Search Results (13,447)

Autism spectrum disorder (ASD) risk reflects genetic susceptibility and modifiable environmental exposures acting during fetal and early postnatal critical periods. Building on our prior two-path model, in which folate receptor autoantibody-driven cerebral folate deficiency and oxidative stress/neuroinflammation converge on disrupted neurodevelopment, this review provides the first full mechanistic treatment of environmental pollutants within that framework. We synthesize evidence across seven exposure categories: micro/nanoplastics, plastic-associated endocrine-disrupting chemicals, ambient/indoor air pollution, tire wear particles and 6PPD-quinone, heavy metals and pesticides, industrial chemicals and persistent organic pollutants, and ultra-processed food intake as a parallel, non-pollutant contributor to the same inflammatory pathway. Human biomonitoring of micro/nanoplastics has progressed beyond detection in the placenta, brain and breast milk to direct evidence of placental genotoxicity and fetal endocrine disruption, complementing rodent data linking early-life exposure to impaired corticogenesis, disrupted microglial synaptic pruning, and ASD-relevant behavioral deficits. Across categories, oxidative stress, barrier disruption, neuroinflammation, endocrine disruption, and epigenetic modification recur as convergent mechanisms acting on trimester- and age-specific windows of vulnerability. Genetic variation in folate pathway and mitochondrial genes, as well as folate/vitamin B sufficiency, are proposed as candidate effect modifiers rather than established protective factors to modify susceptibility to this pollutant burden. Most evidence is associational or mechanistic rather than trial-based; we grade evidence strength and translate findings into biomarker-guided clinical and population-level policy guidance. Full article

Accurate prediction of the mechanical performance of polymer components fabricated by fused deposition modelling (FDM) remains challenging owing to the complex nonlinear relationships between process parameters and material properties, limiting reliable process planning and broader industrial adoption of polymer additive manufacturing. This study develops and experimentally validates a heterogeneous multi-output stacked ensemble learning framework for the simultaneous prediction of tensile strength, flexural strength, compressive strength, Rockwell hardness, and Charpy impact strength of acrylonitrile styrene acrylate (ASA), a high-performance engineering thermoplastic with excellent weatherability and ultraviolet resistance that remains comparatively underexplored in data-driven FDM research. A Definitive Screening Design (DSD) was employed to investigate eight critical process parameters: extrusion temperature (ET), bed temperature (BT), infill density (ID), layer height (LH), print speed (PS), raster angle (RA), build orientation (BO), and cooling fan speed (CFS). Multiple supervised learning algorithms were systematically benchmarked, and the highest-performing complementary models were integrated into a heterogeneous stacked ensemble for simultaneous multi-output prediction. The proposed framework achieved an overall R² of 0.9943 with an overall RMSE of 0.9758, while the individual prediction models attained R² values ranging from 0.9898 to 0.9967. Beyond improving predictive accuracy, the proposed AI-assisted framework provides a data-driven basis for mechanical-property prediction and establishes a surrogate modelling framework that may subsequently be coupled with dedicated optimization or decision-making methods. Full article

Background: Non-communicable disease (NCD) emergencies, including hypoglycaemia, stroke, acute coronary syndrome, and hypertensive crisis, call for prompt action by households and communities. The psychosocial mechanisms associated with emergency management behavior in rural Thailand, however, are not well understood. Objectives: The study pursued three objectives. It described levels of NCD emergency management behavior and the characteristics of emergency events reported during the preceding six months. It examined associations linking knowledge, Health Belief Model (HBM) constructs, social support, participation, self-efficacy, and emergency management behavior. Then, it identified the constructs with the strongest relations to behavior, along with an indirect pathway operating through self-efficacy, via structural equation modelling. Methods: A cross-sectional study analyzed data from 1321 community adults in Sisaket Province, Thailand, drawn from an initial 1407 records after excluding 86 with age below 21 years, incomplete data on SEM variables, or invalid disease-status coding. A structured, interviewer-administered questionnaire measured knowledge of NCD emergencies, HBM constructs, social support, family and community participation, self-efficacy, and emergency management behavior. Covariance-based SEM with item parcels and maximum-likelihood estimation was conducted; knowledge was treated as an observed variable. Results: Mean age was 58.0 years (SD = 14.3); 15.5% reported a recent emergency event. Knowledge was moderate-to-low (mean = 5.79/10; KR-20 = 0.440). Model fit was acceptable (CFI = 0.980; RMSEA = 0.047; SRMR = 0.049). Discriminant validity was supported by the Fornell–Larcker criterion for all constructs. Participation was strongly associated with self-efficacy (β = 0.858) and with emergency management behavior (β = 0.433); self-efficacy was also associated with emergency management behavior (β = 0.354). Participation had the largest total effect on behavior (0.737), combining a direct effect (0.433) and an indirect, self-efficacy-related effect (0.304). All structural-path variance inflation factors (VIF) were below 5.0 except participation behavior (VIF = 6.19), which remained below the more permissive threshold of 10.0. Conclusions: NCD emergency management behavior in this rural community was most strongly associated with family and community participation and with self-efficacy. Because the design was cross-sectional, these findings indicate association rather than confirmed causal or mediating pathways. Programmes should combine scenario-based knowledge with participatory practice, family role preparation, rehearsal of emergency-service (1669) activation, and confidence-building activities. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly embedded in menstrual and reproductive health applications, yet little is known about how women in conservative, rapidly digitizing regions evaluate AI-enabled menstrual tracking tools. This study investigates how perceived cultural fit and data privacy concerns are associated with women’s openness to AI-enabled menstrual tracking in Gulf Cooperation Council (GCC) countries. Methods: We conducted an online cross-sectional survey using a convenience sample of adult women residing in GCC countries (n = 273), measuring openness to AI menstrual tools, perceptions of cultural fit (e.g., respect for religious and cultural norms, endorsement by trusted institutions, Arabic language, GCC-specific design), privacy concerns (e.g., worries about who can access menstrual health data), and key sociodemographic characteristics and smartphone use. Results: Higher perceived cultural-contextual fit was strongly and positively associated with openness to AI-enabled menstrual tracking, whereas composite privacy concerns showed no statistically significant association once cultural fit and sociodemographic factors were controlled. Item-level analyses indicated that beliefs about cultural sensitivity and GCC-specific design were the most robust predictors of openness, while concerns about data access, authority endorsement, and language accessibility played a limited role. Homemakers reported higher openness than employed women. Conclusions: Overall, the findings suggest that, in this sample and model, GCC women’s openness to AI-enabled menstrual tracking was more strongly associated with perceived cultural alignment and local design than with the measured privacy-concern construct, highlighting the importance of culturally grounded, trust-building design and communication strategies for AI-based women’s health applications. Full article

Masonry structures, frequently encountered both in traditional architecture and in historical buildings, occupy a significant place among structural system types. In these systems, the load-bearing elements typically consist of walls made of materials such as brick and stone. Performance evaluation of masonry structures requires more refined modeling processes due to their brittle behavior under seismic effects and their irregular geometric characteristics. In particular, historical masonry buildings require accurate analysis. The analysis critically depends on the precise identification of the actual geometry, material properties, and structural deficiencies. Within the scope of the study, the effectiveness of combining laser scanning technology with Building Information Modeling (BIM) in the structural analysis and condition assessment of masonry structures was investigated through a case study. Laser scanning point cloud data were processed and transferred into a digital environment. Subsequently, BIM-based software was employed to generate a three-dimensional model using the point cloud data of a historic structure located in Cappadocia, Türkiye. Then, the model was transformed into a finite element model for structural analysis. The finite element model was employed for modal characterization and preliminary structural assessment under self-weight. Throughout the process, axis controls of the walls, misalignments, irregularities, structural discontinuities, and possible structure damage were evaluated digitally to identify potentially vulnerable zones. This study applies the existing Scan-to-BIM-to-Finite Element workflow to complex historical masonry, providing a reliable and practical roadmap for geometric documentation and preliminary structural assessment of the building stock. Full article

Building-use information underpins urban building energy modeling, disaster risk assessment, and evidence-based planning, yet semantic labels are missing for most buildings in open databases; the most widely available proxies are OpenStreetMap (OSM) building-type tags, which largely denote type or physical form rather than independently observed current use. Machine learning models that infer such building-type tags from footprint morphology promise scalable label enrichment, but it remains unclear how well such models travel between cities. This exploratory study benchmarks the cross-city transferability of morphology-based OSM building-type-tag inference using an interpretable, openly documented, and computationally lightweight machine learning pipeline; inference of actual current use would require external validation against authoritative records and is treated here as a downstream hypothesis only. From OpenStreetMap extracts of five European cities (Berlin, Amsterdam, Vienna, Barcelona, and Budapest), 3.22 million building footprints were processed and 197,747 labeled buildings were sampled, each described by 22 footprint- and context-level morphometric indicators. Gradient boosting classifiers achieved within-city macro-F1 of 0.831–0.914 (binary residential/non-residential) and 0.595–0.699 (four-class) under spatially blocked cross-validation. On the class-enriched benchmark samples, pairwise transfer retained 85.7% of within-city performance on average, with a minimum of 61.5%; when the same transferred predictions are reweighted to each target’s observed tagged-subset prevalence, mean transferred macro-F1 falls to 0.621 and non-residential precision to 0.09–0.50, and source rankings can reorder, so the class-enriched matrix does not by itself support operational source selection. Transfer degradation was positively associated with the Wasserstein distance between the morphological feature distributions of city pairs (Spearman ρ = 0.66 descriptively; the association is dominated by Amsterdam and largely disappears when Amsterdam pairs are excluded, ρ = 0.13). SHAP analysis indicated that building-size heterogeneity was a recurring predictive signal across the five models, whereas adjacency- and density-related signals varied more across cities. Learning-curve analyses on the class-enriched benchmark indicate that a pooled multi-city model is able to rival locally trained models on comparable class-enriched samples when the target city is morphologically similar to the source pool, but small local samples quickly dominate otherwise; these fractions refer to the class-enriched benchmark samples and do not translate directly into city-level annotation budgets. The proposed pipeline runs end-to-end on a standard computer with exclusively free and open data and software, providing an openly documented benchmark for AI-assisted urban analytics and hypothesis-generating evidence on when morphology-based tag models may be reused across cities. Full article

This study systematically explored immune targets in gastric adenocarcinoma (GAC) suitable for mRNA vaccine development. Based on multi-omics data from public databases, we first screened a set of potential tumor-associated antigen genes. Subsequently, using ten machine learning algorithms, we constructed 101 prognostic models and, through optimization and comparison, selected the Random Survival Forest (RSF) method to establish a clinical prognostic model for GAC consisting of seven genes (TYMP, IFGN, ITGAX, GBP5, GBP4, STAT1, CD84). At both the genetic and protein levels, these genes were closely associated with the antigen presentation process, suggesting the potential functional role of this model in antigen presentation. Further analysis of the immune infiltration characteristics in GAC preliminarily revealed its possible immune evasion mechanisms. Building on this, we designed candidate mRNA vaccine templates for GAC using the mRNAdesigner platform. Additionally, this study investigated the potential roles of the above seven genes in GAC progression and screened small-molecule compounds targeting these genes. Molecular dynamics simulations (MD) were performed to verify the binding stability between these compounds and their corresponding proteins. This study comprehensively simulated the tumor microenvironment (TME) and antigen presentation process in GAC, evaluated the clinical translation potential of the neoantigen prognostic model and its predictive value for immunotherapy, and provided a preliminary design scheme for an mRNA vaccine against GAC. The findings offer new evidence for identifying immune therapy targets in GAC and are expected to advance the development of immunotherapy strategies for GAC. Full article

Buildings account for a substantial share of global energy consumption and greenhouse gas emissions, highlighting the need for accurate large-scale building energy assessment. However, in Türkiye, the limited availability of Energy Performance Certificates (EPCs) and detailed 3D architectural building models restricts comprehensive urban energy analyses. This study proposes a novel GIS and Urban Digital Twin (UDT)-based methodology integrating stereo photogrammetry and Machine Learning (ML) to predict annual building energy consumption and evaluate rooftop solar energy potential. Initially, EPCs were integrated with architectural and photogrammetric 3D building models, while building energy parameters were validated using the BEP-TR2 calculation methodology. Validated datasets were then employed to train and compare Random Forest, XGBoost, CatBoost, and Artificial Neural Network models. For buildings lacking detailed architectural models, geometric attributes were extracted from stereo photogrammetric aerial imagery to enable energy consumption prediction. XGBoost achieved the best predictive performance, yielding an R² of 0.82 using architectural GML data and an R² of 0.83 using photogrammetrically derived data. Subsequently, rooftop solar radiation potential was estimated and compared with predicted annual energy consumption to assess building self-sufficiency. Results revealed that 27% of buildings were fully self-sufficient, while 20% achieved 50–70% self-sufficiency. The proposed framework demonstrates the potential of integrating UDTs, GIS, stereo photogrammetry, and ML to support scalable urban energy planning, renewable energy integration, and sustainable decision-making. Full article

Artificial intelligence (AI) is reshaping the hospitality industry at an unprecedented pace. However, existing hospitality research has overwhelmingly concentrated on customer-facing applications, including service robots, chatbots, personalization, and revenue management, while largely overlooking the internal operational systems that sustain hotel performance. As a result, the concept of AI maturity within back-of-house hotel operations remains theoretically undefined, fragmented across functional domains, and lacks an integrated framework for assessment. This study addresses this critical gap by asking a fundamental research question: What does AI maturity actually mean for hotel back-of-house operations? Drawing upon a systematic literature review following the PRISMA protocol, this study synthesizes evidence from 18 studies spanning the interdisciplinary fields of hospitality management, operations management, information systems, and artificial intelligence to examine how AI is transforming core internal hotel functions. The review identifies current applications, implementation patterns, organizational enablers, barriers to adoption, and emerging trends across human resource management, procurement, finance and accounting, inventory management, housekeeping planning, maintenance, energy management, and managerial decision support. Building on these findings, the study develops the Artificial Intelligence Maturity in Back-of-House Operations (AIM-BoH) Framework, a domain-specific conceptual framework designed to conceptualize AI maturity across hotel back-of-house functions. The framework conceptualizes AI maturity as a multidimensional organizational capability encompassing technological adoption, process automation, decision intelligence, data readiness, human–AI collaboration, governance and ethical preparedness, and measurable operational outcomes. By moving beyond technology-centric perspectives, the framework provides a comprehensive model for understanding how AI creates organizational value through the integration of internal hotel processes. The proposed framework advances hospitality literature by establishing a common theoretical foundation for understanding AI maturity in internal hotel operations while offering hotel executives a structured conceptual lens for considering organizational capability development in the planning of digital transformation initiatives. The article concludes by proposing a research agenda for the empirical validation, refinement, and cross-cultural application of the AIM-BoH Framework, positioning it as a reference model for future hospitality AI research and practice. Full article

Deterministic channel models are essential for high-frequency communication system design in complex urban environments, where multiple propagation mechanisms including reflection, diffraction, and vegetation scattering coexist. This paper proposes a hybrid forward–backward ray tube tracing (HFB-RTT-3D) approach that extends the established ray tracing fusion with multiple diffuse scattering (RT-MDS) framework from natural terrain to urban scenarios by introducing pyramid-shaped diffraction and vegetation scattering ray tubes. A vegetation scattering model based on a leaf-level bidirectional scattering distribution function (BSDF) is established, enabling computationally feasible representation of vegetation effects in deterministic channel prediction. The framework thereby covers buildings, vegetation, and terrain within a unified ray tube data structure. Simulation analyses quantify vegetation modulation of multipath structure and received power across frequencies from 5 to 15 GHz and different canopy sizes. Measurement validation in a campus tree-lined avenue scenario at 2.3 to 5.9 GHz demonstrates that incorporating vegetation scattering reduces the root mean square error (RMSE) of the prediction to 6.11 to 6.30 dB, an improvement of 0.57 to 1.67 dB over the case without vegetation, confirming the effectiveness of the proposed method. Full article

The European Union has set an ambitious goal of achieving net-zero emissions by 2050, and 90% reduction by 2040, through its Green Deal policy. A promising solution to this challenge lies in the adoption of Fifth-Generation District Heating Networks (5GDHNs) that operate at low temperatures, collecting and distributing heat from diverse sources (energy geostructures, asphalt pavement solar thermal collectors, industrial waste heat and waste heat from buildings’ cooling plants). The system’s design allows for heat storage underground, primarily during summer months, with distribution occurring via pipelines during winter. As part of the REHEAT project, a study has been conducted focusing on a simulation model developed using TRNSYS software. This model incorporates solar thermal collectors installed beneath the parking area asphalt pavements as a thermal energy source, coupled with a borehole thermal energy storage system. The setup is designed to meet the heating and cooling demands of a multiplex cinema situated in Northern Italy. The study presents the optimization of the system, reporting the monthly and annual data on energy balances and system efficiency. The findings demonstrate significant energy savings when compared to traditional heating and cooling systems (50.6% non-renewable primary energy reduction) and even greater CO₂ emission reduction (63.2%). Also, the economic analysis reveals positive results both from the point of view of operating costs and taking into account investment costs, highlighting the potential of 5GDHN as a sustainable solution for urban energy needs for a real case as the main novelty of this study. Full article

The rapid advancement of digital twin technologies has increased the importance of establishing interoperable geospatial information standards for national-scale Digital Twin implementation. The Republic of Korea has developed the National Digital Twin (NDT) standard framework based primarily on OGC CityGML 3.0. This study presents a case-study-based analysis of the development and application of the Korean NDT standards, focusing on how a geospatial standard framework can be structured and extended to support a national-scale Digital Twin. A comparative review of international initiatives indicates that existing approaches generally develop CityGML-based 3D city models as geospatial foundations and subsequently extend them toward Digital Twin applications, whereas the Korean NDT establishes a common reference framework and geospatial data models as a standardized foundation for national-scale Digital Twin implementation. The analysis examines the phased development process, the hierarchical relationship among the reference framework, core data model, and application data models, and the organization of multiple spatial domains, including Building, Transportation, Terrain, Indoor Space, and Underground. The applicability of the NDT standards was further examined through pilot dataset implementation and validation from schema, semantic, geometric, and topological perspectives. The analysis also identifies current limitations, including restricted data openness, limited software support for CityGML 3.0, and the need for further mechanisms to integrate dynamic information. Based on these findings, this study discusses future directions for NDT standardization, including model management, standardized data production and validation, dynamic information integration, and technical implementation guidance. The Korean experience provides a case for understanding both the potential and practical challenges of applying CityGML 3.0-based geospatial standards to national-scale Digital Twin initiatives. Full article

Cities create their own local climate, and numerical weather models can reproduce it if given an accurate picture of the urban surface. The local climate zone (LCZ) framework classifies neighborhoods by building height, density, and materials and provides this information to fine-scale weather models. This approach has mostly been evaluated in warm seasons, leaving open how it performs in the cold, air-stagnant winters of high-latitude cities like Ulaanbaatar, Mongolia. We ran nine model versions over one cold week (22–29 February 2024) across three nested domains (12.5, 2.5, and 0.5 km) with LCZ data resolved to 100 m. Run 8 achieved the highest aggregate validation skill, whereas Run 9 was retained as the configuration most suitable for the LCZ-based analysis rather than as the best model overall. Run 9 reproduced near-surface temperature at the urban Bayanzurkh station (R = 0.89) and gave the smallest wind-speed error (1.5 m s⁻¹), while uniquely resolving the inter-class morphological contrasts required here. The dense urban core proved warmer than the non-urban area by 4.1 °C on average and up to 9.3 °C at night in the compact high-rise zone. Yet this warming barely relieves cold stress: the Universal Thermal Climate Index (UTCI) averaged −15.5 °C across the LCZ classes, within the strong-cold-stress range, with only the compact high-rise zone reaching a milder category. In the low-rise “ger districts”, wind speed rather than air temperature governs perceived cold, and compact high-rise form offers the strongest wind shelter. These findings provide a baseline for future scenario testing of winter thermal exposure in Ulaanbaatar. Full article

Amid rising population pressure, heightened volatility in international markets, and increasingly binding domestic resource and environmental constraints, safeguarding food security while maintaining ecological security has become a salient policy and academic concern in China. Within this context, our study investigates whether and how green finance contributes to the dual security of food and ecology. Building on a theoretical analytical framework, we elaborate on the functional role of green finance and empirically assess its effects using provincial panel data from China. We construct a composite index to gauge the level of green finance development and then employ a two-way fixed-effects model with province and year fixed effects to identify the overall impact of green finance, followed by a mediation model to uncover the underlying transmission mechanisms and sub-sample regressions to examine heterogeneity. The empirical evidence indicates that, while green finance significantly improves the dual security of food and ecology, substantial heterogeneity is observed, with stronger effects in southern regions, major grain-producing functional areas, and plain regions. Mechanism analyses further show that green finance advances dual security through three primary channels of agricultural structure adjustment, planting specialization, and agricultural green technological innovation, among which shifts in agricultural production structure, greater centralization of planting, and enhanced agricultural green innovation capacity constitute the dominant transmission mechanisms. By integrating food security and ecological security within a unified framework, we clarify the multidimensional pathways through which green finance supports sustainable agricultural development and provide new empirical evidence on how financial instruments can reconcile production and environmental objectives. Full article

Pixel-level annotation of bi-temporal high-resolution imagery is costly because annotators must distinguish genuine changes from pseudo-changes caused by illumination, seasonality, shadows, and residual misregistration. From a temporal-symmetry perspective, unchanged regions approximately preserve cross-temporal semantic correspondence, whereas genuine building changes introduce localized symmetry breaking between the two acquisition times. This paper presents BSCNet, a semi-supervised framework for binary building change detection that jointly models boundary-sensitive differences and scene-dependent scale preferences. A shared-weight MixTransformer extracts multi-level bi-temporal features. The Edge-Aware Optimization Module suppresses spatially invariant channel responses, enhances residual spatial cues, and predicts a Sobel-supervised edge map. The Parallel Selective Context Module aggregates depthwise-separable branches with different receptive fields and produces an image-level scale distribution. The Multi-scale Edge-Consistent Mean Teacher framework aligns the final prediction, intermediate edge representation, and scale-selection distribution between an exponential-moving-average teacher and the student. Experiments on WHU-CD and LEVIR-CD under 5%, 10%, and 20% labeled-data settings show consistent improvements over RCL, C2F-SemiCD, and CutMix-CD. With 5% labeled data, BSCNet achieves F1/IoU scores of 88.57%/79.49% on WHU-CD and 88.88%/79.98% on LEVIR-CD. An additional UAV-CD evaluation examines transfer to 0.06 m low-altitude UAV imagery containing both building and land changes; under 5% supervision, BSCNet obtains an F1/IoU of 68.07%/51.60%. Progressive ablations confirm complementary gains from the boundary, scale, and consistency components. Full article