Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
What is consciousness? Despite millennia of inquiry, humanity still lacks a consensus definition. This paper proposes a unified, strictly materialist theory: consciousness is not an entity but a process-specifically, the dynamic process that occurs when a specific structure, formed within complex, chaotic circuits, formats and processes input data and produces output. The structure is the carrier; the processing itself is consciousness. Building on this definition, the paper introduces a two-layer architecture that distinguishes basal consciousness (the autonomous survival functions shared by nearly all living organisms) from advanced consciousness (spanning the awareness of basic sensations, self-awareness, metacognition, and abstract reasoning). We argue that the two layers are related not as a hierarchy of subordination, but as a partnership of trust shaped by natural selection. The paper further proposes the mechanism of the self-reinforcing circuit: advanced consciousness is not complete at birth; it must be "awakened" through sustained external stimulation. Once awakened, it consolidates its own existence by continuously reinforcing the neural pathways dedicated to it, sculpting the unique "I" of each individual. This mechanism offers a parsimonious explanation of dissociative identity disorder, personality change after brain injury, and the diversity of human personality. The Awakener model traces the complete evolutionary path: from primitive sensory-interpretation tools, through the emergence of intelligence, the first awakening event, and chained cultural transmission, to the birth of language and writing. We apply this framework to explain why intelligent animals never developed civilization, to predict which species might awaken next, and to redefine the current state of artificial intelligence as a frozen brain-structurally capable of consciousness, but temporally static: a single frame. Within a single, self-consistent framework, the theory unifies the core problems of consciousness research: the definition and essence of consciousness, the hard problem, the two-layer distinction, the physical basis of the "I", personality diversity, the evolutionary origin of consciousness, the boundaries of animal consciousness, the origin of language, the conscious status of AI, free will and the Libet experiments, split-brain interpretation, dreams and sleepwalking, and the relation between the subconscious and System 1/System 2. No immaterial components are required: every step rests on existing experimental evidence or established neuroscientific findings.
Digital twins (DTs) are rapidly emerging as foundational enablers of 6G smart cities, offering real time monitoring, predictive analytics, and autonomous control across transportation, energy, healthcare, and industrial domains. Large scale DT adoption faces critical barriers including cybersecurity vulnerabilities, privacy risks, and the absence of standardized orchestration frameworks. This article presents Fed-DTOrch, a comprehensive end to end architecture that integrates federated intelligence, blockchain based audit trails, and AI governance to achieve secure and privacy preserving DT management. The proposed three tier architecture spans IoT and edge devices, domain specific twins, and a city level orchestrator, employing secure federated learning for model updates, lightweight cryptographic authentication, and tamper proof logging. We quantify the DT threat landscape, perform a standards gap analysis across ISO/IEC 27001, 3GPP TS 33.501, ITU-T IoT risk frameworks, and NIST AI RMF, and introduce a 6G ready security framework incorporating federated AI trust metrics, secure synchronization, and explainable AI audits. Cross domain evaluation across five smart city sectors demonstrates 35-60% privacy gain, 40-55% attack mitigation, 28-40% reliability uplift, 26-30% latency reduction, and >85% compliance readiness with <10% overhead. These results provide the first integrated blueprint that combines federated intelligence, blockchain-based auditability, and standards gap analysis to enable secure, standardized, and interoperable DT orchestration for trustworthy 6G ecosystems.
Li Wang, Xiuming Cheng· IEEE Communications Standard...· 2 citations
Digital twin (DT) technology real-time digital counterparts of physical assets has advanced rapidly across critical sectors. In the 6G era, the integration of DTs with ultra-low latency communication, edge intelligence, and artificial intelligence (AI) promises predictive control, enhanced collaboration, and resilient research ecosystems. Yet, this same convergence expands the attack surface: physical tampering, edge compromise, model hijacking, and adversarial AI pose risks that current security standards only partially address. Existing frameworks such as ISO/IEC 27001, 3GPP SA3, ETSI PDL, GDPR, and NIST AI RMF each contribute, but none fully cover end-to-end DT synchronisation, AI governance, or federated research data protection. This article presents a layered predictive security framework for 6G-enabled DTs in university research management and big data protection. The framework integrates provenance anchoring, anomaly detection, risk forecasting, and explainability dashboards with secure network slicing and federated identity management. We map threats to controls, assess coverage of international standards, identify critical gaps, and propose future standardisation directions. A university case study illustrates practical deployment. The work highlights the urgency of harmonising security and AI standards to ensure interoperable, trustworthy, and privacy-preserving DT ecosystems in next-generation communication systems.
Digital Twin (DT) technologies enable the creation of virtual replicas of learning environments, supporting personalized and real-time educational interventions. However, the integration of Artificial Intelligence (AI) within DT-enabled e-learning introduces critical challenges related to explainability, security, and learner privacy, and lacks a standardized operational framework. This study proposes and validates a comprehensive framework that operationalizes explainability and security for AI models in DT-based e-learning environments, balancing predictive performance, interpretability, and data protection. A quantitative experimental design involving approximately 300 learners evaluates three AI model variants: baseline, explainability-focused, and privacy/ security-enhanced. Predictive modeling employs temporal learner representations with ensemble predictors. Explainability is implemented through post-hoc interpretability techniques such as SHAP and Integrated Gradients, while privacy protection is ensured using Differential Privacy (DP) and Role-Based Access Control (RBAC). Multilevel mixed-effects models are utilized to assess predictive accuracy, explanation fidelity, and privacy guarantees, expressed as $\varepsilon $ -values. Results indicate that incorporating explainability mechanisms increases user trust by approximately 0.8–1.2 points on a Likert scale and enhances explanation fidelity by 25–30%. Integrating privacy controls produces a modest reduction in predictive AUC (up to 8%) but significantly mitigates data leakage risks. The proposed framework offers a standardized and reproducible evaluation suit for the certified deployment of explainable and secure AI systems in DT-enabled e-learning, facilitating transparent trade-offs between performance, interpretability, and privacy.
Edrees A. Alkinani· IEEE Communications Standard...· 1 citation
The quick development of financial technologies and digital transactions has made fraud detection and regulatory compliance more difficult. This study introduces a Financial Digital Twin with Explainable AI over 6G (FinDT-XAI6G) to enhance real-time fraud detection and compliance monitoring in financial systems. The proposed method leverages the ultra-low latency, high bandwidth, and pervasive intelligence capabilities of 6G networks to enable seamless synchronization between digital twins and real-world financial activities. Common modeling approaches ensure interoperability across financial firms, regulatory bodies, and auditing authorities. Embedded artificial intelligence systems continuously examine vast amounts of transactional data, behavioral patterns, and contextual indications to spot anomalies that could be signs of fraud. Financial firms may now fully and transparently explain automated judgments to regulators thanks to Explainable AI (XAI) modules that enhance interpretability. Blockchain-based audit trails also guarantee data integrity, accountability, and traceability across distributed infrastructures. The integration of 6G connectivity allows for real-time monitoring, cross-border compliance validation, and instant anomaly reporting, even in scenarios with huge data volumes. Comparative studies reveal that our 6G-driven digital twin approach significantly improves detection accuracy, reduces false positives, and expedites compliance verification when compared to traditional methods. Additionally, its scenario modeling capabilities enable the proactive assessment of emerging compliance risks in dynamic regulatory and commercial contexts. Overall, this study demonstrates how financial fraud prevention and compliance assurance in next-generation digital economies can be revolutionized by 6G intelligence-powered standardized, AI-integrated digital twins.
It is argued that both AI and bullshitters are untrustworthy informants, and for similar reasons, it is natural to describe AI’s informational outputs as bullshit, as it signals their distinctive kind of epistemic deficiencies, which they share with bullshit.