Search Results (576)

The accelerating climate emergency places the built environment under increasing pressure as both a major source of greenhouse gas emissions and a system highly vulnerable to climate impacts. Buildings contribute substantially to global operational energy use and embodied carbon, while much of the existing stock remains poorly adapted to changing climatic conditions. This paper examines the role of artificial intelligence (AI) in improving energy efficiency, enabling circular material flows, and enhancing resilience across the building lifecycle. Based on a structured synthesis of recent peer-reviewed literature, institutional reports, and documented case examples, the study maps AI applications in design, construction, operation, and end-of-life stages, including generative design, predictive maintenance, digital twins, and construction and demolition waste analytics. The analysis shows how AI can reduce operational energy demand, optimize material use, and support reuse and recycling strategies, while enabling new software-driven business models in the building sector. The paper argues that AI’s effectiveness depends on data availability, interoperability, regulatory alignment, and workforce capabilities, and that its benefits are maximized when integrated with circular economy strategies and supportive policy and financial frameworks. This integrated perspective highlights pathways for reducing emissions and improving the resilience of the built environment under climate stress. Full article

Background/Objectives: Safe delivery and correct identification of newborns are critical aspects of healthcare systems globally. The accreditation of healthcare and standards regulation significantly promotes the adoption of modern technologies to address risks related to infant abduction and misidentification. The effectiveness and extent of these mandates vary across settings and countries. Therefore, this study aims to map and explore modern technologies used for safe newborn delivery and correct identification aligned with healthcare accreditation and regulatory frameworks. Methods: This review adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis extension for scoping reviews (PRISMA-ScR) guidelines. The Problem, Intervention, Comparison, and Outcome (PICO) framework was employed to facilitate the development of the research question. This study examined studies reporting technologies such as radio frequency identification (RFID), biometric identification, and real-time monitoring across healthcare settings for infant protection through the Normalization Process Theory (NPT). Among three databases and search engines (PubMed, Google Scholar, and Web of Science). The risk of bias for each study was assessed using the AACODS Checklist, SQUIRE 2.0 Checklist, TIDieR Checklist, and JBI tools. Results: Out of 8753 records, only 27 reports were eligible to be included in this review. The most frequently reported technologies were RFID systems (11 studies, 37.9%) and biometric systems such as footprint and facial recognition (6 studies, 20.7%). Despite strong technological potential, many healthcare institutions struggled with the adoption of infant protection technologies. Accreditation systems among the high-resource settings actively mandate advanced technologies and support the integration of staff training and simulation drills. Comparably, middle- and low-income regions usually face challenges related to regulatory enforcement, infrastructure, staff readiness, and limited adoption of modern technologies. Conclusions: Accreditation and standards development are critical catalysts for the adoption of modern infant protection technology. Standards must be comprehensible, adaptable, and supported by investment in human resources and infrastructure. Future regulation must focus on strengthening enforcement, continuous quality improvement, and capacity building to achieve sustainable protection across the world. Full article

Crowdsourced geospatial platforms constitute complex socio-technical systems in which data quality and reliability emerge from collective user behavior rather than centralized control. This study proposes a system-oriented, unsupervised machine learning framework to assess the reliability of crowdsourced building data using only intrinsic indicators. The framework is demonstrated through a large-scale analysis of OpenStreetMap building polygons in Tehran. Six intrinsic indicators—reflecting contributor activity, temporal dynamics, semantic instability, and geometric evolution—were normalized using fuzzy membership functions and objectively weighted based on their discriminative influence within a K-means clustering process. Five reliability classes were identified, ranging from very low to very high reliability. The resulting classification exhibited strong internal validity (average silhouette coefficient = 0.58) and pronounced spatial coherence (Global Moran’s I = 0.85, p < 0.001). This approach eliminates dependence on authoritative reference datasets, enabling scalable, reproducible, and feature-level reliability assessment in open geospatial systems. The framework provides a transferable methodological foundation for trust-aware analysis and decision-making in participatory and data-intensive systems. Full article

Coastal port cities depend on global seafood flows, yet their food security is increasingly exposed to price volatility and supply disruptions. This study examines Busan citizens’ perceptions of seafood-related food security and seafood supply chain stability, and derives actionable municipal policy priorities for a trade-dependent port city. Anchored in the FAO four-dimensional framework—availability, access, utilization, and stability—we developed 20 seafood-related attributes and surveyed adult residents in Busan (n = 297). The measurement structure was assessed through reliability checks and exploratory factor analysis, and Importance–Performance Analysis (IPA) was used to map attribute-level priorities and identify the largest importance–performance gaps. Overall, respondents regard seafood food security as highly important but only moderately satisfactory. Availability and utilization perform relatively well, indicating perceived strengths in basic supply conditions and safe consumption, whereas access and stability show lower performance relative to importance, reflecting concerns about affordability, uneven physical access for vulnerable groups, price volatility, and exposure to external shocks. Notably, several stability-related attributes emerge as “Concentrate Here” priorities, highlighting the need for strengthened risk management, early warning communication, and resilience-oriented logistics planning at the city level. By integrating the FAO framework with attribute-level IPA, this study demonstrates how citizen perception data can translate macro food security debates into locally implementable priorities for building sustainable food systems in coastal cities. Full article

Background: The COVID-19 pandemic exposed major deficiencies in global health governance, including fragmented authority, inequitable resource distribution, and weak compliance mechanisms. Although the WHO Pandemic Agreement (2025) addresses several of these gaps, significant operational and institutional challenges remain. This study aims to develop and empirically validate a Global Governance for Health (GGFH) Framework that strengthens leadership, financing, equity, and legal accountability across global, regional, and national levels. Methods: A three-round Delphi study was conducted. Thirty-one experts from diverse sectors, including public health, international law, economics, environment, and diplomacy, evaluated 32 structured governance statements across seven domains. Experts rated all statements using a 7-point Likert scale. Consensus was determined using a strict threshold median ≥ 6; SD ≤ 1.35; ≥75% agreement. Open-text comments were systematically reviewed through thematic analysis. All statements were systematically mapped to the WHO Pandemic Agreement articles to identify areas lacking operational clarity or enforceability. Results: All seven governance domains achieved consensus by Round 3. High agreement emerged on strengthening WHO leadership, implementing sustainable and equitable financing mechanisms, embedding LMIC representation, establishing legal preparedness and capacity-building, and integrating independent accountability tools. Correlation and interdependence analyses demonstrated that governance goals form an integrated, mutually reinforcing system, with financing, equity, and legal frameworks identified as core enablers of effective treaty implementation. Conclusions: The Delphi process validated a comprehensive and operational Global Governance for Health Framework. The GGFH complements the WHO Pandemic Agreement by addressing its unresolved governance, financing, and equity limitations and offers a structured roadmap to guide global pandemic preparedness and treaty implementation. Full article

Soybean is a key global crop for food and oil production, playing a vital role in ensuring food security and supplying plant-based proteins and oils. Accurate information on soybean distribution is essential for yield forecasting, agricultural management, and policymaking. In this study, we developed an Enhanced Chlorophyll Index (NRLI) to improve the separability between soybean and maize—two spectrally similar crops that often confound traditional vegetation indices. The proposed NRLI integrates red-edge, near-infrared, and green spectral information, effectively capturing variations in chlorophyll and canopy water content during key phenological stages, particularly from flowering to pod setting and maturity. Building upon this foundation, we further introduce a pixel-wise compositing strategy based on the peak phase of NRLI to enhance the temporal adaptability and spectral discriminability in crop classification. Unlike conventional approaches that rely on imagery from fixed dates, this strategy dynamically analyzes annual time-series data, enabling phenology-adaptive alignment at the pixel level. Comparative analysis reveals that NRLI consistently outperforms existing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Greenness and Water Content Composite Index (GWCCI), across representative soybean-producing regions in multiple countries. It improves overall accuracy (OA) by approximately 10–20 percentage points, achieving accuracy rates exceeding 90% in large, contiguous cultivation areas. To further validate the robustness of the proposed index, benchmark comparisons were conducted against the Random Forest (RF) machine learning algorithm. The results demonstrated that the single-index NRLI approach achieved competitive performance, comparable to the multi-feature RF model, with accuracy differences generally within 1–2%. In some regions, NRLI even outperformed RF. This finding highlights NRLI as a computationally efficient alternative to complex machine learning models without compromising mapping precision. This study provides a robust, scalable, and transferable single-index approach for large-scale soybean mapping and monitoring using remote sensing. Full article

The escalating threat of wildfires under global climate change necessitates rigorous monitoring to mitigate environmental and socio-economic risks. Burned area (BA) mapping is crucial for understanding fire dynamics, assessing ecosystem impacts, and supporting sustainable land management under increasing fire frequency. This study aims to develop a high-resolution detection framework specifically calibrated for Mediterranean environmental conditions, ensuring the production of consistent and accurate annual BA maps. Using Sentinel-2 MSI time series over Sardinia (Italy), the research objectives were to: (i) integrate field surveys with high-resolution photointerpretation to build a robust, locally tuned training dataset; (ii) evaluate the discriminative power of multi-temporal spectral indices; and (iii) implement a Random Forest classifier capable of providing higher spatial precision than current operational products. Validation results show a Dice Coefficient (DC) of 91.8%, significantly outperforming the EFFIS Burnt Area product (DC = 79.9%). The approach proved particularly effective in detecting small and rapidly recovering fires, often underrepresented in existing datasets. While inaccuracies persist due to cloud cover and landscape heterogeneity, this study demonstrates the effectiveness of a machine learning approach for long-term monitoring, for generating multi-year wildfire inventories, offering a vital tool for data-driven forest policy, vegetation recovery assessment and land-use change analysis in fire-prone regions. Full article

Even though environmental health and climate change are rapidly intensifying the severity of determinants of disease and inequity, training for health professionals in these areas remains fragmented across Europe. To address this gap, the European Medical Association (EMA), in collaboration with the European Network on Climate and Health Education (ENCHE), the International Network on Public Health and Environment Tracking (INPHET) and University College London, convened a one-day hybrid roundtable in London on 17 September 2025, focused on “Preparing Health Professionals for Environmental Health and Climate Change: A Challenge for Europe”. The programme combined keynote presentations on global and European policy, health economics and curriculum design with three disease-focused roundtables (respiratory, cardiovascular and neurological conditions), each examining the following topics: (A) climate and environment as preventable causes of disease; (B) healthcare as a source of environmental harm; and (C) capacity building through education and training. Contributors highlighted how environmental epidemiology, community-based prevention programmes and sustainable clinical practice can be integrated into teaching, illustrating models from respiratory, cardiovascular, surgical and neurological care. EU-level speakers outlined the policy framework (European Green Deal, Zero Pollution Action Plan and forthcoming global health programme) and tools through which professional and scientific societies can both inform and benefit from European action on environment and health. Discussions converged on persistent obstacles, including patchy national commitments to decarbonising healthcare, isolated innovations that are not scaled and curricula that do not yet embed sustainability in examinable clinical competencies. The conference concluded with proposals to develop an operational education package on environmental and climate health; map and harmonise core competencies across undergraduate, postgraduate and Continuing -professional-development pathways; and establish a permanent EMA-led working group to co-produce a broader position paper with professional and scientific societies. This conference report summarises the main messages and is intended as a bridge between practice-based experience and a formal EMA position on environmental-health training in Europe. Full article

Over the past two decades, Building-Integrated Photovoltaics (BIPV) has become a core technology in the green building sector, driven by global carbon-neutrality goals and the growing demand for sustainable design. This review adopts a scalability-oriented perspective and systematically examines 82 peer-reviewed articles published between 2001 and 2025. The results indicate that existing research is dominated by studies on electrical and thermal performance, with East Asia and Europe—particularly China, Japan, and Germany—emerging as the most active regions. This dominance matters for scalability because real projects must satisfy comfort, compliance, buildability, and operation/maintenance constraints alongside energy yield; limited evidence in these dimensions increases delivery risk when transferring solutions across regions and building types. Accordingly, we interpret the observed distribution as an evidence-maturity pattern: performance gains are increasingly well characterized, whereas deployment-relevant uncertainties (e.g., boundary-condition sensitivity and validation depth) remain less consistently reported. Multidimensional integration of thermal, optical, and electrical functions is gaining momentum; however, user-centered performance dimensions remain underexplored. Simulation-based approaches still prevail, whereas large-scale empirical studies are limited. The review also reveals extensive interdisciplinary collaboration but also identifies a notable lack of architectural perspectives. Using Biblioshiny, this study maps co-authorship networks and research structures. Based on the evidence, we propose future research directions to enhance the practical scalability of BIPV, including strengthening interdisciplinary integration, expanding empirical validation, and developing product-level design strategies. Full article

The Soil and Plant Analyzer Development (SPAD) value is a reliable proxy for chlorophyll, yet conventional field measurement remains labor-intensive and spatially limited. Current remote sensing inversion models typically depend on costly multispectral sensors and rarely account for phenological changes, restricting their applicability across orchards and seasons. To overcome these limitations, this study introduces a stage-aware and low-cost SPAD inversion framework for jujube trees, integrating multi-source data fusion and an optimized ensemble model. A two-year experiment (2023–2024) combined UAV multispectral vegetation indices (VI) with RGB-derived color indices (CI) across leaf expansion, flowering, and fruit-setting stages. Rather than using static features, stage-specific predictors were systematically identified through a hybrid selection mechanism combining Random Forest Cumulative Feature Importance (RF-CFI), Recursive Feature Elimination (RFE), and F-tests. Building on these tailored features, XGBoost, decision tree (DT), CatBoost, and an Optimized Integrated Architecture (OIA) were developed, with all hyperparameters globally tuned using a genetic algorithm (GA). The RFI-CFI-OIA-GA model delivered superior accuracy (R² = 0.758–0.828; MSE = 0.214–2.593; MAPE = 0.01–0.045 in 2024) in the training dataset, and robust cross-year transferability (R² = 0.541–0.608; MSE = 0.698–5.139; MAPE = 0.015–0.058 in 2023). These results demonstrate that incorporating phenological perception into multi-source data fusion substantially reduces interference and enhances generalizability, providing a scalable and reusable strategy for precision orchard management and spatiotemporal SPAD mapping. Full article

Understanding how transport conditions and the built environment shape urban CO₂ emissions is critical for low-carbon urban development. This study analyses CO₂ emission intensity across fifty major Chinese cities using integrated ODIAC emissions, VIIRS night-time lights, traffic performance indicators, built-environment morphology, population/POI structure, and socioeconomic controls. We develop a GeoAI workflow that couples XGBoost modelling with SHAP interpretation, congestion-based city grouping, and 1 km grid-level GNNWR to map intra-urban spatial non-stationarity. The global model identifies night-time light intensity as the strongest predictor, followed by population density and building density. SHAP results reveal pronounced nonlinearities, with high sensitivity at low–medium levels and diminishing marginal effects as activity and density increase. Although transport indicators are less influential in the aggregate model, their roles differ across congestion regimes: in low-congestion cities, emissions align more consistently with overall activity intensity, whereas in high-congestion cities they respond more strongly to population distribution, motorisation, and built-form intensity, with less stable relationships. Grid-level GNNWR further shows that key mechanisms are spatially uneven within cities, with local effects concentrating in specific cores and corridors or fragmenting across multiple subareas. These findings demonstrate that emission drivers are context-dependent across and within cities. Accordingly, uncongested cities may gain more from activity-related energy-efficiency measures, while highly congested cities may require congestion-sensitive land-use planning, spatial-structure optimisation, and motorisation control. Integrating explainable GeoAI with regime differentiation and spatial heterogeneity mapping provides actionable evidence for targeted low-carbon planning. Full article

To address challenges such as small target size, high density, severe occlusion, complex background interference, and edge device computational constraints, a lightweight model, GAFNet, is proposed based on YOLO11n, optimized for rice pest detection in field environments. To improve feature perception, we propose the Global Attention Fusion and Spatial Pyramid Pooling (GAM-SPP) module, which captures global context and aggregates multi-scale features. Building on this, we introduce the C3-Efficient Feature Selection Attention (C3-EFSA) module, which refines feature representation by combining depthwise separable convolutions (DWConv) with lightweight channel attention to enhance background discrimination. The model’s detection head, Enhanced Ghost Detect (EGDetect), integrates Enhanced Ghost Convolution (EGConv), Squeeze-and-Excitation (SE), and Sigmoid-Weighted Linear Unit (SiLU) activation, which reduces redundancy. Additionally, we propose the Focal-Enhanced Complete-IoU (FECIoU) loss function, incorporating stability and hard-sample weighting for improved localization. Compared to YOLO11n, GAFNet improves Precision, Recall, and mean Average Precision (mAP) by 3.5%, 4.2%, and 1.6%, respectively, while reducing parameters and computation by 5% and 21%. GAFNet can deploy on edge devices, providing farmers with instant pest alerts. Further, GAFNet is evaluated on the AgroPest-12 dataset, demonstrating enhanced generalization and robustness across diverse pest detection scenarios. Overall, GAFNet provides an efficient, reliable, and sustainable solution for early pest detection, precision pesticide application, and eco-friendly pest control, advancing the future of smart agriculture. Full article

In the field management of maize, phenomena such as missed sowing and empty seedlings directly affect the final yield. By implementing seedling replenishment activities and promptly evaluating seedling growth, maize output can be increased by improving seedling survival rates. To address the challenges posed by complex field environments (including varying light conditions, weeds, and foreign objects), as well as the performance limitations of model deployment on resource-constrained devices, this study proposes a Lightweight Real-Time You Only Look Once (LRT-YOLO) model. This model builds upon the You Only Look Once version 11n (YOLOv11n) framework by designing a lightweight, optimized feature architecture (OF) that enables the model to focus on the characteristics of small to medium-sized maize seedlings. The feature fusion network incorporates two key modules: the Feature Complementary Mapping Module (FCM) and the Multi-Kernel Perception Module (MKP). The FCM captures global features of maize seedlings through multi-scale interactive learning, while the MKP enhances the network’s ability to learn multi-scale features by combining different convolution kernels with pointwise convolution. In the detection head component, the introduction of an NMS-free design philosophy has significantly enhanced the model’s detection performance while simultaneously reducing its inference time. The experiments show that the $mA{P}_{50}$ and $mA{P}_{50:95}$ of the LRT-YOLO model reached 95.9% and 63.6%, respectively. The model has only 0.86M parameters and a size of just 2.35 M, representing reductions of 66.67% and 54.89% in the number of parameters and model size compared to YOLOv11n. To enable mobile deployment in field environments, this study integrates the LRT-YOLO model with the ByteTrack multi-object tracking algorithm and deploys it on the NVIDIA Jetson AGX Orin platform, utilizing OpenCV tools to achieve real-time visualization of maize seedling tracking and counting. Experiments demonstrate that the frame rate (FPS) achieved with TensorRT acceleration reached 23.49, while the inference time decreased by 38.93%. Regarding counting performance, when tested using static image data, the coefficient of determination ($R^2$) and root mean square error (RMSE) were 0.988 and 5.874, respectively. The cross-line counting method was applied to test the video data, resulting in an $R^2$ of 0.971 and an RMSE of 16.912, respectively. Experimental results show that the proposed method demonstrates efficient performance on edge devices, providing robust technical support for the rapid, non-destructive counting of maize seedlings in field environments. Full article

Environmental health concerns remain a major global challenge. In many nations, the adoption of measures to mitigate the negative environmental impacts of construction-related activities has been slow. Prior research has clarified that further study/advancement are required to improve environmental health/sustainability (EHS). To determine the focus of previous studies, this study attempts to identify, analyse, and visualise the trends in research concerning EHS in construction-related domains. The data were obtained from the Scopus database, and the study employed a bibliometric approach. The following keywords were used to search the database: ‘environmental health’ OR ‘ecological health’ OR ‘environmental sustainability’ OR ‘ecological sustainability’ OR ‘Environmental safety’ OR ‘ecological safety’ AND ‘construction industry’ OR ‘building industry’ to retrieve relevant documents. The analysis included co-citation analysis, keyword co-occurrence and trend mapping. The findings revealed four themes: Environmental Sustainability and Energy-Oriented Decision-Making, Low-Carbon Cementitious Materials and Mechanical Performance of Concrete, Waste Management and Circular Economy Practices, and Life Cycle Assessment and Carbon Emission Analysis. The keyword findings revealed very scant research in environmental health unlike environmental sustainability. Spain, China, and Saudi Arabia are the top three in terms of citation-to-publication ratio, indicating strong influence in literature sources. However, India has the highest number of publications. The findings also suggest that more relevant studies are required in African nations and South Asian countries. It further highlighted a knowledge gap that emerging economies must address to enhance the sustainability and environmental performance of construction projects. This bibliometric analysis is unique in its integrated examination of environmental sustainability and environmental health in the construction industry, employing strategic thematic mapping to reveal system-level linkages, contextual gaps, and targeted directions for future research. The conclusions provide scholars and stakeholders in the built environment with a solid theoretical basis, enhancing the industry’s preparedness to mitigate the adverse environmental and climatic impacts of traditional construction methods. Full article

Food systems operate as socio-ecological systems (SES) in which governance, markets, and biophysical constraints interact through feedback. However, how resilience capacities accumulate across sequential shocks, particularly in hyper-arid, import-dependent rentier states, remains under-traced. We analyze Qatar’s food-system SES across three distinct stress tests: the 2017–2021 blockade, the COVID-19 pandemic (multi-node logistics and labor shock), and the post-2022 Russia–Ukraine war (global price and agricultural input-cost shock). Using a qualitative longitudinal case-study design, we combine documentary review with process tracing and a two-layer coding scheme that maps interventions to SES components (actors, governance system, resource systems/units, interactions, outcomes/feedback) and to predominant resilience capacities (absorptive, adaptive, transformative). The results indicate path-dependent capability building: the blockade activated rapid buffering and rerouting alongside early adaptive investments; COVID-19 accelerated adaptive reconfiguration via digitized logistics, e-commerce scaling, and targeted controlled-environment agriculture; and the Russia–Ukraine shock validated an institutionalized portfolio (fiscal buffering, reserves, procurement diversification, and upstream linkages). Across episodes, supply continuity was maintained, but resilience gains also generated water–energy–food tradeoffs, shifting pressures toward energy-intensive cooling/desalination and upstream water demands linked to domestic buffers. We conclude that durable resilience in eco-constrained, import-dependent systems requires explicit governance of these tradeoffs through measurable performance criteria, rather than crisis-driven expansion alone. Full article