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The flow and solidification characteristics of molten steel in thin slab continuous casting mold of medium Mn steel with section of 1 500 mm×90 mm were studied by numerical simulation. The melting and mixing behavior of reactive mold slag was analyzed. The results showed that in case of casting speed of 5 m/min and immersion depth of 300 mm， the temperature of the medium Mn steel fluctuated greatly around about a quarter of the width surface of the lower layer. Due to the slow heat transfer of the shell， the solidified shell of narrow face at the mold outlet was thin and its thickness was only 7.73 mm. The slag rim formed in the continuous casting process of medium Mn steel was slightly smaller in size and mainly consisted of the initial slag layer. The solid slag film was formed by mixing the initial layer and the reaction layer at different stages. The initial solidification of the shell was easy to be uneven due to the change of the slag ingredient in the upper part of the mold.

The examination of the lead–lag effect between different publication types, incorporating a temporal dimension, is very significant for assessing research. In this article, we introduce a novel framework to quantify the lead–lag effect between the research topics of conference papers and journal papers. We first identify research topics via the text‐embedding‐based topic modeling technique BERTopic, then extract the research topics of each time slice, construct and visualize the similarity matrix of topics to reveal the time‐lag direction and finally quantify the lead–lag effect by four proposed indicators, as well as by average influence topic similarity comparison maps. We conduct a detailed analysis of 19,166 bibliographic data for top conference papers and journal papers from 2015 to 2019 in the data mining field, calculate the similarity of topics obtained by BERTopic between each time slice divided by quarters. The results show that journal paper topics lag behind conference paper topics in the data mining field. The most significant lead–lag effect is 2.5 years, with approximately 33.45% of topics affected by this lag. The methodology presented here holds potential for broader application in the analysis of lead–lag effects across diverse research areas, offering valuable insights into the state of research development and informing policy decisions. This article is categorized under:Application Areas > Science and Technology [ABSTRACT FROM AUTHOR]