Abstract
The rapid advancement of warehouse automation has increased the need for intelligent intralogistics solutions that enhance material handling efficiency and optimize space utilization. This research presents a simulation-based methodology that integrates Autonomous Mobile Robots (AMRs) with container loading optimization in a unified decision-support framework that dynamically synchronizes AMR routing with optimized truckload configurations, a feature not commonly addressed jointly in existing literature to improve warehouse operations. By leveraging a hybrid approach combining discrete event and agent-based simulation in FlexSim, the study evaluates the impact of AMR fleet size, routing strategies, and truckload configurations on overall logistics performance. A proof-of-concept industrial case study illustrates how different scenarios influence key performance metrics, such as total operation time and resource utilization. The findings demonstrate that synchronized AMR deployment and optimized container loading strategies contribute to increased throughput, reduced handling time, and enhanced logistics unit utilization. This work provides a framework for dynamic logistics planning, offering valuable insights for companies seeking to enhance warehouse efficiency and sustainability through simulation-driven decision support. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
The adoption of Autonomous Mobile Robots (AMRs) has emerged as a promising solution to enhance efficiency and reduce operational costs for industrial companies. Given the significant cost of AMRs, it is crucial to determine the optimal number and characteristics before making significant investments. This study proposes a decision-support framework based on simulation to assess the impact of integrating AMR robots in a complex distribution center. Additionally, this framework aids decision-makers in determining the optimal fleet size of AMR robots and corresponding charging stations. A simulation model was developed using data from a leading retail company, focusing on pallet movement within the facility, comparing scenarios combining AMRs with other intralogistics implementations. This methodology incorporates uncertainty, variability (statistical distributions to create transportation orders, acceleration, demand and offer fluctuations) and implements fleet management, transportation capacity, demand matching, and resource utilization according to real case scenarios. The proposed model replicates accurate robot coordination and actual deployment environments, ensuring that the tested scenarios approximate the real-world conditions as much as possible. Preliminary findings show results supporting the decision-making for a fleet size to meet weekly production targets, optimize robot utilization, and coordinate charging instances to prevent production stops. Conclusions suggest that the proposed simulation approach is an effective tool for planning and implementing logistics solutions, enabling users to make informed decisions before investing.

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 application of Artificial Intelligence (AI) in the energy sector offers new opportunities for developing flexible, efficient, and sustainable infrastructures. Nevertheless, real-world deployment is still constrained by the lack of large-scale, integrated environments that can evaluate advanced algorithms under realistic operating conditions while ensuring regulatory compliance. This paper presents EnerTEF (which stands for Energy Testing and Experimentation Facility), a federated platform for testing and experimentation in the energy sector designed to address this gap. We introduce a unified TEF architecture that enables full-stack evaluation of intelligent systems, including predictive modeling, optimization, learning under data distribution shifts and federated learning across geographically distributed sites. The framework integrates high-fidelity digital twins, a privacy-preserving data exchange framework and regulatory sandboxing to support transparent, explainable and robust AI development. EnerTEF demonstrates how such a framework can be deployed in critical energy domains through three real-world scenarios including short-term hydropower generation forecasting, coordination between distribution network operators and distributed energy resources and real-time optimization of self-consumption for municipal buildings. Results show that EnerTEF effectively enables the development of novel AI models, improves cross-context generalizability and supports innovation for complex energy infrastructures, ultimately creating a practical, scalable path for addressing different energy-related problems and heterogeneous data.

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 policy-makers 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.