Search Results (5,291)

The unprecedented 2022 wildfire season in Navarra, northern Spain, marked a turning point in regional wildfire management, when seven simultaneous large fires during a June heatwave burned more than 17,000 ha in just a few days, overwhelming suppression capacity and highlighting the limits of a strategy based primarily on ignition prevention and fire suppression. In this study, we implemented a stochastic wildfire modeling system based on the Minimum Travel Time algorithm, historical ignition patterns, spatial fuel data, and spatiotemporal weather variability to assess community and protected area exposure to wildfire. We simulated more than 50,000 fire season replicates under extreme fire weather conditions, estimating annual burn probability across fire intensity classes at 50 m spatial resolution. We then intersected modeled fire perimeters with building footprints representing residential and industrial structures, as well as protected areas, to assess the spatial distribution of exposure across the region. Results showed strong concentration of community exposure, with three fourths of residential and industrial exposure concentrated in just over one third of the total municipal area. Across Navarra, mean annual modeled exposure summed to 120 residential buildings and 16 industrial structures. Across the protected area network, mean annual burned area summed to 90 ha year⁻¹, including 68 ha year⁻¹ at flame lengths greater than 2.5 m, while burned forest area was 16 ha year⁻¹. Protected areas in southern Navarra and forested protected areas in central and northern Navarra showed the highest modeled exposure, identifying priority landscapes where prevention, restoration, and evaluation of managed fire options could support more resilient ecosystems. This study provides a scientific basis for improving wildfire risk governance and strengthening the resilience of communities and protected areas under increasing wildfire pressure in the region. Full article

This study integrated two surveys—one conducted in August 2021 in the Maowei Sea–Beilun Estuary (14 stations) and another in spring 2023 in Qinzhou Bay (16 stations)—to delineate four functional zones (MWS, BLE, WJ, WW). We systematically analysed zooplankton communities and water quality factors and used Structural Equation Modelling SEM to break down the effects of eutrophication on zooplankton into direct and indirect pathways. The results showed that sixteen zooplankton species belonging to six major taxonomic groups were recorded. Copepoda dominated the community, accounting for 50% of the total recorded species. Parvocalanus crassirostris reached a dominance index (Y) of 0.54, higher than historical records (0.35–0.40), indicating increased community homogenization. Salinity increased from 2.0 to 29.6 psu, resulting in community homogenization, and DIN decreased from 1.282 to 0.19 mg/L. Ei averaged 13.49 (MWS) and 12.80 (BLE). Zooplankton biomass rose nearly 7-fold (125 to 880 mg/m³, p < 0.05). SEM revealed a direct inhibitory pathway from eutrophication (−0.36), an indirect pathway via phytoplankton food-quality degradation (−0.14), and one via community high evenness compression (−0.06), for a total of −0.56 (Bootstrap 95% CI significant). The heavy-metal total effect (−0.18) was much weaker. The SEM fit was excellent (CFI = 0.976, RMSEA = 0.038, R2 = 0.68). This study is the first to quantify dual-indicator management thresholds (salinity < 10 psu and DIN > 0.8 mg/L) for early risk warnings in nearshore aquaculture in this region. Full article

Rapid and often chaotic urbanisation in post-Soviet cities such as Almaty challenges the quality, availability, and accessibility of public spaces for residents, given the cities’ historical development. Meanwhile, global research is focused on the concepts of Third Places, coworking spaces in the Western context, and urban experience in cities with transitional economies, but the heritage of centrally planned urban development lacks spatial explicit analysis. The purpose of the current study is to analyse the evolution, current situation, and distribution of collaborative spaces (public spaces that combine work and connectedness) in Almaty. The methodology includes four phases of qualitative analysis: (1) a historical–typological analysis of architectural functions since the beginning of the 20th century until the 2025, (2) spatial mapping analysis of the existing typologies such as libraries, museums, coworking spaces, research and development (R&D) institutions and universities, and community centres, (3) longitudinal statistical analysis, and (4) historical graphic analysis. Analysis is conducted through the lens of advanced levels of human needs that concern self-education and self-development. This approach helped to propose a new definition of collaborative space. The results also show examples of sustainable urban structure with collaborative spaces in Almaty’s old centre (“Zolotoi Kvadrat”—Golden Square) and a critical deficit of new multifunctional spaces for work and socialisation in recently developed districts. The study reveals that Almaty’s evolution occurred through incremental infill development over the old grid, without the integrated development of the public realm and existing structural connections. As a result, the research explores the connection between collaborative spaces and their indirect influence on the general well-being in Almaty. Full article

Digital transformation projects in the construction sector are usually implemented through staged processes involving changing technical conditions, organizational priorities and expert participation. Existing risk assessment studies are often based on static or single-round models, thus limiting their ability to support structural comparisons when assessments are repeated under changing project conditions. To address this issue, this study proposes an unsupervised learning-enhanced DEMATEL–ISM framework for stage-sensitive risk structure analysis in construction digital transformation. DEMATEL and ISM are used to identify causal roles and hierarchical relationships among risk factors within each assessment round, while K-means clustering and principal component analysis are introduced to extract historical relational patterns and incorporate them into subsequent structural modeling. The framework is applied to a digital transformation project in a large construction enterprise using a two-round expert assessment with partial panel continuity. The results show that the baseline structure is mainly driven by tangible resources and strategic planning, whereas the follow-up structure places greater emphasis on data management and intangible organizational capabilities. Comparative and robustness analyses further indicate that the main structural interpretation is not driven by the enhancement layer, threshold selection, panel reduction or individual expert judgement. This study offers a decision-support approach for updating risk structures across assessment rounds and for adjusting risk governance as construction digital transformation progresses. Full article

Croatia’s accession to the European Union in 2013, followed by the abolition of the EU milk quota regime in 2015, exposed a structurally vulnerable dairy and beef sector to the dual pressure of single-market price convergence and post-quota production reallocation. While prior partial equilibrium modelling work on Croatian agriculture has assessed broad accession impacts and CAP reform scenario comparisons, no study has yet quantified the post-quota structural transition trajectory of the Croatian dairy and beef sectors with mechanism-level interpretation under the actually implemented CAP framework. This study simulates and analytically interprets the medium-term (to 2030) trajectories of the Croatian dairy and beef sectors using the AGMEMOD partial equilibrium framework, with an updated Croatian country-level model incorporating CAP 2023–2027 parameters, the post-quota dairy module specification, and historical data extending to 2024. Results indicate continued structural contraction: dairy cow numbers decline from 70.9 thousand head (2024) to 66.0 thousand head (2030); milk production falls 8.6% to 399.6 million litres; total cattle population declines by 4.0% and beef and veal meat production falls 11.0% to 33.1 thousand tonnes. Self-sufficiency rates deteriorate to 41% for milk and 51% for beef by 2030. The study contributes empirical evidence on small Member State post-quota adjustment dynamics, demonstrates that the current CAP 2023–2027 instruments are unlikely to reverse the contraction trajectory under baseline assumptions, and raises explicit food security implications for national agri-food policy. Full article

Item-to-group recommendation identifies the most compatible user groups for a specific item provider to enable precision marketing, such as recommending fruit products to the most receptive consumer communities. Existing graph-based recommendations typically treat social relationships as static binary links, failing to capture variations in interaction intensity driven by user preferences. Moreover, these models largely overlook the structural relevance of intra-group connections, leading to unreliable group representations. To address these challenges, we propose the Preference-Weighted Neighbor-Aware Group Recommendation Network (PNGRN). Specifically, social edges are first reweighted using preference signals derived from historical user–item rating interactions, thereby suppressing socially connected but preference-inconsistent neighbors during aggregation. Structurally cohesive candidate groups are then identified via k-core decomposition, retaining only subgraphs where every member has at least k internal connections. A neighbor-aware graph convolutional network (GCN) module is further introduced to incorporate external social neighborhood features into group representations. This ensures that the learned group profiles reflect both internal structural stability and the external social context. Experiments on three real-world datasets demonstrate that PNGRN consistently outperforms competitive baselines across all evaluation metrics. Notably, on the MovieLens-1M dataset, PNGRN achieves up to a 9.85% improvement in Precision@20 and a 8.98% gain in NDCG@20. These results validate the necessity of coupling topological density with external social influence, and this work offer a scalable framework for precision group-targeted marketing. Full article

Fire protection remains one of the key challenges in the field of architectural heritage conservation, particularly for heritage buildings dominated by timber structures, which face greater difficulties in fire prevention and risk assessment. To systematically evaluate the fire safety performance of high-rise timber heritage buildings, this study takes the Shengjin Pagoda, a typical brick–timber pavilion-style ancient tower in Jiangxi Province, China, as the research object. A three-dimensional performance-based fire assessment framework was developed using Fire Dynamics Simulator (FDS) and PyroSim. Based on field survey data and historical documentation, the geometric characteristics, material properties, and vertical circulation system of the pagoda were reconstructed. Three representative fire scenarios, including bottom-floor ignition, simultaneous multi-level ignition, and wind-driven top-floor ignition, were established to investigate smoke propagation, thermal insulation degradation, and the thermal response of critical timber components under different fire conditions. The results show that brick walls provide effective thermal insulation during the early stages of fire, with efficiency exceeding 90%, but this decreases to approximately 55% in upper regions due to chimney-effect-driven smoke accumulation. Under wind-driven top-floor ignition, exposed dougong components can reach temperatures of 782 °C, resulting in a progressive “top-down and outside-in” failure mechanism. The study reveals the dominant smoke-driven heat transfer pathways and the failure sequence of critical load-bearing elements. Based on these findings, a performance-based fire protection strategy incorporating vertical virtual smoke control zoning and fire-resistance enhancement of key structural components is proposed to support the sustainable conservation of historic high-rise timber structures. Full article

This study presents a hybrid spatio-textual matching approach for integrating historical web-derived address datasets with a municipal reference dataset, using the 2009 administrative delineation of Şişli (Istanbul) as a case study. The proposed approach addresses challenges commonly encountered in data obtained from web archives, such as lexical differences, abbreviations, heterogeneous structures, and missing address information. The methodology consists of three main stages: (i) preprocessing and structuring of web-based address records; (ii) hybrid matching, combining deterministic rules with similarity-based methods; and (iii) post-matching geographic enrichment using an Application Programming Interface (API) to provide supplementary geographic context for matched records. The matching process is conducted exclusively between historical datasets; contemporary geographic information is used only after the completion of the matching process to provide additional contextual information. The methodology integrates token-based, vector-based, and structural similarity measures within a calibrated scoring scheme to improve the matching of ambiguous and inconsistent address records. The results indicate that 65.4% of the records were automatically accepted, 7.3% required manual review, and no suitable candidate was found for 5.4%. Deterministic matching results reveal that strict rule-based approaches are highly sensitive to data integrity and attribute consistency, especially in heterogeneous web-based datasets, highlighting the value of combining multiple similarity measures within a hybrid matching strategy. The API-based enrichment results provide supplementary geographic context regarding the contemporary surroundings of matched records, while historical interpretations remain grounded in the original archival datasets. In this context, the study may contribute to the integration of historical web-based address data with structured municipal datasets under heterogeneous archival data conditions. Full article

West African cuisine is among the world’s most complex and historically significant culinary traditions, shaped by diverse ecosystems, centuries of trans-regional trade, and the cultural heritage of more than three hundred distinct ethnic groups spanning the Atlantic coast and the Sahel. West African cuisine has undergone a significant cultural and culinary transformation in the American food landscape, moving from relative obscurity to mainstream visibility. This entry examines the rise of West African cuisine in the United States, with particular attention to jollof as a cultural symbol of identity, diaspora, and culinary diplomacy. Drawing on academic scholarship, food journalism, and primary cultural sources, the entry traces the historical roots of West African foodways through the transatlantic slave trade and their enduring influence on American culinary traditions. It further explores how contemporary chefs, restaurateurs, and food writers of West African descent, including Eric Adjepong, Pierre Thiam, and Kwame Onwuachi, have elevated the cuisine within American fine dining and popular culture. The entry also addresses the role of social media, particularly the viral “Jollof Wars,” in amplifying West African culinary culture globally, culminating in UNESCO’s recognition of Senegalese jollof rice as an element of intangible cultural heritage. Questions of structural barriers, authenticity, and representation are critically examined. The entry argues that while West African cuisine is experiencing unprecedented visibility in America, its mainstream acceptance remains mediated by cultural filters that risk diluting its complexity and richness. Ultimately, this entry positions West African cuisine not merely as a culinary trend but as a living expression of diasporic identity, cultural resilience, and global influence. Full article

Underground heritage represents a hidden urban resource for cultural regeneration and sustainable tourism, preserving historical layers, wartime memory, and local identity. Positioning the shelters as a form of Underground Built Heritage (UBH), this study examines how concealed civil-defense spaces can be reinterpreted as local cultural heritage resources before systematic reuse. However, enclosed and unfamiliar spaces are often perceived as risky, making adaptive reuse socially sensitive. This study investigates Jingdezhen’s underground air-raid shelters through a scenario-based survey and partial least squares structural equation modeling (PLS-SEM). Using an extended Value-Attitude-Behavior (VAB) framework incorporating perceived authenticity, anticipated affective identification, safety assurance, and perceived risk, this study identifies factors influencing pre-development public acceptance. Results show that public acceptance is shaped by cognitive evaluation of value and anticipated affective identification, while perceived risk constrains behavioral intentions. Perceived authenticity enhances value perception and anticipated affective identification; perceived value strengthens attitudes; safety assurance shows a small but statistically significant negative association with perceived risk, although most variance in perceived risk remains unexplained; and an exploratory moderation analysis further suggested that perceived risk may weaken the attitude–visit intention relationship. Although the estimated model showed a relatively high SRMR, the results are interpreted as prediction-oriented ex ante evidence rather than as a covariance-based model with strong global fit. These findings provide prediction-oriented ex ante evidence for the sustainable reuse of underground heritage, supporting heritage interpretation, risk management, and urban regeneration aligned with SDG 11. Full article

Periodontal disease remains one of the leading causes of tooth loss worldwide, highlighting the need for effective regeneration of alveolar bone, periodontal ligament, and cementum. The structural complexity and unique biological behavior of these tissues have historically posed significant challenges for clinical regeneration strategies. The primary therapeutic approach used is guided bone regeneration; however, it has certain limitations, such as morbidity, low structural integrity and dimensional stability. Recent advances in 3-dimensional (3D) bioprinting have made it possible to fabricate customized scaffolds with precise architecture and spatial organization that closely mimic normal periodontal structures. The incorporation of multifunctional nanocomposite biomaterials and nanoparticles further enhances the performance of the scaffolds by increasing mechanical strength, bioactivity and controlling degradation rates. These advanced scaffolds function as dynamic microenvironments that support cell adhesion, proliferation and differentiation, ultimately promoting tissue regeneration. Furthermore, their multifunctional properties allow for the controlled release of growth factors, anti-inflammatory and antimicrobial agents, as well as the incorporation of stem cells and bioactive molecules that facilitate angiogenesis. This review investigates and critically evaluates modern approaches for the regeneration of periodontal tissues through scaffolds, biomaterials and 3D bioprinting technologies, as well as to assess their effectiveness compared to established clinical practices. Full article

Irregular Hangeul typefaces present a challenging computer vision problem because complete font generation must generalize from a small number of reference glyphs while preserving both structural consistency and stylistic fidelity. This study investigates few-shot learning for the restoration and expansion of irregular and historical Hangeul typefaces through three experiments spanning relatively regular woodblock print, irregular contemporary type, and highly irregular royal calligraphy. We benchmark a GAN-based model (DM-Font), a VQGAN-based model (VQ-Font), and a diffusion-based model (Diff-Font) under limited supervision and evaluate them using pixel-level similarity, structural indicator, OCR usability, and expert assessment. DM-Font established a feasible baseline for historical restoration (mean SSIM 0.77), whereas VQ-Font obtained the highest structural similarity for irregular contemporary typeface when paired with a structurally designed 10-character pangram reference set (SSIM 0.97; OCR accuracy 99.5% on the evaluated glyph set). For highly irregular royal calligraphy, the two models performed comparably on global similarity (SSIM 0.78 vs. 0.80) and on expert ratings (4.2 vs. 4.3); VQ-Font showed more stable structure-sensitive indicators, whereas Diff-Font better preserved stylistic nuance. The findings suggest that reference-set composition substantially affects generation quality under fixed-budget few-shot conditions, and that model choice should be matched to source regularity and restoration objectives. Full article

Wear on historic staircases is often used as evidence for conservation assessment and historical interpretation, yet existing studies are largely descriptive and rarely provide a quantitative explanation of how observed wear relates to long-term pedestrian use. To address this limitation, this paper proposes a physics-constrained inversion framework for analyzing directional preference and wear-related usage regimes from geometric wear profiles of heritage staircases. An Archard-type wear model is extended to account for spatial footfall distribution, cumulative abrasion, material deterioration, and environmental loss, and the reconstruction problem is formulated as an inverse parameter estimation task. Bayesian uncertainty quantification is introduced to estimate posterior distributions, credible intervals, and parameter coupling. A unified workflow is developed for staircase geometry representation, reference surface reconstruction, profile extraction, regularized height field construction, forward simulation, and inverse solution. Nine synthetic scenarios with different usage levels and directional preferences are tested under 1%, 3%, and 5% noise, and the method is further applied to a publicly available three-dimensional heritage staircase model. Under 3% noise, profile correlation coefficients for three representative scenarios reach 0.9646, 0.9807, and 0.9868, indicating strong recoverability of geometric wear morphology under model-consistent conditions. The results indicate that directional preference, material hardness, and some degradation-related parameters are identifiable, whereas pedestrian volume and the wear coefficient show strong compensation. Overall, the proposed framework provides a quantitative basis for identifying directional asymmetry, analyzing parameter identifiability, and supporting geometry-based interpretation in heritage staircase studies. Full article

This study examines how contemporary religious architecture mediates sacred meaning through the interaction of symbolic form, embodied practice, and sensory-spatial conditions, using the Namaste Dagoba at Famen Temple as a case study. Integrating architectural semiotics with exploratory empirical research, the study employs questionnaires and semi-structured interviews, supplemented by architectural field notes, to investigate how visitors perceive and interpret the space. An exploratory structural equation modeling (SEM) framework is used to examine possible relationships among Symbolism and Aesthetic Experience (SAE), Embodied Spatial-Ritual Perception (ESRP), and Perceived Sacred Meaning (PSM). The findings indicate that while symbolic and aesthetic perception provides an initial interpretive basis, perceived sacred meaning appears to be strongly associated with reported embodied spatial experience. Spatial configuration, ritual pathways, mandala-based geometry, and gradients of spatial intensity are interpreted as design conditions that may shape visitors’ reported perception, movement experience, and sense of sacred meaning. The observed mediating role of ESRP suggests that architecture may operate as an experiential interface rather than only as a static symbolic system. By integrating semiotic theory with exploratory questionnaire and interview evidence, the study proposes a tentative embodied and processual model of architectural meaning-making. Rather than suggesting a rupture from historical Buddhist spatial traditions, the study identifies one contemporary design strategy in which inherited cosmological symbolism, ritual movement, threshold experience, and sensory atmosphere are recomposed through a monumental modern architectural vocabulary. Full article

Node-level cargo-volume forecasting in logistics sorting networks requires modeling temporal dynamics together with directed inter-node dependencies and planned topology perturbations. This study addresses 1-h-ahead forecasting under a partial-information boundary, where historical node volumes, the pre-change network structure, and planned route-topology changes are available before prediction, whereas continuous post-change dynamic edge weights and realized post-change graph states are unavailable. We propose a perturbation-aware framework that represents the sorting system as a directed network and integrates temporal features, pre-change structural descriptors, topology-change encodings, perturbation-response proxies, and similarity-assisted support for data-scarce nodes within a unified forecasting protocol. A shared random forest backbone is used only to assess the incremental value of these representations. Experiments on 57 sorting centers show that temporal dynamics dominate under stable-network conditions. Under topology perturbation, topology-change signals reduce test weighted absolute percentage error (WAPE) from 18.10% to 17.11%, and perturbation-response proxies further reduce it to 16.91%. For data-scarce nodes, similarity support reduces test WAPE from 29.43% to 26.68%, with consistent gains under 10%, 20%, and 30% sample-retention settings. These results suggest that the framework provides an interpretable and information-admissible representation strategy for node-level forecasting in directed networked systems. Full article