Search Results (5,087)

Impulsive overtopping generated by dam-break surges is a critical hazard for dikes and flood-protection embankments, especially in reservoirs and mountainous catchments. Unlike classical coastal wave overtopping, which is governed by long, irregular wave trains and usually characterized by mean overtopping discharge over many waves, these dam-break-type events are dominated by one or a few strongly nonlinear bores with highly transient overtopping heights. Accurately predicting the resulting overtopping levels under such impulsive flows is therefore important for flood-risk assessment and emergency planning. Conventional cluster-then-predict approaches, which have been proposed in recent years, often first partition data into subgroups and then train separate models for each cluster. However, these methods often suffer from rigid boundaries and ignore the uncertainty information contained in clustering results. To overcome these limitations, we propose a GMM+MoE framework that integrates Gaussian Mixture Model (GMM) soft clustering with a Mixture-of-Experts (MoE) predictor. GMM provides posterior probabilities of regime membership, which are used by the MoE gating mechanism to adaptively assign expert models. Using SPH-simulated overtopping data with physically interpretable dimensionless parameters, the framework is benchmarked against XGBoost, GMM+XGBoost, MoE, and Random Forest. Results show that GMM+MoE achieves the highest accuracy ($R^2=0.9638$ on the testing dataset) and the most centralized residual distribution, confirming its robustness. Furthermore, SHAP-based feature attribution reveals that relative propagation distance and wave height are the dominant drivers of overtopping, providing physically consistent explanations. This demonstrates that combining soft clustering with adaptive expert allocation not only improves accuracy but also enhances interpretability, offering a practical tool for dike safety assessment and flood-risk management in reservoirs and mountain river valleys. Full article

Urban vitality is a crucial indicator of a city’s sustainable development and the quality of life of its residents. Investigating the spatiotemporal patterns and influencing mechanisms of urban vitality is essential for optimizing the built-environment and improving governance. Using the central urban area of Guiyang, China, as a case study, this research integrates multi-source urban sensing data to investigate the spatiotemporal patterns of urban vitality and their driving factors. Geographically weighted regression (GWR) and machine learning combined with SHapley Additive exPlanations (SHAP) are applied to capture spatial heterogeneity, nonlinear relationships, and threshold effects among influencing variables. Results show that urban vitality exhibits a Y-shaped, single-core, multi-center, and clustered spatial configuration, with slightly higher intensity on weekdays and similar diurnal rhythms across weekdays and weekends. The effects of influencing factors display strong spatial non-stationarity, characterized by a concentric gradient radiating outward from the historic Laocheng core. Building density (BD), residential point density (RED), normalized difference vegetation index (NDVI), and road density (RD) emerge as the dominant contributors to urban vitality, while topographic conditions play a relatively minor role. The relationships between key landscape and built-environment variables and urban vitality are highly nonlinear, with distinct threshold effects. By integrating spatial econometric modeling and explainable machine learning, this study advances methodological approaches for urban vitality research and provides practical insights for landscape-oriented urban planning and human-centered spatial design. Full article

Background/Objectives: Federated learning (FL) offers a way for healthcare institutions to collaboratively train machine learning models without sharing sensitive patient data. This systematic review aims to comprehensively synthesize the ethical dimensions of FL in healthcare, integrating privacy preservation, algorithmic fairness, governance, and equitable access into a unified analytical framework. The application of FL in healthcare between January 2020 and December 2024 is examined, with a focus on ethical issues such as algorithmic fairness, privacy preservation, governance, and equitable access. Methods: Following PRISMA guidelines, six databases (PubMed, IEEE Xplore, Web of Science, Scopus, ACM Digital Library, and arXiv) were searched. The PROSPERO registration is CRD420251274110. Studies were selected if they described FL implementations in healthcare settings and explicitly discussed ethical considerations. Key data extracted included FL architectures, privacy-preserving mechanisms, such as differential privacy, secure multiparty computation, and encryption, as well as fairness metrics, governance models, and clinical application domains. Results: Out of 3047 records, 38 met the inclusion criteria. The most popular applications were found in medical imaging and electronic health records, especially in radiology and oncology. Through thematic analysis, four key ethical themes emerged: algorithmic fairness, which addresses differences between clients and attributes; privacy protection through formal guarantees and cryptographic techniques; governance models, which emphasize accountability, transparency, and stakeholder engagement; and equitable distribution of computing resources for institutions with limited resources. Considerable variation was observed in how fairness and privacy trade-offs were evaluated, and only a few studies reported real-world clinical deployment. Conclusions: FL has significant potential to promote ethical AI in healthcare, but advancement will require the development of common fairness standards, workable governance plans, and systems to guarantee fair benefit sharing. Future studies should develop standardized fairness metrics, implement multi-stakeholder governance frameworks, and prioritize real-world clinical validation beyond proof-of-concept implementations. Full article

This paper analyzes the implementation of the Geoportal SIG FESBAL–UPM, a Participatory Geographic Information System (PGIS) developed within the Master’s and Doctorate programs in Rural Development Project Planning and Sustainable Management at UPM. The study introduces a model integrated with Project-Based Learning (PBL), the Working With People (WWP) framework, and the CFS-RAI principles to address challenges in responsible food systems. The geoportal designed to be applied at the Food Bank–UPM Chair–FESBAL, acts as an innovative instrument for participation among the different stakeholders enabling the spatialization and analysis of data across social, environmental, and governance dimensions. Functionally, it offers a robust foundation for evidence-based decision-making, systematizes geographic information, and visualizes data via the web, supporting research, training, and community engagement actions. Furthermore, this study details the specific projects and activities developed under the three involved action lines: research, training, and community engagement, identifying strengths and weaknesses in each. The findings affirm that this participatory approach ensures that the proposed solutions are aligned with local needs and priorities, increasing the sustainability and long-term success of the projects implemented through the geoportal. Full article

Sustainability assessment in UNESCO World Heritage city centres often treats spatial configuration, functional accessibility, and heritage governance as separate analytical domains. This study addresses this fragmentation by developing a composite assessment framework to evaluate morphological sustainability in historic urban cores. The Morphological Sustainability Model (MSM) and its numerical expression, the Morphological Sustainability Index (MSI), are applied to the Bursa Khans District for the 2020–2025 period. The model integrates Space Syntax variables (integration, connectivity, choice, and intelligibility), 15-Minute City indicators related to proximity, pedestrian accessibility, active mobility, and inclusivity, and Historic Urban Landscape-based governance evaluations derived from UNESCO-compliant management plans. These components are synthesised into six weighted composite indicators (BKH1–BKH6). Results show that the MSI increases from 0.38 in 2020 to 0.51 in 2025 (+34.2%), indicating a strengthened alignment between spatial configuration, pedestrian-oriented functional performance, and heritage governance capacity. The findings reveal a shift from car-oriented axial dominance toward a more pedestrian-centred spatial structure along the historic bazaar spine. Overall, the study demonstrates that the MSI provides a transferable, decision-support-oriented framework for assessing morphological sustainability in historic urban environments. Full article

Cultural ecosystem services (CESs) are the non-material benefits people derive from ecosystems and are important for human well-being. Most research has focused on individual CES supply–demand relationships, with little systematic study of the overall CES structure, interactions, and mechanisms in metropolitan areas. This study takes the Nanjing Metropolitan Area as a case study, integrating multi-source geospatial data and employing the MaxEnt model, self-organizing maps (SOMs), Spearman correlation analysis, and the Optimal Parameters-based Geographical Detector (OPGD). It analyzes supply–demand matching, trade-offs, synergies, and drivers for four CES categories: aesthetic (AE), recreational entertainment (RE), knowledge education (KE), and cultural diversity (CD). The main findings are as follows: (1) CES supply and demand are spatially zoned: the core area has surplus supply, secondary centers are balanced, and the periphery has both weak supply and demand. (2) Three supply–demand bundles have distinct synergy and trade-off patterns: Bundle 1 primarily exhibits strong synergy between AE and CD; Bundle 2 shows a weak trade-off relationship; and Bundle 3 forms a synergy centered on AE. (3) The explanatory power of driving factors exhibits pronounced spatial heterogeneity: Bundle 1 is dominated by non-quantifiable social factors; Bundle 2 features dual synergistic drivers of population and transportation; and Bundle 3 demonstrates synergistic effects driven by facilities and economic factors. Overall, this study contributes an integrated metropolitan-scale framework that connects CES supply–demand mismatch patterns with bundle typologies, interaction structures, and bundle-specific drivers. The results provide an operational basis for targeted planning and coordinated ecological–cultural governance in the Nanjing Metropolitan Area and offer a transferable reference for other metropolitan regions. Full article

Under the pressing imperative of achieving “dual carbon” goals and advancing urban low-carbon transitions, understanding how neighborhood spatial environments influence carbon emissions has become a critical challenge for enabling refined governance and precise planning in urban carbon reduction. Taking the central urban area of Xining as a case study, this research establishes a high-precision estimation framework by integrating Semantic Segmentation of Street View Images and Point of Interest data. This study employs a Geographically Weighted XGBoost model to capture the spatial non-stationarity of emission drivers, achieving a median R² of 0.819. The results indicate the following: (1) Socioeconomic functional attributes, specifically POI Density and POI Mixture, exert a more dominant influence on carbon emissions than purely visual features. (2) Lane Marking General shows a strong positive correlation by reflecting traffic pressure, Sidewalks exhibit a clear negative correlation by promoting active travel, and Building features display a distinct asymmetric impact, where the driving effect of high density is notably less pronounced than the negative association observed in low-density areas. (3) The development of low-carbon neighborhoods should prioritize optimizing functional mixing and enhancing pedestrian systems to construct resilient and low-carbon urban spaces. This study reveals the non-linear relationship between street visual features and neighborhood carbon emissions, providing an empirical basis and strategic references for neighborhood planning and design oriented toward low-carbon goals, with valuable guidance for practices in urban planning, design, and management. Full article

There is little evidence of the implementation of the Principles for Responsible Investment in Agriculture and Food Systems between universities and businesses, and there is even less research that prioritizes people and implements sustainable development with a territorial focus. In this article, we address a form of collaborative work that integrates academia with business, where the Principles for Responsible Investment in Agriculture and Food Systems (CFS-RIA) are seen as an opportunity to promote and strengthen the management of a business in the communities where it operates, and determine a new way of working from its links with the university. The experience is developed in the provinces of Santiago Rodríguez, Valverde (Mao), and Dajabón in the Dominican Republic, with the aim of contributing, using this new approach, to economic, social, environmental, and governance development in the territory. The conceptual and methodological basis for the university–business link is Working With People, a model that integrates key elements of planning such as social learning, collaborative participation, and project management models. The main catalysts of the experience are the business values and the stakeholders who insert the principles into their programs and projects. Among these is an innovative Family Social Responsibility Program with female entrepreneurs and organic banana production. It is concluded that the implementation of the CFS-RIA Principles has a significant impact on the sustainable development of the region and that the university–business link reinforces the social responsibility of companies, providing an opportunity for the entry of new actors. Full article

Quantum technology, a critical 21st-century strategic frontier science, has been a key technological competition between China and the U.S. This study employs natural language processing (NLP) techniques and a technology analytical framework to analyze the quantum technology policies of both countries. While the U.S. emphasized free-market innovation and global technological leadership on quantum technology from 2018 to 2024, China prioritized government-led development and socioeconomic stability. Moreover, the Chinese government adopts a systematic top-down approach characterized by government planning and direct intervention. However, the U.S. fosters innovation through market mechanisms and industry-academia collaboration. U.S. policies have gradually shifted from pure technological innovation to national security considerations. On the other hand, China has moved from breakthrough research to industrial deployment and application. These policy differences reflect distinct political systems and governance models, which may also resonate with their respective cultural traditions and philosophical foundations. Our findings fill a critical gap in comparative quantum technology policy research, offering significant insights for policymakers, researchers, and international stakeholders. Full article

Urban heat islands are among the most intense and unequal climate impacts in Mediterranean cities, with direct effects on health, thermal comfort, and habitability. This reality calls for the incorporation of binding and verifiable climate criteria into spatial planning and urban planning law. This article examines the extent to which the Spanish legal framework—at national, regional, and municipal levels—incorporates measurable standards to mitigate urban heat islands and how it might evolve towards operational climate-responsive urbanism. A legal–analytical and comparative methodology is applied, based on multilevel normative content analysis and a comparison of four autonomous communities, four Spanish cities, and four international reference cases with consolidated metrics. The results show that, despite progress in recognising adaptation, territorial asymmetries persist, enforceable parameters remain scarce, and there is a prevailing reliance on strategic or voluntary instruments. In response to these gaps, the study proposes a coherent set of urban climate standards (urban vegetation, functional soil permeability, roof albedo/cool roofs, green roofs and façades, plot-scale performance indices, urban ventilation, and thermal diagnostics) and a multilevel integration model aimed at guiding legislative reforms and strengthening cities’ adaptive capacity and thermal equity. Full article

Mid-sized Philippine cities commonly rely on fixed-time traffic signal plans that cannot respond to short-term, demand-driven surges, resulting in measurable idle time at stop lines, increased delay, and unnecessary emissions, while adaptive signal control has demonstrated performance benefits, many existing solutions depend on centralized infrastructure and high-bandwidth connectivity, limiting their applicability for resource-constrained local government units (LGUs). This study reports a field deployment of TrafficEZ, a lightweight edge AI signal controller that reallocates green splits locally using traffic-density approximations derived from cabinet-mounted cameras. The controller follows a macroscopic, cycle-level control abstraction consistent with Transportation System Models (TSMs) and does not rely on stationary flow–density–speed (fundamental diagram) assumptions. The system estimates queued demand and discharge efficiency on-device and updates green time each cycle without altering cycle length, intergreen intervals, or pedestrian safety timings. A quasi-experimental pre–post evaluation was conducted at three signalized intersections in El Salvador City using an existing 125 s, three-phase fixed-time plan as the baseline. Observed field results show average per-vehicle delay reductions of 18–32%, with reclaimed effective green translating into approximately 50–200 additional vehicles per hour served at the busiest approaches. Box-occupancy durations shortened, indicating reduced spillback risk, while conservative idle-time estimates imply corresponding CO₂ savings during peak periods. Because all decisions run locally within the signal cabinet, operation remained robust during backhaul interruptions and supported incremental, intersection-by-intersection deployment; per-cycle actions were logged to support auditability and governance reporting. These findings demonstrate that density-driven edge AI can deliver practical mobility, reliability, and sustainability gains for LGUs while supporting evidence-based governance and performance reporting. Full article

The increasing urgency to mitigate plastic pollution has accelerated the shift from linear manufacturing toward circular systems. This review synthesizes current advances in mechanical, chemical, biological, and upcycling pathways, emphasizing how artificial intelligence (AI) is reshaping decision-making, performance prediction, and system-level optimization. Intelligent sensing technologies—such as FTIR, Raman spectroscopy, hyperspectral imaging, and LIBS—combined with Machine Learning (ML) classifiers have improved material identification, reduced reject rates, and enhanced sorting precision. AI-assisted kinetic modeling, catalyst performance prediction, and enzyme design tools have improved process intensification for pyrolysis, solvolysis, depolymerization, and biocatalysis. Life Cycle Assessment (LCA)-integrated datasets reveal that environmental benefits depend strongly on functional-unit selection, energy decarbonization, and substitution factors rather than mass-based comparisons alone. Case studies across Europe, Latin America, and Asia show that digital traceability, Extended Producer Responsibility (EPR), and full-system costing are pivotal to robust circular outcomes. Upcycling strategies increasingly generate high-value materials and composites, supported by digital twins and surrogate models. Collectively, evidence indicates that AI moves from supportive instrumentation to a structural enabler of transparency, performance assurance, and predictive environmental planning. The convergence of AI-based design, standardized LCA frameworks, and inclusive governance emerges as a necessary foundation for scaling circular plastic systems sustainably. Full article

This study applies spectral analysis and singular spectrum analysis (SSA) to mean annual runoff of the Dniester River for 1950–2024 to identify dominant periodic components governing the hydrological regime of this transboundary basin shared by Ukraine and Moldova. The novelty lies in a basin-specific integration in the first systematic application of a combined spectral–SSA framework to the Dniester River, enabling consistent characterization of runoff variability and assessment of large-scale natural drivers. Time series from three gauging stations are analysed to develop data-driven runoff models and medium-term forecasts. Four stable groups of periodic variability are identified, with characteristic timescales of approximately 30, 11, 3–5.8, and 2 years, corresponding to major atmospheric–oceanic oscillations (AMO, NAO, PDO, ENSO, QBO) and the 11-year solar cycle. Cross-spectral and coherence analyses reveal a statistically significant relationship between solar activity and river discharge, with an estimated lag of about 2 years. SSA reconstructions explain more than 80% of discharge variance, indicating high model reliability. Forecast comparisons show that spectral methods tend to amplify long-term trends, CNN–LSTM models produce conservative trajectories, while a hybrid ensemble approach provides the most balanced and physically interpretable projections. Ensemble forecasts indicate reduced runoff during 2025–2028, followed by recovery in 2029–2034, supporting long-term water-resources planning and climate adaptation. Full article

Despite the growing recognition of heritage risk reduction, a comprehensive framework for multi-risk assessment remains notably absent within the context of historic urban landscapes (HULs) across diverse global contexts. This paper aims to fill this gap by developing an assessment framework to address multiple emerging risks in HUL management, considering climate-related, human-induced, and mixed hazards in UNESCO World Heritage properties. A four-step process is established—hazard identification, exposure categorisation, adaptation capacity-building, and vulnerability monitoring and evaluation. Using content analysis, this framework is applied to official reports from 33 World Heritage HUL cases across 33 countries. The results show that, although various hazards have been acknowledged by state parties, local governments prioritise human-induced or natural hazards more often than mixed hazards, leading to a shortage of comprehensive risk management plans and practical actions in most cases. Regarding heritage adaptation, the factors of capacity and governance are widely addressed, demonstrating the commitment of state parties to formulate strategies and solve problems. However, public participation and education practices remain insufficiently implemented, resulting in a relatively low degree of adaptation capacity-building. The proposed multi-risk assessment framework offers a crucial reference for global urban heritage management and risk reduction. Full article

Global water management faces a critical challenge: whilst scholarly consensus recognises that multiple, interacting drivers fundamentally shape water availability and management capacity, operational governance frameworks fail to systematically incorporate this understanding. This disconnect is particularly acute in public good contexts where incomplete knowledge, diverse stakeholder values, and statutory planning mandates create distinct challenges. Using Australia’s Murray–Darling Basin as a pilot case, this research develops and demonstrates a rapid, policy-relevant heuristic for identifying, prioritising, and incorporating drivers of change in complex socio-ecological water systems. Through structured participatory deliberation with 70 experts spanning research, policy, industry, and community sectors across three sequential workshops and 15 semi-structured interviews, we systematically identified key drivers across environmental, governance, economic, social, and legacy dimensions. A risk and sensitivity assessment framework enabled prioritisation based on impact, vulnerability, and urgency. Climate change, drought, water quality events, and cumulative impacts emerged as the highest-priority future drivers, with climate change acting as a threat multiplier, whilst governance drivers show declining relative significance. Using these methodological innovations, we synthesise the I-PLAN heuristic: five interdependent dimensions (Integrative Knowledge, Prioritisation for Management, Linkages between Drivers, Adaptive Agendas, and Normative Collaboration) that provide water planners with a transferable, operational tool for driver identification and bridging to planning and management in data-sparse contexts. Full article