Abstract
This study explores the context of blood donation in Portugal, specifically aiming to understand how communication strategies can effectively recruit young blood donors aged 18 to 24. The research addresses the following question: What is the impact of communication efforts on the recruitment of young blood donors in Portugal? To answer this question, four specific objectives were set: (1) To evaluate the level of awareness among young individuals in this age group regarding blood donation; (2) to analyze and assess the communication strategies employed by the Portuguese Institute of Blood and Transplantation (IPST) to promote blood donation; (3) to investigate the motivations and barriers related to blood donation; and (4) to identify effective communication strategies for encouraging blood donation. To achieve the first objective, which is the primary focus of this article, a content analysis of 14 blood donation campaigns was conducted. For the second objective, an exploratory interview was held with a specialist from the IPST. The third objective is being addressed through a survey involving 390 young individuals, which has already been administered and revealed that over half of the respondents are not blood donors. The findings suggest that future campaigns should adopt more targeted segmentation strategies based on behavioral criteria and make greater use of integrated marketing communication to enhance effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract
Automated assessment tools for programming assignments have become increasingly popular in computing education. These tools offer a cost-effective and highly available way to provide timely and consistent feedback to students. However, when evaluating a logically incorrect source code, there are some reasonable concerns about the formative gap in the feedback generated by such tools compared to that of human teaching assistants. A teaching assistant either pinpoints logical errors, describes how the program fails to perform the proposed task, or suggests possible ways to fix mistakes without revealing the correct code. On the other hand, automated assessment tools typically return a measure of the program's correctness, possibly backed by failing test cases and, only in a few cases, fixes to the program. In this paper, we introduce a tool, AsanasAssist, to generate formative feedback messages to students to repair functionality mistakes in the submitted source code based on the most similar algorithmic strategy solution. These suggestions are delivered with incremental levels of detail according to the student's needs, from identifying the block containing the error to displaying the correct source code. Furthermore, we evaluate how well the automatically generated messages provided by AsanasAssist match those provided by a human teaching assistant. The results demonstrate that the tool achieves feedback comparable to that of a human grader while being able to provide it just in time.

Abstract
In an increasingly competitive tourism landscape, digital marketing strategies must adapt to engage modern travelers effectively. This paper investigates the role of gamification in enhancing the competitiveness of tourist destinations, focusing specifically on Braga, Portugal. The study aims to evaluate how gamification can influence destination attractiveness, enhance tourist experiences, and inform decision-making processes. The study used a mixed-methods approach, combining qualitative and quantitative analyses. The qualitative component consisted of semi-structured interviews with key tourism stakeholders in Braga, revealing insights into the benefits and challenges of implementing gamification strategies. The quantitative analysis involved surveys conducted with tourists, assessing their perceptions of gamified elements and their influence on travel decisions. Findings indicate that tourists perceive gamification positively, particularly regarding features such as achievements, storytelling, and point systems, which significantly affect their travel choices. Stakeholders recognize the potential of gamification to boost tourist engagement and satisfaction, while also emphasizing the need to address implementation challenges. The study concludes that gamification can enhance the attractiveness and competitiveness of tourist destinations, though its success depends on strategic planning, resource allocation, and collaboration among stakeholders. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract

Abstract
Non-formal education seeks to address the limitations of formal education that do not reach all communities and do not provide all new competencies and capabilities that are essential for the integrated development of communities. The role of non-formal education becomes even more relevant in the context of developing countries where significant asymmetries in access to education emerge. This study adopts the Solutions Story Tracker provided by the Solutions Journalism Network to identify and explore solutions based on journalism stories in the non-formal education field. A total of 256 stories are identified and categorized into 14 dimensions. The findings reveal that practical, participatory, and volunteering dimensions are the three most common dimensions in these non-formal education initiatives. Furthermore, two emerging dimensions related to empowerment and sustainability are identified, allowing us to extend the theoretical knowledge in the non-formal education field. These conclusions are relevant for establishing public policies that can involve greater participation by local communities in non-formal education and for addressing sustainability challenges through bottom-up initiatives.

Abstract
Yield forecasting is of immeasurable value in modern viticulture to optimize harvest scheduling and quality management. The number of inflorescences and flowers per vine is one of the main components and their assessment serves as an early predictor, which can explain up to 85-90% of yield variability. This study introduces a sophisticated framework that integrates the benchmark of different advanced deep learning and classic image processing to automate the segmentation of grapevine inflorescences and the detection of single flowers, to achieve precise, early, and non-invasive yield predictions in viticulture. The YOLOv8n model achieved superior performance in localizing inflorescences ( F1-Score (Box) = 95.9%) and detecting individual flowers (F1-Score = 91.4%), while the YOLOv5n model excelled in the segmentation task ( F1-Score (Mask) = 98.6%). The models demonstrated a strong correlation (R-2 > 90.0%) between detected and visible flowers in inflorescences. A statistical analysis confirmed the robustness of the framework, with the YOLOv8 model once again standing out, showing no significant differences in error rates across diverse grapevine morphologies and varieties, ensuring wide applicability. The results demonstrate that these models can significantly improve the accuracy of early yield predictions, offering a noninvasive, scalable solution for Precision Viticulture. The findings underscore the potential for Computer Vision technology to enhance vineyard management practices, leading to better resource allocation and improved crop quality.