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Accurate and timely information related to quantitative descriptions and spatial distributions of urban areas is crucial to understand urbanization dynamics and is also helpful to address environmental issues associated with rapid urban land-cover changes. Thresholding is acknowledged as the most popular and practical way to extract urban information from nighttime lights. However, the difficulty of determining optimal threshold remains challenging to applications of this method. In order to address the problem of selecting thresholds, a Genetic Algorithm-based urban cluster automatic threshold (GA-UCAT) method by combining Visible-Infrared Imager-Radiometer Suite Day/Night band (VIIRS DNB), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI) is proposed to distinguish urban areas from dark rural background in NTL images. The key point of this proposed method is to design an appropriate fitness function of GA by means of integrating between-class variance and inter-class variance with all these three data sources to determine optimal thresholds. In accuracy assessments by comparing with ground truth—Landsat 8 OLI images, this new method has been validated and results with OA (Overall Accuracy) ranging from 0.854 to 0.913 and Kappa ranging from 0.699 to 0.722 show that the GA-UCAT approach is capable of describing spatial distributions and giving detailed information of urban extents. Additionally, there is discussion on different classifications of rural residential spots in Landsat remote sensing images and nighttime light (NTL) and evaluations of spatial-temporal development patterns of five selected Chinese urban clusters from 2012 to 2017 on utilizing this proposed method. The new method shows great potential to map global urban information in a simple and accurate way and to help address urban environmental issues.

In the field of bearing defect detection, Aiming at the problem of low efficiency in manual inspection and prone to missed detections in scenarios with small target defects, and overlapping targets, an improved YOLOv5-based object detection method is proposed. Firstly, in terms of feature extraction, the C3 modules in the original backbone of YOLOv5 are replaced with the finer-grained Res2Block modules to improve the model’s feature extraction ability. Secondly, in terms of feature fusion, a Bidirectional Feature Pyramid Network (BiFPN) is added to the original neck of YOLOv5 to enhance the fusion ability of shallow graphic features and deep semantic features. Finally, the performance of the improved YOLOv5 algorithm is validated through ablation experiments and comparative experiments with other defect detection algorithms, including the Small_obj algorithm the existing method of adding a small target detection head for identifying small target defects. The experimental results demonstrate that the improved YOLOv5 algorithm exhibits high mAP and accuracy in bearing defect detection, enabling more precise identification the types of small target defects on bearings in complex scenarios with multiple coexisting defects and overlapping detection targets, thereby providing valuable reference for practical bearing defect detection.

Despite the importance of high-resolution population distribution in urban planning, disaster prevention and response, region economic development, and improvement of urban habitant environment, traditional urban investigations mainly focused on large-scale population spatialization by using coarse-resolution nighttime light (NTL) while few efforts were made to fine-resolution population mapping. To address problems of generating small-scale population distribution, this paper proposed a method based on the Random Forest Regression model to spatialize a 25 m population from the International Space Station (ISS) photography and urban function zones generated from social sensing data—point-of-interest (POI). There were three main steps, namely HSL (hue saturation lightness) transformation and saturation calibration of ISS, generating functional-zone maps based on point-of-interest, and spatializing population based on the Random Forest model. After accuracy assessments by comparing with WorldPop, the proposed method was validated as a qualified method to generate fine-resolution population spatial maps. In the discussion, this paper suggested that without help of auxiliary data, NTL cannot be directly employed as a population indicator at small scale. The Variable Importance Measure of the RF model confirmed the correlation between features and population and further demonstrated that urban functions performed better than LULC (Land Use and Land Cover) in small-scale population mapping. Urban height was also shown to improve the performance of population disaggregation due to its compensation of building volume. To sum up, this proposed method showed great potential to disaggregate fine-resolution population and other urban socio-economic attributes.

The atherogenic index of plasma (AIP) is a novel marker associated with the risk of prediabetes, yet its interplay with menopausal status remains unclear. This study aimed to investigate the association between AIP and prediabetes among middle-aged Chinese women, and to explore this association jointly with menopausal status. This retrospective cross-sectional study included data of 7,929 middle-aged women who underwent physical examinations in a tertiary hospital in Changsha City, China, from 2015 to 2023. Participants' sociodemographic, physical, and laboratory data were collected from their medical records. The association between AIP and prediabetes was tested using multiple logistic regression models, followed by restricted cubic splines analysis for dose-response relationships. The relationship was further examined through stratified and joint analyses by menopausal status. Among the 7,929 participants, 1,592 (20.08%) were newly diagnosed with prediabetes. After adjusting for confounders, AIP was associated with an increased risk of prediabetes in all women (OR: 1.45, 95% CI: 1.30, 1.62), premenopausal women (OR: 1.51, 95% CI: 1.25, 1.83), and postmenopausal women (OR: 1.44, 95% CI: 1.26, 1.65), with the relationships being approximately linear. Although the multiplicative interaction was not statistically significant (P = 0.973), joint analysis revealed that compared to the low AIP-premenopausal reference group, the high AIP-postmenopausal group had the highest prediabetes risk (OR: 1.61, 95% CI: 1.31, 1.98). This study demonstrated a positive, linear relationship between AIP and the risk of prediabetes in middle-aged women. When considered jointly, a high AIP combined with postmenopausal status identified a subgroup with the greatest associated risk. AIP shows promise as a simple, cost-effective indicator for identifying high-risk individuals, though its clinical utility requires validation in prospective studies.

Urban heat not only affects thermal comfort but also constrains the sustainable development of cities, underscoring the necessity of understanding the temporal response of land surface temperature (LST) to urban characteristics over time. Most existing studies rely on single-overpass satellite observations or daily averages, failing to capture continuous diurnal variability and the time-dependent influence of different drivers. In this study, we reconstructed seasonal hourly LST series for Beijing using an improved diurnal temperature cycle (DTC) model (GEMη) based on MODIS data, and employed a random forest framework to quantify the relative contributions of natural, urban morphological, and anthropogenic factors throughout the diurnal cycle. Unlike previous studies that rely on traditional DTC models and machine learning for largely static or single-scale assessments, our research provides a unified, time-explicit comparison of LST driver dominance across seasons, hourly diurnal cycles, and urban–rural contexts. The results indicate that persistent urban heat island (UHI) effects occur in all seasons, with the maximum intensity reaching approximately 5.0 °C in summer. Generally, natural factors exert a cooling influence, whereas urban morphology and human activities contribute to warming. More importantly, the dominant drivers show strong temporal dependence: a nature-dominated regime prevails in summer, where vegetation exerts an overwhelming cooling effect. Conversely, during transition seasons and winter, LST variability is governed by a mixed-driven mechanism characterized by an hourly-resolved diurnal handoff, in which the dominant contributors shift hour by hour between surface physical properties and anthropogenic proxies. Our findings challenge the static view of urban heat drivers and provide quantitative evidence for developing time-sensitive and seasonally adaptive mitigation strategies, thereby supporting sustainable urban planning and enhancing climate resilience in megacities.

Urban heat island (UHI), referring to higher temperatures in urban extents than its surrounding rural regions, is widely reported in terms of negative effects to both the ecological environment and human health. To propose effective mitigation measurements, spatiotemporal variations and control machines of surface UHI (SUHI) have been widely investigated, in particular based on the indicator of SUHI intensity (SUHII). However, studies on SUHI frequency (SUHIF), an important temporal indicator, are challenged by a large number of missing data in daily land surface temperature (LST). Whether there is any city with strong SUHII and low SUHIF remains unclear. Thanks to the publication of daily seamless all-weather LST, this paper is proposed to investigate spatiotemporal variations of SUHIF, to compare SUHII and SUHIF, to conduct a pattern classification, and to further explore their driving factors across 305 Chinese cities. Four main findings are summarized below: (1) SUHIF is found to be higher in the south during the day, while it is higher in the north at night. Cities within the latitude from 20° N and 40° N indicate strong intensity and high frequency at day. Climate zone-based variations of SUHII and SUHIF are different, in particular at nighttime. (2) SUHIF are observed in great diurnal and seasonal variations. Summer daytime with 3.01 K of SUHII and 80 of SUHIF, possibly coupling with heat waves, increases the risk of heat-related diseases. (3) K-means clustering is employed to conduct pattern classification of the selected cities. SUHIF is found possibly to be consistent to its SUHII in the same city, while they provide quantitative and temporal characters respectively. (4) Controls for SUHIF and SUHII are found in significant variations among temporal scales and different patterns. This paper first conducts a comparison between SUHII and SUHIF, and provides pattern classification for further research and practice on mitigation measurements.

Urban populations face heightened extreme heat risks attributed to urban heat islands and high population densities. Although previous studies have examined global urban population exposure to heatwaves, the influence of urbanization-induced warming is still not quantified. Here, leveraging satellite-derived near-surface air temperature data, we assess the impacts of urbanization-induced warming on heat exposure in 1028 cities worldwide. Additionally, we investigate its role in shaping disparities in heat exposure between global North and South cities. Our findings reveal that urbanization-amplified compound heatwaves exacerbate heat exposure risk in more than 90% of cities, and that this amplification is stronger in high urbanization areas. Moreover, our analysis highlights the potential for overestimating disparities between global North and South cities if urbanization-induced warming is overlooked. The inequality of higher heat exposure in the global South cities than in the global North cities will be narrowed in real scenarios due to more intense urbanization-induced warming in the global North cities. We emphasize the pivotal role of urbanization-induced heatwave intensification in heat exposure assessments and call for its inclusion in future population vulnerability evaluations to extreme heat.

Urban building morphology has a significant impact on the urban thermal environment (UTE). The sky view factor (SVF) is an important structure index of buildings and combines height and density attributes. These factors have impact on the land surface temperature (LST). Thus, it is crucial to analyze the relationship between SVF and LST in different spatial-temporal scales. Therefore, we tried to use a building vector database to calculate the SVF, and we used remote sensing thermal infrared band to retrieve LST. Then, we analyzed the influence between SVF and LST in different spatial and temporal scales, and we analyzed the seasonal variation, day–night variation, and the impact of building height and density of the SVF–LST relationship. We selected the core built-up area of Beijing as the study area and analyzed the SVF–LST relationship in four periods in 2018. The temporal experimental results indicated that LST is higher in the obscured areas than in the open areas at nighttime. In winter, the maximum mean LST is in the open areas. The spatial experimental results indicate that the SVF and LST relationship is different in the low SVF region, with 30 m and 90 m pixel scale in the daytime. This may be the shadow cooling effect around the buildings. In addition, we discussed the effects of building height and shading on the SVF–LST relationship, and the experimental results show that the average shading ratio is the largest at 0.38 in the mid-rise building area in winter.

China has experienced varying degrees of increase or decrease in daytime and nighttime heatwaves, but studies have mostly been at the site or grid scale, and it remains unclear how daytime and nighttime heatwave events in China vary regionally when spatial scales are considered. Here, we redefine the different types of heatwave events in China from 1979 to 2018 as combined day and night heatwave events (CDNHWEs) and independent daytime (nighttime) heatwave events. Due to more pronounced spatiotemporal characteristics, CDNHWEs are the dominant heatwave events in China. Further analysis of the dominant heatwave events indicates that their increase in intensity is stronger at night than during the day, i.e. for CDNHWEs, the daytime-dominated events are gradually replaced by nighttime-dominated events. Compared to 1979–2003, there has been an increase in dominant heatwave events mainly in the south during the day and a nationwide increase at night since 2004. For CDNHWEs, daytime and nighttime processes are regulated by different mechanisms. During the daytime, fewer clouds enhance solar shortwave radiation, favouring daytime heatwaves; however, the increase in aerosols in northern China suppresses solar shortwave radiation. At night, the increase in humidity allows for increased longwave radiation, which favours the formation of nighttime heatwaves across the country. These findings further demonstrate the regional variability of heatwave hazards experienced in China and that targeted heatwave-mitigation measures should be developed based on regional characteristics.

Anthocyanins are important components in skins of red table grapes and contribute to the berries appearance, a key quality characteristic for customers. In recent years, exogenous foliage fertilizers has been applied to grapevines to improve the pigmentation of the fruit. The present study examines the effect on a biostimulant (SUNRED) pre- véraison application in the accumulation of anthocyanins in ‘Red Globe’ grapes, and investigates the related changes in expression of key genes and their enzyme activities in the flavonoid pathways. Additionally, abscisic acid (S-ABA) was also applied to grapevines to evaluate the comparative effect of SUNRED. Our analyses showed that total anthocyanin contents increased in both SUNRED and S-ABA treated grapes; for S-ABA, a 1% dilution (A100) of the commercially available stock solution treatments represented the greatest effect on pigmentation; for SUNRED, a 0.1% dilution (S1000) was most effective. The anthocyanin contents increased by 1.16-fold and 1.4-fold after A100 and S1000 treatments, respectively. The gene expression analyses showed that almost all genes involved in the anthocyanin biosynthesis pathway up-regulated after A100 and S1000 treatments, suggesting that the increment in total anthocyanin content was attributed to the increased expression level of related genes. Moreover, the activities of phenylalanine ammonia-lyase (PAL), chalcone isomerase (CHI), UDP glucose: flavonoid 3-o-glucosyl transferase (UFGT) and dihydroflavonol 4-reductase (DFR), key enzymes for biosynthesis of anthocyanin, were increased by the exogenous treatments. Overall, our findings clearly demonstrate that application of exogenous biostimulant have a positive effect on the pigment characteristics of grape crop.