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Cloud-edge collaborative and graph neural network based load forecasting method for electric vehicle charging stations DENG Bin, ZHANG Zongbao, ZHAO Wenmeng, LUO Xinhang, WU Qiuwei Journal of Shandong University(Engineering Science)    2025, 55 (5): 62-69.   DOI: 10.6040/j.issn.1672-3961.0.2024.219 Abstract （440）      PDF（pc） （2415KB）（33）       Knowledge map    Save Aiming at the problems of privacy protection, computational efficiency, and predictive accuracy in existing forecasting methods for electric vehicle charging stations, a cloud-edge collaborative and graph neural network based load forecasting approach was proposed. A privacy preserving module based on embedding is developed in the cloud, which reconstructs the input data through embedding transformation to prevent potential privacy leakage risks. A method for generating representation with graph structure based on clustering is proposed to provide additional spatiotemporal information and achieve more accurate forecasting. Personalized graph neural network forecasting models are designed for clients based on cloud's graph structure representation, enabling collaborative training of electric vehicle charging stations in different regions while protecting privacy. Experimental results on the Perth dataset demonstrate that the model outperforms benchmark methods in predictive accuracy and that the cloud-edge collaborative framework proposed in this study significantly enhances the performance of graph neural network algorithms in the task of load forecasting for electric vehicle charging stations. Reference | Related Articles | Metrics

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Study on associations between serum per- and polyfluoroalkyl substances levels and blood pressure in residents of Jinan ZHANG Haoyu, XU Fei, LIU Yi, HOU Chengxi, DING Lei Journal of Shandong University(Engineering Science)    2025, 55 (4): 160-172.   DOI: 10.6040/j.issn.1672-3961.0.2024.297 Abstract （309）      PDF（pc） （5752KB）（21）       Knowledge map    Save Human was exposed to per- and polyfluoroalkyl substances(PFASs), which were implicated to be associated with elevated prevalence of hypertension. To evaluate the relationships between individual PFAS and PFAS mixture with blood pressure levels and hypertension risk, 18 PFASs in fasting serum samples collected from 326 individuals in Jinan, China were analyzed with an ultrahigh performance liquid chromatography system coupled with an Orbitrap mass spectrometer. Multivariable linear regression and logistic regression models were utilized to analyze the associations between individual PFAS and systolic blood pressure, diastolic blood pressure, and the risk of hypertension, respectively. To evaluate the joint effects of PFAS mixture, quantile g-computation and Bayesian kernel machine regression models were applied. All the models indicated a positive association between perfluorodecanoic acid mass concentration and diastolic blood pressure, a negative association between perfluorododecanoic acid mass concentration and diastolic blood pressure, and a positive association between perfluoroundecanoic acid mass concentration and risk of hypertension. According to a series of results from this study, it was concluded that both diastolic blood pressure and the risk of hypertension increased with the percentile of PFAS mixture mass concentration among the study population. Reference | Related Articles | Metrics

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Robust unit commitment model with multi-energy coupled system considering gas-heat network dynamics ZHANG Yumin, LI Jingrui, YANG Ming, JI Xingquan, SUN Donglei, XU Bo, WU Fucheng Journal of Shandong University(Engineering Science)    2025, 55 (5): 18-29.   DOI: 10.6040/j.issn.1672-3961.0.2024.163 Abstract （300）      PDF（pc） （10507KB）（41）       Knowledge map    Save The inherent intermittency and uncertainty of renewable energy sources had a challenge to operation decisions of the system. To solve this problem, a robust unit commitment model with multi-energy coupled system considering gas-heat network dynamics was proposed. The mathematical expressions that characterize the dynamic characteristics of gas network and thermal network were established, which were incorporated into the robust unit commitment optimization model of multi-energy coupled system. A multi-dimensional uncertainty set from the perspectives of interval, time, and space to achieve flexible adjustment of wind power absorption boundaries was established. At the same time, concentrating solar power was used to replace the output of some thermal power units to further improve the utilization rate of renewable energy. The column-and-constraint generation algorithm was employed to transform the established min-max-min structure optimization model into a mixed-integer linear programming master-subproblem form for optimization, improving the solution speed of the model. The effectiveness of the proposed model and method was verified on 6-6-8 and 118-20-16 electricity-gas-heat systems, with results indicating that the dynamic characteristics of gas and heat networks can improve the economy of system operation and the utilization rate of renewable energy. Reference | Related Articles | Metrics

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Inverse analysis on the softening curve of steel slag fine aggregate concrete XUE Gang, ZHANG Yifan, LIU Jiangsen, DONG Wei Journal of Shandong University(Engineering Science)    2025, 55 (6): 120-128.   DOI: 10.6040/j.issn.1672-3961.0.2024.249 Abstract （295）      PDF（pc） （11966KB）（14）       Knowledge map    Save In order to study the influence of steel slag fine aggregate volume fraction on the softening characteristics of concrete, the steel slag fine aggregate with stability meeting the specification limit requirements was selected to prepare concrete for wedge splitting test, and the inverse analysis program was established by ISIGHT integrated MATLAB and ABAQUS, and the trilinear softening curve of steel slag fine aggregate concrete was deduced, and the accuracy of finite element analysis of the softening curve was verified based on the wedge splitting tensile test results. The results showed that steel slag could significantly improve the tensile strength of concrete and accelerate the stress reduction of concrete after cracking. When the steel slag volume fraction was 20%, the slope of the second segment of the trilinear softening curve decreased the most. The cracking displacement of concrete at the end of the softening curve decreased with the increase of steel slag volume fraction, and the ductility of concrete deteriorated. The softening curve of steel slag fine aggregate concrete provided a theoretical basis for studying its softening performance. Reference | Related Articles | Metrics