This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structure using deterministic clustering methods and stochastic optimisation approaches to optimally centre the clusters. Similar to other state-of-the-art methods available in the literature, it uses “microclusters” and other established techniques, such as density based clustering. Unlike other methods, it makes use of metaheuristic optimisation to maximise performances during the initialisation phase, which precedes the classic online phase. Experimental results show that OpStream outperforms the state-of-the-art methods in several cases, and it is always competitive against other comparison algorithms regardless of the chosen optimisation method. Three variants of OpStream, each coming with a different optimisation algorithm, are presented in this study. A thorough sensitive analysis is performed by using the best variant to point out OpStream’s robustness to noise and resiliency to parameter changes.

Using an unbalanced panel of 922 non-financial companies publicly listed on the London Stock Exchange during January 1995 and September 2014, this article tests the predictions of Pecking Order Theory (POT), Trade-off Theory (TOT) and Market Timing Theory (MTT) of capital structure through the lens of macroeconomic conditions. We find strong evidence that leverage is negatively associated with the business cycle but positively related to stock market performance, which is consistent with POT. In addition, leverage is negatively related to financial market risk, as predicted by TOT. Furthermore, leverage is positively related to credit supply, which is in line with both the POT and TOT. Finally, there is no evidence in support of MTT. The above results are robust with respect to the measurement of macroeconomic variables, the choice of estimation methods and the inclusion of a dummy variable to account for the effect of the 2008 financial crisis. An important implication is that, because firms tend to be highly levered during business cycle downturns, expansionary fiscal and monetary policies to encourage more business borrowings may not be effective after all.

A sustainable business model (SBM) has become an inevitable trend for the traditional business model to follow the development of the times. Based on scientometrics, we explored and visually analyzed SBM's hotspots and emerging trends using the references co-citation networks, landmark references, burst references, keywords co-occurring networks, timeline map, and burst keywords indicators. Our findings consist of four aspects: hotspots and emerging trends, hotspots evolution history, landmark references, and burst references evolution history. First, the core hotspots are “sustainable innovation” and “open innovation”. Second, the main hotspots have evolved from “sustainable innovation,” “strategy,” “performance,” and “supply chain” from 1998 to 2012 into “resource-based view,” “product-service system,” “China,” “life cycle assessment,” and “conceptual framework” in 2013–2017. And “circular economy,” “renewable energy,” “value creation,” “decarbonization” have appeared since 2019, which are emerging trends of SBM. Third, we have found ten landmark references in total. Articles written by Bocken & Short, Boons & Ludeke-Freund, and Zott & Amit are the three most landmark references that significantly impact the whole field. Last, the top 10 cited references have burst since 2011 and lasted until 2022. The article written by Teece became a long-term cited hotspot from 2011 to 2018. Moreover, the article written by Geissdoerfer & Vladimirova, which burst in 2020–2022, was the most cutting-edge. This research aims to enrich the existing research theories related to SBM and help governments and enterprises capture hotspots and emerging trends of SBM so that they can better understand and study SBM.