Abstract
This study aimed to analyze the association of leadership with turnover intentions and whether this relationship is mediated by employee well-being. The sample consists of approximately 306 individuals working in organizations based in Portugal. The results indicate that transformational leadership has a positive and significant association with turnover intentions, while the relationship between transactional leadership and turnover intentions is negative and significant. Both transformational leadership and transactional leadership have a positive and significant association with well-being. Well-being has a negative and significant association with turnover intentions. Well-being only has a mediating effect on the relationship between transactional leadership and turnover intentions. This study contributes to the advancement of academic research and knowledge about the mechanisms through which transformational and transactional leadership styles can influence employees' turnover intentions, as well as providing empirical evidence on the mediating role of psychological well-being. In addition, practical insights are offered to organizational leaders and managers on adopting practices that foster psychological well-being in the workplace, thereby reducing employee turnover intentions.

Abstract
Abandoned, Lost, or otherwise Discarded Fishing Gear (ALDFG) has a great impact on marine ecosystems. This is not only due to the direct contribution to marine litter production with particular emphasis on plastics but also to the effects of ghost fishing.The Nettag+ project aims to reduce these impacts by acting on three main lines of action: prevention, avoidance, and mitigation. In the first line, direct action and collaboration with fishers and nature protection organizations around Europe aim to establish the fishermen community as guardians of the ocean. These actions with active fishers' collaboration range from training and dissemination activities related to marine litter and ocean protection to direct measures in day-to-day work to minimize and recover litter from the sea.In the prevention line, an acoustic tag designed explicitly for the location of ALDFG was developed in collaboration with research institutions and fishing gear manufacturers. It can be integrated into the fishing equipment for future tracking and recovery. This tool can reduce lost fishing gear retrieval costs and is complemented with robotic solutions to support retrieving operations.To mitigate the effects of existing untagged ALDFG, multisensorial  detection algorithms are being developed to detect and map ALDFG on the sea and to take advantage of autonomous and robotic systems to perform this task.

Abstract
The present critical literature review describes the state-of-the-art innovative proximal (ground-based) solutions for plant disease diagnosis, suitable for promoting more precise and efficient phytosanitary measures. Research and development of new sensors for this purpose are currently a challenge. Present procedures and diagnosis techniques depend on visual characteristics and symptoms to be initiated and applied, compromising an early intervention. Also, these methods were designed to confirm the presence of pathogens, which did not have the required high throughput and speed to support real-time agronomic decisions in field extensions. Proximal sensor-based systems are a reasonable tool for an efficient and economic disease assessment. This work focused on identifying the application of optical and spectroscopic sensors as a tool for disease diagnosis. Biophoton emission, fluorescence spectroscopy, laser-induced breakdown spectroscopy, multi- and hyperspectral spectroscopy (HS), nuclear magnetic resonance spectroscopy, Raman spectroscopy, RGB imaging, thermography, volatile organic compounds assessment, and X-ray fluorescence were described due to their relevant potential. Nevertheless, some techniques revealed a low technology readiness level (TRL). The main conclusions identify HS, single and multi-spatial point observation, as the most applied methods for early plant disease diagnosis studies (88%), combined with distinct feature selection (FeS), dimensionality reduction (DR), and modeling techniques. Vegetation indices (28%) and principal component analysis (19%) were the most popular FeS and DR approaches, highlighting the most relevant wavelengths contributing to disease diagnosis. In modeling, classification was the most applied technique (80%), used mainly for binary and multi-class health status identification. Regression was used in the remaining (21%) scientific works screened. The data was collected primarily in laboratory conditions (62%), and a few works were performed in field conditions (21%). Regarding the study's etiological agent responsible for causing the disease, fungi (53%) and viruses (23%) were the most analyzed group of pathogens found in the literature. Overall, proximal sensors are suitable for early plant disease diagnosis before and after symptom appearance, presenting classification accuracies mostly superior to 71% and regression coefficients superior to 61%. Nevertheless, additional research regarding the study of specific host-pathogen interactions is necessary.

Abstract
This paper explores the potential of the educational gamification platform known as SCORE as a novel solution to address challenges related to student disengagement and the increasing preference for gaming. Faced with observed disinterest among first-year Computer Engineering students, particularly intensified during the Covid-19 era, the study advocates for integrating the educational gamification platform to create a dynamic and engaging learning environment. SCORE is presented as an innovative alternative to conventional teaching methods, fostering deeper understanding and motivation among students. Positioned to catalyze holistic student development, encompassing critical thinking and problem-solving skills, SCORE emerges as a leading player in the evolving landscape of educational gamification. The document provides a comprehensive overview of the motivating factors for this investigation, laying the groundwork for a detailed analysis of SCORE and the role of educational games in effective teaching methods. Anticipated outcomes encompass enriched pedagogical practices and a solid foundation for future research endeavors. Positioned as one among many alternatives, SCORE contributes to the ongoing discourse on innovative teaching methods, offering valuable insights for educators and researchers exploring ways to enhance the learning experience. With the evolution of technology, SCORE, alongside other educational games, aims to take a significant step forward in academic terms, enabling students to achieve the best possible results while remaining motivated in their academic journey. © 2025 Elsevier B.V., All rights reserved.

Abstract
Purpose - The objective of this study is to integrate various theories of identity within entrepreneurship and derive insights and propositions that enhance the understanding of how an incubation program influences the formation and development of entrepreneurial team identity. Design/methodology/approach - This study adopts a qualitative multiple case study design to explore how entrepreneurial team identity develops within ventures incubated at CEU iLab. The analysis is based primarily on interviews with individual entrepreneurs from five selected ventures, complemented by secondary data to enrich and contextualize the findings. Findings - The findings revealed the interconnections between entrepreneurial team formation processes, social interactions, networking, entrepreneurial team stability, feedback mechanisms, team dynamics and intrateam trust and legitimacy. Moreover, the cultivation of a culture defined by trust, open communication and the active integration of feedback mechanisms played a pivotal role in the creation of collaborative team environments. Furthermore, the process of building an entrepreneurial team is heavily reliant on shared vision, common values, complementary skill sets, intrateam trust and pre-existing relationships. Originality/value - This study addresses a notable gap in the existing literature by studying how entrepreneurial teams and individual entrepreneurial team members manage to form and develop their entrepreneurial identity. By focusing on the dynamic processes behind identity formation within teams, this research provides novel insights into the motivations that drive individuals and teams to engage in entrepreneurial activities. This focus on the interplay between identity and team processes represents a distinctive and timely addition to the field.

Abstract
The implementation of deep learning approaches based on instance segmentation data remains a challenge for customized scenarios, owing to the time-consuming nature of acquiring and annotating real-world instance segmentation data, which requires a significant investment of semi-professional user labour. Obtaining high-quality labelled data demands expertise and meticulous attention to detail. This requirement can significantly impact the overall implementation process, adding to the complexity and resource requirements of customized scenarios with diverse objects. The proposed work addresses the challenge of generating labelled data for large-scale robotic bin-picking datasets by proposing an easy-to-use automated framework designed to create customized data with accurate labels from CAD models. The framework leverages a photorealistic rendering engine integrated with physics simulation, minimizing the gap between synthetic and real-world data. Models trained using the synthetic data generated by this framework achieved an Average Precision of 86.95%, comparable to the performance of models trained on real-world datasets. Furthermore, this paper provides a comprehensive multi-metric analysis across diverse objects representing distinct industrial applications, including naval, logistics, and aerospace domains. The evaluation also includes the use of three distinct instance segmentation networks, alongside a comparative analysis of the proposed approach against two generative model techniques.