Abstract
This article describes the construction path of a Responsible Research and Innovation (RRI) tool, starting with a systematic literature review of all responsible innovation tools to extract the essential dimensions and exclude overlapping. Those dimensions were presented in a series of workshops within a Research and Innovation Action European Project where 35 Innovation Actions (IA) were developed. Focusgroup methodology was followed, including the IA's leaders, to generate discussion around the sixteen dimensions and the meanings of the different grades of the Likert scale of an assessment tool to be applied to innovation processes and results.

Abstract
The phase-matching conditions for exciting surface plasmon resonances (SPR) in plasmonic films are typically satisfied via prism, optical fibers or grating-assisted coupling. We recently showed that plasmonic nanospheres can act as local emitters, exciting SPR waves on thin films-termed nanoparticle-induced SPR (NPI-SPR). This structure holds promise for sensing, but the effects of optical fiber geometry and nanoparticle anisotropy on the response were unexplored. This study examines these factors, showing that an etched multimode fiber with a 200 mu m core diameter, taper ratio of 4, and etching angle of 20 degrees optimizes interaction with plasmonic nanoparticles. Tuning the nanoparticle aspect ratio from 1 to 3 shifts the NPI-SPR band from 780 to 1580 nm, with excitation highly dependent on the incident light angle. Notably, for light incident parallel to the film plane, a refractive index sensitivity exceeding 1000 nm/RIU is achieved. This efficient light emission combines the field locality enhancements of plasmonic nanoparticle-on-film structures with the emission efficiency of plasmonic nanoantennas, advancing plasmonic optical fiber chemical and biosensors.

Abstract
We introduce a novel blockchain-based interledger authorisation scheme, a unique solution designed to support delegated authorisation management in cross-domain cloud environments. This innovative approach enables client applications, acting on behalf of a user, to request access to specific resources owned by a different organization. It benefits cross-domain multi-cloud applications, such as large-scale collaboration projects involving multiple cloud service providers. Multi-cloud presents authorisation challenges because of complex access control operations in multiple distinct domains. OAuth2 is the state-of-the-art choice for delegated authorisation. However, it falls short in handling such complex interactions with multiple authorisation servers and resource servers, and this is our motivation. Compared with OAuth2, our approach overcomes the limitations of a centralized model and achieves coordinated distinct delegations among multiple entities without a thirdparty trusted component. authorisation servers are blockchain participants and designated access token issuers, ensuring minimum client data collection and promoting client privacy. Multiple smart contracts are deployed in our multi-ledger blockchain environment to guarantee the provenance of the authorisation operations and facilitate scalable cross-domain authorisation. Our hybrid interledger model combines security techniques such as blockchain-based device authentication, hashed time-lock contracts (HTLCs), and service discovery. The blockchain-based device authentication ensures secure and trusted interactions, HTLCs enable time-bound transactions, and service discovery simplifies finding and connecting to the required cloud services. Our experimental evaluation provides insights into the scalability of our approach, which achieves decentralized authorisation in a multi-cloud environment with acceptable latencies compared with related works and shows stable throughput against concurrent authorisation requests in a multi-ledger system.

Abstract
This paper explores the integration of SolidWorks, LabVIEW, and Arduino as a comprehensive and cost-effective approach to teaching robotics to undergraduate students. In scenarios where real hardware is unavailable or prohibitively expensive, this methodology offers significant advantages. SolidWorks enables students to design and simulate robotic components in a virtual environment, fostering a deep understanding of mechanical design and engineering principles. LabVIEW provides an intuitive graphical interface for programming and control, allowing students to develop and test their algorithms. Finally, Arduino, as an open-source hardware platform, bridges the gap between virtual simulations and physical implementation, offering a hands-on experience with minimal financial investment. Together, these tools create a robust educational framework that enhances theoretical knowledge through practical application, encourages innovation, and prepares students for real-world engineering challenges. The paper concludes that this integrated approach not only mitigates the limitations of resource constraints but also enriches the learning experience by providing a versatile and accessible platform for robotics education.

Abstract
The main aims of this chapter were to explore metaverse branding by identifying the main trends and contributions in extant literature. Through a bibliometry and the critical analysis of the main contributions in the literature, the chapter proposes a metaverse branding conceptualization, which shows how immersive metaverse experiences that provide multi- dimensional value enhance brand engagement, which leads to increased brand awareness, brand love, satisfaction, trust, and brand equity. These factors ultimately drive online and offline purchases and strengthen brand loyalty. Overall, this chapter and the proposed framework provide relevant insights for both managers defining metaverse branding strategies, and researchers interested in these topics. © 2025, IGI Global Scientific Publishing. All rights reserved.

Abstract
Prolog is a programming language that provides a high-level approach to software development. Python is a versatile programming language that has a vast range of libraries including support for data analysis and machine learning tasks. We present a Prolog-Python interface that aims at exploiting Prolog deduction capabilities and Python's extensive libraries. Our novel interface was built using a divide and conquer methodology. In a first step, we implemented a set of C++ classes that can be matched to Python classes; next, we used an interface generator to export the relevant classes. Finally, we use C code to actually convert between the two realms. In order to demonstrate the usefulness of the interface, we enhance an Inductive Logic Programming System with a visualization capabilities and show how to interface with a standard classifier.