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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Radio-Frequency IDentification (RFID) technologies automate the identification of objects and persons, having several applications in retail, manufacturing, and intralogistics sectors. Several works explore the application of RFID systems in robotics and intralogistics, focusing on locating robots, tags, and inventory management. This paper addresses the challenge of intralogistics cargo trolleys communicating their characteristics to an autonomous mobile robot through an RFID system. The robot must know the trolley's relative pose to avoid collisions with the surroundings. As a result, the passive tag on the cargo communicates information to the robot, including the base footprint of the trolley. The proposed RFID system includes the development of a controller board to interact with the frontend integrated circuit of an external antenna onboard the industrial mobile robot. Experimental results assess the system's readability distance in two distinct environments and with two different antenna modules. All the code and documentation are available in a public repository.

Abstract
Purpose: The Internet of Things (IoT) is currently acting as a critical component of the digitalization process by connecting physical devices to the digital world. It is assumed that IoT serves as both a driver and enabler of digitalization. Accordingly, this study investigates the significance of digitalization in enhancing small and medium-sized enterprises (SMEs) firm performance using IoT as a mediator. Design/methodology/approach: Relying on a sample of 393 SMEs in Portugal, the study used a survey method and questionnaire to gather data, while utilizing the structured equations model to explore the relationship between the constructs of the research model. Findings: The findings show that digital infrastructure and value chains are central to digitalization. Technology, data analytics, digital skills and transformation strategies directly and jointly enhance firm performance. The study also highlights the mediating role of IoT in this relationship and stresses the need to consider industry dynamics, digital readiness and strategic goals when assessing IoT’s impact on SME performance. Originality/value: This study provides valuable insights into the core of digitalization, emphasizing the need for SMEs to effectively integrate digital infrastructure, digital value chains and IoT-driven technologies to drive performance and long-term success. © 2025, Fernando Almeida and Edet Okon.

Abstract

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
Multimodal perception systems enhance the robustness and adaptability of autonomous mobile robots by integrating heterogeneous sensor modalities, improving long-term localisation and mapping in dynamic environments and human-robot interaction. Current mobile platforms often focus on specific sensor configurations and prioritise cost-effectiveness, possibly limiting the flexibility of the user to extend the original robots further. This paper presents a methodology to integrate multimodal perception into a ground mobile platform, incorporating wheel odometry, 2D laser scanners, 3D Light Detection and Ranging (LiDAR), and RGBD cameras. The methodology describes the electronics design to power devices, firmware, computation and networking architecture aspects, and mechanical mounting for the sensory system based on 3D printing, laser cutting, and bending metal sheet processes. Experiments demonstrate the usage of the revised platform in 2D and 3D localisation and mapping and pallet pocket estimation applications. All the documentation and designs are accessible in a public repository.