Computational and ML methods in MOF based supercapacitors - from mechanistic understanding to future materials design

Inside the black box: A comprehensive survey of explainable AI benchmarks, protocols, technical analysis, and open gaps

End-to-End Federated Intelligence for Secure and Standardized Digital Twin Orchestration in 6G Smart Cities

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.

AI and Bullshit

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.

Neuro-Symbolic Reasoning with Attentive Graph Neural Networks and Rule Extraction

This paper presents a novel approach to neuro-symbolic reasoning by integrating attentive graph neural networks (GNNs) with rule extraction techniques. The core idea is to leverage the pattern recognition capabilities of GNNs for initial data processing while simultaneously extracting symbolic rules that encapsulate domain knowledge. The attentive mechanism within the GNN allows for selective focus on relevant graph features, improving both the accuracy and interpretability of the learned representations. These representations are then used as the basis for logical reasoning and decision-making, providing a more robust and explainable AI system compared to purely neural or symbolic approaches. We demonstrate the effectiveness of this hybrid system through a detailed explanation of the architecture and its theoretical underpinnings. The key contribution lies in the synergistic combination of graph representation learning and symbolic knowledge discovery, resulting in a system capable of handling complex reasoning tasks.

The Awakener: A Unified Materialist Theory of Consciousness as a Dynamic Process

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.

Multidimensional trust perceptions of AI medical conversational agents: framework development and scale validation

Artificial intelligence is rapidly becoming embedded in everyday life through an expanding range of applications, services, and products. As its potential to improve diagnosis, personalize treatment, and enhance operational efficiency becomes increasingly evident, healthcare is undergoing profound transformation. However, trust and distrust operate as dual mechanisms shaping technology diffusion: trust facilitates adoption, whereas distrust constrains large-scale deployment. Trust therefore remains a persistent barrier to the widespread use of artificial intelligence in healthcare services. At present, empirical evidence on the pathways linking trust and acceptance of AI medical conversational agents (AIMCAs) remains limited. Grounded in trust theory, this study aimed to develop and validate a multidimensional trust-perception scale for AIMCAs, establish its dimensional structure and psychometric quality, and examine its associations with an external acceptance-related behavioral criterion. Methodologically, the study first used grounded-theory-informed abductive qualitative analysis to identify the structure of public trust perceptions of AIMCAs and generated and screened measurement items through expert Q-sorting; independent samples were then used for exploratory and confirmatory factor analyses, followed by assessments of internal consistency, test-retest reliability and absolute agreement, within-construct indicator convergence, discriminant validity, and criterion-related validity. Parallel analysis and the scree plot jointly supported a five-factor solution. Principal axis factoring with Direct Oblimin oblique rotation yielded a 15-item, five-dimensional structure, with the five common factors explaining 76.07% of the total variance. Confirmatory factor analysis further supported a five-dimensional structure comprising cognitive trust, affective trust, functional trust, human-like trust, and interactional trust; model-fit indices, standardized factor loadings, latent-variable correlations, and residual diagnostics collectively provided evidence for its internal structure. The Fornell-Larcker criterion and bootstrap confidence intervals for HTMT jointly provided evidence for internal discriminant validity among the five AIMCA trust dimensions. An ordinal logit model using actual use frequency as an external behavioral criterion was statistically significant overall, likelihood-ratio χ²(5) = 71.963, p < 0.001, McFadden pseudo-R² = 0.125. Interactional trust showed the strongest association with higher use frequency (OR = 3.096, 95% CI [2.257, 4.246]). The resulting scale captures multidimensional public trust perceptions of AIMCAs and provides a structured measurement basis for research on acceptance-related behavior. The findings support a 15-item, five-dimensional structure comprising cognitive trust, affective trust, functional trust, human-like trust, and interactional trust. An ordinal logit model using self-reported AIMCA use frequency as an external behavioral criterion provided additional criterion-related evidence. The scale can be used to characterize multidimensional public trust perceptions of AIMCAs and provides a structured measurement foundation for subsequent research on acceptance, use intention, continuance intention, and actual use.