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Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models

Scientific reports 2025年第1期15卷 17836页

作者： Sherif S M Ghoneim Mohammed Baz Ali Alzaed Yohannes Tesfaye Zewdie Department of Electrical Engineering College of Engineering Taif University Taif 21944 Saudi Arabia. Department of Computer Engineering College of Computers and Information Technology Taif University Taif 21944 Saudi Arabia. Architectural Engineering Program - Civil Engineering Department Faculty of Engineering Taif University Taif 21944 Saudi Arabia. Department of Electrical and Computer Engineering Faculty of Technology Debre Markos University P. BOX 269 Debre Markos Ethiopia. yohannes_tesfaye@dmu.edu.et.

Power transformer plays a crucial role in the power networks. Most of the transformer malfunctions was due to failure in the insulating systems. The utilities keen on the contentious operation of the power network, so early detection of the transformer faults avoiding the undesirable outage of the transformer from the service. The insulating paper state is an indication to the transformer health and aging of it may lead to failure of the transformer, so some periodic and routine test must be performed on the insulting oil to get information about the insulating paper condition. The value of the degree of polymerization (DP) is a key of the insulating paper state. Various recommended tests such as the dissolved gases (DGA), breakdown voltage (BDV), oil interfacial tension (IF), oil acidity (ACI), moisture content (MC), oil color (OC), dielectric loss (Tan δ), and furans concentration specifically (2-furfuraldehyde (2-FAL)) were carried out to correlate between these variables and the DP and then to the insulating oil state. The collected data from these tests were used to supply XGBoost/LightGBM to build artificial intelligence model to predict the insulating paper state. The results indicated that the great ability of the proposed model to predict the insulating state with high accuracy. Of these various configurations for these two classification models, one achieved perfect prediction accuracy (100%) of the insulating paper state prediction accuracy. The other configurations showed accuracy values from 1.0 down to 0.955.

关键词： Dissolved gas analysis Furnic compounds Insulating oil Insulating paper Power transformer XGBoost/LightGBM

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Bending wave based characterization of viscoelastic materials 23

Bending wave based characterization of viscoelastic material...

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23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Miller, Max Malakooti, Sadeq Taghvaee, Tahereh Xiang, Ning Lu, Hongbing Leventis, Nicholas Graduate Program in Architectural Acoustics Rensselaer Polytechnic Institute United States Department of Mechanical Engineering University of Texas Dallas United States Department of Chemistry Missouri University of Science and Technology United States

ISBN: (纸本)9783939296157

Obtaining broadband dynamic mechanical properties of viscoelastic materials is challenging. Commercially available characterization equipment is typically limited to about 500 Hz. Ultrasonic testing is a common strategy but requires time-temperature superposition extrapolation to audible frequencies. These techniques are indirect and sensitive to inhomogeneities. Modal methods do not yield broadband data and can fail for materials with high loss factors. A new relatively simple characterization method, relying on bending wave excitation and laser Doppler vibrometer measurement, is proposed. Complex elastic moduli and phase speeds are now attainable over the entire range of building acoustics frequencies. The materials under test are an array of polyurea aerogels with fixed chemical composition but tailorable nanomorphologies. Low cost and ease of synthesis add appeal to materials already noted for their sound transmission loss performance [4]. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Bending Characterization Damping Flexural Transmission

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Groundwater quality assessment using few-shot learning with prototypical, Siamese, and matching networks

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Journal of Water Process engineering 2025年 75卷

作者： Abdessamed Derdour Mohammed Baz Ali Alzaed Amanuel Kumsa Bojer Sherif S.M. Ghoneim Artificial Intelligence Laboratory for Mechanical and Civil Structures and Soil University Center of Naama P.O. Box 66 Naama 45000 Algeria Laboratory for the Sustainable Management of Natural Resources in Arid and Semi-Arid Zones University Center of Naama 45000 Naama Algeria Department of Computer Engineering College of Computers and Information Technology Taif University Taif 21944 Saudi Arabia Architectural Engineering Program - Civil Engineering Department Faculty of Engineering Taif University Taif 21944 Saudi Arabia Ethiopian Artificial Intelligence Institute PO Box 40782 Addis Ababa Ethiopia Department of Electrical Engineering College of Engineering Taif University Taif 21944 Saudi Arabia

Groundwater quality assessment is critical for sustainable water resource management, especially in hyperarid regions like Adrar, Algeria, where data scarcity and environmental challenges hinder traditional monitoring methods. This study explores the application of three Few-Shot Learning (FSL) algorithms: Prototypical Networks, Siamese Networks, and Matching Networks, for groundwater quality classification using limited datasets. The dataset comprises 166 groundwater samples from the Adrar region, characterized by five quality classes: “Excellent,” “Very Good,” “Good,” “Satisfactory,” and “Unsatisfactory.” Results demonstrate that Prototypical Networks outperform other FSL algorithms, achieving 93 % accuracy with 10 support samples per class, while Siamese and Matching Networks achieve 90 % and 88 % accuracy, respectively. The study highlights the potential of FSL in addressing data scarcity, offering a cost-effective and efficient approach for groundwater quality assessment in data-scarce regions. The findings underscore the importance of FSL in complementing traditional methods, particularly in hyper arid areas where data collection is challenging.

关键词： Water resource management Data scarcity Classification algorithms Hyperarid regions Accuracy evaluation

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Relative humidity levels associated with increases in microbial growth and MVOC emissions in carpet and drywall 16

Relative humidity levels associated with increases in microb...

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16th Conference of the International Society of Indoor Air Quality and Climate: Creative and Smart Solutions for Better Built Environments, Indoor Air 2020

作者： Haines, Sarah R. Hall, Emma C. Misztal, Pawel K. Goldstein, Allen H. Adams, Rachel I. Dannemiller, Karen C. Environmental Sciences Graduate Program Ohio State University ColumbusOH43210 United States Department of Civil Environmental and Geodetic Engineering College of Engineering Ohio State University ColumbusOH43210 United States Division of Environmental Health Sciences College of Public Health Ohio State University ColumbusOH43210 United States Department of Civil Architectural and Environmental Engineering University of Texas at Austin AustinTX78712 United States Department of Environmental Science Policy and Management University of California BerkeleyCA94720 United States Department of Plant and Microbial Biology University of California BerkeleyCA94720 United States

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Bayesian inference for layered porous material analysis 48

Bayesian inference for layered porous material analysis

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48th International Congress and Exhibition on Noise Control engineering, INTER-NOISE 2019 MADRID

作者： Xiang, Ning Fackler, Cameron J. Horoshenkov, Kirill Graduate Program in Architectural Acoustics Rensselaer Polytechnic Institute 110 8th Street TroyNY12180 United States Department of Mechanical Engineering University of Sheffield Sheffield S YorksS1 3JD United Kingdom

ISBN: (纸本)9788487985317

In noise control applications, porous materials are often inhomogeneous with depth. For better understanding of sound absorbing mechanism, the depth-dependent inhomogeneity of the porous media is approximated by multilayered porous absorbers of finite-thickness layers with each layer being considered as homogeneous. This work applies the Bayesian probabilistic inference to analyze multilayer porous materials, developing a method to determine simultaneously the number of constituent layers and the physical properties of each layer. The Bayesian analysis is based on experimental measurements of the acoustic surface impedance of a potentially multilayered material sample with multilayer porous acoustic propagation models. The number of layers considered in the propagation model is varied so as to formulate a finite set of different porous materials models, and Bayesian model selection is applied in estimating the model with a parsimonious number of layers present in the sample. Once the number of layers has been determined, Bayesian parameter estimation inversely estimates the physical properties of each layer. © INTER-NOISE 2019 MADRID - 48th International Congress and Exhibition on Noise Control engineering. All Rights Reserved.

关键词： Porous materials

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Comparative analysis of SWAT and SWAT coupled with XGBoost model using Optuna hyperparameter optimization for nutrient simulation: A case study in the Upper Nan River basin, Thailand

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Journal of Environmental Management 2025年 388卷 126053页

作者： Chayut Pinichka Srilert Chotpantarat Kyung Hwa Cho Wattasit Siriwong Center of Excellence on Hazardous Substance Management (HSM) Chulalongkorn University Bangkok Thailand International Health Policy Program Ministry of Public Health Nonthaburi Thailand Department of Geology Faculty of Science Chulalongkorn University Bangkok Thailand Center of Excellence in Environmental Innovation and Management (EnvIMM) Sustainable Research Institute Chulalongkorn University Thailand School of Civil Environmental and Architectural Engineering Korea University Seoul 02841 South Korea College of Public Health Sciences Chulalongkorn University Bangkok Thailand

Agricultural runoff leading to nitrate (NO 3 -N) and orthophosphate (PO 4 -P) contamination poses significant environmental and public health risks. This study integrates the Soil and Water Assessment Tool (SWAT) with eXtreme Gradient Boosting (XGBoost), optimized using Optuna hyperparameter tuning, to enhance predictions of nutrient concentrations in water bodies. The hybrid SWAT-XGBoost model combines physical hydrological processes with machine learning for improved accuracy. The optimized model demonstrated significant improvements over SWAT alone, with R 2 values increasing by up to 50 % and RMSE decreasing by up to 75 % across datasets. These results highlight the enhanced predictive capabilities of the hybrid approach in capturing nutrient transport dynamics. SHAP analysis further identified key factors, such as sediment dynamics and nutrient mineralization, as dominant drivers of contamination, providing actionable insights for effective watershed management. By integrating SHAP analysis, the study identified key processes influencing nutrient transport, such as sediment dynamics and nutrient mineralization, offering deeper insights into pollution pathways. This approach provides a scalable and adaptable framework for improving watershed management, supporting sustainable agricultural practices, and mitigating contamination risks. The findings highlight an approach that combines physical modeling with machine learning to effectively address complex environmental challenges.

关键词： Machine learning Nutrient simulation Optuna SWAT Thailand XGBoost

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Thermo-responsive poly( N -isopropylacrylamide) (PNIPAM) hydrogel particles improve workability loss and autogenous shrinkage in cement paste

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CEMENT 2022年 10卷

作者： Anastasia N. Aday Mohammad G. Matar Jorge Osio-Norgaard Wil V. Srubar Materials Science and Engineering Program University of Colorado Boulder ECOT 441 UCB 428 Boulder CO 80309-0428 USA Department of Civil Environmental and Architectural Engineering University of Colorado Boulder ECOT 441 UCB 428 Boulder CO 80309-0428 USA Department of Mechanical Engineering University of Colorado Boulder ECOT 441 UCB 428 Boulder CO 80309-0428 USA

In this work, we show that non-superabsorbent, thermo-responsive poly(N-isopropyl acrylamide) (PNIPAM) hydrogel particles (< 250 μm) can reduce autogenous shrinkage in cement paste and improve early-age stiffening that can be caused by traditional superabsorbent polymers (SAPs). Swelling measurements in DI water and cement filtrate solution suggest that SAP-induced early-age stiffening is caused by its super-absorbency in low-ionic solutions – a behavior not exhibited by non-superabsorbent PNIPAM. Addition of PNIPAM resulted in a 29% and 60% reduction in autogenous shrinkage strain at 14 days when used alone (0.3 wt% PNIPAM) and in combination with SAP (0.15% PNIPAM, 0.15% SAP), respectively, compared to a Control with no polymer addition. Furthermore, an addition of 0.3 wt.% PNIPAM exhibited a ∼29% and ∼37% decrease in static yield stress compared to a Control and 0.3 wt% SAP-modified cement pastes, respectively. Taken together, the results provide initial evidence to suggest that the use of hydrogels as internal curing agents may not necessarily require super-absorbency to reduce autogenous shrinkage. Non-superabsorbent hydrogels, like PNIPAM, may help reduce autogenous shrinkage while alleviating the effects of SAP-induced early-age stiffening.

关键词： Superabsorbent polymers Poly( N -isopropyl acrylamide) (PNIPAM) Static yield stress Autogenous shrinkage

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Full house: Microbial and nonmicrobial volatile organic compounds competing for "residency" in indoor environments at cycling relative humidity 16

Full house: Microbial and nonmicrobial volatile organic comp...

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16th Conference of the International Society of Indoor Air Quality and Climate: Creative and Smart Solutions for Better Built Environments, Indoor Air 2020

作者： Hall, Emma C. Haines, Sarah Dannemiller, Karen C. Marciniak, Katarzyna Robin, Weber Goldstein, Allen H. Adams, Rachel Misztal, Pawel K. Department of Civil Architectural and Environmental Engineering University of Texas at Austin AustinTX78712 United States Environmental Sciences Graduate Program Ohio State University ColumbusOH43210 United States Department of Civil Environmental and Geodetic Engineering College of Engineering Ohio State University ColumbusOH43210 United States Division of Environmental Health Sciences College of Public Health Ohio State University ColumbusOH43210 United States Department of Environmental Science Policy and Management University of California BerkeleyCA94720 United States Department of Plant and Microbial Biology University of California BerkeleyCA94720 United States School of Chemistry University of Edinburgh EdinburghEH9 3JJ United Kingdom

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3D noise modeling of an urban environment: Simulated and measured noise characteristics of the UDC campus

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Proceedings of Meetings on Acoustics 2024年第1期46卷

作者： Lirane Mandjoupa Dorian Davis Samba Gaye Shehabaldin Mohamed Etochukwu Ofodile Lei Wang Wagdy Mahmoud Max Denis University of the District of Columbia Washington DC 20008 USA liranekertesse.mandj@udc.edu dorian.davis@udc.edu samba.gaye@udc.edu Shehabaldin.Mohamed@udc.edu etochukwu.ofodile@udc.edu Department of Civil and Architectural Engineering and Construction Management University of Cincinnati Cincinnati OH 45221 USA wang4li@ucmail.uc.edu Computer Engineering Program Director Department of Electrical and Computer Engineering University of the District of Columbia Washington DC 20008 USA wmahmoud@udc.edu Department of Mechanical Engineering University of the District of Columbia Washington DC 20008 USA max_f_denis@***

In this work, a 3-D numerical method is employed to predict the noise propagation within the University of the District of Columbia (UDC). UDC is located within an urban environment in Washington, DC. Sound pressure level measurements of various noise sources including traffic noise are obtained. Of particular interest is the method’s ability to accurately simulate the urban space. Accuracy is evaluated comparatively between simulated and measured results of acoustic parameters in the mid-to-high-frequency regimes. Specifically, reverberation time, energy decay time, and sound pressure level. The results and limitations of the particle-tracing method for predicting sound propagation in urban environments are discussed.

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Comparing real-time ultraviolet-induced fluorescence with hexosaminidase activity for assessing airborne fungal spore loads in occupied K-12 classrooms 15

Comparing real-time ultraviolet-induced fluorescence with he...

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15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018

作者： Nieto-Caballero, Marina Gomez, Odessa M. Shaughnessy, Richard J. Hernandez, Mark Civil Environmental and Architectural Engineering University of Colorado Boulder United States Indoor Air Program University of Tulsa Oklahoma United States

ISBN: (纸本)9781713826514

The goal of this demonstration study was to compare fluorescent aerosol cytometry with select enzyme activity for their ability to assess the airborne loads of fungal biomass in occupied settings. Within the same suburban area and season, simultaneous collocated measurements, targeting airborne fungal concentrations, were acquired from 28 occupied classrooms in 11 different public schools. As judged by ANOVA and Spearman's ranking, a statistically significant association was observed between results from these different analytical methods. © 2018 15th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2018. All rights reserved.

关键词： Enzyme activity

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