Search Results (548)

This paper examines the iconography of the Mother of God holding a book in Early Christian and Medieval art, focusing on representations in which a book or scroll functions as an attribute of the Virgin Mary. Particular attention is given to scenes depicting Mary in relation to the Christ Child, Christ Pantocrator, and the Magnificat. The study explores the symbolic significance of the book and scroll through the textual tradition of the Church Fathers. Adopting the methodological approach to the iconographical structure developed by André Grabar, the paper centers on three interconnected case studies. First, it offers a close re-examination of a Marian scene on the ivory relief of the Werden casket (9th c.) of which the meaning is hard to understand. Second, it analyzes the depiction of the Mother of God in the vault of the crypt of Epiphanius at San Vincenzo al Volturno (9th c.), with particular emphasis on motifs that associate the image with the theme of Mary’s Triumph. Finally, it considers a fresco of Mary and Christ enthroned from the Egyptian monastery of Deir al-Suryan (10th c.), treating these works as semantically and conceptually related. Through this comparative analysis, the paper advances several interpretations of the Magnificat as articulated in Early Christian visual culture and developed in later periods with the contribution of the Byzantine theology. Given the well-established influence of Early Christian art on both the Carolingian Renaissance in the West and the Byzantine East, the shared iconographical details identified here—both formal and conceptual—are understood as deriving from a common visual tradition rooted in Antiquity. Full article

Background: Liver transplant recipients are highly susceptible to infectious complications due to surgical invasiveness and immunosuppressive therapy, and post-transplant bloodstream infection is associated with substantial morbidity and mortality. Although several prediction models for bloodstream infection have been proposed, most focus on emergency department or general ward populations and rely on black-box approaches. This limits their applicability and clinical interpretability in liver transplant settings. Therefore, this study aimed to develop predictive models for post-transplant bloodstream infection using preoperative and perioperative clinical data and to derive an interpretable risk equation through symbolic regression. Methods: We conducted a retrospective observational study including 245 adult liver transplant recipients treated at a single tertiary center. Clinical and laboratory variables were extracted from electronic medical records and analyzed using standard statistical methods. For prediction tasks, multiple conventional machine learning models were developed and compared with a symbolic regression-based model. Predictive performance and model interpretability were evaluated using discrimination metrics and Shapley Additive Explanations. Results: Post-transplant bloodstream infection occurred in 82 patients (33.4%). In the test set, conventional machine learning models showed modest discriminative performance (area under the curve, 0.53–0.64). The symbolic regression model achieved comparable discrimination (area under the curve, 0.63) while providing transparent, threshold-based risk equations. While conventional models primarily relied on laboratory variables, symbolic regression additionally identified perioperative clinical factors and viral serologic markers as important predictors. Discussion: Although overall predictive performance was modest, symbolic regression highlighted viral serologic markers as potential indicators of immunologic vulnerability, extending beyond standard laboratory predictors. Conclusions: This interpretability-focused approach may inform future risk stratification models incorporating richer perioperative data. Full article

Addressing the prevalent issue of “physical preservation but spiritual silence” in the revitalisation of China’s national industrial heritage, this study proposes and empirically validates a “dual-track narrative” design framework that systematically translates cultural values into spatial experiences. The framework integrates a “figure–history” narrative, which crystallises historical lineage and symbolic spirit through spatial sequences, commemorative landmarks, and authentic remains, with a “scene–activity” narrative, which transforms former production spaces into dynamic, culturally vibrant stages through ecological restoration displays, industrial landscape transformation, and flexible activity implantation. Using Nantong Guangsheng Oil Mill as a single-case study, the research employs qualitative methods including archival analysis, field observation, and semi-structured interviews to examine how the dual-track framework operates in practice. The findings reveal that the “figure–history” narrative manifests in a walkable “time corridor” along the north–south axis, where architectural remnants from different eras are organised to materialise Zhang Jian’s industrial salvation ethos and the collective memory of generations of workers. Meanwhile, the “scene–activity” narrative activates underutilised spaces—such as the repurposing of acid treatment ponds into constructed wetlands and paved grounds into public stages—enabling ongoing cultural production, community interaction, and ecological healing. The study demonstrates that the dual-track framework bridges the historical and contemporary dimensions often treated separately in heritage practice, establishing a systematic “translation mechanism” from cultural decoding to design intervention. Theoretically, it contributes to industrial heritage research by integrating narratology, memory studies, heritage interpretation, and situationism into a coherent design methodology. Practically, it offers decision-makers evaluation criteria beyond the preservation-versus-development binary, provides designers with a mode of creative transformation grounded in material authenticity, and suggests to operators a content-driven, event-based model for sustaining heritage spaces. By spatialising and eventising narratives, the dual-track approach enables industrial heritage to function as a catalyst for cultural identity, social vitality, and economic sustainability, offering a transferable paradigm for the adaptive reuse of industrial heritage in contemporary urban contexts. Full article

Prosignification is defined as the process through which the subject generates new meanings by engaging in aesthetic experience, critical reflection, and creative action. Unlike general theories of meaning-making, which primarily describe the cognitive organization of experience, prosignification foregrounds the symbolic–expressive dimension as the central site of meaning production. It refers to the individual and collective capacity to construct meaning from expressive and symbolic experiences, integrating cognitive, emotional, social, and cultural dimensions of learning through intentional creative mediation. Prosignification operates between knowledge construction and subjective experience, enabling learners to connect conceptual understanding with personal interpretation and emotional involvement. Whereas knowledge construction emphasizes epistemic development and transformative learning focuses on perspective transformation through critical reflection, prosignification centers on the aesthetic reconfiguration of experience through symbolic creation and interpretation. Rooted in constructivist and experiential approaches, it unfolds through active, student-centred methodologies, particularly in Project-Based Learning contexts. However, its distinctive contribution may lie in integrating reflection, expression, and creation as interdependent mechanisms of meaning generation. Art education constitutes a particularly relevant context for this process, as its symbolic nature fosters the embodied and shared construction of meaning. Thus, prosignification cannot be reduced to cognitive restructuring or attitudinal change but involves the expressive re-symbolization of lived experience. Full article

Smart-city programmes in emerging economies often produce climate-risk registers, dashboards, and narrative reports that do not lead to real changes in technical specifications or budget decisions. This study examines how the internal audit function can transform such symbolic compliance into enforceable climate-governance practices within Egypt’s state-led smart-city developments. This paper applies an interpretive single-case study design, drawing on interviews, documents, and field observations to analyse how climate-risk signals move from operational systems into governance, procurement, and reporting routines. A unified risk-and-control framework is introduced that integrates enterprise risk management, internal control over sustainability information, and the requirements of the international climate-disclosure standards. The findings show that an internal audit provides the enforcement mechanism that converts climate-scenario breaches into mandatory amendments to design clauses, acceptance tests, and operating and capital expenditure decisions across critical assets such as coastal protection, water systems, district cooling, mobility, and data-centre infrastructure. This study offers a practical governance architecture—such as threshold-to-specification tables, climate-weighted procurement gates, quarterly compliance certifications, and verifiable data-lineage controls—that enables public managers to embed accountable and transparent climate resilience within smart-city programmes. This research contributes to sustainability governance by demonstrating how an internal audit moves climate-risk management from narrative reporting toward enforceable, auditable action. Full article

This paper is the first attempt to interpret the visual ‘construemes’ by the constructivist poet Alexei N. Chicherin, published in the anthology Mena vsekh which appeared in Moscow in1924. ‘Construemes’ can be considered the most enigmatic artifacts of the Russian avant-garde. Although ‘construemes’ can be easily confused with meaningless visual zaum (‘the transrational’), Chicherin’s actions and the very nature of his personality prevent one from interpreting ‘construemes’ as actionist endeavors to scandalize or a ‘play on nonsense’. Analysis of the poet’s treatise Kan-Fun published in Moscow in 1926 required finding the key to deciphering the ‘construemes’, reveals the positivist nature of Chicherin’s visual–phonological exercises. In the treatise, the poet argues for the primacy of the eye and vision. He illustrates synthetic ‘signs’ or ‘pictograms’ with the quotidian example of propaganda posters, capable of influencing millions more effectively than words alone. The study emphasizes the enigmatic nature of the titles of Chicherin’s books, the Nietzschean subtexts of his self-presentation, encrypted allusions to the esoteric and magical tradition of the Tarot, and religious symbolism. Sixteen illustrations help the understanding of Chicherin’s logic behind the creation of his four ‘construemes’, including the most mysterious composition called ‘Raman’ (‘the shortest Kan-Fun Novel in the world’). The structure of this text synthesizes the verbal, visual–graphic, acoustic (phonological symbols) and musical (notes) levels. The article also examines Chicherin’s proven techniques: the appropriation of the sacred dimension and self-presentation as an actor possessing genuine knowledge and capable of competing alone with the entire literary environment. Full article

Zero Trust Network Access (ZTNA) has emerged as a fundamental paradigm for securing cloud-native and distributed computing environments. However, existing ZTNA implementations remain largely limited by static policy enforcement and opaque machine-learning-based anomaly detection mechanisms, which often lack contextual adaptability, policy awareness, and interpretable decision-making capabilities. These limitations create significant challenges in dynamic multi-cloud environments where access behavior continuously evolves and security decisions must be both accurate and explainable. To address these challenges, this study proposes Cognitive ZTNA framework, a unified neuro-symbolic trust enforcement framework that integrates transformer-based behavioral trust modeling with ontology-guided symbolic reasoning. The proposed architecture enables continuous trust evaluation by combining behavioral access patterns with explicit policy semantics through a hybrid trust fusion mechanism. This design allows the system to capture long-range behavioral dependencies while maintaining policy-compliant and interpretable access control decisions. The framework is evaluated using the CloudZT-Bench-2025 dataset, comprising 4.2 million cross-platform access events derived from enterprise security telemetry, AWS CloudTrail logs, and simulated adversarial scenarios. Experimental results demonstrate that Cognitive ZTNA achieves Precision = 0.96, Recall = 0.93, and F1-score = 0.95, significantly outperforming rule-based and machine-learning baselines while reducing the false positive rate to 0.03. In addition, the system maintains real-time feasibility with an average decision latency of 24 ms and explanation latency below 5 ms, while achieving 92% analyst-rated explanation sufficiency. These findings demonstrate that integrating behavioral intelligence with symbolic policy reasoning enables adaptive, interpretable, and policy-aware Zero Trust enforcement. The proposed framework therefore provides a practical foundation for next-generation ZTNA systems capable of supporting secure, transparent, and context-aware access control in modern cloud environments. Full article

Unreinforced masonry walls exhibit limited resistance to lateral loads and, therefore, frequently require strengthening interventions. Carbon fiber reinforced polymer (CFRP) systems provide an efficient retrofit solution; however, current design procedures defined in structural guidelines require repetitive trial calculations to determine the necessary reinforcement amount. This study introduces a hybrid computational process that integrates metaheuristic optimization with symbolic regression to generate direct analytical equations for the estimation of the required CFRP area. First, a comprehensive database containing 1300 optimal strengthening scenarios was generated using the Jaya optimization algorithm under the constraints specified in ACI 440.7R and ACI 530. The resulting dataset was subsequently processed through symbolic regression using the PySR platform to identify explicit mathematical relationships between structural parameters and the optimum CFRP area. Most traditional machine learning approaches operate as black-box predictors. In contrast, the proposed approach generates interpretable closed-form expressions that can be used directly in engineering calculations. Two models were derived from the Pareto-optimal solution set. The first model is a simplified equation emphasizing algebraic simplicity. The second model prioritizes prediction accuracy. The simplified formulation achieved a coefficient of determination of approximately 0.992. The accuracy-focused model achieved a value above 0.997 with very low prediction errors. Validation studies with independent test samples showed that the obtained equations are reliable. The average error for the simplified model is below 4%, and for the high-accuracy model, it is approximately 2%. The results demonstrate that combining the optimization-generated datasets with symbolic regression makes it possible to obtain transparent design equations. These equations eliminate iterative design processes and provide a fast and reliable estimation tool for CFRP strengthening of masonry walls. Full article

Benchmark-driven evaluation helps distinguish between planning quality and interface reliability when large language models are utilized for embodied reasoning in simulation. Our submission to the Embodied Agent Interface Challenge (EAI) is evaluated across four stages of the pipeline. These being goal interpretation, subgoal decomposition, action sequencing, and transition modeling. The tasks run in the BEHAVIOR and VirtualHome simulators, which use constrained action vocabularies, fixed-object inventories and symbolic state representations within a standard evaluation protocol. Our system accesses the OpenAI API using GPT-4.1 for BEHAVIOR, GPT-4.1-mini for VirtualHome, and GPT-5-mini in later exploratory experiments across both environments. The schemas for each task determine how the outputs are structured, and outputs are regenerated when they do not follow the specification. On the final public leaderboard, our system ranked eighteenth overall with a score of 57.92, achieving 68.88 on BEHAVIOR and 46.96 on VirtualHome. In this paper, we describe our approach and discuss what these observations suggest about the strengths and limitations of current language models when used for embodied reasoning. Full article

Traditional architectural decorative patterns are increasingly reused in contemporary design, yet the link between object selection and design generation often remains experience-driven: public perceptual differences are rarely formalized, and evaluation outcomes seldom constrain generative decisions. This study proposes a perceptual demand-driven layered filtering and design response model (PD–LFDR) that treats traditional architectural decorative patterns as comparable and traceable design resources. Perceptual inputs from multiple stakeholders are converged via Kansei-based semantic aggregation into four core dimensions—symbolism, heritage authenticity, recognition and regionality—and are organized as a perceptual evaluation matrix. Grey relational analysis (GRA) is then applied using an expected perceptual level as the reference sequence to identify representative pattern samples suitable for design intervention. An empirical study on decorative patterns from Shaanxi vernacular dwellings demonstrates a closed-loop workflow: (i) first-round GRA filters representative theme samples, (ii) a second-round GRA selects operable minimal gene units, and, under a unified parametric rule set and a traceable two-layer parameter basis (parameter domain definition and parameter selection), (iii) multiple alternatives are generated and re-evaluated through a third-round GRA to support scheme selection. Robustness checks indicate stable rankings under moderate parameter and weight variation, improving interpretability, reproducibility, and decision efficiency for the computational translation of regional cultural visual resources. Full article

Scientific publishing is currently constrained by an unstructured narrative bottleneck paradigm, which increasingly diverges from the scale, complexity, and computational nature of modern research. Despite rapid advancements in data generation and analysis, scientific knowledge is predominantly disseminated as static narrative artifacts, thereby limiting reproducibility, machine accessibility, and cumulative integration. This study explores how scientific communication can be restructured to facilitate scalable validation and reliable knowledge accumulation. We propose the Object-Oriented Scientific Information paradigm, wherein scientific contributions are represented as executable, machine-interpretable objects that integrate structured data, reproducible methodologies, and formally encoded semantic claims. To operationalize this paradigm, we delineate the architecture of an Autonomous Knowledge Engine, a modular neuro-symbolic system that combines domain-specialized Mixture-of-Experts routing, formal verification of claims, and an information-theoretic filter based on marginal information gain. This architecture enables continuous validation, redundancy control, and the integration of scientific contributions within an active knowledge graph. The analysis demonstrates that Object-Oriented Scientific Information (OOSI) and Autonomous Knowledge Engine (AKE) fundamentally differ from existing document-based, executable, and semantic publishing models by shifting epistemic control from narrative evaluation to computational verification. We conclude that transitioning toward a computable scientific record is essential for sustaining reliable and self-correcting science in the context of accelerating knowledge production. Full article

In this work, a formal asymptotic framework based on infinite number expressions is employed to investigate structural relations associated with the Dirichlet representation of the Riemann zeta function. Within this framework, infinite number objects are interpreted through asymptotic representatives and serve as symbolic encodings of asymptotic behavior in the regime x → ∞. A divergent real series is constructed from the sum of entries of an n × n matrix in the asymptotic limit n → ∞ and analyzed in relation to the squared modulus of a Dirichlet-type series. When the common parameter coincides with the imaginary part of a nontrivial zero of the Riemann zeta function on the critical line, the framework yields a structured cancellation mechanism, leading to parameter-dependent decay or convergence toward the constant −γ/2. Additional formal asymptotic relations are derived linking nontrivial zeros, divergent expressions, and the Euler–Mascheroni constant. The theoretical analysis is accompanied by numerical computations in double-precision arithmetic, which serve as consistency checks of the predicted asymptotic behavior. The proposed approach provides a coherent representative asymptotic methodology for organizing and analyzing identities involving divergent expressions arising in analytic number theory. The resulting relations are interpreted within this representative framework and are intended as structural asymptotic identities rather than classical equalities of divergent series. Full article

Unlike conventional virtual idols like Hatsune Miku, which rely on pre-set voice libraries and stage scripts, AI-customized virtual idols achieve real-time interaction through generative artificial intelligence, continuously iterating their personality traits, language style, and even value expression along with fan and user interactions. AI-customized virtual idols, as pre-defined cultural commodities in the digital age, tend to focus on static, functional interpretations and have not yet fully entered the dynamic construction process as “subjects in the process of generation.” This study, based on a deep mediation perspective, employs a research method combining app roaming and semi-structured interviews to focus on the sociological examination of young fan groups’ use of AI tools to customize virtual idol companionship. It explores the reciprocal relationship between fan groups and customized virtual idols. The study finds that the AI-customized idols fan group constitutes a typical “actor group,” and its interaction practices are essentially a “fluid interaction” of human–machine intimacy. Young fan groups mainly interact with AI-customized virtual idols based on materiality, cognition, visibility, and emotional frames, thereby generating rich meaning production and symbolic imagination during the usage process. Fan groups and AI-customized virtual idols have developed different relationship paths, including mutual attachment, returning to normalcy, seeking substitutes, or direct withdrawal, revealing the inherent contradictions and tensions in digital intimacy, as well as the self-adjustment strategies of individuals under the mediation of technology. This process presents a “human-machine-idol” triadic relationship framework, becoming a new paradigm for intimacy in the digital age. Full article

Background/Objectives: Municipal hospitals in transitional health systems operate under structural resource constraints that complicate managerial decision-making and shape patient perceptions. This study examines how patients interpret resource allocation and evaluate the ethical and legitimacy consequences of alternative strategic priorities. Methods: A qualitative research design was employed using semi-structured patient interviews. Participants were recruited using purposive sampling based on predefined inclusion criteria: age over 18, hospitalization for digestive symptoms, undergoing diagnostic investigations, and provision of informed consent. Thematic analysis identified key expectation domains related to technological modernization, workforce capacity, infrastructure, and relational communication. These themes were translated into core governance variables and integrated into a conceptual simulation model comparing three allocation scenarios: technological investment, human resource expansion, and status quo preservation. Results: Findings show that patient evaluations extend beyond satisfaction to include distributive fairness, symbolic modernization, and institutional legitimacy. Simulation findings suggest that technological investment strengthens symbolic legitimacy and perceived equity but may increase workload and fiscal exposure; workforce expansion enhances relational justice and operational stability yet leaves modernization gaps; and status quo preservation maintains short-term fiscal balance while risking gradual legitimacy erosion. Conclusions: The study demonstrates that satisfaction metrics alone are insufficient for governance evaluation. Integrating ethical analysis, organizational legitimacy theory, participatory input, and systems thinking provides a structured framework for assessing resource allocation trade-offs in resource-constrained municipal hospitals. Full article

A new nearness grey incidence model that not only measures the degree of correlation but also measures the direction is proposed to analyze the distribution characteristics of air pollutants in the Beijing–Tianjin–Hebei region, from 2016 to 2024. To be specific, improvements in this proposed model lie in the following aspects: First, the calculation method of the symbol judgement factor is updated, so that the final nearness grey incidence degree can better reflect the nearness degree and nearness direction between any two sequences, which improves the stable robustness of the grey incidence degree. Second, the anti-fluctuation factor is introduced into the new model, and the relative volatility between series is included in the calculation process of the grey incidence degree. Third, several practical properties of the proposed model are elaborated to further interpret the feasibility and adaptability of the proposed model. In experiments, based on the daily data of the six pollutants in the Beijing–Tianjin–Hebei region from 2016 to 2024, using the new model as a tool, the main pollutants in the Beijing–Tianjin–Hebei region are identified, the temporal and spatial distribution of different pollutants is analyzed, the changes in trends of air pollution processes in the past 9 years are identified, and a comparison with pollution levels of other cities in Beijing–Tianjin–Hebei and Beijing is also detailed. Finally, the superlative performance of the proposed model is confirmed by the model comparison, Monte Carlo analysis and example analysis. Full article