The paradigm shift introduced by Industry 5.0 has intensified the focus on enhancing worker autonomy through new technologies within decision-making contexts. Despite the growing body of research on this phenomenon, there is a notable lack of guidance on the appropriate methods for investigating this issue. This literature review aims to identify and characterize existing research and evaluate the most commonly used methods. The findings reveal various methods, each with significant limitations, and uneven coverage of new technologies studied to enhance decision-making processes and autonomy at work. Case studies emerge as the predominant method, but their inherent characteristics and opportunistic nature present significant limits to the establishment of structured research, enabling a comprehensive and generalizable study of such issues. However, the adoption of use cases is now emerging as a promising alternative to meet these challenges, but the development of a structured and coherent research framework is still needed.
The implementation of Digital Twin in healthcare significantly contributes to the diagnosis of pathologies, the suggestion of treatment scenarios, and the simulation of surgical interventions. Our focus is specifically on the application of Digital Twins in ophthalmology. The integration of artificial intelligence in the processes of prediction, monitoring, and simulation of pathologies brings numerous benefits, but also presents ethical risks and challenges. These risks include patient data confidentiality, accuracy of simulations and compliance with professional ethical standards. This paper emphasizes the necessity of in-depth discussions regarding the implementation of ethical standards to ensure the responsible use of artificial intelligence in medicine.
This paper describes an agent-based approach addressing the unique challenges of mobile multi-robot order picking in the logistics sector. Given the significant growth in the courier, express, and parcel service market, there's an increasing need for systems that not only cope with a rising demand but also adapt to the dynamic logistics environment. This study introduces enhancements to an Agent-Oriented Software Engineering methodology, focussing on information-related aspects that are of increasing importance in the era of Industry 4.0. By systematically developing a Multi-agent System that employs a decentralized control strategy, this research offers a scalable solution to optimize multi-robot tasks in warehousing and order fulfillment processes.
In the last 60 years, Programmable Logic Controllers (PLCs) have become central to manufacturing systems, driving real-time control, data acquisition, safety management, and machine communication. Their reliability and deterministic behaviour contributed to their ongoing importance, even as Industry 4.0 evolves. Simplified programming languages such as GCODE, IEC 61131, and IEC 61499 were developed to make complex behaviour programming accessible to non-experts. Technology providers also created proprietary languages or adapted standards to match hardware advancements. These languages balanced expressiveness and lightweight execution to meet the limited computational power of 1970s technology. However, as intelligent industrial systems increasingly integrate concepts like collaboration, cognition, and self-management, traditional automation languages showing their limitations. Enhanced computational power is shifting the focus from lightweight languages to more expressive ones. This paper highlights the urgent need for new automation programming languages that align with the sophisticated requirements of modern intelligent manufacturing systems. Such languages must harness today's computational advancements to ensure future systems are robust, efficient, and adaptable to the complexities of contemporary industrial environments. Based on these statements, a research agenda is proposed, composed of three main steps: efficient machines-agents design, cognitive work analysis and definition of enriched languages.
Large Language Models (LLMs) are rapidly evolving and increasingly utilized in a wide range of applications. They offer significant advantages over traditional Natural Language Processing approaches for analyzing requirements written in natural language. When designing intelligent systems according to Industry 4.0 principles, these requirements are often written by stakeholders from different domains, possibly leading to defects and misunderstandings. In this paper, we explore the potential of LLMs for detecting various defects, such as ambiguities, inconsistencies and incompleteness in requirements. We present an approach for improving current requirements engineering processes by defining a conversational interface specifically designed for requirements engineering based on LLMs. This interface allows stakeholders from different domains to interact with requirements without the need for extensive knowledge about the underlying models used by Artificial Intelligence, i.e., mainly the LLMs. This will ultimately provide a bridge between stakeholders and developers to create a common understanding of the requirements. The evaluation results of this approach show that it is possible to detect defects in requirements with a promising level of accuracy and respond with valuable suggestions for improving the quality of requirements.
