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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.

Abstract
This study delves into the critical role of third-party applications in enhancing motorcycle interface design, particularly focusing on their potential to revolutionize the rider experience through innovative UI/UX designs. As motorcycles increasingly serve as primary modes of transportation in urban environments, safety concerns escalate, needing effective solutions. The research scrutinizes the main safety challenges faced by motorcycle riders, the influence of thirdparty applications on safety outcomes, and the advantages these applications offer over traditional dashboards. In this context, it is presented a Systematic Literature Review (SLR) methodology. Specifically, articles were selected based on predefined inclusion and exclusion criteria. Results indicate that third-party applications provide customizable solutions that not only offer real-time information about the motorcycle's condition but also mitigate safety risks by minimizing distractions. However, the literature reveals a lack of studies specifically addressing these applications, underscoring their potential as pioneering initiatives to reinforce motorcycle safety.

Abstract
This case study aims to analyze the impact of the digital transformation on Delta Cafés. To this end, a consumer survey was carried out to understand their perception of the brand, from the products to how the company works and its actions. The sample consisted of 140 participants. An attempt was also made to understand whether the participants’ perceptions were independent of whether or not they were Delta Cafés customers. The chi-square test results indicate that Delta Cafés customers are more aware of the brand, both because of the coffee and because of its founder, are more aware of the social responsibility actions carried out by the company and are more involved with organizations that positively impact society. Finally, an interview was conducted with Delta's Systems Director, who has been with the company for 12 years and is currently responsible for 54 employees out of a total of around 3500 employees. The interview lasted approximately 1 h and 15 min, via Zoom. The big challenge is to automatically manage all the processes associated with finance, logistics and production. Delta is looking to digitize the whole organization, which will free people from superfluous tasks and allow them to perform others that matter more. Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) and Electronic Data Interchange (EDI) have all been implemented, as well as e-commerce and m-commerce (Delta Mobile). Dehumanization and loss of human capital are a risk of digitization which Delta Cafés seeks to avoid through training—one of the pillars of the organization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract