Abstract
Cyber-physical systems (CPS) rapidly expand within industrial contexts in a new era of digitalization, processing power, and inter-device communication capabilities. These advancements integrate technologies such as the Internet of Things (IoT), artificial intelligence (AI), and cloud and edge computing, granting processes and operations a high degree of autonomy. In addition, these interconnections foster collective intelligence arising from information exchange and collaboration between components, often outperforming individual capabilities. This collective intelligence manifests in fault detection and diagnosis (FDD) tasks within CPS, as it significantly improves the flexibility, performance, and scalability. However, the inherent complexity of CPS poses challenges in determining the best configuration of the collaboration parameters, such as when and how to collaborate, wherein incorrect adjustments may lead to decision errors and compromise the system's performance. With this in mind, this paper proposes seven metrics to evaluate collaboration performance for fault detection and diagnosis in multi-agent systems (MAS)-based CPS, evaluating when the collaboration is beneficial or when the collaboration parameters need to be adjusted. The experiments focus on collaborative fault detection in temperature and humidity sensors within warehouse racks, where the proposed evaluation metrics point out the impact of collaboration on the detection task, as well as possible actions to be adopted to improve the agent's performance.

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
Mutation testing has evolved beyond academic research, is deployed in industrial and open-source settings, and is increasingly part of universities' software engineering curricula. While many mutation testing tools exist, each with different strengths and weaknesses, integrating them into educational activities and exercises remains challenging due to the tools' complexity and the need to integrate them into a development environment. Additionally, it may be desirable to use different tools so that students can explore differences, e.g.. in the types or numbers of generated mutants. Asking students to install and learn multiple tools would only compound technical complexity and likely result in unwanted differences in how and what students learn. This paper presents FRAFOL, a framework for learning mutation testing. FRAME provides a common environment for using different mutation testing tools in an educational setting.

Abstract
In the global transformation towards a sustainable energy system, the implementation of energy efficiency measures and demand flexibility play a crucial role. Dynamic window shading of building facades poses a great potential to reduce, shift, and modulate a building's electricity consumption by blocking solar heat gains and thereby avoiding expensive Heating, Ventilation, and Air-Conditioning (HVAC) operation to cool the building. However, the installation of dynamic facade systems is often cost-prohibitive with expensive building wiring and interconnection. An integrated direct current (DC) nanogrid is proposed instead, which eliminates any electrical interconnection, by combining all components - generation, storage, and shading element into a self-contained unit. This study seeks to assess the unique design criteria of such Integrated Facade Node (IFN) system given infrequent but high-power use, coincidence of dynamic facade operation with solar renewable photovoltaic (PV) power generation, and unusual placement of the PV generator along the building facade. Optimal IFN sizes based on a deterministic sizing algorithm for a south facing building perimeter are analyzed and installation cost savings of $64,000 (65%) for a medium office building, with the potential to increase up to 91%, are presented.

Abstract
This document presents the Tacks summary of Trends on Gamification, Generative AI, Multidisciplinary Technological Resources, Engineering Education, New Trends in Mechatronics, Diversity Gap in STEM, Laboratories in STEM Education at TEEM 2023, which was held in Bragança (Portugal) from October 25–27. These sessions were held as tracks of the International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM’23). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract