Search Results (908)

Differential enrichment of shale oil hydrocarbon fractions exerts a fundamental control on the spatial distribution of “sweet spots” and the efficiency of unconventional resource recovery. This study investigates the continental shales of the Upper Es4 Member in the Dongying Sag, Bohai Bay Basin, through an integrated analytical framework combining Laser Scanning Confocal Microscopy (LSCM), Scanning Electron Microscopy (SEM), and high-pressure mercury intrusion. By moving beyond qualitative observations, we characterize the micro-scale partitioning of light and heavy fractions and establish a deterministic hierarchy of controlling factors. Our results indicate the following. (1) Mineral composition functions as a “primary geochemical filter,” where carbonate minerals exhibit a preferential adsorption affinity for light fractions (≤ C₁₈), while clay minerals facilitate the selective retention of heavy components (> C₁₈). (2) Pore–throat architecture acts as a “secondary mobility modulator.” A statistically significant linear correlation (R² = 0.72, p < 0.05) was identified between mean pore diameter and the light-to-heavy fluorescence ratio, suggesting that interconnected macropores in carbonate laminae provide low-resistance conduits for light oil accumulation, whereas isolated mesopores in argillaceous matrices promote heavy-component sequestration. (3) Thermal maturity (Ro) drives a progressive shift in the light-to-heavy ratio, enhancing oil fluidity and regulating the transition from adsorption-dominated to migration-dominated enrichment. This study clarifies the lithofacies-dependent coupling mechanisms between mineral diagenesis and pore-scale fractionation, providing a semi-quantitative conceptual model for shale oil sweet-spot prediction in complex lacustrine basins. Full article

Singular Design Foresight (SDF) is proposed as a foundational methodological framework for advancing Design Foresight (DF) toward a more explicit, traceable, and evaluable scientific discipline. The framework formalizes DF as a structured cycle in which qualitative foresight inputs—such as signals, trends, and expert interpretations—are progressively transformed into analyzable representations that support decision closure under conditions of structural uncertainty. SDF combines an expert-defined conceptual universe with semantic projections to relate textual and contextual evidence to anticipatory constructs, enabling the generation of traceable indicators and structured configurations of viable futures. Within this architecture, the Stakeholder Viability Principle (SVP) functions as a filtering mechanism that delimits relevant futures according to continuity, agency, and axiological coherence, while Social Singularity captures context-specific critical transitions that shape when and why decision closure becomes necessary. The framework is organized in alignment with Design Science Research (DSR), adopting an evaluation logic centered on validity, utility, and attribution. Rather than presenting conclusive system-level validation, the article synthesizes summative evidence from previously published studies on semantic projections, singularity detection, and mixed expert–corpus foresight applications to support the plausibility, internal coherence, and operational feasibility of the proposed framework, while delimiting full integrated validation as a future research objective. SDF does not aim to provide deterministic prediction; instead, it enables auditable anticipatory representations and justified closure under uncertainty. In this sense, the framework is compatible with forecasting understood as the production of evaluable anticipations under explicit assumptions, while preserving the interpretive and situated character of strategic decision-making. Full article

Cells contain an unexpectedly large diversity of lipid molecules. Modern lipidomics studies have revealed that even a single cell type can harbor hundreds to thousands of distinct lipid species that differ in headgroup structure, acyl chain length, and degree of unsaturation. While this remarkable diversity is now well established, its biological significance remains incompletely understood. Why do cells maintain such complex lipidomes? In this review, we examine several conceptual frameworks that may help explain the origin and functional significance of lipid diversity. First, the physical properties of biological membranes impose constraints on lipid composition, as variations in lipid structure influence membrane fluidity, curvature, thickness, and phase behavior. Second, lipids can regulate membrane protein function through specific interactions and through the physical environment of the lipid bilayer. Third, lipid metabolism generates signaling molecules that participate in diverse regulatory pathways. Fourth, lipid metabolic networks continuously remodel membrane composition, producing dynamic lipidomes that can adapt to physiological conditions. Finally, evolutionary processes have shaped membrane lipid composition across different domains of life, suggesting that lipid diversity may reflect long-term adaptation to functional and environmental constraints. Taken together, these perspectives suggest that lipid diversity is unlikely to be a simple byproduct of metabolism. Instead, the cellular lipidome may emerge from the interplay of membrane biophysics, metabolic network architecture, protein regulation, and evolutionary pressures. Understanding why cells contain thousands of lipid species therefore represents an important challenge for modern cell biology and may reveal fundamental principles governing the organization of biological membranes. Full article

Interleukin-5 (IL-5) has long been positioned as a lineage-restricted cytokine primarily responsible for eosinophil differentiation and survival. However, emerging mechanistic and clinical evidence supports a broader conceptual shift: IL-5 should no longer be viewed solely as an eosinophil growth factor, but as a context-dependent regulator embedded within dynamic airway immune networks. Drawing on advances in eosinophil subset biology, receptor signaling, and tissue-level immune crosstalk, this review reframes IL-5 biology through the lens of systems-level inflammatory regulation across airway and systemic eosinophilic diseases. Recent data reveal functional heterogeneity between resident and inflammatory eosinophil subsets, challenging the assumption that blood eosinophilia uniformly reflects pathogenic activity. In parallel, functional IL-5 receptor expression has been identified on multiple structural and immune cell populations—including epithelial cells, mast cells, plasma cells, basophils, neutrophils, and fibroblasts—supporting a broader tissue-signaling paradigm. Experimental and translational studies further link IL-5 to epithelial integrity, airway remodeling, and mucus pathology, suggesting structural and network-level effects beyond simple eosinophil depletion. Comparative analyses across asthma, chronic rhinosinusitis with nasal polyps, and COPD demonstrate that eosinophilic inflammation is biologically heterogeneous and context-dependent. While IL-5-targeted therapies yield consistent benefit in severe asthma, therapeutic responses in other airway diseases appear to be shaped by local tissue architecture and mixed inflammatory programs. Together, these observations illustrate a paradigm shift from pathway-specific inhibition toward network-informed disease control and highlight key areas for future mechanistic investigation. Full article

This study proposes a sensor-free approach for indoor fire detection that leverages existing Wi-Fi infrastructure as a passive sensing modality. By extracting Channel State Information (CSI) from prevalent 802.11n Wi-Fi signals and applying an unsupervised deep anomaly detection model, the approach conceptualizes the wireless environment as a sensorless detection field capable of identifying combustion-induced perturbations without requiring any physical sensors. CSI data were collected in both normal and flame-induced states under three combustion conditions (gasoline, wood, plastic), each introducing unique signal perturbations. These data, which exhibit diverse signal perturbations, were used as input to four unsupervised deep anomaly detection architectures: a variational autoencoder (VAE), a 1D convolutional autoencoder (CNN-AE), a long short-term memory autoencoder (LSTM-AE), and a hybrid CNN-LSTM autoencoder. Each architecture was trained exclusively on baseline data to learn compact latent representations of normal signal patterns. Among the evaluated architectures, CNN-AE achieved perfect detection across all scenarios, showing high responsiveness to signal disruptions. LSTM-AE tracks prolonged combustion but struggles with fast-onset anomalies. VAE maintains low error during baseline but misses sharp deviations. These findings validate that Wi-Fi CSI encodes latent combustion features. The method requires no additional sensors and operates on existing signals, enabling scalable smart building integration via lightweight software updates. Full article

Epigenetic mechanisms profoundly regulate gene expression, developmental trajectories, and phenotypic variation, extending biological influence beyond DNA sequence alone. A growing body of evidence suggests that environmental exposures, including pollutants, drugs, stress, and diet, can induce germline and early embryonic epimutations that alter developmental programs with lasting consequences for neurodevelopmental and cognitive outcomes. However, the fields most relevant to these processes have largely developed independently. These include germline epigenetics, early embryonic patterning, neurodevelopment and cognitive regulation, and intergenerational or transgenerational inheritance. Each field has its own conceptual frameworks and mechanistic models. This fragmentation obscures the biological reality that these systems are tightly interconnected: environmentally induced epigenetic perturbations in gametes can reshape the epigenetic landscape of the early embryo, influence lineage allocation during gastrulation, and ultimately modify the molecular architecture of the developing central nervous system. A systems–biology perspective capable of linking germline epimutations and early embryonic epigenetic instability to later neurodevelopmental and cognitive phenotypes and their potential inheritance is therefore required. This review synthesizes current evidence across these traditionally isolated domains and proposes a coherent mechanistic framework linking germ cell epimutations and early embryonic epigenetic instability to the emergence of neurodevelopmental and cognitive phenotypes. By bridging these conceptual gaps, we aim to establish a cohesive foundation for understanding how early epigenetic disruptions generate long-lasting and in some cases heritable effects on brain development and cognitive function. Full article

This paper extends the metacybernetic framework by grounding its conceptual descriptions in first principles of information physics. We demonstrate that for living systems to organise efficiently under uncertainty, they must adhere to a strict recursive pattern, a “fractal seed” originating in the third-order interaction between potential and action. By utilising Fisher Information Field Theory (FIFT) within an Informational Realism paradigm, we formalise this process through variational analysis on an implicate–explicate manifold. Under a rigorous informational parsimony constraint (a functional analogue of the holographic principle), we treat the J-field as the dispositional reservoir of latent potential and the I-field as the operative field of structured configurations, and show how their autopoietic coupling generates the system’s Potential–Actuation trait poles as a scale-invariant viability structure This coupling reveals that the boundary substructure, which encodes the holographic content, directly conditions the emergent superstructure through a deterministic parity rule inherited from the dyadic logic of the minimal generic living system represented by ${\widehat{\theta }}_2$. Drawing on the application of Fisher Information, we show that maintaining informational parsimony requires the system’s architecture to oscillate: odd-numbered orders express two traits (dyads), whereas even-numbered orders express three (triads). This produces a canonical 2–3–2–3–2 sequence, preventing a combinatorial explosion of traits as systemic depth increases. We present the Cogitor5 model as a complete fifth-order exemplar of this rule, demonstrating how this rhythmic structural pattern enables self-evolution, systemic coherence, and collective intelligence in both biological and artificial agencies. Full article

Artificial Intelligence (AI) has traditionally been developed according to a model-centric paradigm, in which progress is driven by increasingly sophisticated learning architectures applied to largely fixed datasets. However, this paradigm exhibits well-known limitations, including sensitivity to label noise, distribution shifts, adversarial perturbations, and limited transparency and reproducibility. These issues indicate that many of the current bottlenecks of AI systems arise from deficiencies in data rather than from model design. In this paper, we adopt and formalize the Data-Centric Artificial Intelligence (DCAI) paradigm, which places data quality, semantic consistency, and representativeness at the core of the AI lifecycle. From this perspective, performance, robustness, interpretability, and regulatory compliance are primarily achieved through systematic data engineering, including data curation, enrichment, validation, and continuous monitoring, rather than through repeated model re-engineering. The contributions of this work are threefold. First, a conceptual framework is provided to clarify the epistemic and methodological foundations of DCAI and distinguish it from traditional model-centric approaches. Second, a data-centric lifecycle is presented, covering training data development, inference data design, and data maintenance and integrating techniques such as semantic data representation, active learning, synthetic data generation, and drift-aware quality control. Third, the role of DCAI in the context of Generative AI is analyzed, showing how data-centric practices are essential to ensure robustness, accountability, and responsible deployment of large-scale generative models. Overall, this work positions DCAI as a coherent methodological and technological framework for the development of trustworthy, resilient, and sustainable AI systems, making a research contribution and providing a reference model for industrial and regulatory contexts. Full article

Traditional food systems have historically sustained nutrient adequacy under conditions of environmental variability and limited food diversity, yet their underlying nutritional mechanisms remain insufficiently integrated into contemporary nutrition science. This article attempts to provide a conceptual synthesis of how traditional dietary practices may function as informal nutrient optimization strategies. Drawing on evidence from nutrition science, food chemistry, and human physiology, it examines how food processing techniques (e.g., fermentation, soaking, germination, and thermal treatment), food pairing, and structural properties of foods influence nutrient bioavailability, absorption, and metabolic responses. Across diverse dietary contexts—including Mediterranean, agrarian cereal–legume, and East Asian-type patterns—recurring mechanisms emerge that can boost mineral solubility, improve protein digestibility and amino acid balance, facilitate vitamin absorption, and modulate glycemic responses. These effects are mediated not only by nutrient content but by interactions within the food structure and at the meal level. The synthesis supports a reframing of traditional diets as functional nutritional architectures in which processing and dietary configuration may enhance nutrient utilization efficiency. From this perspective, nutrient adequacy arises from coordinated structural features rather than from maximal nutrient density alone. The findings can be influential in contemporary nutrition research and policy, highlighting the need to move beyond reductionist intake-based models toward integrated approaches that account for bioavailability, metabolic handling, and dietary context. Several transferable principles of nutrient optimization are proposed, offering a framework for designing nutritionally efficient and resilient diets in modern settings. Full article

This study reexamines the concept of techne within computer-aided architectural practices and proposes a Lakatosian research program framework to understand its historical continuity and contemporary transformation. Since its ancient origins, techne has been not only a knowledge of making but a mode of thinking and knowing that unifies mental and material production within a single practice. To analyze its transformation, Imre Lakatos’s methodology of research programs is adopted as a framework, and techne is examined as a research program. Within this framework, the hard core of techne is defined as the craftsman–tool–material–knowledge relationship grounded in the unity of making and knowing, while code, software, parametric systems, and algorithmic feedback mechanisms are interpreted as components of the protective belt in the digital age. The study specifically focuses on the relationship between the designer and the tool. Bernard Cache’s Objectile practice is presented as a case study that illustrates the architect’s transformation in the digital age from a user of tools to a developer of tools. The findings reveal the following principal patterns: tool development emerges as a conceptual necessity and an epistemic act; knowledge in digital practice acquires a hybrid structure between explicit and tacit knowledge; and feedback loops reshape not only design but also the tool itself. Consequently, techne is repositioned as a progressive research program that remains productive in architecture at both theoretical and operational levels. Full article

Aircraft conceptual design is an iterative process that seeks to obtain a feasible design that meets a series of mission and configuration requirements. Starting with several guesses regarding the initial sizing and aerodynamics of the future aircraft, a first resulting general layout is found, which is then subjected to trade studies where initial assumptions are altered in search of a refined design. With the aim of enhancing design solutions and reducing time costs derived from calculations, the authors of the present paper have developed ARCADE (AiRcraft ConceptuAl DEsign Tool), a framework that automates, in multiple thematic modules, the steps and calculations needed for the conceptual design process of fixed-wing aircraft. This work presents the basis for the early architecture of ARCADE, developed in Python and focused on the use of data retrieved from existing aircraft for the first design hypotheses. Initial findings of the use of ARCADE show a small relative error between the first parameter guesses, made based on similar aircraft, and the results of the next design iteration, which are independent of reference aircraft. This suggests that the design parameters of the target aircraft are accurately guessed when using existing aircraft information for the initial estimations of this process. Full article

Resolving the dichotomy between wide detection ranges and low mechanical hysteresis remains a critical challenge in flexible electronics, largely governed by the intrinsic viscoelastic creep of polymeric dielectrics. Drawing inspiration from the distinctive load-bearing mechanisms of traditional Chinese Sparrow Brace architecture, we report a mechanically optimized tilted micro-architecture designed to enhance structural resilience. Unlike conventional soft elastomeric pillars that easily succumb to mechanical failure, this BOPS-based tilted geometry provides excellent load-bearing capacity, effectively preventing premature failure. Finite element analysis (FEA) confirms that this tilted geometry forces a fundamental shift from conventional bulk compression to structural bending. Because this bending-dominated architecture drives rapid elastic recovery, it significantly mitigates the severe effects of the polymer’s viscoelastic creep under the tested loading conditions, achieving reliable signal reversibility with low hysteresis. We fabricated this specific architecture via programmable femtosecond laser direct writing (FsLDW) on biaxially oriented polystyrene (BOPS) films, harnessing the material’s entropy-driven self-growth kinetics. By merging this localized growth mechanism with the architectural design, we effectively bypassed the complexities of traditional molding, achieving mask-free, in situ growth of large-scale, highly uniform dielectric micro-arrays. The resulting sensor delivers a remarkably broad working range (up to ~2.28 MPa) coupled with a negligible recovery error (~1.3%), an agile dynamic response (~70/80 ms), and consistent operational durability. Ultimately, this work combines architecture-inspired structural design with advanced femtosecond laser surface microengineering, providing a conceptually novel and scalable pathway for next-generation flexible sensing. Full article

Digital Twins (DTs) are increasingly explored in tourism and hospitality as enabling technologies for smart destinations, service optimization, and data-driven decision-making. Yet these environments are inherently human-centered. Existing DT implementations, however, are largely technology-driven and focus mostly on infrastructures and operational processes. This study presents a systematic literature review of DT applications in tourism and hospitality. It combines a comparative taxonomy with a technological and data-oriented analysis to examine how these systems are currently conceptualized, implemented, and integrated. The review analyzes 42 studies, classifying them by application level, twin focus, architectural approach, and human integration. The results show a strong dominance of destination- and facility-level DTs, limited human-centered models, and a prevalent use of varied sensing technologies. There is limited attention to interoperability and semantic integration. Governance, socio-technical aspects, and real-time synchronization mechanisms are also mostly underexplored. Based on these findings, this study identifies key research gaps and calls for a shift towards Social Digital Twins (SDTs). SDTs integrate human actors, social interactions, and governance within unified modelling frameworks. This transition will require advances in semantic and ontology-driven architectures. Greater attention to privacy, trust, and user acceptance in data-intensive service environments is also needed. Full article

Digital tectonics is often discussed through design–production integration, computational form generation, and digital fabrication, yet frameworks that systematically explain how architectural knowledge is constituted in process remain limited. This study addresses that gap by approaching digital tectonics not as an instrumental or formal design approach, but as a knowledge regime. Methodologically, it combines a conceptual–genealogical approach with an onto-methodological reading strategy grounded in Deleuze’s ontology of becoming and De Landa’s assemblage methodology and develops a core reading matrix. The study shows that knowledge in digital tectonics intensifies across potential setup, the productive threshold, behavioral stability, and feedback. Within this model, architectural making knowledge is understood not as a fixed content represented in advance, but as an operative process that concentrates decision-making within production and is reorganized through feedback. The article concludes by proposing an analytical reading model that redefines digital tectonics not merely as a technical or formal category, but as an onto-methodological problem field in which architectural knowledge is constituted in process. Full article

Cucurbit crops—including cucumber (Cucumis sativus), watermelon (Citrullus lanatus), and melon (Cucumis melo)—are of major economic and nutritional importance worldwide. Yet their productivity and quality are severely compromised by foliar fungal diseases, particularly powdery mildew (PM), downy mildew (DM), and target leaf spot (TLS). While PM and DM have been extensively studied, TLS has emerged as an increasingly prevalent and damaging disease in key production regions, yet it remains comparatively understudied—especially with respect to its molecular basis and comparative pathobiology relative to PM and DM. Current reliance on chemical fungicides is hampered by escalating pathogen resistance and concerns over residual toxicity, whereas conventional breeding approaches face inherent limitations in pyramiding durable, broad-spectrum resistance against multiple pathogens. In this context, cucumber has emerged as a pivotal model species for dissecting foliar disease resistance mechanisms in cucurbits, supported by a high-quality reference genome, extensive resequencing datasets, diverse germplasm collections, and an efficient Agrobacterium-mediated transformation system. Despite these advantages, existing reviews predominantly address PM or DM resistance in isolation; comprehensive syntheses integrating TLS resistance advances—and critically, cross-disease comparisons of genetic architecture, transcriptional reprogramming, and defense signaling—are notably scarce. Furthermore, the translational pipeline—from gene discovery and functional validation to deployment in marker-assisted or genome-edited breeding—lacks systematic evaluation. Here, we provide a focused, cucumber-centered review that (i) synthesizes recent progress in mapping QTLs and GWAS loci, and characterizing key resistance-associated gene families (such as NLRs, RLKs, PR genes) conferring resistance to PM, DM, and TLS; (ii) integrates transcriptomic, epigenomic, and proteomic evidence to delineate conserved versus pathogen-specific host responses; (iii) highlights breakthroughs and unresolved questions in TLS resistance research, including the roles of novel susceptibility factors and non-canonical immune regulators; and (iv) critically assesses bottlenecks in translating resistance genes into practical breeding outcomes—such as linkage drag, functional redundancy, and genotype-by-environment interactions—and proposes empirically grounded strategies for accelerating molecular design of multi-disease-resistant cultivars. Collectively, this review aims to bridge fundamental insights with applied breeding goals, offering a conceptual and strategic framework for integrated management of foliar fungal diseases and the development of durable, broad-spectrum resistance in cucurbits. Full article