Abstract
Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another concern lies in their need for transparency. Consequently, this paper addresses those problems by proposing an online solution for identifying and explaining spam reviews, incorporating data drift adaptation. It integrates (i) incremental profiling, (ii) data drift detection & adaptation, and (iii) identification of spam reviews employing Machine Learning. The explainable mechanism displays a visual and textual prediction explanation in a dashboard. The best results obtained reached up to 87% spam F-measure.

Abstract
Collaborative robots, or cobots, are increasingly used by manufacturing companies to meet the demands for greater flexibility and to adapt to the trend of mass customisation in production. When considering the adoption of cobots, companies enter a critical decision-making phase. This study aims to identify the relevant decision factors for adopting collaborative robots (cobots) in manufacturing medium-sized enterprises (SMEs) in Portugal, using a combined framework of Technology-Organisation-Environment (TOE), Diffusion of Innovations (DOI) theory, and Institutional Theory. Data was collected through an online survey distributed to Portuguese manufacturing companies, yielding 78 valid responses. Analysis conducted using SmartPLS 4 revealed that top management support, resource availability, and industry pressure significantly influence the adoption decision. However, factors such as the relative advantage of cobots, compatibility with existing processes, organisational innovativeness, human resources quality, and external support did not significantly impact SMEs' adoption of cobots. These findings enhance the understanding of technology management, specifically the process of adopting cobots in manufacturing. The insights from this study help managers focus on the key factors critical for successful cobot adoption, supporting decision-makers in making more informed choices.

Abstract
The circular economy is increasingly being considered as a potential model to replace the prevailing end-of-life approach by establishing a closed-loop flow. The importance of different supply chain (SC) actors in this process has been recognized as a critical aspect of the development of sustainable production-consumption models. Consumers play a crucial role in this context, as they have a dual function: ensuring the correct disposal of used products; and consuming products from circular sources. However, the different roles consumers play (refuse, rethink, reduce, reuse, repair, refurbish, remanufacture, repurpose, recycle, recover) in circular SCs are still unclear. Through a systematic literature review, this paper aims to contribute to a better understanding of the influence of consumers on the adoption of circular supply chain (CSC) practices and to identify the main drivers and barriers regarding the adoption of circular practices. The results demonstrate that the topic is recent and has gained ground in the literature. An in-depth qualitative analysis was carried out with the 74 papers identified and shows that the most commonly addressed R-strategies are reuse, recycle and repair. The main motivations and challenges towards a greater adoption of circular practices are related to (or lack of) environmental beliefs and financial benefits.

Abstract

Abstract
The Regulation (EU) 2019/6 establishes that the veterinary prescriptions should follow a cascade, according to their availability of the market. In sum, the veterinarian is authorized to use a medicine for human use only if there is no product available for the same or other therapeutic indication, in the same or another animal species. This study aims to analyse the application of Regulation (EU) 2019/6 in the pharmacological prescription at the Veterinary Hospital of the University of León. A total of 121 clinical cases, 89 dogs (73.55%) and 32 cats (26.45%) were included. Results revealed that 95 medicines were prescribed, 51 (53.68 %) as veterinary medicines and 44 (46.32 %) as human medicines. From the human medicines, 22 (50.00%) did not have a veterinary alternative in the market; four (9.00%) presented a veterinary medicine in the appropriate formulation for the species; 10 (23.00%) had no alternative in the desired formulation; and 8 (18.00%) had no alternatives for the target species. This study suggested that the cascade was not strictly followed, and several reasons may justify it, such as the lack of veterinary products, different formulations, and differences in costs. An effective, safe and sustainable use of the therapeutic option available can only be accomplished with a rational use of the prescription cascade and a correct use of the Regulation (EU) 2019/6. © 2024 State University of Santa Catarina. All rights reserved.

Abstract
Breast cancer, the most prevalent cancer among women, poses a significant healthcare challenge, demanding effective early detection for optimal treatment outcomes. Mammography, the gold standard for breast cancer detection, employs low-dose X-rays to reveal tissue details, particularly cancerous masses and calcium deposits. This work focuses on evaluating the impact of incorporating anatomical knowledge to improve the performance and robustness of a breast cancer classification model. In order to achieve this, a methodology was devised to generate anatomical pseudo-labels, simulating plausible anatomical variations in cancer masses. These variations, encompassing changes in mass size and intensity, closely reflect concepts from the BI-RADs scale. Besides anatomical-based augmentation, we propose a novel loss term promoting the learning of cancer grading by our model. Experiments were conducted on publicly available datasets simulating both in-distribution and out-of-distribution scenarios to thoroughly assess the model's performance under various conditions.