Search Results (1,533)

Religious buildings such as synagogues, churches, and mosques, which are central to religious, cultural, and social life, have served important purposes throughout history as sacred spaces where art, architecture and performance converge. Although these sacred spaces offer unique spatial contexts that deepen individuals’ spiritual experiences through their physical, symbolic, and atmospheric qualities, empirical studies examining this relationship remain limited. This study aims to investigate the impact of biophilic design features within the Yivli Minaret Mosque, one of the oldest Islamic monuments in Antalya, constructed during the 13th-century Anatolian Seljuk Period, on the spiritual experiences of congregation members, and to identify the key psychological mechanisms shaping this relationship. The methodology of the study is based on a mixed-methods approach that combines expert assessments conducted using the Biophilic Interior Design Matrix (BID-M), which integrates proven scientific data with artistic perspective within a historical and symbolic religious structure, with survey data obtained from 359 mosque congregation members. The findings indicate that the mosque exhibits medium-to-high levels of biophilic design characteristics and that the relationship with nature is established indirectly through historical, cultural, and ecological contexts and symbolic representations rather than directly through natural elements. In this respect, the biophilic characteristics of sacred spaces are not merely an artistic and aesthetic approach, but an element that supports individuals’ relationship with nature and their restorative and spiritual experience. Overall, the study reveals that spiritual experience cannot be considered independently of its spatial context and that sacred spaces related to nature support spiritual experience. Full article

This study introduces a high-order numerical scheme for solving 1D second-order hyperbolic telegraph equations (HTEs) with variable coefficients. We employ a generalized temporal discretization (TD) of order p via the Rothe approach, combined with a spatial spectral collocation (SCM) method using generalized shifted Jacobi polynomials (GSJPs). By utilizing a Galerkin-type basis that structurally satisfies homogeneous boundary conditions (HBCs)—including Dirichlet or Neumann types—we achieve a global error bound of $\mathcal{O}({\left(\Delta \tau \right)}^p+N^{-s})$, where $\Delta \tau$ denotes the temporal step size and s represents the spatial regularity of the exact solution (ExaS). The proposed algorithm, Rothe-GSJP, allows for an optimal balance between the temporal and spatial parameters, minimizing computational effort for high-precision engineering applications such as Phase-Locked Loop (PLL) modeling. Numerical experiments performed on an i9-10850 workstation show that the scheme always reaches the machine precision floor of ${10}^{-16}$. While the framework supports temporal orders up to $p=6$, the results indicate that $p\in \{2,3,4\}$ provides an optimal balance between high-order precision and absolute stability. The Rothe-GSJP method proves to be a robust, efficient, and highly accurate alternative to traditional solvers for hyperbolic systems. Full article

Village landscapes within an 80 km radius of Lubumbashi (south-eastern Democratic Republic of the Congo) are undergoing rapid spatial transformation driven by subsistence agriculture, charcoal production, and mining activities. This study analyzes how these transformations are spatially perceived and organized across five village territories of the Lubumbashi Charcoal Production Basin using an adapted version of Kevin Lynch’s perceptual model. Landscape elements were independently identified by trained cartographic observers and by local community members. A comparison of the resulting maps yields a Sørensen similarity index ranging between 70% and 75% across villages, indicating strong convergence in spatial interpretation despite differences in expertise. Among the perceptual components, districts and landmarks account for nearly half of all identified elements and comprise the most perceptible anthropogenic disturbances. Spatial analysis shows that areas perceived as negatively impacted represent between 40% and 79% of total village surfaces. Deforestation associated with post-cultivation fallow dominates in Makisemu (47.6%) and Texas (64.4%), while woodland degradation linked to charcoal production is particularly pronounced in Mwawa (39.0%) and Luisha (25.1%). Mining-related disturbances, including soil and water alteration, are especially evident in Nsela (24.6%). These findings demonstrate that Lynch’s framework, although originally developed for urban systems, can effectively structure perception in diffuse rural woodland environments when methodologically adapted. Perception-based cartography therefore provides a robust complementary tool to biophysical monitoring for understanding the spatial footprint of anthropogenic pressures at the village scale and informing ecosystem restoration strategies. Full article

Crohn’s disease (CD) is a chronic inflammatory disorder arising from the convergence of genetic susceptibility, immune dysregulation, environmental exposures, and perturbations of the gut microbiome. This review advances a developmental and compartment-aware framework for interpreting dysbiosis in CD, integrating spatial heterogeneity, transmural pathology, and mesenteric interactions. By synthesizing evidence on microbial composition, functional metabolism, and host-immune crosstalk, we describe a dysbiotic profile shaped by disease location, inflammatory activity, and therapeutic exposure, while also considering the emerging roles of non-bacterial members. We propose that microbiome alterations in CD reflect inflammation-driven ecosystem instability rather than a static taxonomic imbalance. Moving beyond descriptive compositional profiling toward a dynamic ecological model that incorporates disease trajectory and anatomical compartmentalization is essential to refine disease stratification and guide future microbiome-informed precision therapies. Full article

The Burgers–Huxley equation is a nonlinear partial differential equation that incorporates convective, diffusive and reactive effects and arises in various reaction–diffusion and fluid flow models. In this paper, a numerical method based on the method of lines is proposed for its solution. The spatial derivatives are approximated using a third-order finite difference scheme, which converts the governing partial differential equation into a system of ordinary differential equations. The resulting semi-discrete system is solved in time using the classical fourth-order Runge–Kutta method. The stability and convergence properties of the proposed scheme are analyzed to establish its numerical reliability. Several numerical experiments are presented to illustrate the accuracy and efficiency of the method. The computed results confirm that the proposed approach provides accurate and stable solutions for the Burgers–Huxley equation. Full article

Reliable and up-to-date measurements of public space remain scarce in small and medium-sized towns (SMSTs), where conventional geospatial datasets are often outdated, inconsistent, or inaccessible. This study presents a low-cost and fully reproducible computational pipeline that integrates nadir RGB imagery captured by a DJI Mini 3 UAV with a lightweight instance-segmentation model (Ultralytics YOLOv12-seg) and GIS-based post-processing to derive class-specific surface indicators at the neighborhood scale. The workflow consists of four components: autonomous UAV acquisition over three representative zones of Sesquilé, Colombia; planar mosaic generation and georeferencing using ad hoc ground control points; fine-tuning of a YOLOv12-seg model trained on locally annotated images; and transformation of predicted masks into OSM and GeoPackage geometries for metric analysis. The trained model achieved stable convergence with mask mAP50 ≈ 0.85 and mAP50–95 ≈ 0.70, supported by balanced precision–recall behavior across classes. Spatial outputs exhibit coherent morphological contrasts between the analyzed zones. Buildings occupy 48.17% of the mapped area, vegetation 25.88%, and transport- and plaza-related public space (roadways, sidewalks, and hardscape areas) 25.95%. These proportions capture a clear gradient from a dense urban core to less consolidated peripheral sectors. Results demonstrate that very-high-resolution UAV imagery, combined with open-source deep-learning tools and structured GIS post-processing, can reliably produce operational public-space indicators for SMSTs at low cost. The methodology provides an accessible and scalable framework for evidence-based urban assessment in municipalities with limited technical and financial resources. Full article

This paper extends the data-driven computational mechanics paradigm to nonlinear materials characterized by the Duncan–Chang Elastic-Bulk (E-B) constitutive model. Unlike in linear elastic systems, geotechnical media exhibit stress-dependent tangent moduli and non-convex constitutive manifolds. We propose a recursive nested data-driven solver that dynamically adapts the phase-space distance metric to account for pressure-dependent hardening. A rigorous mathematical analysis of convergence is provided, demonstrating that the solver’s performance is governed by the local transversality between the conservation law constraint set and the nonlinear material manifold. We derive explicit error bounds that couple spatial discretization resolution with material data density. Numerical experiments using triaxial test data from a high-altitude region validate the theoretical predictions, showing that the proposed scheme demonstrates convergence in single-element tests. Full article

Skin lesion segmentation has achieved notable progress in recent years; however, accurate delineation remains challenging due to complex backgrounds, ambiguous boundaries, and low lesion-to-skin contrast. To address these issues, we propose the lesion-aware triple-path feature fusion network (LTPNet), an end-to-end framework that progressively processes features through extraction, refinement, and aggregation stages. In the extraction stage, we incorporate a general foreground–background attention to suppress background interference and accelerate model convergence. In the refinement stage, we introduce an attentive spatial modulator (ASM) to jointly exploit local structural cues and global semantic context for precise spatial modulation. We further develop a lesion-aware lite-gate attention (LALGA) module that performs local spatial feature modulation and global channel recalibration tailored to lesion characteristics. In the aggregation stage, we propose a triple-path feature fusion (TPFF) module that explicitly models feature relationships across scales via three complementary pathways: a common path (CP) for semantic consistency, a saliency path (SP) for highlighting co-activated regions, and a difference path (DP) for accentuating structural discrepancies. Extensive experiments on in-domain and cross-domain datasets show that LTPNet achieves superior segmentation accuracy with reasonable inference efficiency and model complexity, demonstrating its potential for efficient and reliable clinical decision support. Full article

Since the emergence of big data analytics, forecasting future population distributions in specific regions has become a popular prominent research topic. Accurate predictions offer benefits for urban planners, traffic management authorities, and commercial stakeholders. However, most studies have concentrated on population dynamics within isolated locations, often overlooking broader spatial fluctuations across larger geographic areas. This narrow scope limits the practical utility of such predictions. Therefore, generative adversarial networks (GANs) have been employed to estimate population counts across multiple locations within expansive regions. Despite their potential, many GAN-based models encounter significant challenges when tasked with predicting numerous locations simultaneously, resulting in prolonged training times or failure to achieve convergence. To address these limitations, we developed a novel random number generation method to improve the training efficiency and convergence stability of GANs. We also set a new identification criterion to ensure that the large-scale population distributions generated by GAN closely reflect real-world conditions. The developed model in this study was validated using actual telecommunications-based pedestrian flow data from Taiwan, demonstrating its effectiveness and practical feasibility. Full article

Satellite–terrestrial spectrum sensing plays a crucial role in enhancing spectrum efficiency through reusing spectra. However, in a satellite–terrestrial converged system, the large SNR range, non-Gaussian signal characteristics and noise uncertainty pose significant challenges for spectrum sensing. In this paper, we investigate a downlink spectrum sensing framework where multi-terrestrial BSs act as a secondary system to sense idle satellite spectra through a multi-domain feature-level sensing signal fusion. To enhance the characterization of signal/noise features, we provide a fusion strategy of multi-features including energy, power spectral density, cyclic autocorrelation function, higher-order moments, sparse ratio, and I/Q samples, constructing two feature tensors of statistical features and an I/Q component. Then, we propose a deep-learning-enabled multi-feature fusion spectrum sensing method (DL-MFFSSnet) based on a dual-branch deep neural network architecture with the constructed two feature tensors as inputs. In the statistical feature processing branch, CNN and channel self-attention are incorporated to capture intra-channel correlations and inter-channel relative contributions of different feature modalities. In the I/Q branch, multi-scale dilated convolutions and spatial self-attention are introduced to analyze dependencies across different temporal positions and multi-scale spatial features. The feature map extracted from both branches passed through fully connected layers for deepwise feature fusion, achieving accurate spectrum sensing. Extensive simulation results demonstrate that the DL-MFFSSnet method outperforms the existing state-of-the-art algorithms. Full article

This study investigates the evolution of the structures of China’s domestic intercity tourism information flow networks, an increasingly important issue in an information-driven society. Moving beyond prior research that primarily emphasizes urban node attributes and multidimensional distances, this study applies social network analysis to develop an integrated analytical framework that incorporates endogenous structural effects, exogenous network effects, node attributes, and similarity effects. Using tourism information flows in China as an empirical proxy, the study examines the mechanisms underlying the formation and persistence of intercity relationships within the country. The results indicate that the self-organization of microscopic network structures plays a significant role in both tie formation and persistence, particularly through reciprocity, cyclicity, and convergence. Notably, the effect of cyclicity reversed during the COVID-19 pandemic and changed direction from relationship formation to persistence. In addition, cultural distance (proxied by dialect distance), geographical distance, and institutional distance significantly inhibit both the formation and persistence of intercity tourism information flows. Changes in urban node scale and node similarity also exert significant influences on network evolution. This study deepens the understanding of the spatial structural dynamics of China’s domestic intercity tourism information flows and provides a conceptual basis for future research on the evolutionary mechanisms of tourism network structures within a domestic context. Its direct significance lies in promoting sustainable urban tourism development, network resilience, and adaptive governance of urban systems. Full article

To address the task-grouping problem for air–ground integrated Unmanned Aerial Vehicle (UAV) swarm missions in three-dimensional (3D) environments, this study proposes a data-preprocessing and hybrid initialization clustering method based on 3D spatial features. A dual-modal prototype meta-heuristic optimization model, Dual-Prototype Metaheuristic K-Means (DPM-Kmeans), is constructed accordingly. First, to overcome spatial information loss in high-dimensional task allocation, a 3D spatial task data preprocessing technique and a hybrid initialization strategy based on the golden spiral distribution are designed. This ensures the diversity and environmental adaptability of the initial solutions. Second, a dual-modal prototype optimization framework incorporating row prototypes (local refinement) and column prototypes (global combination) was constructed using meta-heuristics and clustering algorithms. The prototype-driven replacement update mechanism simultaneously performs global and local search, balancing the algorithm’s exploration and exploitation capabilities while expanding the solution space. This effectively addresses premature convergence issues in complex search spaces. Simultaneously, a collaborative multi-constraint, dynamically weighted optimization model was constructed, incorporating task requirements and flight distance constraints to ensure that the grouping scheme approximates the global optimum. Simulation results demonstrate that compared to traditional K-means and mainstream meta-heuristic optimization algorithms, DPM-Kmeans achieves an overall improvement of 2–10% in Sum of Squared Errors (SSE), Silhouette Coefficient (SC), and Davies–Bouldin Index (DB) metrics. It exhibits superior convergence speed and solution quality, proving the method’s excellent scalability and robustness in multi-constraint, large-scale 3D scenarios. Full article

Building safe and resilient cities is a key objective of China’s urbanisation and a prerequisite for high-quality development. This study assesses urban resilience in 73 mining cities from 2014 to 2023 using a composite index system (30 indicators) structured around robustness, resistance, and recovery. We integrate ARIMA-based forecasting, kernel density estimation, and Dagum Gini decomposition to characterise spatiotemporal dynamics and quantify regional inequality. Urban resilience increases steadily over the study period and can be characterised by three sequential stages, with further gains forecast for 2024–2030. Spatially, high-resilience cities shift from a dispersed pattern to belt-like and clustered agglomerations, consistent with an increasingly stratified centre–periphery structure. Inequality is driven primarily by between-region disparities: the East performs best, followed by the Central region, whereas the West and Northeast lag behind, revealing a pronounced gap between the Northeast and the East, alongside relatively convergent Central–West trajectories. These patterns are associated with interacting differences in location and market development, fiscal capacity and transition pathways, infrastructure endowment and ecological constraints, and institutional and demographic dynamics. The findings underscore the need for place-based regional coordination and targeted investments to strengthen recovery-related capacities. Full article

Unmanned aerial vehicles (UAVs), a key enabler for the Internet of Things’ (IoT) evolution to 3D spatial dimensions, play a critical role in data collection across fields. However, path planning in obstacle-rich and threat-prone environments remains a core bottleneck for their safe and efficient operation. Traditional meta-heuristic algorithms suffer from insufficient exploration, slow convergence, and local optima issues. To address this, we propose an enhanced multi-mechanism DBO algorithm (MMDBO), integrating SPM chaotic mapping, dynamic global exploration, adaptive T-distribution, and dynamic weight mechanisms. Comparative experiments against five classical algorithms on 12 benchmarks test functions and three complex terrains show MMDBO achieves superior performance across the majority of key path-planning metrics—including flight trajectory length, altitude profile fidelity, and path smoothness—while incurring only a modest increase in computational time. The results of the statistical test further indicate that the MMDBO algorithm significantly outperforms the comparison algorithms in both convergence speed and accuracy. These advances deliver actionable, highly reliable guidance for UAV flight path optimization. Full article

Automated waste classification is essential for improving recycling efficiency and supporting sustainable waste management systems. However, conventional convolutional neural network (CNN) approaches primarily focus on localized feature extraction, which may limit their ability to capture complex spatial relationships in heterogeneous waste materials. This study proposes DeepWasteSort-SI-SSO, a Vision Transformer (ViT)-based framework enhanced with a Self-Improved Sparrow Search Optimization (SI-SSO) strategy for hyperparameter tuning. The optimization process focuses on key training parameters, including learning rate, batch size, and dropout rate, to improve convergence stability and reduce the risk of suboptimal local minima. The framework was evaluated on a balanced four-class waste image dataset (paper, wood, food, and leaves; N = 4000) using a five-fold cross-validation protocol. Experimental results achieved an average accuracy of 95.5% (±0.007), a macro-averaged AUC-ROC of 0.975, and a Cohen’s Kappa coefficient of 0.938, indicating strong agreement between predicted and true labels. Comparative experiments against ResNet-50 and a baseline ViT configuration suggest that SI-SSO optimization improves performance stability with only a modest increase in computational cost. These findings highlight the potential of optimized Transformer-based approaches for automated waste image classification under controlled evaluation conditions. Full article