Open Access

With the advent of the cryptographic security ex-tensions for PROFINET Security Class 2/3, as specified by PROFIBUS & PROFINET International, there arises a signif-icant difference between the establishment of a conventional (insecure) and a secure application relation (AR). However, to the best of our knowledge, there is no study yet that numeralizes how much delay is induced by the computational and communication overhead of the secure AR establishment. Therefore, we study its performance by runtime measurements using an experimental hardware setup. We answer two research questions: (1) how much additional runtime delay is induced by the currently specified security extensions when performing a secure AR establishment, and (2) what is the impact of currently unspecified options to reduce this delay? In particular, we study the options to use the Extensible Authentication Protocol (EAP) with Transport Layer Security (TLS) for full handshakes and session resumption, version 1.2 and 1.3, certificate-based and pre-shared-key-based (PSK) ciphersuites, as well as EAP-PSK. We gain the insight that the currently specified extensions lead to a best case delay of 17.5 ms and a worst case delay of 288.6 ms for full handshakes and to 7.3 and 9.8 ms for resumed sessions. With the currently unspecified option to use pre-shared-key-based ciphersuites, yet providing perfect forward security, the best case delay for full handshakes is reduced by 25.7 % and the worst case delay by 91.1 %. With the currently unspecified EAP-PSK option, which does not provide PFS, the largest reduction is achieved by 78.4 and 98.7 % related to best and worst case full handshakes. The authors are members of the PROFINET Working Group CBIPG 10 Security.

In industrial production, Explainable Artificial Intelligence offers the opportunity to better understand AI models, not only to understand their decisions but also to be able to improve processes, which is possible even without the necessary experience. There are several widely used methods to implement this for Artificial Intelligence models, but no uniform standards and definitions. There are also still some hurdles on a technical and legal level as well as trust on the human side. Ultimately, the application of such techniques is particularly important in this area, but this is often not possible without the well-functioning and widespread use of Artificial Intelligence in manufacturing industry. However, it is a further step towards intelligent manufacturing and the acceptance of Artificial Intelligence in this area. The publication not only shows the latest developments, but also some use cases of Explainable Artificial Intelligence in manufacturing. Not only legal aspects but also ethical aspects are highlighted. In conclusion, it can be stated that Explainable Artificial Intelligence methods per se are well established, but their implementation and use in practice is lagging behind. A major point here in the future will be the explanation of generative artificial intelligence and user-centred explanations for non-domain experts. A standardised definition of explainability and standards for evaluating the individual methodologies must also be created

Transportation planners are increasingly relying on AI to optimize logistics and solve persistent challenges. However, as AI advances rapidly, most software vendors are unable to evaluate and implement all new developments. This paper uses bibliometric methods to track and evaluate the emerging trend of neurosymbolic AI, which combines neural networks with symbolic AI to improve decision making. By analyzing literature and citation data, we gain insights into the development and impact of neurosymbolic AI. The results provide a scalable approach for practitioners to efficiently identify and evaluate AI trends to facilitate the strategic adoption of technologies and innovations in transportation planning.