Abstract
This paper introduces the Kabsch Marker Estimation Algorithm (KMEA), a new, robust multi-marker localization method designed for Autonomous Mobile Robots (AMRs) within Industry 4.0 (I4.0) settings. By integrating the Kabsch Algorithm, our approach significantly enhances localization robustness by aligning detected fiducial markers with their known positions. Unlike conventional methods that rely on a limited subset of visible markers, the KMEA uses all available markers, without requiring the camera's extrinsic parameters, thereby improving robustness. The algorithm was validated in an I4.0 automated warehouse mockup, with a four-stage methodology compared to a previously established marker estimation algorithm for reference. On the one hand, the results have demonstrated the KMEA's similar performance in standard controlled scenarios, with millimetric precision across a set of error metrics and a mean relative error (MRE) of less than 1%. On the other hand, KMEA, when faced with challenging test scenarios with outliers, showed significantly superior performance compared to the baseline algorithm, where it maintained a millimetric to centimetric scale in error metrics, whereas the other suffered extreme degradation. This was emphasized by the average reduced results of error metrics from 86.9% to 92% in Parts III and IV of the test methodology, respectively. These results were achieved using low-cost hardware, indicating the possibility of even greater accuracy with advanced equipment. The paper details the algorithm's development, theoretical framework, comparative advantages over existing methods, discusses the test results, and concludes with comments regarding its potential for industrial and commercial applications by its scalability and reliability.

Abstract
In today's knowledge-driven economy, collaboration among stakeholders is essential for the framing of innovative trends, with knowledge-intensive business services (KIBS) playing a core role in addressing market demand. Users' involvement in shaping products and services has been considered in innovation ecosystem frameworks. Fewer risks in service/product development, and more sustainability and market acceptance, are a few of the benefits arising from including the user community (UC) in innovation partnerships. However, the need for resources, absorptive capacity and tacit knowledge, among other capabilities, is often a reason for overlooking this important contributor. KIBS possess a vast knowledge base, cater to digital tools, and mediate and propel innovation with different partners, benefiting from exclusive cognitive proximity to remix extant knowledge with emergent information from communities into new products and services. The aim of this study is to assess and quantify the effect of the collaboration with UC through three active forms of collaboration (co-creation, mass customization, and personalization) on different innovation types developed in KIBS. The significance of the user community was proven across all innovation types. Robustness analysis confirmed the results for both P-KIBS and T-KIBS. P-KIBS may be better suited to co-creation policies for product and service innovation, personalization of processes, and organizational and marketing innovations. T-KIBS can focus on mass customization, ensuring good innovation success. Additionally, co-creation with user community is best for product innovation.

Abstract
Predictive maintenance methods play a crucial role in the early detection of failures and errors in machinery, preventing them from reaching critical stages. This paper presents a comprehensive study on a real-world dataset called MetroPT3, with data from a Metro do Porto train's air production unit (APU) system. The dataset comprises data collected from various analogue and digital sensors installed on the APU system, enabling the analysis of behavioural changes and deviations from normal patterns. We propose a data-driven predictive maintenance framework based on a Long Short-Term Memory Autoencoder (LSTM-AE) network. The LSTM-AE efficiently identifies abnormal data instances, leading to a reduction in false alarm rates. We also implement a Sparse Autoencoder (SAE) approach for comparative analysis. The experimental results demonstrate that the LSTM-AE outperforms the SAE regarding F1 Score, Recall, and Precision. Furthermore, to gain insights into the reasons for anomaly detection, we apply the Shap method to determine the importance of features in the predictive maintenance model. This approach enhances the interpretability of the model to support the decision-making process better.

Abstract
STEM education advances at the same rate as the need for new and more evolved tools. This article introduces the latest version of the Kid Grígora educational robot, based on the work of Barradas et al. (2019). Targeted for students aged 8 to 18, the robot serves as an interdisciplinary teaching tool, integrated into STEM curricula. The upgraded version corrects what we’ve learned from a real test with 177 students from a Portuguese school and adds other features that allow this new robot to be used in even more educational STEM and problem-solving scenarios. We focused on the creation of a second beta version of the prototype, named Stemie, and its heuristic evaluation by three experts. After all the issues and suggestions from the experts have been resolved and implemented, the new version is ready for usability evaluation. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

Abstract
This paper studies a real world dedicated parallel machine scheduling problem with sequence dependent setups, different machine release dates and additional resources (PMSR). To solve this problem, two previously proposed models have been adapted and a novel objective function, the minimisation of the sum of the machine completion times, is proposed to reflect the real conditions of the manufacturing environment that motivates this work. One model follows the strip-packing approach and the other is time-indexed. The solutions obtained show that the new objective function provides a compact production schedule that allows the simultaneous minimisation of machine idle times and setup times. In conclusion, this study provides valuable insights into the effectiveness of different models for solving PMSR problems in real-world contexts and gives directions for future research in this area using complementary approaches such as matheuristics. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Process Mining offers a powerful framework for uncovering, analyzing, and optimizing real-world business processes. Petri nets provide a versatile means of modeling process behavior. However, traditional methods often struggle to effectively compare complex Petri nets, hindering their potential for process enhancement. To address this challenge, we introduce PetriNet2Vec, an unsupervised methodology inspired by Doc2Vec. This approach converts Petri nets into embedding vectors, facilitating the comparison, clustering, and classification of process models. We validated our approach using the PDC Dataset, comprising 96 diverse Petri net models. The results demonstrate that PetriNet2Vec effectively captures the structural properties of process models, enabling accurate process classification and efficient process retrieval. Specifically, our findings highlight the utility of the learned embeddings in two key downstream tasks: process classification and process retrieval. In process classification, the embeddings allowed for accurate categorization of process models based on their structural properties. In process retrieval, the embeddings enabled efficient retrieval of similar process models using cosine distance. These results demonstrate the potential of PetriNet2Vec to significantly enhance process mining capabilities.