen the characteristic wavelengths and realized the identification of the fluorescence spectrum. It is found that in the RF model composed of 54 trees, the selected 196 characteristic wavelengths of the fluorescence spectrum can minimize the analysis error (0.56%). In addition, the selected characteristic wavelength information was fed into other common classifiers to construct a fluorescence spectrum identification model, which further proved the effectiveness of BiPLS-RF for wavelength selection for LIF spectroscopy of power transformer oil. The results show that it is feasible to use BiPLS-RF to screen the characteristic wavelength of LIF spectroscopy and apply it to transformer fault diagnosis, which provides a new solution for transformer fault diagnosis.
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Affiliation(s)
- Feng Hu
- Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center, Anhui University of Science and Technology, Huainan 232001, Anhui, PR China; School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, PR China
| | - Yijie Hu
- Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center, Anhui University of Science and Technology, Huainan 232001, Anhui, PR China; School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, PR China.
| | - Yan Ge
- Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center, Anhui University of Science and Technology, Huainan 232001, Anhui, PR China; School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, PR China
| | - Rongying Dai
- Langxi Power Supply Company, State Grid Anhui Electric Power Co. Ltd., Xuancheng 242100, Anhui, PR China
| | - Zhen Tian
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, PR China
| | - Enhan Cui
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, PR China
| | - Hang Wu
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, PR China
| | - Yuewen Zhang
- School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, PR China
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Lv J, Du L, Lin H, Wang B, Yin W, Song Y, Chen J, Yang J, Wang A, Wang H. Enhancing effluent quality prediction in wastewater treatment plants through the integration of factor analysis and machine learning. BIORESOURCE TECHNOLOGY 2024; 393:130008. [PMID: 37984668 DOI: 10.1016/j.biortech.2023.130008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/30/2023] [Accepted: 11/09/2023] [Indexed: 11/22/2023]
Abstract
Precisely predicting the concentration of nitrogen-based pollutants from the wastewater treatment plants (WWTPs) remains a challenging yet crucial task for optimizing operational adjustments in WWTPs. In this study, an integrated approach using factor analysis (FA) and machine learning (ML) models was employed to accurately predict effluent total nitrogen (Ntoteff) and nitrate nitrogen (NO3-Neff) concentrations of the WWTP. The input values for the ML models were honed through FA to optimize factors, thereby significantly enhancing the ML prediction accuracy. The prediction model achieved a highest coefficient of determination (R2) of 97.43 % (Ntoteff) and 99.38 % (NO3-Neff), demonstrating satisfactory generalization ability for predictions up to three days ahead (R2 >80 %). Moreover, the interpretability analysis identified that the denitrification factor, the pollutant load factor, and the meteorological factor were significant. The model framework proposed in this study provides a valuable reference for optimizing the operation and management of wastewater treatment.
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Affiliation(s)
- Jiaqiang Lv
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China; School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Lili Du
- Central Plains Environmental Protection Co., LCD., Zhengzhou 450000, China
| | - Hongyong Lin
- Central Plains Environmental Protection Co., LCD., Zhengzhou 450000, China
| | - Baogui Wang
- Central Plains Environmental Protection Co., LCD., Zhengzhou 450000, China
| | - Wanxin Yin
- College of the Environment, Liaoning University, Shenyang 110036, China
| | - Yunpeng Song
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Jiaji Chen
- Sino-Danish Center for Education and Research, University of Chinese Academy of Sciences, Beijing, China
| | - Jixian Yang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Aijie Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China; School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Hongcheng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China; School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.
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Qian W, Zhou J, Shou S. Exploration of m 6A methylation regulators as epigenetic targets for immunotherapy in advanced sepsis. BMC Bioinformatics 2023; 24:257. [PMID: 37330481 DOI: 10.1186/s12859-023-05379-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 06/06/2023] [Indexed: 06/19/2023] Open
Abstract
BACKGROUND This study aims to deeply explore the relationship between m6A methylation modification and peripheral immune cells in patients with advanced sepsis and mine potential epigenetic therapeutic targets by analyzing the differential expression patterns of m6A-related genes in healthy subjects and advanced sepsis patients. METHODS A single cell expression dataset of peripheral immune cells containing blood samples from 4 patients with advanced sepsis and 5 healthy subjects was obtained from the gene expression comprehensive database (GSE175453). Differential expression analysis and cluster analysis were performed on 21 m6A-related genes. The characteristic gene was identified based on random forest algorithm, and the correlation between the characteristic gene METTL16 and 23 immune cells in patients with advanced sepsis was evaluated using single-sample gene set enrichment analysis. RESULTS IGFBP1, IGFBP2, IGF2BP1, and WTAP were highly expressed in patients with advanced sepsis and m6A cluster B. IGFBP1, IGFBP2, and IGF2BP1 were positively correlated with Th17 helper T cells. The characteristic gene METTL16 exhibited a significant positive correlation with the proportion of various immune cells. CONCLUSION IGFBP1, IGFBP2, IGF2BP1, WTAP, and METTL16 may accelerate the development of advanced sepsis by regulating m6A methylation modification and promoting immune cell infiltration. The discovery of these characteristic genes related to advanced sepsis provides potential therapeutic targets for the diagnosis and treatment of sepsis.
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Affiliation(s)
- Weiwei Qian
- Tianjin Medical University, Tianjin, 300203, China
- Department of Emergency, Shangjin Nanfu Hospital, West China Hospital, Sichuan University, Chengdu, 610044, Sichuan, China
| | - Jian Zhou
- Department of Immunology, International Cancer Center, Shenzhen University Health Science Center, Shenzhen, 518060, China
| | - Songtao Shou
- Department of Emergency, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin, 300052, China.
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Intelligent Health Monitoring of Cable Network Structures Based on Fusion of Twin Simulation and Sensory Data. Symmetry (Basel) 2023. [DOI: 10.3390/sym15020425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The precise and effective prognosis of safety risks is vital to ensure structural safety. This study proposed an intelligent method for the health monitoring of cable network structures, based on the fusion of twin simulation and sensory data. Firstly, the authors have established a framework that integrate simulation data with sensory data. The authors have established a high-fidelity twin model using genetic algorithm. The mechanical parameters of the structures were obtained based on the twin model. The key components of the structure are captured by using Bayesian probability formula and multiple mechanical parameters. The fusion mechanism of twin simulation and random forest (RF) was established to capture the key influencing factors. The coupling relationship between structural safety state and key factors was obtained, and the safety maintenance mechanism was finally formed. In view of the risk prognosis of the structure, the establishment method for the database of influencing factors and maintenance measures was formed. The authors used the Speed Skating Gymnasium of 2022 Winter Olympic Games (symmetric structure) as the case study for validating the feasibility and effectiveness of the proposed method. The theoretical method formed in this study has been applied to the symmetric structure, which provides ideas for the safety maintenance of large symmetric structures. Meanwhile, this research method also provides a reference for the health monitoring of asymmetric structures.
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Shi J, Guo Z, Chen H, Xiao Z, Bai H, Li X, Niu P, Yao J. Artificial Intelligence-Assisted Terahertz Imaging for Rapid and Label-Free Identification of Efficient Light Formula in Laser Therapy. BIOSENSORS 2022; 12:826. [PMID: 36290963 PMCID: PMC9599775 DOI: 10.3390/bios12100826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/28/2022] [Accepted: 10/02/2022] [Indexed: 06/16/2023]
Abstract
Photodynamic therapy (PDT) is considered a promising noninvasive therapeutic strategy in biomedicine, especially by utilizing low-level laser therapy (LLLT) in visible and near-infrared spectra to trigger biological responses. The major challenge of PDT in applications is the complicated and time-consuming biological methodological measurements in identification of light formulas for different diseases. Here, we demonstrate a rapid and label-free identification method based on artificial intelligence (AI)-assisted terahertz imaging for efficient light formulas in LLLT of acute lung injury (ALI). The gray histogram of terahertz images is developed as the biophysical characteristics to identify the therapeutic effect. Label-free terahertz imaging is sequentially performed using rapid super-resolution imaging reconstruction and automatic identification algorithm based on a voting classifier. The results indicate that the therapeutic effect of LLLT with different light wavelengths and irradiation times for ALI can be identified using this method with a high accuracy of 91.22% in 33 s, which is more than 400 times faster than the biological methodology and more than 200 times faster than the scanning terahertz imaging technology. It may serve as a new tool for the development and application of PDT.
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Affiliation(s)
- Jia Shi
- Tianjin Key Laboratory of Optoelectronic Detection Technology and System, School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, China
- Key Laboratory of Opto-Electronics Information Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
| | - Zekang Guo
- Tianjin Key Laboratory of Optoelectronic Detection Technology and System, School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, China
| | - Hongli Chen
- Tianjin Key Laboratory of Optoelectronic Detection Technology and System, School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, China
| | - Zhitao Xiao
- Tianjin Key Laboratory of Optoelectronic Detection Technology and System, School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, China
| | - Hua Bai
- Tianjin Key Laboratory of Optoelectronic Detection Technology and System, School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, China
| | - Xiuyan Li
- Tianjin Key Laboratory of Optoelectronic Detection Technology and System, School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, China
| | - Pingjuan Niu
- Tianjin Key Laboratory of Optoelectronic Detection Technology and System, School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, China
| | - Jianquan Yao
- Key Laboratory of Opto-Electronics Information Technology (Ministry of Education), School of Precision Instruments and Opto-Electronic Engineering, Tianjin University, Tianjin 300072, China
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