Search Results (70,490)

Access to the sky is a key element of residential environmental quality. In densely built-up urban areas, exposure to the sky is often limited not only quantitatively but, above all, directionally. Traditional illuminance metrics, such as the Sky View Factor (SVF) or Daylight Factor (DF), describe the proportion of visible sky or the amount of light in an averaged manner, without considering its relationship to the functional organisation of the human field of view.This article introduces the Relative Retinal Image (RRI) metric, which evaluates directional access to the sky through geometric analysis of viewing directions in relation to functional zones of the visual field, without reconstructing perceived images or simulating physiological processes. Within this geometric framework, human vision is interpreted as operating simultaneously in two visual cones: a narrow central cone responsible for acute, conscious vision (RRI-A), and a wider peripheral cone enabling the reception of low-resolution but spatially stable stimuli (RRI-B). For clarity, three concentric central ranges are distinguished: foveal (0–2.5°), sharp central (0–5°), and extended interpretative central vision (up to 10°). The proposed approach provides a geometry-based analytical tool that complements existing daylight metrics in the assessment of sustainable residential environments, without formulating normative or biological design prescriptions. Based on geometric and graphical analyses and a case study of the Józefowiec housing estate in Katowice, the results indicate that the directional structure of the sky view may be lost despite compliance with conventional planning criteria. Full article

This study examines the socioeconomic and behavioral determinants, together with spatial heterogeneity, influencing the adoption of solar irrigation pumps in Bangladesh. Five study regions of Bangladesh were sampled using stratified random sampling to collect 257 respondents, who were familiar with both solar and diesel pumps, to justify the energy transition, ensuring sample equity throughout the regions. Income inequality among respondents was assessed using the Lorenz curve, revealing that the bottom 50% of respondents only earned 20% of total income, while a Gini coefficient of 0.46 indicated moderate to high income disparity. To determine whether socioeconomic factors and spatial heterogeneity significantly influence adoption decisions, a Firth’s penalized likelihood logistic regression model was employed, complemented by predictive and average marginal effects for regional categories. The results identified that training, social influence, large household size and income are the prominent drivers for solar pump adoption. Based on the significant spatial heterogeneity, we further recorded a five-point Likert scale response to design region-wise policy recommendations for the fast diffusion of solar pumps. Financial incentives emerged as the most critical policy lever, with 89.10% of respondents expressing strong agreement and a mean score of 4.83. Overall, these findings highlight the central role of socioeconomic and spatial factors in shaping adoption behavior and suggest that policy interventions should prioritize targeted financial and technical support to promote the equitable and rapid diffusion of solar irrigation technologies. Full article

Waste is increasingly recognized as misplaced biomass, underscoring its potential for reintegration into sustainable environmental management strategies. Biomass pyrolysis has emerged as a promising value-adding process capable of enhancing material properties for diverse applications. In this study, discarded Pili (Canarium ovatum Engl.) nutshells (PS) were utilized as a pyrolysis feedstock to upgrade their fuel characteristics. Pyrolysis conditions were optimized using response surface methodology (RSM) based on a central composite design (CCD) to maximize fixed-carbon content and higher heating value (HHV). The optimized biochar achieved a maximum fixed-carbon content of 86.15% and an HHV of 32.10 MJ/kg at a pyrolysis temperature of 600 °C and a residence time of 60 min, values comparable to those of conventional coal. Under these optimized conditions, the fixed-carbon content and HHV of the precursor biomass were enhanced by up to 254.7% and 58.4%, respectively. Statistical analysis indicated that pyrolysis temperature was the most significant factor influencing both fixed-carbon content and HHV (p < 0.05). The optimized biochar exhibited low volatile matter (8.88%), low ash content (4.97%), and low atomic ratios (H:C = 0.291; O:C = 0.077), indicating a high degree of carbonization and thermal stability. Energy-dispersive X-ray (EDX) analysis identified alkali and alkaline earth metals (Ca, Mg, Na), which contributed to the ash fraction, with minor heavy metals present, predominantly Pb. Hence, these findings enhance understanding of how pyrolysis conditions affect PS–biochar properties, improving fuel quality indicators. Full article

The blood–brain barrier (BBB), a core component of the neurovascular unit (NVU), meticulously regulates material exchange between the blood and brain parenchyma, serving as a critical barrier for maintaining the homeostasis of the central nervous system (CNS). Neuroinflammation, a pivotal response of the CNS to injury and disease, can disrupt NVU homeostasis when excessive or persistent, acting as a core pathogenic driver of various intractable neurological disorders. Chemokines, as key signaling molecules guiding the directional migration of immune cells, form the central hub mediating the dynamic regulation of neuroinflammation and the BBB. However, existing studies mostly focus on single disease systems or chemokine families, neglecting the bidirectional heterogeneity of different chemokine axes in BBB regulation and the common regulatory rules across diseases, while lacking systematic exploration of clinical translation challenges caused by the redundancy and spatiotemporal heterogeneity of the chemokine network. This review systematically clarifies the bidirectional regulatory effects of the core axes of the three major chemokine families (e.g., CCL2/CCR2, CXCL12/CXCR4, CX3CL1/CX3CR1) on the BBB. For the first time, we integrate a multi-dimensional regulatory model based on concentration, location, and time to analyze their molecular mechanisms and regulatory heterogeneity in promoting BBB disruption under pathological conditions versus mediating barrier repair and neuroprotection under specific spatiotemporal conditions. Combined with advancements in cutting-edge models such as microfluidic chips, we discuss the clinical translation progress of chemokine research, including potential biomarkers and targeted therapeutic strategies, and propose precise breakthrough paths for the two core challenges of network redundancy and spatiotemporal heterogeneity. Finally, we construct a complete research framework for chemokine-mediated regulation of NVU homeostasis, providing novel insights and directions for restoring BBB function and treating intractable neurological diseases. Full article

This paper establishes a graph-theoretic framework for idealization semigroups arising from Bruck–Reilly extensions. Building on a recent study by Wazzan and Ozalan, we introduce five graph families—${\Gamma }_E$, ${\Gamma }_0$, ${\Gamma }_{\mathrm{Cay}}$, ${\Gamma }_{\mathcal{K}}$, and $\Gamma \left(G_k\right)$—each encoding a distinct algebraic facet of $S{B}_i(\bowtie )B$. We prove explicit correspondences linking combinatorial invariants to algebraic structure: diameter captures generating efficiency and semilattice height; girth signals short relations; chromatic number bounds idempotent cardinalities and $\mathcal{D}$-class counts; clique number measures maximal commuting subsets; and Laplacian spectra encode ideal size and Schützenberger groups. Our central result demonstrates that Green’s relations are combinatorially recoverable from graph pairs. For commutative $S{B}_i(\bowtie )B$, $({\Gamma }_E,{\Gamma }_{\mathcal{K}})$ uniquely determines $\mathcal{J}$-order, $\mathcal{D}$-classes, and $\mathcal{H}$-classes via neighborhood inclusions, bipartite components, and automorphism orbits, yielding the first algorithmic reconstruction of ideal-theoretic structure from graph data. The framework is implemented in SageMath as a reproducible open-source toolkit validated on concrete examples. This work synthesizes algebraic graph theory, semigroup theory, and computational mathematics into a unified algebraic-combinatorial dictionary, providing both new analytical tools and a methodological template for studying algebraic constructions via graph invariants. Full article

The vaginal microbiome (VM), a complex and dynamic microbial ecosystem, is now recognized as a central determinant of female reproductive and gynecologic health. Under homeostatic conditions, a Lactobacillus-dominant ecosystem maintains vaginal acidity, provides colonization resistance, and modulates mucosal immunity. Conversely, vaginal dysbiosis—characterized by Lactobacillus depletion and anaerobic or aerobic overgrowth—is associated with infectious vaginitis, increased susceptibility to sexually transmitted infections, and non-infectious conditions such as genitourinary syndrome of menopause. This review provides an integrated overview of the composition, functional characteristics, and host interactions of the VM across health and disease. We highlight major mechanisms by which microbial dysbiosis contributes to disease pathogenesis, including biofilm formation, altered microbial metabolism, and immune dysregulation. In addition, we discuss the translational potential of the VM as a source of diagnostic and prognostic biomarkers and as a target for emerging microbiome-dependent therapeutic strategies. Collectively, current evidence supports the view that vaginal dysbiosis is a heterogeneous and context-dependent state driven by distinct pathogen- and host-related mechanisms, underscoring the importance of prioritizing microbiome restoration rather than pathogen eradication alone. Full article

How digital technology can effectively drive ecological collaborative governance in trans-administrative river basins is a core prerequisite for achieving intelligent ecological governance of river basins. Based on 402 micro survey responses collected from water conservancy, environmental protection and other relevant departments in the middle reaches of the Yangtze River Basin, this study identifies the characteristics of digital technology from three dimensions: tool, power and capacity. By integrating factor analysis and the mediating effect model, it empirically examines the impact of digital technology on inter-provincial ecological collaboration in river basins and its underlying mechanism. The results show that: (1) Digital technology exerts a significantly positive driving effect on inter-provincial ecological collaboration in the middle reaches of the Yangtze River Basin, and this conclusion remains robust after conducting robustness tests including the re-measurement of digital technology and the exclusion of interference from smart water conservancy pilot projects. (2) Mechanism analysis reveals that central government support and public participation play partial mediating roles in the relationship between digital technology and inter-provincial ecological collaboration, and both variables exert a masking mediating effect on the sustainability of inter-provincial ecological collaboration. These findings provide micro-evidence-based policy implications for optimizing the digital collaborative governance system of river basins. Full article

Background: Lip aging is a heterogeneous and visually complex process, yet a standardized morphological classification applicable to clinical practice is still lacking. Current approaches mainly focus on volumetric loss or perioral rhytids, while the geometric features of the lips, including borders, projection, and eversion, remain poorly codified. Methods: Fifty anonymized lip images acquired under standardized conditions using digital facial imaging were independently evaluated by five physicians experienced in esthetic medicine. Images were classified according to three predefined morphotypes representing distinct patterns of lip aging. Inter-rater reliability was assessed using Fleiss’s kappa statistic. Results: Three recurrent morphotypes were consistently identified: devolumized lips, central lips, and chapped lips. Overall, 87% of images were assigned to one of the three morphotypes by at least four of five evaluators, while 13% were classified as undefined due to mixed features. Inter-rater agreement was substantial (κ = 0.89; 95% CI 0.79–0.99), confirming high reproducibility of the proposed classification. Conclusions: This study proposes a simple and reproducible image-based morphotypic classification of lip aging that captures recurrent visual patterns within this cohort. The framework may facilitate standardized clinical communication, support personalized rejuvenation strategies, and provide a foundation for future quantitative imaging studies and AI-based phenotype recognition in esthetic and reconstructive practice. Full article

In the context of rapid urban tourism expansion and the growing emphasis on equitable and sustainable transport development, understanding how transport systems support different types of attractions has become increasingly important. This study investigates how attraction hierarchy and functional type interact with public transport accessibility to shape urban tourism patterns and equity. Whereas prior work emphasizes objective metrics, the alignment between perceived accessibility and actual transport conditions remains understudied. Using Wuhan’s A-rated and popular unrated attractions as a case, we have developed an innovative “ objective–perceived coupling framework that integrates GIS network analysis, travel cost matrix, non-parametric testing, and online comment text mining methods to examine how scenic spot levels (A-level and unrated popular scenic spots) and functional types interact with the public transportation system from both objective and perceptual dimensions. Results show: (1) A-rated attractions cluster in suburbs with low accessibility, while unrated sites concentrate centrally with high rail-bus connectivity, revealing a “high-grade–low-accessibility” mismatch. (2) Accessibility varies by type: natural sites are lowest, cultural/leisure venues intermediate, and comprehensive sites highest due to multimodal hub proximity. (3) Sentiment and topic analyses based on transport-related review content suggest that some A-rated attractions receive less favorable evaluations of access conditions (e.g., transfers, waiting, last-mile walking, wayfinding, and parking), whereas many popular unrated sites are evaluated more positively in these transport-specific aspects. (4) Quadrant analysis shows many highly rated attractions fall into a “low objective–low perceived” disadvantage, while most unrated ones exhibit strong objective–perceived coupling. These findings underscore structural imbalances among administrative grading, attraction function, and transit provision, offering evidence for optimizing public transport service to tourist attractions. They help optimize the spatial structure of urban tourism, improve resource allocation efficiency, guide differentiated scenic spot development strategies, and promote sustainable and experience-oriented urban tourism governance. Full article

This article presents a simple and understandable approach to the automatic assessment of the severity of late blight on tomato leaves. We collect our own dataset of 5245 RGB images of healthy and diseased tomato leaves and determine five ordinal classes: healthy (0%) and four infection levels (0.1–10%, 11–25%, 26–50%, and ≥51% of the affected area). Each image is segmented using the global definition of the Otsu threshold, followed by morphological purification, after which seven textural and geometric characteristics are extracted from the contours of the lesion: contrast, number of contours, average and standard deviation of the contour area, average and standard deviation of the contour perimeter, and average area-to-perimeter ratio. All characteristics are normalized and used as input data for the XGBoost classifier. The dataset is randomly split into 80% training and 20% test images, resulting in an independent test set of 1049 images. In this test set, the proposed model provides an overall accuracy of 0.93 and an F1 macro score of 0.93 points, while for each F1 class, it varies from 0.90 to 0.97. The confusion matrix shows a stable difference between neighboring severity levels, while the analysis of the importance of the features confirms the relevance of contour descriptors for characterizing the size and shape of the lesion. This method only runs on a central processor, requires a small amount of memory, and outputs interpretable output data, making it suitable for use in greenhouses and farms with limited computing resources. We also discuss the limitations associated with the boundaries between neighboring classes and the potential shift in the subject area, and we outline directions for expanding the approach to multi-sheet scenes and explicit ordinal loss functions. Full article

Benchmarking embodied carbon in residential building stock accurately would involve a high volume of data sharing and would pose serious privacy and competitive issues among building construction stakeholders. This study introduces a new federated learning-based building stock modeling system (FedCarbon) that can allow embodied carbon to be evaluated collaboratively without data aggregation at a central place. The architecture proposed enables construction firms, cities, and providers of construction materials to collectively train predictive models at the same time as data sovereignty is achieved via a hierarchical federated aggregation mechanism with attention-based client weighting. A differentiated privacy scheme that is adaptively calibrated on noise guarantees the privacy of individual projects and allows for statistically significant benchmarking based on heterogeneous building portfolios. The framework also includes a gradient compression scheme based on momentum, which incurs an 82.6% reduction in communication overhead over traditional federated averaging-based methods and still maintains model convergence. The effectiveness of the approach is demonstrated with the help of comprehensive validation with the UCI Energy Efficiency Dataset, which includes 768 residential building configurations, and the Embodied Carbon in European Buildings Database, which includes 2340 residential units in 12 European jurisdictions. It has been experimentally shown that FedCarbon has a 94.2% prediction accuracy (R2) on embodied carbon intensity, with a mean absolute error of 21.4 kgCO₂e/m², and that (ε, δ) differential privacy can be guaranteed with ε = 1.0 and −δ = 10⁻⁵. This structure opens up building stock knowledge and hastens industry-wide implementation of low-carbon building strategies. Full article

This review critically analyzes the evolution of passive heat transfer enhancement in internal flows, charting a paradigm shift from momentum-based flow perturbation to the precise engineering of vortex structures. The central thesis is that the highest-performance, next-generation thermal systems will be realized through ‘flow field programming’—a unified design paradigm that intelligently architects vortex-topology and surface architecture across scales using smart materials, additive manufacturing, and artificial intelligence. This progression is traced from classical devices such as twisted tapes, which generate global swirl, to bio-inspired aerofoil inserts that efficiently produce discrete longitudinal vortices. The synergy achieved in compound systems—through the integration of geometries or the combination of inserts with advanced fluids—is identified as a key mechanism for surpassing traditional performance limits. Furthermore, applications in microscale and phase-change heat transfer, where surface engineering dominates, are explored. The novelty of this work lies in its synthesis of the underlying vortex-generation physics across diverse techniques and scales, introducing ‘flow field programming’ as a forward-looking framework for adaptive thermal management. This evolution—from static geometries to intelligent, responsive designs—is positioned to dramatically improve energy sustainability by enabling more compact, efficient, and adaptive thermal management across power generation, advanced electronics, and renewable energy systems. Full article

The integration of renewable energy sources (RESs) into modern power systems has introduced significant challenges in maintaining system stability and reliability. Among these challenges, load frequency control (LFC) has become a vital area of research. The variable nature of RESs, such as wind and solar, along with their intermittent availability, necessitates advanced management systems for effective frequency regulation. LFC plays a crucial role in ensuring the stability and performance of electrical power systems by managing frequency through the balance of supply and demand, accounting for variations in load, generation, and other disturbances within the system. In traditional power systems, LFC is achieved through a combination of primary, secondary, and tertiary control mechanisms. However, the advent of smart grids has considerably complicated and enhanced the potential for LFC. In these smart grids, which leverage digital communication, sensors, and automation technologies, LFC becomes more intricate and adaptable. These systems not only utilize traditional centralized control but also incorporate RESs, decentralized resources, energy storage solutions, and real-time data to improve frequency management. This research methodically evaluates current LFC techniques using a hierarchical control and technology-focused framework, classifying approaches as conventional, intelligent, and hybrid control schemes within centralized and decentralized system architectures. An evaluative analysis reveals that while intelligent and hybrid control strategies markedly enhance dynamic frequency response and robustness with substantial renewable energy source (RES) integration, persistent challenges remain regarding controller coordination, scalability, computational requirements, and real-time execution. The analysis highlights adaptive hybrid intelligent control schemes, namely those that combine data-driven learning with physical system models, as the most promising avenue for future research, particularly in low-inertia and highly dispersed smart grid scenarios. Full article

High-quality ecological environments are vital for sustainable agro-pastoral development. This study evaluated the spatiotemporal dynamics of ecological environment quality (EEQ) in Zalait Banner from 2000 to 2022 using the Remote Sensing Ecological Index (RSEI) and explore the correlation between various factors and EEQ via Geodetector. Results show a fluctuating upward RSEI trend over 22 years. EEQ hit a low in 2004, with “poor” areas peaking at 30.77%, followed by a significant recovery between 2009 and 2013. Spatially, the region exhibits a “high in the west/northeast, low in the central-south/southeast” pattern. Notably, the central-south region, despite early recovery, has shown continuous deterioration since 2009, requiring urgent remediation. Geodetector analysis revealed that land surface temperature (LST) is the dominant single factor (q = 0.87) influencing EEQ, followed by land use/cover (LULC) and air temperature. Interaction analysis indicates that the synergy between RSEI’s four components (NDVI, WET, NDBSI, and LST) provides the highest explanatory power, while socioeconomic factors (GDP, population) and topography show weaker effects. These findings could provide a scientific basis for local ecological management, with future research planned for the Qinghai–Tibet Plateau. Full article

We study the production of charged particles in ultrarelativistic collisions by using the string fragmentation model. To do this, we describe the $p_{\mathrm{T}}$ spectrum as the convolution of the Schwinger mechanism with string tension fluctuations that account for the stochastic nature of QCD. We found that heavy-tailed distributions are required to adequately reproduce the power-law tail of the $p_{\mathrm{T}}$ spectrum experimentally observed. Additionally, the heavy-tailed characteristic is also necessary for the KNO scale invariance of intense color interactions modeling hard processes in this framework. In this way, the initial state admits a nonextensive picture, leading to a final state out of equilibrium, in which particle production occurs in small regions at different temperatures. Applying this framework to ALICE data, we observe trends in the power-law exponent as a function of event multiplicity and collision centrality. These trends are consistent with enhanced hard-particle production in small systems and with high-$p_{\mathrm{T}}$-particle suppression in heavy-ion collisions. Full article