Abstract
The sustainable utilization of forest biomass for bioenergy production is increasingly challenged by the variability and unpredictability of raw material availability. These challenges are particularly critical in regions like Central Portugal, where seasonality, dispersed resources, and wildfire prevention policies disrupt procurement planning. This study investigates two flexibility strategies-dynamic network reconfiguration and operations postponement-as policy relevant tools to enhance resilience in forest-to-bioenergy supply chains. A novel mathematical model, the mobile Facility Location Problem with dynamic Operations Assignment (mFLP-dOA), is proposed and solved using a scalable matheuristic approach. Applying the model to a real case study, we demonstrate that incorporating temporary intermediate nodes and adaptable processing schedules can reduce costs by up to 17% while improving operational responsiveness and reducing non-productive machine time. The findings offer strategic insights for policymakers, biomass operators, and regional planners aiming to design more adaptive and cost-effective biomass supply systems, particularly under environmental risk scenarios such as summer operation bans. This work supports evidence-based planning and investment in flexible logistics infrastructure for cleaner and more resilient bioenergy supply chains.

Abstract
Renewable energy resources are crucial for addressing global economic and environmental challenges. Energy communities, which unite consumers to pursue shared energy goals, present a promising solution for reducing energy costs and enhancing sustainability. This study analyzes the optimal sizing and operation of energy community resources, formulating the problem as mixed-integer linear programming (MILP) models. Two tools are employed: one for daily operation, calculating energy setpoints for community assets such as battery energy storage systems (BESS) and electric vehicles (EVs), and another for sizing photovoltaic (PV) panels and BESS capacities to minimize costs while optimizing local energy trades. Due to the high computational demands of MILP, three optimization methods are compared: deterministic, hybrid particle swarm optimization (PSO), and evolutionary PSO (EPSO). The hybrid PSO method handles binary and continuous variables efficiently, while EPSO introduces diversity to improve solution quality in complex scenarios. These metaheuristic approaches address the trade-off between solution accuracy and computational effort, providing reliable tools for decision-makers in energy communities.

Abstract
This workshop proposal is the third edition of a workshop which has been organised at EICS 2023 and EICS 2024. This edition aims to bring together researchers and practitioners interested in the engineering of interactive systems that embed AI technologies (as for instance, AI-based recommender systems) or that use AI during the engineering lifecycle. The overall objective is to identify (from experience reported by participants) methods, techniques, and tools to support the use and inclusion of AI technologies throughout the engineering lifecycle for interactive systems. A specific focus is on guaranteeing that user-relevant properties such as usability and user experience are accounted for. Contributions are also expected to address system-related properties, including resilience, dependability, reliability, or performance. Another focus is on the identification and definition of software architectures supporting this integration.

Abstract
Effective selection of forecasting algorithms for time series data is a challenge in machine learning, impacting both predictive accuracy and efficiency. Metalearning, using features extracted from time series, offers a strategic approach to optimize algorithm selection. The utility of this approach depends on the amount of information the features contain about the behavior of the algorithms. Although there are several methods for systematic time series feature extraction, they have never been compared. This paper empirically analyzes the performance of each feature extraction method for algorithm selection and its impact on forecasting accuracy. Our study reveals that TSFRESH, TSFEATURES, and TSFEL exhibit comparable performance at algorithm selection accuracy, adeptly capturing time series characteristics essential for accurate algorithm selection. In contrast, Catch22 is found to be less effective for this purpose. In particular, TSFEL is identified as the most efficient method, balancing dimensionality and predictive performance. These findings provide insights for enhancing forecasting accuracy and efficiency through judicious selection of meta-feature extractors. © 2025 Elsevier B.V., All rights reserved.

Abstract
Conflict-free Replicated Data Types (CRDTs) allow optimistic replication in a principled way. Different replicas can proceed independently, being available even under network partitions and always converging deterministically: Replicas that have received the same updates will have equivalent state, even if received in different orders. After a historical tour of the evolution from sequential data types to CRDTs, we present in detail the two main approaches to CRDTs, operation-based and state-based, including two important variations, the pure operation-based and the delta-state based. Intended for prospective CRDT researchers and designers, this article provides solid coverage of the essential concepts, clarifying some misconceptions that frequently occur, but also presents some novel insights gained from considerable experience in designing both specific CRDTs and approaches to CRDTs.

Abstract
This article focuses on the creation and evaluation of synthetic data to address the challenges of imbalanced datasets in machine learning (ML) applications, using fake news detection as a case study. We conducted a thorough literature review on generative adversarial networks (GANs) for tabular data, synthetic data generation methods, and synthetic data quality assessment. By augmenting a public news dataset with synthetic data generated by different GAN architectures, we demonstrate the potential of synthetic data to improve ML models' performance in fake news detection. Our results show a significant improvement in classification performance, especially in the underrepresented class. We also modify and extend a data usage approach to evaluate the quality of synthetic data and investigate the relationship between synthetic data quality and data augmentation performance in classification tasks. We found a positive correlation between synthetic data quality and performance in the underrepresented class, highlighting the importance of high-quality synthetic data for effective data augmentation.