Multivariate Load Forecasting of Integrated Energy System Based on CEEMDAN-CSO-LSTM-MTL by WANG Yongli, LIU Zeqiang, DONG Huanran, LI Dexin, CHEN Xin, GUO Lu, WANG Jiarui

“…Firstly，preprocess the collected raw load data and calculate the actual load value considering system energy loss； Secondly，the maximum information coefficient （MIC） is used to analyze the correlation between multiple loads and between multiple loads and weather factors，and to extract strongly correlated variables of multiple loads； Once again，the strongly correlated variables of multiple loads are substituted into CEEMDAN，and the load data is decomposed into stationary subsequences； Then，the feature sequence is substituted into the LSTM-MTL shared layer and the CSO algorithm is used to optimize the prediction model，achieving collaborative prediction of multiple loads； Finally，the performance of the constructed model was validated using a multivariate load dataset from a chemical park in Jilin City，Jilin Province，China. The results show that compared with traditional prediction models，the constructed model can effectively improve the prediction accuracy of multiple loads in the integrated energy system.…”
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Experimental Study on Repairing and Strengthening Seismically Damaged RC Frames by Installing H-Shaped Steel by Yuebing Li, Qushenglin Song, Hang Wang, Liang Shan, Dawei Liu, Shuang Xing

“…The results showed that the seismic performance of the repaired and strengthened frames exceeded that of the original frame, and the failure mode of the frame changed from joint shear and column-end bending to ideal beam-end yielding. …”
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Predicting nighttime black ice using atmospheric data for efficient winter road maintenance patrols by Jinhwan Jang

“…During winter, hazardous black ice can form anywhere on roads under various weather conditions, including fog, frost, rime ice, freezing rain, and snow. To prevent accidents caused by black ice, nighttime road maintenance patrols have been conducted in Korea since December 2019, following a tragic accident on slippery pavement. …”
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