Abstract
Many of today’s domains of application of Machine Learning (ML) are dynamic in the sense that data and their patterns change over time. This has a significant impact in the ML lifecycle and operations, requiring frequent model (re-)training, or other strategies to deal with outdated models and data. This need for dynamic and responsive solutions also has an impact on the use of computational resources and, consequently, on sustainability indicators. This paper proposes an approach in line with the concept of Frugal AI, whose main aim is to minimize the resources and time spent on training models by re-using models from a pool of past models, when appropriate. Specifically, we present and validate a methodology for similarity-based model selection in data streaming environments with concept drift. Rather than training a new model for each new block of data, this methodology considers a pool with only a subset of the models and, for each new block of data, will select the best model from the pool. The best model is determined based on the distance between its training data and the current block of data. Distance is calculated based on a set of meta-features that characterizes the data, and on the Bray-Curtis distance. We show that it is possible to reuse previous models using this methodology, leading to potentially significant saving of resources and time, while maintaining predictive quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
This paper advocates for Digital Twin (DT) technology as a pivotal solution to address the complexities of Complex Operations Environments (COEs). Recognizing the need for a thorough understanding of COEs and their DTs, a methodology is introduced to bridge existing gaps. Given the lack of a universal definition, the approach leverages the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Latent Dirichlet Allocation (LDA) to extract insights, facilitating the development of a comprehensive definition for COE and DT. The methodology integrates Ontology and Systems Modelling Language (SysML) to provide a semantic and conceptual model of COE and DT. Ontology enriches the semantic understanding, exploring existence and entity relationships, while SysML ensures clear and concise communication through standardized graphical representation. This paper aims to present a methodology to achieve a precise understanding of COEs and their corresponding DTs, providing a robust foundation for addressing operational complexities in dynamic environments.

Abstract
The footwear industry is energy-intensive and, consequently, a source of large amounts of greenhouse gas emissions every year. Issues related to climate change and growing conflicts on a global scale that impact the prices of raw materials and energy prices have led companies in the footwear industry to take actions to mitigate these impacts. Among these actions is the growing focus on producing its energy from energy systems based on renewable sources and battery energy storage units. This paper addresses the energy-efficient manufacturing scheduling in footwear industries with onsite energy production from a photovoltaic system with batteries. The problem is formulated as a mixed integer linear programming problem. Different objectives are presented, depending on the priorities of the entity that owns the footwear factory, namely, minimizing operation costs, minimizing CO2 emissions, or both. The case study is footwear factory located in Portugal that uses a manufacturing process based on injection molding. The results show the effectiveness of the proposed approach, with active demand side management playing a fundamental role in shifting periods of higher energy consumption to periods of lower prices or lower CO2 emissions. Also, Pareto fronts are depicted to make the trade-off between CO2 emissions and operation costs. As expected, the reduction of CO2 emissions promotes an increase on operation costs. Furthermore, a sensitivity analysis is carried out on the increase in photovoltaic capacity and battery capacity. The results show that increasing photovoltaic and battery capacity promotes reductions in costs up to 30% and in the emissions up to 37%.

Abstract
Lean and green (L&G) manufacturing in Industry 4.0 (I4.0) has brought many advantages in manufacturing industries by minimizing waste and maximizing efficiency with integration of renewable energy sources and sustainable materials. Multi-layer Stream Mapping (MSM) is a new framework for the performance assessment of complex manufacturing processes. MSM is used for multi-domain analysis of manufacturing processes to assess resources, and processes, that are used to identify Non-ValueAdded (NVA) procedures or steps that consume unnecessary time and resources, and/or release emissions and waste that can no longer be reused or recycled to be eliminated or replaced to create a Value Added (VA) process flow that avoids waste in a clean, green and environmental friendly manner. This paper presents the implementation of the L&G strategy through MSM in metal working production systems. In metalworking production systems, the variables of operational performance and resources consumption considered are process time, number of operators, consumables, raw material, and energy. These can be suitably used for reduction in water emissions, gas emissions, solid waste and scrap generated in metalworking production systems.

Abstract
To address the increasing complexity of product characteristics, demand fluctuations, and higher costs of raw materials, along with pressures for fast-er integration of decarbonized energy resources, manufacturing companies require flexible production systems. These systems should minimize waste, achieve faster cycle times, and deliver high-quality products to stay competitive. In this regard, Product Design-for-Excellence (DfX) principles have gained significant importance in recent years. DfX enables all management levels to perform quick and comprehensive design inputs and performance evaluations, leveraging product lifecycle management platforms. LeanDfX, a dedicated Lean approach for product development performance assessment, has been previously proposed. This work builds upon LeanDfX by presenting a multi-dimensional approach to support design and performance assessment of production systems throughout its lifecycle. This approach coherently integrates different production knowledge areas and strategic foundations (e.g., Lean Manufacturing, Strategic Aspects, Sustainability, and Circular Economy) for the effectiveness and efficiency evaluation of production systems. The research hypothesis revolves around the translational strategy of extending and transforming the LeanDfX methodology for application in production system design within factory operations. This new architecture is presented in the context of the European project RENEE, devoted to designing and deploying remanufacturing processes for a more sustainable, circular, and competitive industry.

Abstract
As the need for enhanced material performance continues to escalate in several sectors, addressing complex parameters such as economic feasibility, ease of manufacturing, and production volume, rises the need for multidomain decision-making tools. In order to explore and streamline this process, this study employed the novel Material Design-for-eXcellence methodology to investigate polymer material selection in aeronautical and power transformer components, using additive manufacturing. The study assessed the X's selected (mechanical, thermal, physical, cost, dielectric, and environmental) by assigning weights to these factors, and identifying the optimal materials for each application. In the aeronautical context, PEI+GF30 was chosen as the best solution, attaining an overall effectiveness of 79 %, primarily due to its exceptional mechanical characteristics. The use of a thermoplastic can lead to lighter components while ensuring the same technical performance, enabling longer flight duration. Conversely, in the energy sector for power transformers, PSU obtained a 78 % score, largely attributable to its outstanding dielectric properties. The application of additive methods on transformers' insulating parts leads to optimized channels for the mineral oil, enhancing its thermal and dielectric performance. The obtained results underscored the importance of tailored material selection approaches, adjusted to specific application requirements. The importance of comprehending and adapting to diverse contexts for effective material design and implementation is also highlighted.