PurposePrevious research concerning automatic extraction of research topics mostly used rule-based or topic modeling methods, which were challenged due to the limited rules, the interpretability issue and the heavy dependence on human judgment. This study aims to address these issues with the proposal of a new method that integrates machine learning models with linguistic features for the identification of research topics.Design/methodology/approachFirst, dependency relations were used to extract noun phrases from research article texts. Second, the extracted noun phrases were classified into topics and non-topics via machine learning models and linguistic and bibliometric features. Lastly, a trend analysis was performed to identify hot research topics, i.e. topics with increasing popularity.FindingsThe new method was experimented on a large dataset of COVID-19 research articles and achieved satisfactory results in terms of f-measures, accuracy and AUC values. Hot topics of COVID-19 research were also detected based on the classification results.Originality/valueThis study demonstrates that information retrieval methods can help researchers gain a better understanding of the latest trends in both COVID-19 and other research areas. The findings are significant to both researchers and policymakers.

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This study examined home country bias in the New England Journal of Medicine (NEJM). Bibliographic information of publications in the NEJM from 2000 to 2019 was collected as data. Bibliometric analyses were performed to investigate the number of papers with one or more US‐based authors in the NEJM and compare this to other journals. Citation counts of US‐based and non‐US papers were calculated and compared. The results show that US‐based papers account for 65.44% of the NEJM papers, and the US share of the NEJM papers has remained stable in the examined years whereas in other journals the percentage decreased. More importantly, US‐based papers in the NEJM display significantly lower research impact compared with non‐US papers. The results suggest that US‐based papers are favoured by the NEJM and a home country bias may exist in the publication process of the journal.

The increasing penetration of renewable energy resources in the distribution network has posed great uncertainties and challenges for the system security operation. To model various uncertain factors like the wholesale market price and renewable energy generation in the active distribution network (ADN), a similarity measurement method considering the amplitude, volatility and variation trend is proposed. The Latin hypercube sampling method and Graph Pyramid clustering algorithm are adopted to obtain the comprehensive typical scenario set. Furthermore, this study proposes a scenario-based stochastic day-ahead optimal economic dispatch approach based on typical scenario set. The energy trading between the distribution system and the wholesale energy market, various distributed generators, network topology and power flow model are jointly formulated in the proposed operation model. The effectiveness and scalability of the proposed approach are verified using the IEEE 33-bus system. Numerical simulation results under different implementation scenarios indicate that the proposed approach offers a high computational efficiency and promotes the security and economy of the distribution system operation, which has a promising industrial application value.

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