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Fault diagnosis and degree evaluation of steam generator heat transfer tube rupture based on hybrid method

ANNALS OF NUCLEAR ENERGY 2025年 211卷

作者： Jiang, Yingying Xia, Hong Zhou, Zhuoran Yin, Wenzhe Jia, Zhujun Huang, Xueying Zhang, Jiyu Zhu, Yihu Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Harbin 150001 Peoples R China Xi An Jiao Tong Univ Natl Innovat Platform Ctr Ind Educ Integrat Energy Xian 710049 Peoples R China

As critical pressure boundary components in pressurized water reactor, the integrity of steam generator heat transfer tubes is continually challenged by adverse operational environments. Direct monitoring of their condition is unfeasible. A hybrid fault diagnosis method for detecting and evaluating the damage of steam generator heat transfer tubes was proposed by this paper. This method constructed pseudo bond graph model by acquiring in-depth knowledge of steam generators and the causal relationships among its various parameters. To overcome the expert system's limitation in evaluating the extent of damage, a random forest algorithm was employed for severity assessment. Furthermore, particle swarm optimization-based swarm intelligence method was utilized to optimize the hyperparameters of the random forest model. Performance tests indicated that the proposed method could diagnose damage in heat transfer tubes with high accuracy and promptness and effectively assesses the extent of damage in the heat transfer tubes of steam generators.

关键词： Natural circulation steam generator Heat transfer tube rupture Fault severity assessment Pseudo bond graph random forest algorithm

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Research on the refinement of atmospheric weighted average temperature model in Xi'an based on machine learning

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ADVANCES IN SPACE RESEARCH 2025年第1期75卷 353-366页

作者： Shen, Yu Liu, Ning Zhang, Shuangcheng Zhu, Xuejian An, Ningkang Changan Univ Coll Geol Engn & Geomat Xian 710054 Peoples R China

One of the most important parameters for calculating the atmospheric precipitable water vapor (PWV) is the atmospheric weighted mean temperature (Tm). Meteorological parameters that are measured typically constrain the accuracy and regional applicability of commonly used empirical models. When precipitable water is inverted using the Global Navigation Satellite System (GNSS), it results in erroneous Tm being obtained. Thus, in this paper, a small-scale Tm model for Xi'an is proposed by least squares method based on data provided by the European Centre for Mesoscale Weather Forecasts (ECMWF) from 2019 to 2022, while improving the functional model's capacity to fit nonlinear residuals through the random forest method and enabling it to predict peaks more accurately. It is based on a random forest to confirm the weighted average temperature model's correctness (RF Tm). When comparing the RF Tm model's projected Tm accuracy to the GPT3, UNB3, and regional Bevis models, using the 2023 ERA5 data as the experimental data, the improvements are 23.6 %, 18.6 %, and 11.4 %, respectively. With the sounding station data as the reference value, the BIAS of RF Tm is only 1.51 K. In determining the atmospheric weighted average temperatures, the two models' accuracy was comparable to that of the Long Short-Term Memory neural network model (LSTM). However, the LSTM model requires short-term forward data for practical usage, the RF Tm model compensated for the shortfall. The RF Tm model is used to invert the atmospheric precipitable water vapor at the appropriate monitoring stations using data from GNSS monitoring stations in Xi'an. It was demonstrated that the RF Tm model's PWV inversion accuracy outperformed the PWV inverted by the GPT3 model by at least 30 %. Moreover, Xi'an rainfall data integration shows that the PWV inverted by the RF Tm model reacts to brief rainfall episodes efficiently. To sum up, this study's RF Tm model provides a dependable and effective approach to meteor

关键词： Global satellite navigation system Weighted mean atmospheric temperature Precipitable water vapor random forest algorithm Zenith wet delay

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Machine learning-driven simultaneous quantification of Cd(II) and Cu(II) on Co2P/CoP heterostructure: enhanced electrochemical signals via activated Co-P electron bridge

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JOURNAL OF HAZARDOUS MATERIALS 2025年 491卷 138030页

作者： Gao, Zhi-Wei Yu, Yan Chen, Shi-Hua Li, Yong-Yu Liu, Zi-Hao Yang, Meng Li, Pei-Hua Song, Zong-Yin Huang, Xing-Jiu Chinese Acad Sci Inst Solid State Phys Key Lab Environm Opt & Technol HFIPS Hefei 230031 Peoples R China Chinese Acad Sci Inst Solid State Phys Environm Mat & Pollut Control Lab HFIPS Hefei 230031 Peoples R China Univ Sci & Technol China Dept Mat Sci & Engn Hefei 230026 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Environm Hefei 230088 Peoples R China

Simultaneous quantification of multiple heavy metal ions remains a significant challenge in electrochemical methods, as complex high-throughput data from signal interference cannot be accurately analyzed through individual expertise and calibration curves. In this study, machine learning techniques were introduced to co-detect Cd(II) and Cu(II), with their electrochemical interference mechanisms explored on highly active Co2P/ CoP heterostructures. The random forest (RF) model initially identified key feature variables in response currents, which were subsequently input into the convolutional neural network (CNN) to uncover the relationship between electrochemical signals and ion concentrations, demonstrating excellent reliability with R2 values of 0.996 for both Cd(II) and Cu(II). The root mean square error (RMSE) values for Cd(II) and Cu(II) were 0.0177 and 0.0206 mu M, respectively, indicating high predictive accuracy. The experiments and theory calculations revealed that Cu(II) preferentially bonded with P sites over Cd(II). Enhanced electron transfer from Co to P atoms and weakened Cu-P bonds facilitated Cu(II) reduction and desorption from Co2P/CoP, thereby boosting electrochemical signals, while Cd(II) signals were inhibited due to active site loss. Herein, the integration of machine learning provides robust support for simultaneous detection of multiple analytes, accelerating the practical application of electrochemical methods in environmental monitoring.

关键词： Active P sites Competitive adsorption Key feature currents random forest algorithm Convolutional neural network algorithm

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Mapping groundwater-related flooding in urban coastal regions

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JOURNAL OF HYDROLOGY 2025年 657卷

作者： Marshall, Montana Dubois, Emmanuel Cherif, Saleck Moulaye Ahmed Dubath, Candice Oumarou, Wague Mariethoz, Gregoire Perona, Paolo Swiss Fed Inst Technol Lausanne EPFL Platform Hydraul Constructions Lausanne Switzerland Reg Nouakchott Nouakchott City Hall Nouakchott Mauritania Mauritania Natl Ctr Water Resources Ctr Natl Ressource Eau CNRE Nouakchott Mauritania Univ Lausanne UNIL Inst Earth Surface Dynam Geostat Algorithms & Image Anal Res Grp GAIA Lausanne Switzerland

Coastal urban environments are facing an increasing risk of flooding from both surface water and groundwater due to globally elevated water levels, causing substantial socio-economic damages, threatening livelihoods, and hindering economic development. To date, assessing the risk of groundwater-related flooding has primarily relied on groundwater modeling and analytical solutions, but these approaches face the challenge of limited groundwater observations. In this context, spatio-temporal mapping of groundwater-related flooding in urban environments using satellite imagery offers a promising option to augment limited groundwater field measurements. However, the ability of satellite imagery to capture the dynamics of groundwater fluctuations, which can induce saturation excess flooding, has not been assessed previously. This study is the first to compare flood detection estimates derived from Sentinel-2 satellite imagery with those obtained by analyzing the difference between topography and the piezometric surface, using in-situ groundwater level measurements. This research uses the coastal city of Nouakchott, Mauritania as a case study, and covers the period from 2015 to 2023. The satellite images were processed to map flooded areas using a supervised classification (random forest algorithm) and water detection spectral indices (MNDWI and NDPI). The novelty of this study lies in the successful mapping of flooding in a data-scarce coastal urban environment. For the first time, this study demonstrated that satellite imagery, combined with machine learning methods, effectively captures the spatio-temporal dynamics of groundwater-related flooding and complements low-frequency in- situ groundwater level measurements. Results showed that flooded area peaked during the wet season (July to September) due to rainfall infiltration, and continuously decreased during the rest of the year. This work also demonstrated that mapping groundwater-related flooding based on groundwater

关键词： Saturation excess flooding Coastal city Groundwater rise Sentinel-2 random forest algorithm Supervised classification

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Analysis of Machine Learning algorithms for Real-Time Gallbladder Stone Identification from Ultrasound Images in Clinical Decision Support Systems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-28页

作者： Hong, Chen Zafar, Imran Ayaz, Muhammad Mazhar Kanwal, Rimsha Kanwal, Faheem Dauelbait, Musaab Bourhia, Mohammed Jardan, Yousef A. Bin Hubei Univ Sci & Technol Sch Biomed Engn & Med Imaging Wuhan Peoples R China Univ Faisalabad Fac Sci Dept Biochem & Biotechnol Faisalabad Pakistan Cholistan Univ Vet & Anim Sci Fac Vet Sci Dept Parasitol Bahawalpur Pakistan Univ Lahore Inst Mol Biol & Biotechnol IMBB Lahore Pakistan Univ Bahr El Ghazal Freedowm Stree Wau 91113 Sudan Ibn Zohr Univ Fac Sci Lab Biotechnol & Nat Resources Valorizat Agadir 80060 Morocco King Saud Univ Coll Pharm Dept Pharmaceut POB 11451 Riyadh Saudi Arabia

Global epidemiology of gallstones in the twenty-first century affects millions of individuals, and ultrasound diagnostics effectively assess gallbladder size and function and detect abnormalities. This study collected datasets from local hospitals and reliable online sources for analysis using advanced CV/IP tools and WEKA. Image preprocessing techniques, including cropping, resizing, and grayscale conversion, were applied to 90 ultrasound images, extracting 600 ROIs with 21 features spanning binary, histogram, and texture attributes. The dataset was divided into balanced training and validation subsets, and supervised learning algorithms were optimized via cross-validation and grid search. Circular patterns were processed iteratively, with specific dimensions (512 x 512 for width/height, 32 x 32 for radius/blur, 128 x 128 for columns/rows). The performance of various machine learning classifiers was evaluated using accuracy, precision, recall, F1 score, AUC-ROC, MCC, Kappa GDR, and Dice Index, ensuring strong classification of normal and abnormal samples. The random forest (RF) classifier achieved the highest performance with an accuracy of 96.33%, followed by the MLP and Logit Boost classifiers with 95.67% and 95.40% accuracy rates, respectively. The RF model also exhibited the highest precision (0.9542), recall (0.9732), F1 score (0.9636), and a Dice Index (0.9649) with an MCC of 0.925, ROC area of 0.988, Kappa (0.921), and specificity of 95.34%-indicating its strong ability to balance true positives and negatives while minimizing misclassifications. The MLP classifier also performed well with a precision of 0.9477, a recall of 0.9665, and an F1 score of 0.957, while Logit Boost had similar results with a precision of 0.9411 and a recall of 0.9665. Other classifiers, such as the Bayes Net and J48 classifiers, showed slightly lower performance with accuracy rates of 94.67% but still exhibited good precision and recall, making them viable alternatives. This study h

关键词： Ultrasound imaging Real-time gallbladder stone identification Machine learning algorithms random forest algorithm Clinical decision support systems

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Machine Learning-Driven Archaeological Site Prediction in the Central Part of Jharkhand, India Using Multi-parametric Geospatial Data

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2025年第1期53卷 259-276页

作者： Pal, Sanjit Kumar Maity, Shubhankar Bera, Amit Ghosh, Debajit Kumar, Anil Indian Inst Technol Indian Sch Mines Dept Appl Geophys Dhanbad 826004 Jharkhand India Visva Bharati Dept Ancient Indian Hist Culture & Archaeol Santini Ketan 731235 West Bengal India

The central part of Jharkhand, India, harbours a complex history shaped by ancient civilisations, notably Buddhism, Jainism, and Brahmanism, necessitating a meticulous identification of potential archaeological sites. This study employs a cutting-edge machine learning approach to predict the suitability of archaeological sites in the region, marking a significant evolution in the documentation of such sites. Machine learning-based integration of 12 geoenvironmental datasets using a random forest model reveals a nuanced spatial distribution of potential archaeological sites, categorised into four suitability zones: high, moderately high, moderately low, and low. The region with the best-anticipated suitability comprises around 20.33% of the research area, whereas the area with the lowest expected suitability comprises nearly 41.81%. High suitability zones, characterised by gentle terrain, open vegetation, fertile soils, and water proximity, suggest conditions conducive to human habitation and archaeological preservation. Conversely, low suitability zones exhibit rugged terrain, dense vegetation, poor soil quality, limited water availability, and remoteness from natural resources, indicating potential hindrances to human occupation and archaeological preservation. The model exhibited high predictive accuracy, as evidenced by the ROC-AUC score of 88.3%, enhancing its reliability. Specific locations within the study area demonstrate varying degrees of suitability, providing valuable insights for archaeological site management, cultural heritage preservation, and land-use planning, which will support the restoration and conservation plan of the heritage sites. Furthermore, this machine learning-based archaeological site prediction study underscores its potential applicability in historically rich regions globally, showcasing its significance in uncovering and preserving our shared human history.

关键词： Geoenvironmental datasets GIS and remote sensing approach Prioritising exploration random forest algorithm Archaeological site prediction

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Classification and Diagnosis of the Rotor Unbalance Parameters via Hybridized EMD and RF

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2025年第4期13卷 1-6页

作者： Gohari, Mohammad Ghorbani, Mohammad Hossein Arak Univ Technol Fac Mech Engn Daneshgah St Arak *** Markazi Iran Univ Federico II PRISMA LAB Naples Italy Arak Univ Technol Fac Mech Engn Arak Iran

Background: Todays, using of rotating machineries are very popular in industry centers. Since these machineries are used as moving or dynamic parts of various systems, they encounter different types of faults. If these faults are not fixed properly, they can cause failures with costly problems. One of the major defects in rotating machineries is unbalance. Methods: In this research, it is attempted to classification and predict unbalance parameters by Empirical Mode Decomposition (EMD) and random forest (RF) algorithm with higher accuracy and simple way. A rotating test rig with four disks capable of placing different weights at different distances from the shaft center has been used in this study. The vibrational results were recorded for each of each test separately. The accelerations of the bearings, obtained from these results, were processed using empirical mode decomposition and then used as inputs for the random forest algorithm. Results: After training the algorithms comparing the predicted values with the empirical data indicates the effectiveness of these method in determining the location and severity of the fault. Conclusion: The accuracy of hybridizing EMD and RF show acceptable accuracy in locating of unbalance position in terms of disk number (75% accuracy), mass (82%) and radius of eccentricity (88%).

关键词： Unbalance in rotating machinery Multi disk rotor Empirical mode decomposition random forest algorithm Unbalance parameters

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Predictors of depression among Chinese college students: a machine learning approach

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BMC PUBLIC HEALTH 2025年第1期25卷 1-11页

作者： Luo, Lin Yuan, Junfeng Wu, Chenghan Wang, Yanling Zhu, Rui Xu, Huilin Zhang, Luqin Zhang, Zhongge Guizhou Normal Univ Sch Phys Educ Guiyang 550075 Peoples R China Guizhou Ctr Dis Control & Prevent Guiyang 550001 Peoples R China

BackgroundDepression is highly prevalent among college students, posing a significant public health challenge. Identifying key predictors of depression is essential for developing effective interventions. This study aimed to analyze potential depression risk factors among Chinese college students using the random forest algorithm (RFA) and to explore gender differences in risk *** cross-sectional study was conducted with 10,043 undergraduate students from Guizhou Normal University. Thirty-three variables were analyzed using RFA. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D), with a score of >= 16 indicating depression risk. The variables included sociodemographic characteristics, physical and psychological health indicators, behavioral and lifestyle factors, socioeconomic conditions, and family mental health *** RFA identified several factors associated with depression risk, with suicidal ideation, anxiety, and sleep quality exhibiting the strongest associations. Other significant predictors included academic stress, BMI, vital capacity, psychological resilience, physical fitness test scores, major satisfaction, and social network use. The model achieved an accuracy of 87.5% and an AUC of 0.927. Gender-stratified analysis suggested different patterns: physical fitness indicators showed stronger associations with depression risk among male students, while BMI was more strongly associated with depression risk among female *** cross-sectional study identified factors associated with depression risk among Chinese college students, with psychological factors showing the strongest associations. Gender-specific patterns were observed, suggesting the importance of considering gender differences when developing mental health interventions. However, longitudinal studies are required to establish causal relationships and validate these findings through intervention trials.

关键词： College student depression Machine learning Predictors Gender differences random forest algorithm

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Ground visibility prediction using tree-based and random-forest machine learning algorithm: Comparative study based on atmospheric pollution and atmospheric boundary layer data

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ATMOSPHERIC POLLUTION RESEARCH 2024年第11期15卷

作者： Wang, Fuzeng Liu, Ruolan Yan, Hao Liu, Duanyang Han, Lin Yuan, Shujie Chengdu Univ Informat Technol Coll Elect Engn Chengdu 610225 Peoples R China Chengdu Meteorol Off Pengzhou Meteorol Adm Chengdu 611930 Peoples R China Chengdu Univ Informat Technol Sch Atmospher Sci Chengdu 610225 Peoples R China Nanjing Joint Inst Atmospher Sci Key Lab Transportat Meteorol China Meteorol Adm Nanjing 210041 Peoples R China Shanghai Maritime Univ Coll Ocean Sci & Engn Shanghai 200135 Peoples R China

To mitigate haze impacts, three visibility simulation schemes were designed using decision tree and random forest algorithms, leveraging atmospheric boundary layer meteorological data, pollutant concentrations, and ground observations. The optimal approach was identified to investigate the boundary layer's effect on simulations. The results showed that the simulation effect of the random forest algorithm for two haze processes was better than that of the decision tree algorithm. In the first haze process, the random forest algorithm had a more significant reduction in root mean square error than the decision tree algorithm in the same visibility range (Scheme 3, visibility<200 m, mean absolute error reduced by 5.9%, root mean square error reduced by 19.1%). Simulation models significantly improved the accuracy of the models by adding atmospheric boundary layer observation data to the two fog-hazes process visibility. However, the addition of atmospheric boundary layer meteorological data in the first haze process had a better improvement effect (random forest: visibility<200 m, mean absolute errors of 25.0 (relative error<12.5%) and 25.5 m (relative error<12.8%) in Scheme 2 and 3, respectively). The addition of atmospheric boundary-layer pollutant concentrations data was more effective in the second haze process (random forest: visibility<200 m, scheme 2 and scheme 3 had mean absolute errors of 25.6 (relative error<12.8%) and 11.1 m (relative error<5.6%), respectively). The influence of atmospheric boundary layer meteorological data and pollutant data on the two fog processes is affected by the cause of the fog process.

关键词： Decision tree algorithm random forest algorithm Visibility prediction Haze Artificial intelligence

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Research and application of optimization of physical education training model based on multi-objective differential evolutionary algorithm

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SYSTEMS AND SOFT COMPUTING 2025年 7卷

作者： Wu, Man Wenzhou Polytech Wenzhou 325035 Peoples R China

With the development of computer science, various algorithm models are gradually applied in various fields of life. In order to study the application of the multi-objective differential evolution algorithm in the field of sports transportation. Based on the improvement of multi-objective differential evolution algorithm, this paper proposes the training model of PE education, and compares the prediction results and the actual results. The specific conclusions are as follows: (1) MODE algorithm is better to other algorithms in convergence speed and accuracy;MODE algorithm can not only reach the optimal particle position quickly, but also fluctuate around the best point.(2) AMODE-MPS has great potential for dealing with complex and multiple objectives.(3) There are significant differences between the prediction performance of the proposed algorithm model and the statistical performance, in which the statistical performance is significantly higher than the predicted performance.(4) The proposed model can basically meet the prediction requirements. Although there are some differences between the prediction results and the actual results, this is because the statistical process is affected by the weather, physical condition and other factors. The results show that the PE training model has good results in practice, so this paper can provide reference for the improvement of PE teaching model.

关键词： Multi-objective differential evolutionary algorithm random forest algorithm Mirror point screening method Physical education

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