Predictive Maintenance (PdM) emerged as one of the pillars of Industry 4.0, and became crucial for enhancing operational efficiency, allowing to minimize downtime, extend lifespan of equipment, and prevent failures. A wide range of PdM tasks can be performed using Artificial Intelligence (AI) methods, which often use data generated from industrial sensors. The steel industry, which is an important branch of the global economy, is one of the potential beneficiaries of this trend, given its large environmental footprint, the globalized nature of the market, and the demanding working conditions. This survey synthesizes the current state of knowledge in the field of AI-based PdM within the steel industry and is addressed to researchers and practitioners. We identified 219 articles related to this topic and formulated five research questions, allowing us to gain a global perspective on current trends and the main research gaps. We examined equipment and facilities subjected to PdM, determined common PdM approaches, and identified trends in the AI methods used to develop these solutions. We explored the characteristics of the data used in the surveyed articles and assessed the practical implications of the research presented there. Most of the research focuses on the blast furnace or hot rolling, using data from industrial sensors. Current trends show increasing interest in the domain, especially in the use of deep learning. The main challenges include implementing the proposed methods in a production environment, incorporating them into maintenance plans, and enhancing the accessibility and reproducibility of the research.

Automated driving systems are safety-critical cyber-physical systems whose safety of the intended functionality (SOTIF) can not be assumed without proper argumentation based on appropriate evidences. Recent advances in standards and regulations on the safety of driving automation are therefore intensely concerned with demonstrating that the intended functionality of these systems does not introduce unreasonable risks to stakeholders. In this work, we critically analyze the ISO 21448 standard which contains requirements and guidance on how the SOTIF can be provably validated. Emphasis lies on developing a consistent terminology as a basis for the subsequent definition of a validation strategy when using quantitative acceptance criteria. In the broad picture, we aim to achieve a well-defined risk decomposition that enables rigorous, quantitative validation approaches for the SOTIF of automated driving systems.

The first IEA/AIE conference was organized in 1988 in Tullahoma, Tennessee. Since that time, the conference has been held internationally in many countries including Germany, Scotland, Australia, Spain, Egypt, Hungary, England, Italy, France, Japan, Poland, China, Taiwan, Netherlands, and South Korea. The conference has always been sponsored by ISAI and all conferences have been held in cooperation with AAAI. AI, and Intelligent Systems in Health Care and The focus of the 2017 conference was on research mHealth for Health Outcomes advances in new and innovative intelligent systems' IEA/AIE-2017 also organized two workshops: ASP methodologies and their applications in solving reallife, Technologies for Querying Large-Scale Multiple-complex problems. In many worldwide applications, Source Heterogeneous Web Information, cochaired there is a real need to develop intelligent systems by Odile Papini, Salem Benferhat, Laurent Garcia, that deal with complex, open, and dynamic and Marie-Laure Mugnier; and Computer Animation information systems.