Abstract
essential for achieving the Sustainable Development Goals (SDGs), particularly in regions aiming to balance energy efficiency, waste management, and urban development. This study explores the application of multicriteria decision-making and statistical techniques to evaluate municipal sustainability, with a focus on renewable energy, using the Alto Minho region of Portugal as a case study. The analysis incorporates 12 SDG indicators across ten municipalities, addressing energy consumption, urban renewal, and waste management. Cluster analysis revealed distinct groups of municipalities, highlighting disparities in sustainability performance. Municipalities such as Melgaco and Moncao excelled in energy-related metrics, while others showed strengths in waste management and urban renewal. The Analytic Hierarchy Process (AHP) emphasized the importance of renewable energy indicators, revealing notable changes in rankings when energy-related criteria were prioritized. Ponte de Lima and Melgaco ranked highest under energy-focused weighting schemes, showcasing their leadership in energy efficiency and renewable adoption. The findings underscore the need for targeted policies to enhance sustainability across municipalities, particularly in regions lagging in energy performance.

Abstract
The study aimed to explore how communication can influence young individuals to become blood donors. It sought to answer a key question: how do communication strategies impact the recruitment of donors within this age group? The research was structured around four primary objectives. First, it evaluated young people’s knowledge about blood donation through a content analysis of 14 campaigns. Second, it examined the communication strategies implemented by the Portuguese Institute of Blood and Transplantation (IPST) via an exploratory interview with an expert from the organization. Third, it investigated the motivations and barriers affecting young people’s willingness to donate, using a survey conducted with 390 participants, which revealed that more than half of respondents were not blood donors. Finally, it identified the most effective communication strategies and actions to promote blood donation. The findings suggest that future campaigns should prioritize precise segmentation based on behavioral criteria and adopt integrated marketing communication more broadly. This approach is expected to enhance the effectiveness of initiatives aimed at increasing donor recruitment among young people.

Abstract
This paper aims to examine how Portugal and Brazil leveraged digital marketing to redefine their country brands during and after the COVID-19 pandemic. By focusing on the application of innovative digital strategies in tourism and culture, the research highlights the transformative potential of digital tools in overcoming pandemic-related challenges. Specifically, the study identifies key approaches such as the use of social media, data analytics, virtual reality, and influencer marketing that were strategically employed to maintain global engagement, foster international visibility, and support economic recovery. The results demonstrate that integrating digital marketing into country branding strategies not only sustained international recognition but also accelerated the adoption of sustainable tourism practices. By analyzing the cases of Portugal and Brazil, this paper provides actionable insights for policymakers and practitioners seeking to align tourism growth with global sustainability goals. These findings underscore the critical importance of digital transformation in enhancing the resilience and competitiveness of the tourism sector in a post-pandemic world.

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.

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.