Abstract
The combination of class imbalance and overlap is currently one of the most challenging issues in machine learning. While seminal work focused on establishing class overlap as a complicating factor for classification tasks in imbalanced domains, ongoing research mostly concerns the study of their synergy over real-word applications. However, given the lack of a well-formulated definition and measurement of class overlap in real-world domains, especially in the presence of class imbalance, the research community has not yet reached a consensus on the characterisation of both problems. This naturally complicates the evaluation of existing approaches to address these issues simultaneously and prevents future research from moving towards the devise of specialised solutions. In this work, we advocate for a unified view of the problem of class overlap in imbalanced domains. Acknowledging class overlap as the overarching problem - since it has proven to be more harmful for classification tasks than class imbalance - we start by discussing the key concepts associated to its definition, identification, and measurement in real-world domains, while advocating for a characterisation of the problem that attends to multiple sources of complexity. We then provide an overview of existing data complexity measures and establish the link to what specific types of class overlap problems these measures cover, proposing a novel taxonomy of class overlap complexity measures. Additionally, we characterise the relationship between measures, the insights they provide, and discuss to what extent they account for class imbalance. Finally, we systematise the current body of knowledge on the topic across several branches of Machine Learning (Data Analysis, Data Preprocessing, Algorithm Design, and Meta-learning), identifying existing limitations and discussing possible lines for future research.

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Abstract
In recent years, learning from streaming data, commonly known as stream learning, has enjoyed tremendous growth and shown a wealth of development at both the conceptual and application levels. Stream learning is highly visible in both the machine learning and data science fields and has become a hot new direction in research. Advancements in stream learning include learning with concept drift detection, that includes whether a drift has occurred; understanding where, when, and how a drift occurs; adaptation by actively or passively updating models; and online learning, active learning, incremental learning, and reinforcement learning in data streaming situations.

Abstract
Due to the advancement of power electronics devices and control techniques, the modular multilevel converter (MMC) has become the most attractive converter for multiterminal direct current (MTDC) grids thanks to its most relevant features, such as modularity and scalability. Despite their advantages, conventional MMCs face a major challenge with: i) fault-tolerant operation strategy; ii) energy losses in conversion; iii) lack of DC fault handling capability. This paper provides a systematic review to identify the gaps in the literature about Intelligent Fault-Tolerant Protection Schemes for multi-terminal HVDC grids. Through the bibliometric analysis, it was possible to identify topics still to be developed within the four main clusters (Offshore wind farms, Wind turbines, Voltage Source Converters, and Wind power). The research topic opens three research paths: the first is the analysis of failures in HVDC (High Voltage Direct Current) grid equipment by the FDD (Fault Detection and Diagnosis) method; the second is failure analysis by the IFDD (Inverse Fault Detection and Diagnosis) method and the third is the possibility of interconnecting the different energy generation zones with different frequencies. © The Authors.

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This study analysis the entrepreneurial intention (EI) of 236 students attending higher education considering a heterogeneous sample of students with diverse profiles and work experience, which allows exploring its role in EI. The findings confirm that understanding EI requires considering a multidimensional model that includes attitude toward entrepreneurship, perceived entrepreneurial capacity, perceived social norms, and entrepreneurship education (EE). Furthermore, students with professional experience manifest a higher EI and a higher perception of the role that EE can play in the process of establishing a start-up.

Abstract
This article fills a gap in the literature by exploring e-shoppers' views on the ability of verbal and visual cues to represent scents of unknown perfumes. In-depth face-to-face interviews were conducted with 27 consumers from Brazil, Iran, and Portugal. Results demonstrate that visual cues could complement verbal descriptions in conveying the type of scent of perfumes. In addition, this study identified a set of associations between several colors and types of scents. Overall, this article argues that consistent combinations of perfume components' symbolic and sensory verbal descriptions, colors, and images should be developed to effectively convey the scent of an unknown perfume, which can attract more e-shoppers and eventually boost online sales. Cross-cultural comparisons are also highlighted. The present study advances the knowledge of how perfume companies and e-tailors can take the advantage of implementing sensory cues to facilitate the online purchase of a typical experience product. Copyright © 2023 (Barbosa, Oliveira, Chkoniya, Mahdavi).