Abstract
Non-adherence to medication is a pervasive issue worldwide, affecting 50% of prescription users, resulting in suboptimal therapy outcomes and premature death. One of the key factors contributing to non-adherence is the complexity associated with managing medication regimens. To address this challenge, an automated pill dispenser “SelfMed, your medication partner” has been proposed. This study focuses on studying determinants of medication non-adherence, its ramifications and alternatives available in the market in order to increase medication adherence among adults aged 60 and over. The overarching goal of this research is to evaluate whether digital solutions like SelfMed are required for addressing non-compliance issues in Portugal while assessing their effectiveness over time for our target audience within the marketplace. The research was conducted using primary data collected through a questionnaire distributed to users and care institutions/companies in Portugal (Northern area). According to the analysis, 40% of users and 40% of care companies are interested in obtaining SelfMed to simplify the complex medication management and prescription regimen for the end user. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract
The aim of this study was to conduct a qualitative analysis research that will support the internal and external analysis, as well as the strategic options to be developed for a Marketing Plan for the Polytechnic Institute of Viana do Castelo. The methodology used was through exploratory interviews using a script based on nine key dimensions for creating a marketing plan. The script was applied through face-to-face interviews with the president of the Polytechnic Institute of Viana do Castelo. We obtained six interviews that could be analyzed in the nine dimensions. It was possible to understand that the elements suggest some changes in the different dimensions of the script. One of the points with the most observations for improvement was communication, mostly internal. However, all the improvements and growth that the institute has achieved over the years were acknowledged. It was also possible to realize that the six people interviewed have the same perspective and make similar observations on the different questions asked. With the interviews, it will be possible to outline the best structure and path for the strategic marketing plan. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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
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
This study examines the social factors influencing the acceptance of autonomous buses, with a focus on per-ceived benefits, safety, and comfort. It also explores whether these factors differ among residents of cities with varying sizes and urban mobility solutions. A survey was conducted in three Polish cities, collecting data from 1,160 respondents. Structural Equation Modelling (SEM) was used to analyse relationships between perceived benefits, safety, comfort, and future intentions to use autonomous buses. Results indicate that safety and comfort positively influence future intentions to use autonomous buses. However, the effect of perceived benefits varies across cities, suggesting that urban mobility conditions shape public acceptance. The study focuses on Polish cities, which may limit generalizability. Future research should examine other geo-graphical contexts. Findings provide insights for policymakers and manufacturers on enhancing public trust and promoting autonomous bus adoption. Improving public awareness and addressing safety concerns may increase societal acceptance of autonomous mobility. The study uniquely assesses how city characteristics influence social acceptance of autonomous buses.

Abstract
The increasing global burden of lung diseases necessitates the development of improved diagnostic tools. According to the WHO, hundreds of millions of individuals worldwide are currently affected by various forms of lung disease. The rapid advancement of artificial neural networks has revolutionized lung disease diagnosis, enabling the development of highly effective detection and classification systems. This article presents dual channel neural networks in image feature extraction based on classical CNN and vision transformers for multi-label lung disease diagnosis. Two separate subnetworks are employed to capture both global and local feature representations, thereby facilitating the extraction of more informative and discriminative image features. The global network analyzes all-organ regions, while the local network simultaneously focuses on multiple single-organ regions. We then apply a novel feature fusion operation, leveraging a multi-head attention mechanism to weight global features according to the significance of localized features. Through this multi-channel approach, the framework is designed to identify complicated and subtle features within images, which often go unnoticed by the human eye. Evaluation on the ChestX-ray14 benchmark dataset demonstrates that our hybrid model consistently outperforms established state-of-the-art architectures, including ResNet-50, DenseNet-121, and CheXNet, by achieving significantly higher AUC scores across multiple thoracic disease classification tasks. By incorporating test-time augmentation, the model achieved an average accuracy of 95.7% and a specificity of 99%. The experimental findings indicated that our model attained an average testing AUC of 87%. In addition, our method tackles a more practical clinical problem, and preliminary results suggest its feasibility and effectiveness. It could assist clinicians in making timely decisions about lung diseases.