The article explores the importance of cybersecurity in Industry 4.0, specifically focusing on safeguarding industrial automation systems from cyberattacks. It discusses essential security measures, network monitoring, and employee training, emphasizing the significance of risk management for ensuring industry stability. Adherence to industrial security standards, such as ISA/IEC 62443, is crucial (ISA—International Society of Automation, IEC—International Electrotechnical Commission). The article outlines cyberthreat challenges and their impact on various sectors, including healthcare and finance, as well as the risks faced by large industrial enterprises. Additionally, it elucidates evolving cybersecurity strategies and principles, underscoring the necessity for continuous, multi-layered protection. Collaboration with operators, strict information security policies, and robust incident response plans are emphasized. The importance of risk monitoring and adaptability to ever-changing threat landscapes is highlighted, emphasizing the collaborative and flexible nature of cybersecurity in the face of escalating digital threats.

The fascinating tribological phenomenon of carbon nanotubes (CNTs) observed at the nanoscale was confirmed in our numerous macroscale experiments. We designed and employed CNT-containing nanolubricants strictly for polymer lubrication. In this paper, we present the experiment characterising how the CNT structure determines its lubricity on various types of polymers. There is a complex correlation between the microscopic and spectral properties of CNTs and the tribological parameters of the resulting lubricants. This confirms indirectly that the nature of the tribological mechanisms driven by the variety of CNT–polymer interactions might be far more complex than ever described before. We propose plasmonic interactions as an extension for existing models describing the tribological roles of nanomaterials. In the absence of quantitative microscopic calculations of tribological parameters, phenomenological strategies must be employed. One of the most powerful emerging numerical methods is machine learning (ML). Here, we propose to use this technique, in combination with molecular and supramolecular recognition, to understand the morphology and macro-assembly processing strategies for the targeted design of superlubricants.

Classification is one of the most-common machine learning tasks. In the field of GIS, deep-neural-network-based classification algorithms are mainly used in the field of remote sensing, for example for image classification. In the case of spatial data in the form of polygons or lines, the representation of the data in the form of a graph enables the use of graph neural networks (GNNs) to classify spatial objects, taking into account their topology. In this article, a method for multi-class classification of spatial objects using GNNs is proposed. The method was compared to two others that are based solely on text classification or text classification and an adjacency matrix. The use case for the developed method was the classification of planning zones in local spatial development plans. The experiments indicated that information about the topology of objects has a significant impact on improving the classification results using GNNs. It is also important to take into account different input parameters, such as the document length, the form of the training data representation, or the network architecture used, in order to optimize the model.