Background: The convergence of Information Technology (IT) and Operational Technology (OT) in Industrial Automation and Control Systems (IACS) has expanded the cyber-attack surface, creating critical risks where security failures can propagate into physical safety hazards. Traditional, static risk assessment methods are inadequate for this complex, converged environments, and the application of standards like IEC 62443 remains a significant challenge.
Objective: This paper designs and validates a novel, hybrid cybersecurity risk assessment (CRA) methodology that integrates Model-Driven Engineering (MDE), explicit safety-security interdependency analysis, and dynamic attack path modeling. The objective is to provide a systematic, semi-automated framework to operationalize the IEC 62443 standard within a "Safety-Security by Design" paradigm.
Methods: We propose a four-phase methodology: (1) automated system modeling and asset identification using MDE principles; (2) integrated threat analysis mapping cyber-threats to physical safety hazards; (3) dynamic risk modeling using attack path analysis to identify critical vulnerability chains; and (4) risk evaluation and mitigation alignment with IEC 62443 Security Levels (SLs). The methodology was validated using a case study of a modular manufacturing testbed.
Results: The application of the methodology successfully identified critical attack paths exploiting IT-OT boundaries that were missed by traditional static analyses. The MDE approach automated the discovery of safety-critical assets, and the interdependency analysis (Phase 2) explicitly linked specific cyber-vulnerabilities to high-priority safety hazards.
Conclusion: The proposed hybrid methodology offers a more robust, dynamic, and integrated approach to IACS cybersecurity. By embedding risk assessment within a model-driven framework, it enables the systematic identification of safety-critical risks and provides a clear roadmap for implementing IEC 62443 controls.

The integration of computer vision into real-world applications is often hindered by the high computational demands of deep learning models and the associated cost of hardware. This paper presents a novel, comprehensive solution for the real-time digitization of a physical chessboard, specifically designed for low-cost embedded platforms. We introduce a complete system that combines efficient classical computer vision algorithms for chessboard localization with a custom-designed, lightweight Convolutional Neural Network (CNN) for piece recognition. The core of our innovation lies in the use of a low-cost, open-source RISC-V platform, which is augmented with a custom, tightly coupled hardware accelerator to offload the most computationally intensive tasks. Our system demonstrates a chessboard localization accuracy of 99.5% and a piece recognition accuracy of 98.2%. Critically, it achieves an average processing latency of 150 milliseconds per frame, enabling true real-time operation on hardware that is an order of magnitude more affordable and power-efficient than conventional GPU-based systems. This work validates a crucial hardware-software co-design paradigm for deploying complex AI tasks on resource-constrained devices, paving the way for a new generation of affordable, intelligent edge applications.