In recent years, both the Internet of Things (IoT) and blockchain technologies have been highly influential and revolutionary. IoT enables companies to embrace Industry 4.0, the Fourth Industrial Revolution, which benefits from communication and connectivity to reduce cost and to increase productivity through sensor-based autonomy. These automated systems can be further refined with smart contracts that are executed within a blockchain, thereby increasing transparency through continuous and indisputable logging. Ideally, the level of security for these IoT devices shall be very high, as they are specifically designed for this autonomous and networked environment. This paper discusses a use case of a company with legacy devices that wants to benefit from the features and functionality of blockchain technology. In particular, the implications of retrofit solutions are analyzed. The use of the BISS:4.0 platform is proposed as the underlying infrastructure. BISS:4.0 is intended to integrate the blockchain technologies into existing enterprise environments. Furthermore, a security analysis of IoT and blockchain present attacks and countermeasures are presented that are identified and applied to the mentioned use case.

This study evaluates the effectiveness of cybersecurity architectures in mitigating AI-driven cyberattacks. A key tool in this evaluation is the Layered Rubric Security Score (LRS), a structured threat model-based MITRE ATT&CK framework that has been developed specifically for this purpose. LRS utilises architectural layers, cross-detection, containment, and automated response capabilities for assessment. Modern reference architectures from aerospace enterprises, designed to mitigate attacks like Citrix Bleed and RedLine Stealer, are assessed and compared. Findings reveal significant gaps in handling adaptive and LLM-based threats, particularly in the integration of CASB and EDR/XDR. LRS introduces a scoring methodology and proposes mitigation strategies aligned with Zero Trust Architecture, supporting the evidence-based improvement of AI-resilient defence infrastructures.

Blockchain and Industry 4.0 have the potential to revolutionize the way entities work together in the industrial environment. Former paper-based and manual tasks, such as maintenance, will be replaced by smart contracts in combination with automatic data collection. We consider different data sources and possibilities integrating them into a blockchain ecosystem in consideration of integration and security aspects. We identify potential security threats around the blockchain and propose potential countermeasures. Finally, we provide three solutions to protect blockchain users against three threats targeting data corruption, data protection and input falsification.

Background: Novice teachers often struggle to apply their content and pedagogical content knowledge in real teaching situations, a phenomenon known as the theory–practice gap. Classroom simulations offer an opportunity to bridge this gap by integrating practical, risk‐free experiences into early university teacher education. Enriching these simulations with chatbots could enhance this process by providing tailored, adaptive learning support, potentially improving pre‐service teachers' diagnostic skills. Objectives: This study investigates pre‐service biology teachers' ability to diagnose virtual students' evolutionary explanations in the classroom simulation SCR ^Bio and the effect of a retrieval‐based chatbot system. Methods: A sample of 107 pre‐service biology teachers diagnosed 3153 virtual students' evolutionary explanations using three SCR ^Bio settings: without a chatbot (SCR‐only), with a knowledge bot (SCR‐Kbot), and with both a knowledge bot and a process‐based feedback bot (SCR‐Kbot+Pbot). Results and Conclusions: Pre‐service biology teachers were able to broadly categorize student explanations in a classroom simulation; however, they struggled to accurately diagnose specific misconceptions, especially in mixed‐scientific responses. Regression analysis revealed that pedagogical content knowledge and cognitive load had a significant influence on judgement accuracy. Although chatbot‐based support was accessed more frequently when combined with process prompts (SCR‐Kbot+Pbot group), it did not result in significant improvements in accuracy. These findings highlight the need for targeted training for diagnostic skills and suggest that the integration of adaptive digital tools alone may not be sufficient to enhance diagnostic competence without deeper pedagogical embedding.