Abstract
The development of technologies to enable robots to operate autonomously in challenging forest environments is crucial for promoting effective natural resource management and preventing forest fires, standing out as a priority on environmental conservation and public safety agendas. This article presents a detailed discussion on the development of an innovative sensory architecture, specifically designed to integrate a wide range of advanced sensors. The main objective of this architecture is to provide highly accurate inputs to a system, thereby empowering a forest robot to make autonomous and adaptive decisions in real-time. To achieve this ambitious goal, the proposed sensory architecture defines a comprehensive set of crucial variables, which are carefully selected and strategically integrated. This design results in a distributed system capable of processing multiple subsystems in parallel and efficiently. This innovative approach enables the conversion of a conventional forest mulcher machine into a fully autonomous and highly intelligent forest robot. Furthermore, the article details the procedures and methodologies used to experimentally validate the robustness and effectiveness of the developed system. Through rigorous testing and comprehensive analyses, the system's ability to handle a variety of adverse environmental conditions and typical operational challenges in forest environments is demonstrated. These experimental validations are essential to ensure the reliability and accuracy of the system in real-world situations. © 2024, International Frequency Sensor Association (IFSA). All rights reserved.

Abstract
Epileptic seizures are clearly characterized by their displayed behavior, the semiology, which is used in diagnosis and classification as a base for therapy. This article presents a novel 4K 3D video recording and reviewing system for epilepsy monitoring, introducing a novel perspective and allowing continuous recording and review of 3D videos in the epilepsy monitoring unit (EMU), providing significantly more detail than the current clinical systems, which can lead to the recognition of more Movements of Interest (MOIs) and may reduce inter-rater variability. To put the system to an initial test in clinical practice the article presents three real-world examples of subtle MOIs, that could only be appreciated on the 4K-video, but not on the VGA-video, recorded as part of the clinical routine. In conclusion, a 4K-RGB recording, 3D cropping, and 3D video playing system was developed, implemented, and tested for realworld clinical scenarios, considering the specific requirements of clinical monitoring in EMUs. The new data acquisition setup can support clinical diagnosis, which may lead to new insights in the field of epilepsy and the development of AI approaches in the future.

Abstract

Abstract
Human resources play a crucial role in the success of small- and medium-sized enterprises (SMEs), and in today’s competitive recruitment landscape, leveraging technology can be instrumental in enhancing these processes. Organizations and HR departments increasingly adopt artificial intelligence solutions to streamline recruitment and selection procedures. By doing so, SMEs can improve operational efficiency while enabling human resource (HR) specialists to focus on crucial tasks, enhancing candidate experience throughout the recruitment process. However, adopting artificial intelligence (AI) in recruitment remains limited among SMEs. We can attribute this to various factors, including a need for more capacity among SME managers to evaluate and leverage AI’s potential and concerns related to costs and risks associated with its implementation. Given that SMEs constitute 90% of businesses and contribute over 50% of global employment, it is crucial to address this issue and research ways to enhance recruitment processes specifically tailored for SMEs. Our research aims to explore the benefits, challenges, and necessary organizational resources for SMEs to adopt AI effectively in recruitment processes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
The need for interoperability between robots of different brands and navigation typologies, graph-based and free navigation, is increasing and this has led to the development of a new approach to empower a graph and ROS-based robot fleet manager for the management of free navigation mobile robots. For this implementation and validation, in real tests, the OMRON LD-90 was the mobile robot platform chosen, which has the particularity of not allowing the execution of a waypoints sequence. A software module was developed to exchange data between a non-ROS-based mobile robot and a specific ROS-based robot fleet manager. This is an approach applicable to any free navigation Autonomous Mobile Robot (AMR) with the necessary adaptations regarding the information provided by the different robot brands.

Abstract
The application of surface treatments to cork stoppers is presently a common practice in the wine industry, designed to achieve maximum performance and optimal costumer experience of premium products. Unfortunately, current coating techniques lack efficient process control tools, often resulting in faulty products being detected too late, already in use, compromising performance, product quality and mining consumer confidence. In this work a fully automated system equipped with machine vision and automatic feeding of corks, was coupled with an imaging LIBS setup and used to perform a benchmarking against conventional quality control methods. Results clearly demonstrate the capability of the new LIBS system to effectively evaluate in real time the quality of silicone-based surface coatings in cork stoppers, effectively working as a tool for process control providing a route for effective optimization.