Abstract
The increasing adoption of the Digital Twin concept and technology for managing complex physical assets has led to the emergence of Digital Twin Ecosystems, where interconnected digital twins generate additional value. However, ensuring seamless data sharing and interoperability among diverse systems presents significant challenges. Although research on digital twin architectures has advanced, gaps remain in addressing data governance, security, and stakeholders' trust. This study performs a comprehensive literature review to investigate architectural solutions to overcome challenges in digital twin ecosystems. The findings identify key requirements such as interoperability, governance, and data management, emphasizing the role of Data Spaces as enablers of secure data sharing. By structuring the requirements for digital twin ecosystem architectures, this paper identifies gaps suggesting future research on scalable and sustainable digital twin ecosystem implementations. These insights are expected to contribute to the development of frameworks that integrate technical advances with organizational and regulatory considerations, ultimately fostering the adoption of digital twin ecosystems across industries.

Abstract
This paper evaluates an Augmented Reality (AR) solution designed to support quality control in a assembly line inspection station before body marriage at a European automotive manufacturer. A threephase methodology was applied: an AS-IS assessment, a formative evaluation of an intermediate prototype, and a summative evaluation under real production conditions. The AR solution aimed to improve task standardization, non-value-added time (NVAT), and enhance operator accuracy. The results showed that operators successfully developed inspections using the AR tool, identifying and correcting non-conformities (NOKs) while maintaining task duration. Participants valued having contextual information directly in their field of vision and reported increased rigor and consistency. However, usability and ergonomic improvements were noted, such as headset weight, gesture interaction, and visibility over dark components. The findings highlight AR's potential to support operator autonomy and accuracy in industrial environments while emphasizing the need for human-centered design and integration to ensure long-term adoption.

Abstract
This study presents a photovoltaic (PV)-based electric vehicle (EV) charging system designed to optimize energy use and support isolated microgrid operations. The system integrates PV panels, DC/AC, AC/DC, and DC/DC converters, voltage and frequency droop control, and two energy management algorithms: Power Sharing and SEWP (Spread Energy with Priority). The DC/AC converter demonstrated high efficiency, with stable AC output and Total Harmonic Distortion (THD) limited to 1%. The MPPT algorithm ensured optimal energy extraction under both gradual and abrupt irradiance variations. The DC/DC converter operated in constant current mode followed by constant voltage regulation, enabling stable power delivery and preserving battery integrity. The Power Sharing algorithm, which distributes PV energy equally, favored vehicles with a higher initial state of charge (SOC), while leaving low-SOC vehicles at modest levels, reducing satisfaction under limited irradiance. In contrast, SEWP prioritized low-SOC EVs, enabling them to achieve higher SOC values compared to the Power Sharing algorithm, reducing SOC dispersion and enhancing fairness. The integration of voltage and frequency droop controls allowed the station to support microgrid stability by limiting reactive power injection to 30% of apparent power and adjusting charging current in response to frequency deviation.

Abstract
Evaluating diffusion properties of novel optical clearing (OC) agents is critical for advancing medical imaging. Tartrazine (TTZ), a strong absorbing dye, has shown promise in enhancing tissue transparency, yet its diffusion properties remain uncharacterized. In this work, OC treatments with TTZ-water solutions with varying osmolarities were performed, and the diffusion times (tau) that characterize the tissue dehydration and the RI matching mechanisms were estimated. From kinetic T-c measurements during treatment, tau values of water and TTZ were estimated in muscles as 60.0 s and 416.0 s, respectively. Corresponding diffusion coefficients (D) were derived from sample thickness data measured during treatments where the unique fluxes of TTZ and water occur. The respective D values were then calculated as 1.9 x 10(-6) cm(2)/s for water and 3.6 x 10(-7) cm(2)/s for TTZ. These findings provide key insights into TTZ diffusion in skeletal muscle and support its potential as an effective OC agent.

Abstract
Today, logistics activities are driven by the pressing need to simultaneously increase efficiency, reduce costs, and promote sustainability. In our research, we tackle this challenge by adapting a general vehicle routing problem with deliveries and pickups to accommodate different types of customers. Customers requiring both delivery and pickup services are mandatory, while those needing only a pickup service (backhaul customers) are optional and are only visited if profitable. A mixed-integer linear programming model is formulated to minimize fuel consumption. This model can address various scenarios, such as allowing mandatory customers to be served with combined or separate delivery or pickup visits, and visiting optional customers either during or only after mandatory customer visits. An adaptive large neighborhood search is developed to solve instances adapted from the literature as well as to solve a real-case study of a beverage distributor. The results show the effectiveness of our approach, demonstrating the potential to utilize the available capacity on vehicles returning to the depot to create profitable and environmentally friendly routes, and so enhancing efficient, cost-effective, and sustainable logistics activities.

Abstract
This paper explores a Cycle-GAN architecture based on diffusion models for translating cardiac CT images with and without contrast, aiming to enhance the quality and accuracy of medical imaging. The combination of GANs and diffusion models has demonstrated promising results, particularly in generating high-quality, visually similar contrast-enhanced cardiac images. This effectiveness is evidenced by metrics such as a PSNR of 32.85, an SSIM of 0.766, and an FID of 42.348, highlighting the model’s capability for accurate and detailed image generation. Although these results indicate substantial potential for improving diagnostic accuracy, challenges remain, particularly concerning the generation of image artefacts and brightness inconsistencies, which could affect the clinical validation of these images. These issues have important implications for the reliability of the images in real medical diagnoses. The results of this study suggest that future research should focus on optimizing these aspects, improving the handling of artefacts, and investigating alternative architectures further to enhance the quality and reliability of the generated images, ensuring their applicability in clinical settings © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.