Abstract
This project was developed to improve the cost estimation process of new products within the Product Development Department of a furniture manufacturer. This work involved developing a methodology using Machine Learning (ML) models trained on products' existing data to predict the cost of new innovative ones based on similarities and given data. The ML models used were Linear Regression (LR), Light Gradient-Boosting Machine (LGBM), Random Forest (RF), and Support Vector Machine (SVM). The proposed methodology considers the estimation of the total cost of producing a product, which encompasses both material and operational costs. Throughout this project, several analyses were developed to identify and evaluate different independent variables that could explain the behaviour of these two cost components. The suitability of the different variables was studied by applying several ML models, and a set of functions that return an estimate of the cost as a function of these predictor variables was obtained. The proposed approach, which incorporates ML models into more complex variables to predict, resulted in a 19.29% reduction in estimation error.

Abstract
The increasing deployment of solar photovoltaic technologies has intensified concerns regarding end-of-life waste management and the recovery of critical raw materials. Given the socio-environmental and economic significance of photovoltaic panels’ waste, their integration into circular economy and industrial symbiosis strategies is becoming imperative. However, current sustainability assessment methods remain fragmented, with few frameworks adequately supporting informed decision-making across sustainability dimensions. This study conducts a literature review of existing decision-support frameworks that integrate Life Cycle Assessment and Multi-criteria Decision Analysis in the context of industrial symbiosis. The results reveal limited applications of this hybrid methodology specifically targeting photovoltaic waste streams. Key challenges, gaps, and trends were identified being shared into particular inputs and holistic outputs. Based on this synthesis, the paper proposes foundational features for a robust framework tailored to the industrial symbiosis of the photovoltaic waste context, emphasizing dynamic modeling and the critical role of digital tools. This work contributes both a conceptual roadmap and a practical foundation for researchers, policymakers, and industry actors seeking to enhance the sustainability and circularity of photovoltaic waste through industrial symbiosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Ambient intelligence (AmI) refers to environments where smart devices, sensors, and AI-driven systems work seamlessly to enhance human interactions with their surroundings. Through the combination of real-time data, context-awareness, and adaptive learning, AmI enables environments to respond proactively to user needs, improving efficiency, comfort, and decision-making. However, since AmI systems are inherently human-centric and often operate autonomously, they must be designed with robust ethical, privacy, and safety considerations. Ensuring that these systems function reliably, fairly, and without harm is crucial, especially in sensitive domains like healthcare, security, and smart infrastructure. This work introduces a novel tool, conceptualized as an AmI Digital Twin, which allows developers to simulate or monitor AmI data streams, and develop and thoroughly test AmI applications before and during their real use. Built on a modular architecture leveraging technologies like React.js, Node.js, Kafka, Faust, MongoDB, InfluxDB, Grafana, and Docker, the platform ensures adaptability to different application environments, scalability, and ease of deployment. Besides the description of the tool itself, we provide some early validation results in common AmI tasks such as anomaly and concept drift detection. The tool is available in a public repository, and comes pre-packaged with a set of applications for AmI use-cases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
This paper aims to clarify the characteristics of Digital Twins (DTs) in their most advanced conceptual development, Cognitive Digital Twins (CDTs), and analyze their support for the implementation of the Circular Economy (CE). A systematic literature review was conducted using a specially developed five-dimensional analytical framework to characterize DT proposals and their potential for CE based on an established framework for circularity strategies. The study indicates that cognitive and hybrid DT approaches tend to cover high levels of interoperability, data flow, system levels, and cognitive processes. However, CDT use in CE demands harmonizing different strategies to cover the complete product lifecycle, which recent research on DTs has not fully addressed. This study is the first to systematically review cognitive digital twins and their relation to circularity, offering an analytical framework that can be expanded for future research in various application areas of Industry 5.0.

Abstract
This paper presents a case study to illustrate the application of the directed qualitative content analysis (DQCA) technique to focus group transcriptions for data-driven qualitative persona creation, with broader applicability in human-computer interaction and software development. Using a case study from a project focused on creating an e-grocery marketplace for facilitating short agrifood supply chain trade in the Portuguese context, we demonstrate and validate how DQCA can systematically generate personas that reflect real user needs. For the focus group session, we involved one of the project's stakeholders: family farmers. Furthermore, we propose how these personas can be integrated into the Rational Unified Process software development methodology, guiding decision-making, user-centered design, and prioritization throughout all its phases. Despite being rooted in the e-grocery domain, this paper's methodological approach and insights into generating and integrating user-centered personas in software development processes apply to a broader range of industries and projects, offering guidelines for practitioners and researchers in diverse contexts.

Abstract
In online three-dimensional packing problems where items are received one by one and require immediate packing decisions without prior knowledge of upcoming items, considering the static stability constraint is crucial for safely packing each arriving item in real time. Unstable loading patterns can result in risks of potential damage to items, containers, and operators during loading/unloading operations. Nevertheless, static stability constraints have often been neglected or oversimplified in existing online heuristic methods in the literature, undermining the practical implementation of these methods in real-world scenarios. In this study, we analyze how different static stability constraints affect solutions' efficiency and cargo stability, aiming to provide valuable insights and develop heuristic algorithms for real-world online problems, thus increasing the applicability of this research field. To this end, we embedded four distinct static stability constraints in online heuristics, including full-base support, partial-base support, center-of-gravity polygon support, and novel partial-base polygon support. Evaluating the impact of these constraints on the efficiency of a wide range of heuristic methods on real instances showed that regarding the number of used bins, heuristics with polygon- based stabilities have superior performance against those under full-base and partial-base support stabilities. The static mechanical equilibriumapproach offers a necessary and sufficient condition for the cargo static stability, and we employed it as a benchmark in our study to assess the quality of the four studied stability constraints. Knowing the number of stable items under each of these constraints provides valuable managerial insight for decision-making in real-world online packing scenarios.