Estimated reading time: 5 minutes A week ago, a supermarket cyberattack heated fridges in hundreds of French stores to 50°C. Brokers and insurers say

Contemporary smart homes fail to address the built environment’s climate crisis contribution, being trapped in a mechanistic paradigm that decouples intelligence from the body, marginalises its human occupants, and elevates passive consumption over regeneration. We explore a novel conceptual framework—the Cyberphysical Living System (CPLS) that rethinks smart home ontologies by synthesising metabolic theory, cybernetics, and semantic architectures into a hybrid human–intelligent system, asking: what would it mean to design a home that metabolises rather than consumes? Grounded in validated bioelectrochemical and AI technologies, this model envisions the home as an ecosystem of actors in tripartite symbiosis: a metabolic 'gut' (integrated bioreactors), an explainable cognitive ‘brain’ (a cognitive digital twin), and human occupants. Central to this vision is a proposed hybrid ontology—a semantic framework that bridges architecture and computer science that translates architectural metabolic design patterns (codified biological design principles) into a machine-readable format, which could enable AI to adapt to dynamic biological processes and human activities beyond data monitoring. We outline a metabolic-based methodology centred on hybrid human–AI systems. Exploring its societal implications, we also consider redefining comfort as metabolic security, transforming occupants into literate stewards, and repositioning the home as a regenerative and productive asset. We conclude the future of sustainable dwelling may lie in cultivating living architectural metabolisms—which could re-embed human life within the living world's regenerative logic. The paper’s original contribution lies in synthesising metabolic architecture with hybrid AI systems through a proposed hybrid ontology, offering a new conceptual foundation for human-centred, regenerative design.

Using sustainability insights to help strengthen IT and OT security

Using sustainability insights to help strengthen IT security

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An elevator Digital Twin System Using oneM2M

Many gadget makers could use crash course in cybersecurity.

The Organization for Security and Cooperation in Europe (OSCE) has released a new technical guide urging a unified approach to physical and cybersecurity for the protection of critical infrastructure.

The world of industrial cyberphysical systems is facing a daunting challenge in fault diagnosis, with traditional manual and expert-driven methods often proving expensive, slow, and error-prone. However, researchers are now exploring the potential of quantum computers to solve this problem, leveraging their ability to process vast amounts of data exponentially faster than classical computers. Studies have shown that quantum computers can significantly outperform classical PCs in terms of runtime, making them a promising solution for industrial fault diagnosis. In fact, researchers have proposed novel approaches using Grover's algorithm and the Quantum Approximate Optimization Algorithm (QAOA) to diagnose faults efficiently and accurately. These approaches have been evaluated on an IBM Falcon processor, demonstrating impressive results. Moreover, an integrated approach combining machine learning techniques with quantum computing has been proposed, which can learn a diagnosis model from process data and use it for diagnosis. This approach has shown significant promise in terms of runtime and accuracy, outperforming traditional methods. As researchers continue to explore the potential of quantum computers in industrial fault diagnosis, several future directions emerge, including scalability, hybrid approaches, and real-world applications. By addressing these challenges and opportunities, researchers can unlock the full potential of quantum computers in solving industrial fault diagnosis problems, leading to significant improvements in efficiency, safety, and productivity.