Search Results (389)

The northward shift in climate zones and the urban heat island effect demand passive cooling for building roofs in northern regions. Artificial turf is a lightweight candidate, but existing studies treat it as homogeneous material, overlooking blade morphology and roof-scale thermal performance. This study conducted a scaled indoor experiment using a 1 m³ building model. Three artificial turfs with different blade lengths (Type A long, Type B medium, Type C short) were compared against concrete and XPS roofs under simulated summer solar radiation. Results show that blade morphology governs thermal performance. Type A exhibited the lowest peak surface temperature (48.9 °C vs. 53.4 °C and 60.6 °C), and its interface temperature (37.0 °C) was 15.1–19.0 °C lower than Types B and C, attributed to a static air insulation layer and enhanced convection. Its cooling rate (0.98 °C/min) was 1.69–2.33 times faster. Compared to concrete and XPS, Type A had lower surface temperature, less downward heat conduction, and a 29.3 °C drop in 30 min (concrete: 22.3 °C; XPS: 21.7 °C), showing urban heat island mitigation potential. Its heat flux reduction ratio reached 42.9%, with equivalent thermal resistance of ~0.40 m²·K/W, reducing summer peak indoor temperature by 3–6 °C in aging buildings. Double-layer stacking underperformed a single long-blade layer due to heat accumulation. Optimised long-blade turf challenges the view that low albedo inevitably causes high temperature, offering dual benefits of insulation and rapid dissipation for passive cooling in urban renewal. Full article

Modern smart cities increasingly depend on resilient and intelligent energy infrastructures to maintain critical urban services during large-scale disturbances and multi-fault conditions. Conventional restoration approaches are often limited by centralized operation, delayed response, and inadequate coordination of distributed energy resources (DERs) under emergency conditions. To address these challenges, this paper proposes a Federated AI-Driven Urban Energy Resilience Framework for Smart City Critical Infrastructure Restoration using Virtual Power Plant (VPP) coordination, blockchain-enabled peer-to-peer (P2P) energy trading, and intelligent distributed energy management. The proposed framework is validated on the IEEE 118-bus radial distribution system under severe dual-fault outage conditions, representing urban disaster-induced infrastructure interruptions. Critical urban service zones, including healthcare support systems, emergency loads, smart residential sectors, and EV charging corridors, are considered during the restoration process. The Seagull Optimization Algorithm (SOA) is employed to optimize DER dispatch and improve restoration performance under operational constraints. A progressive restoration strategy comprising conventional outage conditions, VPP-assisted restoration, blockchain-enabled decentralized energy trading, and AI-driven coordinated restoration is analyzed. Simulation results demonstrate that the proposed framework significantly enhances urban energy resilience by increasing load restoration from 55.05% to 94.20%, reducing Energy Not Supplied (ENS), improving voltage stability, and lowering interruption-related economic losses. The minimum bus voltage improves to 0.965 p.u. under the proposed coordinated restoration strategy. The results show that coordinated VPP operation and blockchain-based energy sharing can support reliable restoration of critical urban infrastructure during major outage conditions. The results indicate that integrating AI-assisted VPP coordination with secure decentralized energy trading can effectively support smart city critical infrastructure continuity during extreme outage conditions. The proposed framework provides a scalable and resilient solution for future intelligent urban energy systems and disaster-resilient smart city applications. Full article

To investigate the relationship between fishery resources community structure (mainly fish and crustaceans) and environmental factors in the artificial reef waters off Nanri Island, surveys were conducted in November 2021, 2022, and 2023. Shannon–Wiener diversity index (H′), Margalef richness index (D), Pielou evenness index (J′), and resource density index (RD) were employed to characterize the community structure. From 2021 to 2023, DO and petroleum hydrocarbons exhibited significant interannual variation (p < 0.05), whereas DIP, DIN, and SS showed highly significant interannual variation (p < 0.01). Spatially, DO, COD, DIN, and petroleum hydrocarbons varied more than other factors. Both diversity and richness indices rose over the study period, with mean H′ rising from 1.693 to 1.942 and mean D from 2.107 to 2.474. The evenness index (J′) declined in 2022 but then increased to 0.787. In contrast, the resource density index (RD) dropped sharply in 2022 (107.2) and partially recovered in 2023 (155.4), though it remained below the 2021 level (218.5). Redundancy analysis revealed that five environmental variables (DIP, DIN, petroleum hydrocarbons, SS, and DO) primarily shaped the fishery resource community structure in the artificial reef area. This study provided reference data for artificial reef management and sustainable fishery development. Full article

Neoclassical tearing modes (NTMs) are magnetohydrodynamic instabilities that generate magnetic islands in tokamak plasmas, degrading confinement and potentially limiting high-performance operation. Their stabilization typically requires precise alignment and appropriate injection of electron cyclotron (EC) power beams, making real-time control a challenging task. In this work, we present a proof-of-principle study aimed at investigating the potential role of neural networks in the control of plasma instabilities. The objective is not the design of a controller for a specific machine, but rather to study how a learning-based agent can autonomously discover effective stabilization strategies through reinforcement learning. A synthetic environment based on a tokamak scenario is used as a test bed for this investigation; the specific scenario plays no essential role in the methodological conclusions. The controller is trained using reinforcement learning techniques and operates solely on a representation of the magnetic island width, without relying on equilibrium reconstruction or explicit knowledge of the deposition location relative to the island. Two control tasks are considered: pure angular alignment and combined angular alignment with power control. The strategies that autonomously emerge are consistent with hand-designed approaches reported in the literature, while the framework remains flexible for incorporating additional objectives such as power minimization. This exploratory study establishes a framework for assessing the potential advantages of data-driven approaches in magnetic island control and provides a basis for future investigations aimed at improving alignment and suppression strategies in fusion plasmas. Full article

The direct-quadrature (dq) axis control method is a widely employed approach for off-grid and grid-connected inverters in solar photovoltaic (PV) systems that can regulate active and reactive power control. Conventional fixed-gain dq-axis PI controllers may exhibit degraded transient performance and reduced harmonic suppression capability under highly dynamic operating conditions. This article proposes an innovative control scheme of an inverter-based islanded microgrid consisting of PV generation and battery energy storage systems (BESS) that can deliver stable power sharing and robust voltage regulation even under highly dynamic operating conditions. An improved inverter control method based on an artificial neural network-based proportional integral (ANN-PI) controller is investigated to accurately control the dq-axis approach for the DC-link and voltage control loops. The suggested system was validated under MATLAB/Simulink to prove the effectiveness of the proposed controller. The achieved results indicate that the ANN-PI controller presents a high convergence speed and low overshoot with a low total harmonic distortion (THD) index of 3.9% under resistive and inductive loads, thus meeting the IEEE power quality standards. Full article

Islanded microgrids face considerable operational difficulties because of the inconsistency of renewable energy sources and ongoing dependence on diesel power. This study offers a comparative assessment of a traditional rule-based energy management system versus an AI-augmented energy management system for a hybrid island microgrid that includes photovoltaic generation, wind generation, battery energy storage, and diesel generator. The suggested AI-driven controller incorporates short-term predictions and heuristic scheduling to enhance dispatch choices. Simulations using MATLAB and Simulink Ver-sion 25.2.0.2998904 (R2025b) over a 24 h period show enhanced management of battery state-of-charge, decreased operation of the diesel generator, and greater use of renewable energy. The findings show a decrease in fuel usage and carbon dioxide emissions of around 63% in comparison to the baseline rule-based approach. Full article

Cities are artificial ecosystems that suffer most from environmental issues and climate change. Urban Heat Island (UHI) effects represent an increasing challenge, especially for compact Mediterranean cities characterized by high population density and extensive impervious surfaces. This study assessed localized microclimatic conditions within the small Maltese coastal town of Isla through a 15-day summer field monitoring campaign. Air temperature, relative humidity, and wind speed were measured across urban locations characterized by different levels of vegetation coverage and thermal vulnerability. The analysis combined descriptive statistics, Mann–Whitney U testing, and Multiple Linear Regression (MLR) models. In addition, site-specific Nature-based Solutions (NbS) scenarios were proposed as context-sensitive strategies to support urban heat mitigation and climate resilience. The results highlighted distinct microclimatic responses between the sites investigated. In particular, the MLR analysis suggested that non-vegetated areas were more sensitive to short-term atmospheric variability associated with wind speed and relative humidity fluctuations. These findings suggest that urban vegetation may contribute not only to localized cooling, but also to increased microclimatic stability within compact Mediterranean urban environments. Full article

This study developed an ecological environment evaluation framework tailored for islands in Jiangsu and validated its applicability using nine representative islands. The evaluation system encompasses 14 indicators across three dimensions: ecological, socio-economic, and policy-climate. By coupling the Time-varying Entropy Weight method with a Bayesian Network, the framework quantifies the dynamic impacts of policy interventions, extreme weather, and human activities. To enhance model accuracy under small-sample conditions, machine learning and deep learning techniques were integrated to construct a multi-layer ensemble evaluation model. The results indicate that this model improves prediction accuracy by 11.3% and reduces the root mean square error by 33.3%. The assessment results reveal significant differences in ecological quality among islands of different types. Natural-type inhabited islands maintain relatively high ecological quality through the synergy of ecological conservation and industrial activity, whereas artificial-type inhabited islands experience significant negative impacts from industrial development. Uninhabited islands generally score around 10, indicating relatively stable ecological conditions but high natural vulnerability. This framework provides a high-precision quantitative approach for dynamic evaluation of island ecological quality under small-sample constraints and offers a scientific basis for customized, island-specific conservation and development management strategies. Full article

Amid growing concerns over global warming, ensuring the outdoor thermal comfort (OTC) of public urban spaces is crucial for creating liveable and resilient cities. This study focused on the intensification of the urban heat island (UHI) effect and the heat stress experienced by the vulnerable older population. Evidence was found through the case study in a highly vulnerable area of Porto, with a high ageing ratio. The primary aim was to assess the influence of design-based adaptation strategies on OTC using ENVI-met, with a specific focus on older adults. Thermal stress was evaluated using the Physiological Equivalent Temperature (PET) index. The analysis confirms that older adults consistently experience higher PET values (+2–5 °C) and larger areas of thermal discomfort than active-age adults. Simulations reveal that the effectiveness of adaptation measures depends on the characteristics of the urban space but enhanced green infrastructure achieves the most significant heat mitigation results. Artificial shading only provides localized thermal relief. Cool pavements contribute meaningfully by lowering surface heat storage and reducing longwave radiation. However, their impact on PET, beneficial or detrimental, depends significantly on the morphology of the outdoor space and the materials used. In the analysed street canyon, PET was higher in the central hours of the day for both age ranges, when the pavement material had a higher albedo. An effective heat mitigation needs a combination of vegetation-based strategies and climate-responsive materials to ensure comfortable and age-inclusive public spaces. This research presents an actionable methodological approach for evaluating and enhancing OTC, advocating the use of microclimate simulations in a carefully selected set of public spaces within an intervention urban area to define effective climate adaptation measures for each space. Full article

Rapid urbanization and complex topography complicate Urban Heat Island (UHI) spatio-temporal dynamics. Traditional models and coarse-resolution imagery often fail to capture fine-scale, spatially non-stationary seasonal driving mechanisms. This study investigates the multi-dimensional drivers of surface thermal dynamics in Kunming, a typical low-latitude plateau city, using seasonal median LST composite (2018–2025). Integrating eXtreme Gradient Boosting (XGBoost) with eXplainable Artificial Intelligence (XAI) models decoupled the nonlinear impacts of these drivers. Results reveal a seasonal thermal dichotomy: Summer exhibits the most intense UHI effect with extreme peak temperatures, while Spring presents an anomaly where natural and vegetated Local Climate Zones (LCZs) show pronounced warming. SHapley Additive exPlanations (SHAP) analysis identified a seasonal rotation: anthropogenic and structural factors dominate Summer and Autumn warming, whereas natural and topographic regulators govern Spring and Winter. GeoShapley deconstruction demonstrated strong spatial non-stationarity. Building-density warming is amplified in poorly ventilated urban cores, and fragmented vegetation’s cooling is offset by anthropogenic heat during peak summer. This study provides new insights into the seasonal drivers of urban thermal environments in plateau cities. Full article

Climate change exerts growing pressure on the built environment, intensifying urban heat stress, altering microclimatic conditions, and increasing energy demand and health risks. Urban areas, characterized by dense construction and extensive soil sealing, are particularly susceptible to thermal anomalies such as Urban Heat Islands (UHIs), making thermal assessment a crucial element in adaptation and mitigation strategies. This research provides an updated and critical review of methodologies for the thermal evaluation of the built environment, with a focus on remote sensing as an emerging and integrative measurement paradigm. The study presents a comprehensive framework of detection systems, including satellite and aerial remote sensing, ground-based monitoring, and hybrid approaches, complemented by analytical and modeling techniques that combine physical and data-driven methods. A comparative assessment of open-access satellite sensors is carried out, analyzing spatial, spectral, and temporal resolutions and their relevance to urban-scale applications. The integration of remote sensing data with artificial intelligence, machine learning, and cloud-based processing is highlighted as a key advancement for improving interpretative, predictive, and decision-support capabilities. The findings indicate that such integration represents a new frontier for multiscale thermal analysis, supporting resilient urban planning, enhanced energy efficiency, and effective climate change mitigation policies. Full article

A tropical depression (TD) formed over the northern part of the South China Sea and affected Hong Kong during 25–26 June 2025. Based on the historical database, there were not many TDs following a similar track in the past, namely, a northwestward track towards Hainan Island and the Leizhou Peninsula. This paper serves to document a number of aspects of the forecasting service for this TD, including: (1) consideration of the upgrade of the system from a tropical disturbance to a TD, possible further upgrade into a tropical storm, and the location of the centre in “multiple centre” situation (broad tropical cyclone centre and a mesocyclone embedded in the convection near the centre); (2) forecasting of the intensity and the impact on local winds; and (3) wind structure analysis based on dropsonde and wind profiler data. Moreover, this case demonstrates that artificial intelligence models are proven to provide earlier alerting of the possible occurrence of this TD and its subsequent movement towards the coast of southern China, whereas the conventional physics-based models remain useful in the forecasting of the impact of TD on the winds in Hong Kong for the operation of the tropical cyclone warning signal services. Full article

Bovine ocular squamous cell carcinoma (BOSCC) is the most common ocular tumour in cattle, with a multifactorial aetiology involving ultraviolet (UV) radiation, genetic factors, pigmentation, and management practices. A detailed epidemiological characterisation of BOSCC in the Azores, Portugal, is provided, with particular emphasis on its spatial distribution and potential risk determinants. Data were obtained through an epidemiological questionnaire completed by field veterinarians between August 2023 and March 2025. A total of 85 BOSCC cases were recorded across 62 farms—45 on Terceira Island and 17 on São Miguel Island. All affected animals were adult Holstein Friesian dairy cows, managed under extensive pasture-based systems. The nictitating membrane was the most frequently affected structure (69.5%), and multiple lesions occurred in 20% of the cases. Farms located at 200–400 m of altitude presented the highest number of cases. Continuous exposure to UV under pasture-based management represents the main environmental risk factor. Although periocular pigmentation may provide partial protection, other environmental and genetic factors can also contribute to tumour development. Artificial insemination is considered a promising preventive tool, enabling genetic selection for protective traits such as periocular pigmentation. This research provides the first regional epidemiological characterization of BOSCC in the Azores, highlighting the interplay among environmental, genetic, and management-related factors in disease occurrence. Full article

Rapid urbanisation and climate change are intensifying urban heat stress, posing significant challenges for climate-responsive urban planning. Digital and data-driven approaches, including GIS, remote sensing, microclimate simulation, and artificial intelligence (AI), have advanced urban climate analysis; however, their capacity to support human-centred planning remains insufficiently synthesised. This review analyses 78 peer-reviewed studies (2015–2025) to evaluate how digital methods address urban microclimate and outdoor thermal comfort. The reviewed studies are classified into four methodological groups: spatial data analytics, simulation-based models, parametric and optimisation workflows, and AI-driven or hybrid approaches. The results show that the majority of studies rely on proxy indicators, such as land surface temperature and sky view factor, while physiologically based comfort indices (e.g., PET and UTCI) are applied in a limited proportion of studies and remain largely confined to microscale simulations. A persistent scale mismatch is identified between large-scale analytics and pedestrian-level thermal experience, alongside geographic and climatic biases, particularly in hot-arid regions. Unlike previous reviews, this study integrates digital methodologies, urban microclimate processes, and human-centred thermal comfort within a unified framework. The findings provide actionable insights for planners and designers by supporting the integration of thermal comfort into multi-scale, climate-responsive decision-making. Full article

Urban Heat Islands (UHIs) are widely analyzed using Land Surface Temperature (LST), yet most studies remain limited to single cities, rely on a single machine-learning model, analyze LST alone, and use inconsistent Surface Urban Heat Island Intensity (SUHII) definitions, which restrict cross-city comparability and broader generalization. This study introduces an explainable artificial intelligence (XAI) framework implemented in Google Earth Engine (GEE) to analyze census-tract summer surface heat (2018–2024) across eight climatically contrasting U.S. cities. The main novelty is a standardized tract-scale cross-city framework that jointly models LST and SUHII using a consistent SUHII definition, a common physical predictor set, city-held-out nested cross-validation, and SHAP-based interpretation, allowing absolute surface heat to be distinguished from relative within-city heat anomaly; this combination is rarely implemented within a single urban heat study. Multiple machine-learning models were evaluated, with ensemble trees performing best: Extreme Gradient Boosting (XGBoost) best predicted SUHII (R² = 0.879; RMSE = 0.213), while Extra Trees best predicted LST (R² = 0.908; RMSE = 0.745 °C). SHapley Additive exPlanations (SHAP) indicate that SUHII is driven primarily by impervious surface fraction and surface moisture availability, whereas LST is structured by latitude and mean summer air temperature. Overall, the framework provides interpretable multi-city attribution of urban surface heat drivers with demonstrated cross-city generalization. Full article