Search Results (95)

Urban heat inequality represents a critical barrier to inclusive climate-resilient governance. While existing research has extensively mapped surface temperature patterns, the dynamic evolution of human thermal stress and the divergent regulatory efficiencies of cooling features across socio-economic contexts remain poorly understood. This study integrates multi-source datasets from 15 typical Chinese cities, employing a machine learning framework and GeoShapley interpretation to resolve the drivers of heat inequality across spatio-temporal and mechanistic dimensions. The findings demonstrate that high-density urbanization in China leads to a spatial synchronization of wealth and heat exposure, contrasting with the “Luxury Effect” observed in low-density Western contexts and indicating that high-income urban cores bear significantly higher absolute thermal stress. This inequality exhibits pronounced seasonal dynamics, where extreme summer conditions non-linearly amplify exposure gaps between socio-economic groups. Crucially, the results identify a systemic failure of cooling mechanisms in low-income communities, where the empirical thermal response of physical features deviates from expected patterns, failing to mitigate or even exacerbating perceived heat stress. These results emphasize that urban mitigation should move beyond quantitative resource expansion toward efficiency restoration, utilizing targeted spatial optimization to achieve precision climate justice. Full article

Urban planners increasingly require high-resolution thermal comfort maps to prioritize heat adaptation, yet validating the necessary microclimate models against standard field instruments remains methodologically fraught. This study establishes an integrated measurement–modeling framework applied to a study area in Strasbourg, France. First, we evaluate the radiative physics of the LASER/F model against net radiometer measurements at a specific sub-canopy location and against incoming shortwave radiation pyranometer records across three instrumentation sites. Results demonstrate high accuracy for longwave fluxes ($R^2>0.95$) but reveal that simplified tree geometry leads to condition-dependent shortwave discrepancies. Second, we quantify the systematic divergence between Mean Radiant Temperature derived from black globe measurements and six-directional simulations across seven sites. We analyze how these inevitable discrepancies, stemming mainly from geometric mismatch, propagate into the Universal Thermal Climate Index (UTCI), resulting in (71.5–75.5%) diurnal exact categorical agreement. Finally, spatial application of the model uncovers a “masked risk”: while temporal averaging suggests that 100% of the district remains safe (mean UTCI < 32 °C), duration-based analysis reveals that 72.8% of surfaces actually experience critical heat stress for over a quarter of the period. To address these hidden exposure risks, we propose a “Combined Risk Score” (CRS) that integrates thermal intensity and critical exposure duration on an absolute, dataset-independent scale, with a sensitivity analysis demonstrating that spatial risk prioritization is invariant to the intensity–duration weighting choice at the operational threshold. Full article

For irregularly shaped 820CrMnTi carburizing and nitriding parts, the challenges of high reflectivity-induced overexposure, low surface contrast, and interference from minute burrs in industrial online inspection are addressed in this paper. An innovative precision detection method integrating adaptive imaging and a dual-drive heterogeneous coupling model (RGFCN) is proposed. Such parts, due to surface photovoltaic characteristic changes caused by carburizing and nitriding heat treatment and the complex on-site lighting environment, are prone to local overexposure and “false out-of-tolerance” measurements caused by outlier sensitivity in traditional inspections. First, an innovative programmatic adaptive exposure control algorithm based on grayscale histogram feedback is introduced, which dynamically adjusts imaging parameters in real time to effectively suppress high-brightness overexposure under specific working conditions. Second, a novel adaptive main-axis scanning strategy is designed to construct a dynamic follow-up coordinate system, eliminating projection errors introduced by random positioning from a geometric perspective. Additionally, Gaussian gradient energy fields are combined with the Huber M-estimation robust fitting mechanism to suppress thermal noise while automatically reducing the weight of burrs and oil stains, achieving “immunity” to non-functional defects. Meanwhile, a data-driven innovative compensation approach is introduced. Based on sample training, gradient boosting decision trees (GBDTs) are integrated to explore the nonlinear mapping relationship between multidimensional feature spaces and system residuals, achieving implicit calibration of lens distortion and environmental coupling errors. By simulating factory conditions with drastic 24 h day–night lighting fluctuations and strong oil stain interference, statistical analysis of over 1000 mass-produced parts shows that this method exhibits excellent robustness in complex environments. It reduces the false out-of-tolerance rate caused by burrs by over 90%, and the standard deviation of repeated measurements converges to the micrometer level. This effectively addresses the visual inspection challenges of irregular, highly reflective parts on dynamic production lines. Full article

Rapid urbanization, environmental degradation and climate variability are intensifying the exposure of urban populations to multiple, interacting hazards in megacities. In India’s capital, Delhi, extreme heat, worsening air quality and flood-related stress overlap in impacted areas, exacerbated by high population density in low-lying zones and extensive built-up cover. This study develops an integrated spatial framework for assessing relative multi-hazard risk potential in Delhi by combining remote sensing, climate reanalysis, land use and demographic datasets into a predictive modeling system to support urban resilience planning. A comprehensive suite of twenty-two predictors representing thermal stress, air quality, surface indices, topography, hydrology, land use land cover (LULC), and demographic data was derived from diverse Earth observation sources. A cloud-native workflow leveraging Google Earth Engine (GEE) and Python 3 harmonized these predictors to train a Light Gradient Boosting Machine (LightGBM) model with five-fold spatial cross-validation. Quantile regression was used to estimate lower (P10) and upper (P90) predictive bounds, which are interpreted here as empirical predictive intervals around the modeled risk surface rather than as a strict separation of different uncertainty types, while SHapley Additive exPlanations (SHAP) decomposed the non-linear contributions of individual features. The model achieved predictive accuracy (R² = 0.98, MAE = 0.01), with residuals centered near zero and consistent performance across spatial folds, demonstrating strong generalizability. Road density (63.4%) and population density (25.9%) emerged as the primary predictors of the modeled risk surface, followed by building density and NO₂ concentration. Conversely, vegetation cover (NDVI) functioned as a critical mitigating buffer. Spatial risk maps identified persistent high-risk clusters in eastern and northeastern Delhi, coinciding with dense transport networks and industrial zones. The integrated P90 mapping framework provides spatially explicit and uncertainty-aware information on relative multi-hazard risk potential to guide targeted interventions, such as transport corridor mitigation and urban greening in Delhi and other rapidly urbanizing cities. Full article

Polymer–ceramic hybrid composites are emerging as attractive candidates for lightweight, corrosion-resistant absorber components in solar thermal collectors; however, their adoption is constrained by the intrinsically low thermal conductivity of polymers, processing-induced anisotropic heat transport, interfacial thermal resistance at tube/laminate joints, and durability challenges under outdoor exposure. This review provides a collector-centered synthesis of polymer–ceramic hybrid materials, emphasizing the translation of composite properties into collector-level outcomes rather than conductivity enhancement alone. A structure–property–performance mapping approach is presented to connect directional thermal conductivity ((k_in-plane), (k_perp)), thermal diffusivity, heat capacity, coefficient of thermal expansion, and service temperature with collector performance parameters such as heat removal effectiveness, overall heat losses, and stagnation behavior. Ceramic fillers (e.g., boron nitride, aluminum nitride, silicon carbide, alumina) are examined for stable conduction-network formation, coating compatibility, and long-term reliability, while carbon fillers (graphite, graphene nanoplatelets, carbon nanotubes) are evaluated for combined heat spreading and solar absorption benefits, with attention to emissivity penalties. Hybrid ceramic–carbon architectures and multilayer absorber designs are identified as the most promising routes to balance thermal transport, optical selectivity (high solar absorptance and low thermal emittance), manufacturability, and durability under UV, humidity, and thermal cycling. Full article

Background/Objectives: Lower urinary tract symptoms (LUTSs), particularly nocturia and urgency, often intensify during the menopause transition and may worsen with caffeine intake and cold exposure. This review aims to synthesize evidence relevant to a hypothesized caffeine–cold interaction in transitional menopause, focusing on water homeostasis and the estrogen–transient receptor potential melastatin 8 (TRPM8) cold-sensory axis, and to propose potentially actionable, nutrition-centered intervention candidates for future testing. Methods: Structured narrative review of PubMed, Embase, Web of Science, and citation tracking (inception–January 2026). Evidence was mapped into a mechanistic framework distinguishing established from hypothesis-generating links; no formal systematic-review study selection or meta-analysis was performed. Results: Caffeine can increase urine output via renal mechanisms (adenosine receptor antagonism and natriuresis) and may lower bladder sensory thresholds. Because half-life is long and variable, afternoon intake can extend into sleep, potentially increasing awakenings and nocturnal voids. Human studies link colder indoor environments to nocturia/overactive bladder, and passive pre-bedtime heating is associated with fewer nocturnal voids. We propose that repeated nighttime cold may amplify caffeine-related diuresis and may shift urine production toward the night, while estradiol decline may heighten TRPM8-mediated cold sensory gain, potentially contributing to urgency/frequency flares. A testable 2 × 2 cold × caffeine framework can operationalize dose, timing, and metabolism, pairing voiding diaries and bedroom temperature sensing with copeptin profiling. Conclusions: Transitional menopause may represent a susceptibility window in which endocrine instability and estradiol decline could plausibly increase sensitivity to indoor cold exposure and caffeine intake, potentially contributing to nocturia and urgency. The hypothesis label ‘dual hormone suppression’ (attenuated nocturnal AVP signal plus estradiol decline) may provide a mechanistic substrate for cold-exacerbated nocturnal polyuria, while an estrogen–TRPM8 axis may amplify cold-evoked urgency. Potentially actionable candidates include chronobiological caffeine timing/management and low-burden thermal strategies; nevertheless, menopause-stage-specific epidemiologic and clinical evidence for a caffeine × cold interaction remains limited and several mechanistic links are extrapolated, so prospective diary- and biomarker-enabled studies and controlled trials are needed to validate mechanisms and refine cold-sensitive endotypes. Full article

With global warming, high-temperature stress has become a primary abiotic factor limiting maize yield and quality. Exposure to heat stress induces sunscald on maize leaves, which severely impairs photosynthesis and ultimately leads to yield reduction. In this study, we used the heat-tolerant inbred line Zheng58 and the heat-sensitive inbred line HSBN, both of which are cultivated maize (Zea mays L. subsp. mays) inbred lines, as parents to construct F₂ and F_2:3 populations consisting of 257 lines. Phenotyping for sunscald at the flowering stage was performed across three field environments. The F₂ population was genotyped using the Maize 10K SNP array to construct a genetic map containing 1728 single nucleotide polymorphism (SNP) markers. The map spanned 1406.22 cM, with an average marker density of 0.81 cM per marker. Eight quantitative trait loci (QTLs) associated with heat tolerance were identified in the F₂/F_2:3 populations, distributed on chromosomes 1, 4, 5, and 8, collectively explaining 3.43% to 35.44% of the phenotypic variation. Among them, the stable QTL qHT1-2 on chromosome 1 was consistently detected across all three environments, explaining 11.41% to 35.44% of the phenotypic variation. Additionally, a major QTL, qHT1-3, was identified on the same chromosome, accounting for 33.70% of the phenotypic variation. Transcriptome analysis of flowering-stage leaves from both parents revealed 9262 differentially expressed genes (DEGs). Of these, 21 DEGs were co-localized within the eight QTL intervals. The genes Zm00001eb013260, Zm00001eb012720, Zm00001eb013600, and Zm00001eb013100 exhibited highly significant differential expression between the parental lines, these four genes are identified as candidate genes in response to heat stress in maize, and their specific biological functions require further functional validation. Full article

Rapid urbanization and climate change are intensifying heat exposure in cities, making effective adaptation strategies essential. This study presents a streamlined digital twin modeling framework for simulating the impact of nature-based solutions (NBSs) on outdoor thermal comfort, developed within the Intelligent Communities Lifecycle (ICL) software suite. The approach automates the import of urban geometry from OpenStreetMap and integrates geolocated weather data, enabling users to efficiently test scenarios involving NBSs and surface material modifications. Outdoor thermal comfort is quantified using the Universal Thermal Climate Index (UTCI), with results visualized through an interactive cloud-based 3D platform to support participatory urban planning. The methodology is demonstrated in Meunierstraat, Leuven (Belgium), where three planning alternatives are compared across seasonal extremes. Simulations show that targeted NBS interventions, particularly temporary participatory measures, can improve thermal comfort under extreme heat. However, the benefits are seasonally dependent and spatially heterogeneous, emphasizing the value of high-resolution, scenario-based analysis. This integrated workflow enhances both technical evidence and stakeholder engagement. While the tool is capable of linking outdoor comfort improvements with building energy performance and carbon emissions, the present paper focuses solely on the outdoor thermal comfort results, leaving indoor–outdoor coupling analysis as a direction for future work. Full article

As extreme heat events become increasingly frequent worldwide, there is an urgent need for fine-scale assessment of urban heat risk and for identifying its key determinants. Conventional approaches often struggle to capture complex intra-urban spatial heterogeneity, limiting effective heat risk governance and resource allocation. This study applies the Hazard–Exposure–Vulnerability–Adaptation (HEVA) framework by integrating remote sensing, road network, and socio-demographic data. Using the CRITIC weighting method, we quantify and map a street-level heat risk index (HRI) in Tianjin, China. We further employ geographically weighted machine learning models to identify dominant drivers and to characterise nonlinear effects, interaction patterns, and spatially varying relationships. Model reliability is assessed by benchmarking geographically weighted models against global nonlinear baselines under three-fold cross-validation; GW-XGBoost achieves comparable explanatory power to the best global model (R² = 0.672) while yielding lower prediction errors (MAE = 0.142), supporting robust spatial inference. Results show that elevated heat risk is not confined to the urban core; instead, it is more pronounced in peripheral transitional zones around central districts. These areas often exhibit coincident heat stress and high population exposure, a higher concentration of vulnerable groups and ageing residential neighbourhoods, and comparatively limited access to medical and cooling resources. Mechanistically, greater development intensity is generally associated with higher heat risk, whereas higher vegetation cover tends to reduce risk; however, the strength and, in some locations, the direction of these effects vary substantially across streets. These findings suggest that heat risk management should prioritise peripheral transitional zones. Targeted interventions should balance development intensity, expand effective greening and shading, and improve the provision and accessibility of healthcare and cooling services to reduce street-level heat risk. Full article

Resilience to climate change is a complex concept, especially in metropolitan areas where diverse services and stakeholders interact. Promoting sustainable climate adaptation, a resilience assessment method focused on regional areas and nature-based solutions is presented, along with its open-access, web-based platform, supporting resilience assessment, planning, and monitoring. Floods, droughts, heat or cold waves, windstorms, and forest fires can be assessed. A framework for holistic assessment and other framework, addressing critical infrastructure, are integrated. Four resilience dimensions are assessed: organizational (governance, social aspects, finance); spatial (exposure, impacts, and mapping); functional (service management, interdependencies); and physical (infrastructure robustness, redundancy). Strategic services comprise, e.g., water, waste, and natural areas. Resilience capacities, e.g., to prevent, respond, and recover from disruptions, are also assessed. The paper emphasizes new developments and assessment. Practical step-by-step guidance aligned with assessment purposes is included, aiming to address observed limitations (e.g., fragmented service provision, communication silos, data constraints). Overall results of a Spanish metropolitan area (AMB) and an exploratory application to an Austrian rural case (SLR) are also presented. Following the guidelines, AMB progressed from an essential to a comprehensive assessment. Overall, almost 1/3 of the metrics are advanced or progressing. SLR assessed its resilience capabilities regarding electrical infrastructure. Full article

Problem Statement: Two global trends, including aging populations and the acceleration of global warming, are increasing the risk of heat-related illness, challenging the health of populations, and the sustainability of healthcare systems. Global warming refers to the increase in the Earth’s average surface temperature, generally attributed to the greenhouse effect, which is occurring at three times the rate of the pre-industrial era. The global population of older adults, defined here as individuals aged 60 and over, is expected to reach over 2 billion by mid-century. This population is particularly vulnerable to heat-related illness, specifically disruption of thermoregulation from excessive exposure to environmental heat due to metabolic and cognitive changes associated with aging. Objectives: This review examines heat-related illness and its impact on older adults within a socio-ecological framework, considering both drivers and mitigation strategies related to global warming, the built environment, social determinants of health, healthcare system responses, and the individual. The authors were motivated to create a conceptual model within this framework drawing on their lived experiences as healthcare providers interacting with older adults in a large urban area of the southwestern US, known for its extreme heat and extensive heat island effects. Based on this framework, the authors suggest actionable strategies supported by the literature to reduce the risks of morbidity and mortality. Methods: The literature search utilized a wide lens to identify evidence supporting various aspects of the hypothesized framework. In this sense, this review differs from systematic and scoping reviews, which seek a complete synthesis of the available literature or a mapping of the evidence. The first author conducted the literature search and synthesis, while the second and third authors reviewed and added publications to the initial search and conceptualized the socio-ecological framework. Discussion: This study is unique in its focus on a global trend that threatens the well-being of a growing population. The population health focus underscores social determinants of health and limitations of existing healthcare systems to guide healthcare providers in reducing older adults’ vulnerability to heat-related illness. This includes patient education regarding age-related declines in extreme heat tolerance, safe and unsafe physical activity habits, the impact of prescription drugs on heat tolerance, and, importantly, identifying the symptoms of heatstroke, which is a medical emergency. Additional strategies for improving survivability and quality of life for this vulnerable population include improved emergency response systems, better social support, and closer attention to evidence-based treatment for heat-related health conditions. Full article

The combination of gold nanoparticles (AuNPs) with hydrogels has drawn significant interest in the design of smart materials as advanced platforms for biomedical applications. These systems endow light-responsiveness enabled by the AuNPs localized surface plasmon resonance (LSPR) phenomenon. In this study, we propose a nanocomposite hydrogel in which gold nanorods (AuNRs) are included in an agarose–carbomer–hyaluronic acid (AC-HA)-based hydrogel matrix to study the correlation between light irradiation, local temperature increase, and drug release for potential light-assisted drug delivery applications. The gel is obtained through a facile microwave-assisted polycondensation reaction, and its properties are investigated as a function of both the hyaluronic acid molecular weight and ratio. Afterwards, AuNRs are incorporated in the AC-HA formulation, before the sol–gel transition, to impart light-responsiveness and optical properties to the otherwise inert polymeric matrix. Particular attention is given to the evaluation of AuNRs/AC-HA light-induced heat generation and drug delivery performances under near-infrared (NIR) laser irradiation in vitro. Spatiotemporal thermal profiles and high-resolution thermal maps are registered using fiber Bragg grating (FBG) sensor arrays, enabling accurate probing of maximum internal temperature variations within the composite matrix. Lastly, using a high-steric-hindrance protein (BSA) as a drug mimetic, we demonstrate that moderate localized heating under short-time repeated NIR exposure enhances the release from the nanocomposite hydrogel. Full article

Post-industrial waterfronts have become key arenas of urban transformation, where heritage, public space and social equity intersect. This study examined the Xuhui Waterfront in Shanghai under the ‘One River, One Creek’ initiative, which converted former industrial land into a continuous riverfront corridor of parks and cultural venues. The research aimed to evaluate whether this large-scale renewal enhanced social equity or produced new forms of exclusion. A tripartite analytical framework of distributive, procedural and recognitional justice was applied, combining spatial mapping, remote-sensing analysis of vegetation and heat exposure, housing price-to-income ratio assessment, and policy review from 2015 to 2024. The results showed that the continuity of the riverfront, increased greenery and adaptive reuse of industrial structures improved accessibility, environmental quality and cultural enjoyment. However, housing affordability became increasingly polarised, indicating emerging gentrification and generational inequality. This study concluded that this dual outcome reflected the fiscal dependency of state-led renewal on land-lease revenues and high-end development. It suggested that future waterfront projects could adopt financially sustainable yet inclusive models, such as incremental phasing, public–private partnerships and guided self-renewal, to better reconcile heritage conservation, public-space creation and social fairness. Full article

Urban heat vulnerability is an increasing public health concern, particularly in rapidly urbanizing regions of southern France. This study aims to quantify and map the Heat Vulnerability Index (HVI) for Toulouse and to analyze its temporal trends to identify high-risk zones and influencing factors. The assessment integrates recent years’ remote sensing data of pollutant emissions, land use/land cover and land surface temperature, statistical data of climate-related mortalities, and socioeconomic and demographic factors. Following a detailed analysis of recent real-time air quality and weather data from multiple monitoring stations across the city of Toulouse, it was observed that Urban Pollution Island (UPI) and Urban Heat Island (UHI) are closely interlinked phenomena. Their combined effects can significantly elevate the annual mortality risk rate by an average of 2%, as calculated using AirQ+ particularly, in densely populated urban areas. Remote sensing data was processed using Google Earth Engine and all factors were grouped into three key categories: heat exposure, heat sensitivity, and adaptive capacity to derive HVI. Temporal HVI maps were generated and analyzed to identify recent trends, revealing a persistent increase in vulnerability across the city. Comparative results show that 2022 was the most critical summer period, especially evident in areas with limited vegetation and extensive use of heat-absorptive materials in buildings and pavements. The year 2024 indicates resiliency and adaptation although some areas remain highly vulnerable. These findings highlight the urgent need for targeted mitigation strategies to improve public health, enhance urban resilience, and promote overall human well-being. This research provides valuable insights for urban planners and municipal authorities in designing greener, more heat-resilient environments. Full article

This study establishes a robust small-angle neutron scattering (SANS) methodology for the quantitative characterization of γ matrix channel widths in the nickel-based single-crystal superalloy DD10. By combining SANS with TEM analyses and modeling the one-dimensional SANS data via a polydisperse lamellar model, we accurately determined the channel width distribution across macroscopic sample volumes. In the virgin state, the mean channel widths were nearly isotropic, measuring 17.8 ± 0.1 nm along [002] and 20.5 ± 0.1 nm along [020]. After standard heat treatment (solution and two-step aging), significant anisotropic coarsening was observed, with widths increasing to 36.8 ± 0.2 nm along [002] and 28.0 ± 0.1 nm along [020], indicating stress-free rafting. Elemental mapping revealed substantial redistribution of key alloying elements: Al content in γ′ precipitates increased by 2.6 at.%, while Cr in the γ channels rose by 5.9 at.%. These quantitative results demonstrate that SANS provides reliable, bulk-statistical insights into nanoscale channel geometry, highlighting its critical role in influencing elemental diffusion kinetics and microstructural evolution during thermal exposure. Full article