Vehicular Ad Hoc Networks (VANETs) support safety-critical and traffic-optimization applications through low-latency, reliable V2X communication. However, securing integrity and auditability with blockchain is challenging because conventional BFT-style consensus incurs high message overhead and latency. We introduce CoCoChain, a concept-aware consensus mechanism tailored to VANETs. Instead of exchanging full payloads, CoCoChain trains a sparse autoencoder (SAE) offline on raw message payloads and encodes each message into a low-dimensional concept vector; only the top-k activations are broadcast during consensus. These compact semantic digests are integrated into a practical BFT workflow with per-phase semantic checks using a cosine-similarity threshold $\theta =0.85$ (calibrated on validation data to balance detection and false positives). We evaluate CoCoChain in OMNeT++/SUMO across urban, highway, and multi-hop broadcast under congestion scenarios, measuring latency, throughput, packet delivery ratio, and Age of Information (AoI), and including adversaries that inject semantically corrupted concepts as well as cross-layer stress (RF jamming and timing jitter). Results show CoCoChain reduces consensus message overhead by up to 25% and confirmation latency by 20% while maintaining integrity with up to 20% Byzantine participants and improving information freshness (AoI) under high channel load. This work focuses on OBU/RSU semantic-aware consensus (not 6G joint sensing or multi-base-station fusion). The code, configs, and an anonymized synthetic replica of the dataset will be released upon acceptance.

Vehicular ad hoc networks (VANETs) are a critical aspect of intelligent transportation systems, improving safety and comfort for drivers. These networks enhance the driving experience by offering timely information vital for safety and comfort. Yet, VANETs come with their own set of challenges concerning security, privacy, and design reliability. Traditionally, vehicle authentication occurs every time a vehicle enters the domain of the roadside unit (RSU). In our study, we suggest that authentication should take place only when a vehicle has not covered a set distance, increasing system efficiency. The rise of the Internet of Things (IoT) has seen an upsurge in the use of IoT devices across various fields, including smart cities, healthcare, and vehicular IoT. These devices, while gathering environmental data and networking, often face reliability issues without a trusted intermediary. Our study delves deep into implementing Teegraph in VANETs to enhance authentication. Given the integral role of VANETs in Intelligent Transportation Systems and their inherent challenges, we turn to Hashgraph—an alternative to blockchain. Hashgraph offers a decentralized, secure, and trustworthy database. We introduce an efficient authentication system, which triggers only when a vehicle has not traversed a set distance, optimizing system efficiency. Moreover, we shed light on the indispensable role Hashgraph can occupy in the rapidly expanding IoT landscape. Lastly, we present Teegraph, a novel Hashgraph-based technology, as a superior alternative to blockchain, ensuring a streamlined, scalable authentication solution. Our approach leverages the logical key hierarchy (LKH) and packet update keys to ensure data privacy and integrity in vehicular networks.

Vehicular ad hoc networks (VANETs) are a critical aspect of intelligent transportation systems, improving safety and comfort for drivers. These networks enhance the driving experience by offering timely information vital for safety and comfort. Yet, VANETs come with their own set of challenges concerning security, privacy, and design reliability. Traditionally, vehicle authentication occurred every time it entered the domain of the Road Unit (RSU). In our study, we suggest authentication should take place only when a vehicle hasn’t covered a set distance, increasing system efficiency. The rise of the Internet of Things (IoT) has seen an upsurge in the use of IoT devices across various fields, including smart cities, healthcare, and vehicular IoT. These devices, while gathering environmental data and networking, often face reliability issues without a trusted intermediary. IBM anticipates blockchains, having evolved over the past decade, to be pivotal in IoT technology. Our study delves deep into implementing Teegraph in VANETs to enhance authentication. Given VANET’s integral role in Intelligent Transportation Systems and its inherent challenges, we turn to Hashgraph—an alternative to blockchain. Hashgraph offers a decentralized, secure, and trustworthy database. We introduce an efficient authentication system, which triggers when a vehicle hasn’t traversed a set distance, optimizing system efficiency. Moreover, we shed light on the indispensable role Hashgraph can occupy in the rapidly expanding IoT landscape. Lastly, we present Teegraph, a novel Hashgraph-based technology, as a superior alternative to blockchain, ensuring a streamlined, scalable authentication solution. Our approach leverages the logical key hierarchy (LKH) and packet update keys to ensure data privacy and integrity in vehicular networks.

This article analyzes the DCCII/2024 Decree on Artificial Intelligence issued by the Vatican City State, focusing on its unique status within a theocratic legal system, its doctrinal foundation in Catholic Social Teaching, and its comparative alignment with regulatory frameworks from the European Union, the United States, and UNESCO. Through an interdisciplinary approach — legal, political, and theological — the study shows that the Vatican regulation represents a singular normative model, marked by theological anthropology, doctrinal coherence, and institutional stability. The Vatican is presented as a distinctive and morally consistent voice in the global ethical debate on artificial intelligence.

The expanding deployment of generative AI in professional domains-legal practice, medical diagnosis, financial analysis-has renewed the question whether artificial systems can be held accountable for their outputs. This Article argues that the question has been consistently misframed: the dominant literature asks whether AI should be granted legal personhood without first asking whether AI can satisfy the ontological conditions that legal personhood presupposes. We develop the Three-Pillar Test, an analytical instrument that formalizes three copulative conditions for legal accountability: Deliberative Suspension (genuine interruption of automatism, not mere processing), Evaluation without Determination (rational traceability of singular judgment, not statistical optimization), and Signature-as-Commitment (exposure to irreversible loss by an identifiable subject). Drawing on philosophical anthropology (Gehlen), the theory of judgment (Kant, Arendt), and the ontology of commitment (Jakobs, Taleb), we demonstrate that these conditions translate into verifiable legal indicators grounded in positive law. Five objections are examined: the corporate analogy, the technical improvement argument, insurance pools, the pragmatic objection, and functional neutrality. Each is shown to presuppose rather than eliminate the three pillars. The test is agnostic regarding artificial consciousness and operates entirely on observable institutional conditions. A companion article applies the test empirically to generative legal technology.

The rise of generative AI has not produced a legal vacuum in EU copyright law: the substantive originality threshold remains harmonised around the concept of the author’s «own intellectual creation», operationalised through free and creative choices attributable to a human author. The present disruption is primarily evidentiary, not doctrinal. In AI-mediated, black-box production environments, courts retain the relevant legal standard but lack workable tools to verify the creative causal nexus between human decisions and the expressive features of the final output. This article diagnoses this evidentiary fixation crisis, reconstructs the EU originality acquis, decomposes the generative act into evidentiary categories, and formulates the doctrinal rule of the prompt as instruction, laying the groundwork for a harmonisable procedural standard.