Abstract
Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A* (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA* through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm.

Abstract
Since supply chains have become complex and tracking a product's journey, from raw materials to the end of it's life has become more difficult. Consumers are demanding greater transparency about the materials origins and environmental impact of the products they buy. These new requirements, togeher with European Commission Green Deal strategy, lead to the concept of digital product passport (DPP). DPP could be seen as an instrument to boost circularity, however the DPP architecture and governance model still undefined and unclear. Data Governance in the context of the DPP acts as the backbone for ensuring accurate and reliable data within these passports or data models, leading to flawless traceability. This article approaches the DPPs and it's governance challenges, explaining how they function as digital repositories for a product's life cycle information and the concept of Data Governance. By understanding how these two concepts work together, we will explore a short use case within the footwear industry to show how DPP governance architecture might work in a distributed environment.

Abstract

Abstract
The development of simple, selective, and cost-effective methods for quantification of bovine serum albumin (BSA) is currently very important for assessing milk quality (and safety). In this work, a new surface plasmon resonance (SPR) sensor was developed, consisting of imprinted hydrogel-based nanoparticles (nanoMIPs) immobilized on gold platforms, to quantify BSA in bovine milk. The nanoMIPs prepared for recognition of BSA were synthesized by the precipitation polymerization approach, using a synthetic BSA epitope (VVSTQTALA) as template. The spherical MIP nanoparticles (NPs) had an average size of 60 nm. The binding studies performed revealed that the binding affinity of the prepared nanoMIPs to BSA (KD = 7.1 × 10-6 mol L-1) was comparable to that obtained by a natural BSA antibody (KD = 2.5 × 10-6 mol L-1). The plasmonic sensor incorporating the MIP nanomaterials achieved a limit of detection (LOD) of 1.02 × 10-6 mol L-1 (0.068 mg mL-1) and a limit of quantification (LOQ) of 3.39 × 10-6 mol L-1 (0.225 mg mL-1), over a linear range from 2.0 × 10-6 mol L-1 to 1.5 × 10-5 mol L-1. Moreover, the selectivity studies revealed a significant sensor response towards casein and a negligible response towards vancomycin. In the end, the optical sensor was tested against commercial milk samples, showing promising viability for detection of BSA as the value reported by the plasmonic sensor ((1.0 ± 0.1) × 10-4 mol L-1) was very close to that obtained by size exclusion-high-performance liquid chromatography (SEC-HPLC). © 2025 The Author(s)

Abstract
This paper examines the effectiveness of various synthetic data generation methods for deterministic wind power forecasting. Specifically, this work evaluates four approaches - Gaussian Mixture Models (GMMs), t-Copula, DoppelGANger, and FCPFlow - by comparing the forecasting performance, measured using Mean Absolute Error and Root Mean Squared Error, of models trained on synthetic versus real datasets. Our findings indicate that statistical methods (such as GMM and t-Copula) achieve notably better performance under limited data availability. However, the deep generative model FCPFlow yields superior results when sufficient training data is available. These findings suggest that the choice of synthetic data generation method should be informed by the specific data availability context.

Abstract
The widespread availability of wireless networking, such as Wi-Fi, has led to the pervasiveness of always connected mobile devices. These devices are provided with several sensors that allow the collection of large amounts of data, which pose a threat to personal privacy. It is well known that Wi-Fi connectivity information (e.g. BSSID) can be used for inferring user locations. This has caused the imposition of limitations to the access to such data in mobile devices. However, other sources of information about wireless connectivity are available, such as the Received Signal Strength Indicator (RSSI). In this work, we show that RSSI can be used to infer the presence of a user at common locations throughout time. This information can be correlated with other features, such as the hour of the day, to further learn semantic context about such locations with a prediction performance above 90%. Our analysis shows the privacy implications of inferring user locations through Wi-Fi RSSI, but also emphasizes the fingerprinting risk that results from the lack of protection when accessing RSSI measurements.