Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

Flooding is one of the most destructive natural phenomena that happen worldwide, leading to the damage of property and infrastructure or even the loss of lives. The escalation in the intensity and number of flooding events as a result of the combination of climate change and anthropogenic factors motivates the need to adopt real-time solutions for mapping flood hazards and risks. In this study, a methodological framework is proposed that enables the assessment of flood hazard and risk levels of severity dynamically by fusing optical remote sensing (Sentinel-1) and GIS-based data from the region of the Trieste, Monfalcone and Muggia Municipalities. Explainable machine learning techniques were utilised, aiming to interpret the results for the assessment of flood hazard. The flood inventory was randomly divided into $70\%$, used for training, and $30\%$, employed for testing. Various combinations of the models were evaluated for the assessment of flood hazard. The results revealed that the Random Forest model achieved the highest F1-score (approx. 0.99), among others utilised for generating flood hazard maps. Furthermore, the estimation of the flood risk was achieved by a combination of a rule-based approach to estimate the exposure and vulnerability with the dynamic assessment of flood hazard.

Physics-Informed Neural Networks are neural networks which combine data-driven deep learning and physical models by incorporating physical laws into their loss function. They are promising and flexible solvers or surrogates for problems governed by partial differential equations, including forward and inverse cases, and during the last few years they have been applied to a variety of problems in physics and engineering. In this survey, we present a comprehensive analysis of PINNs while focusing on their applications in materials science. We examine their foundational form and components, expand on enhancing strategies, and explore various aspects of their practical implementations. For materials modeling, problem cases are systematically organized based on the main scope of publications found in the literature. For this survey, problems are categorized into heat transfer related problems, including multiphysics cases, materials properties and design, materials durability, stress analysis, as well as problems related to porous media and multi-phase materials. Finally, we discuss the current challenges and limitations of PINNs, their advantages, and potential future research directions.

In high-stakes manufacturing environments, the opacity of "black-box" Machine Learning (ML) models presents significant risks to safety, trust, and efficiency. While Explainable AI (XAI) offers a solution, its practical application landscape remains fragmented. This paper addresses this gap by providing a systematic review and novel synthesis of XAI adoption in manufacturing. Rather than merely summarizing known XAI techniques, we introduce a unique, application-centric categorization, analyzing 91 carefully selected articles across six key industrial domains: Predictive Maintenance, Additive Manufacturing, Process Optimization, Product Quality, Product Development, and Supply Chain Management. Our analysis reveals data-driven insights into publication trends and methodology choices, highlighting a definitive shift toward local, model-agnostic methods like SHAP. By structuring the review around manufacturing-specific challenges, we move beyond a general overview to provide a targeted analysis of the field’s current state. We conclude by identifying critical, domain-specific gaps and proposing actionable directions for future research to foster a more effective and trustworthy integration of advanced AI into the human workforce.