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
This study explores prompt engineering for automated white-box integration testing of RESTful APIs using Large Language Models (LLMs). Four versions of prompts were designed and tested across three OpenAI models (GPT-3.5 Turbo, GPT-4 Turbo, and GPT-4o) to assess their impact on code coverage, token consumption, execution time, and financial cost. The results indicate that different prompt versions, especially with more advanced models, achieved up to 90% coverage, although at higher costs. Additionally, combining test sets from different models increased coverage, reaching 96% in some cases. We also compared the results with EvoMaster, a specialized tool for generating tests for REST APIs, where LLM-generated tests achieved comparable or higher coverage in the benchmark projects. Despite higher execution costs, LLMs demonstrated superior adaptability and flexibility in test generation.

Abstract

Abstract
Recent verification tools aim to make formal verification more accessible for software engineers by automating most of the verification process. However, the manual work and expertise required to write verification helper code, such as loop invariants and auxiliary lemmas and assertions, remains a barrier. This paper explores the use of Large Language Models (LLMs) to automate the generation of loop invariants for programs in Dafny. We tested the approach on a curated dataset of 100 programs in Dafny involving arrays, strings, and numeric types. Using a multimodel approach that combines GPT-4o and Claude 3.5 Sonnet, correct loop invariants (passing the Dafny verifier) were generated at the first attempt for 92% of the programs, and in at most five attempts for 95% of the programs. Additionally, we developed an extension to the Dafny plugin for Visual Studio Code to incorporate automatic loop invariant generation into the IDE. Our work stands out from related approaches by handling a broader class of problems and offering IDE integration.