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.

Abstract
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).

Abstract
Artificial Intelligence has been introduced in many applications, namely in artificial vision-based systems with object detection tasks. This paper presents an object localization system with a motivation to use it in autonomous mobile robots at robotics competitions. The system aims to allow robots to accomplish their tasks more efficiently. Object detection is performed using a camera and artificial intelligence based on the YOLOv4 Tiny detection model. An algorithm was developed that uses the data from the system to estimate the parameters of location, distance, and orientation based on the pinhole camera model and trigonometric modelling. It can be used in smart identification procedures of objects. Practical tests and results are presented, constantly locating the objects and with errors between 0.16 and 3.8 cm, concluding that the object localization system is adequate for autonomous mobile robots. © 2023 IEEE.

Abstract