38,443 research outputs found
Diabetes, impaired fasting glucose and their relations to plasma pro-inflammatory cytokines: a population-based study in China
No full textH. Zuo, Z. Shi, X. Hu, M. Wu, Z. Guo and A. Hussai
Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR
No full textThe demand for water resources during urbanization forces the continuous exploitation of groundwater, resulting in dramatic piezometric drawdown and inducing regional land subsidence (LS). This has greatly threatened sustainable development in the long run. LS modeling helps understanding the factors responsible for the ongoing loss of land elevation and hence enhances the development of prevention strategies. Data-driven LS models perform well with fewer variables and faster convergence than physically-based hydrogeological models. However, the former models often cannot simultaneously reflect the temporal nonlinearity and spatial correlation (SC) characteristics of LS under complex variables. We proposed a LS spatiotemporal model which considers both nonlinear and spatial correlations between LS and groundwater level change of exploited aquifers. It is based on deep learning method and LS time series detected by permanent scatterer-interferometric synthetic aperture radar (PS-InSAR). The LS time series and hydrogeological properties are constructed as a spatiotemporal dataset for model training. The spatiotemporal LS model, geographically weighted long short-term memory (GW-LSTM), is constructed by integrating SC with LSTM. This latter is a deep recurrent neural network approach incorporating sequential data. The model is validated by a case study in the Beijing plain. The results show that the accuracy of the proposed model can be greatly improved considering the spatial correlation between LS and influencing factors. Furthermore, the comparison between the LSTM and GW-LSTM models reveals that groundwater level variation is not a unique causation of LS in the study area. The developed model deals with the spatiotemporal characteristics of LS under multiple variables and can be used to predict LS under different scenarios of groundwater level variations for the purpose of monitoring and providing evidence to support the prevention of future LS
Soilscape of the west-central Taiwan: The footprints in soil pedogenesis and geomorphic environment
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Explicit the urban waterlogging spatial variation and its driving factors: The stepwise cluster analysis model and hierarchical partitioning analysis approach
No full textUrban waterlogging is a hydrological cycle problem that seriously affects people's life and property. Characterizing waterlogging variation and explicit its driving factors are conducive to prevent the damage of such disasters. Conventional methods, because of the high spatial heterogeneity and the non-stationary complex mechanism of urban waterlogging, are not able to fully capture the urban waterlogging spatial variation and identify the waterlogging susceptibility areas. A more robust method is recommended to quantify the variation trend of urban waterlogging. Previous studies have simulated the waterlogging variation in relatively small areas. However, the relationship between variables is often ignored, which cannot comprehensively reveal the dominant drivers affecting urban waterlogging. Therefore, a novel approach is proposed that combined stepwise cluster analysis model (SCAM) and hierarchical partitioning analysis (HPA) within a general framework and verifies the applicability through logistic regression, artificial neural network, and support vector machine. According to the dominant driving factors, different simulation scenarios are established to analyze waterlogging density variation. Results found that the SCAM provides accurate and detailed simulated results both in urban centers where waterlogging frequently occurs and urban fringe with few waterlogging events, which shows an excellent performance with a high classification accuracy and generalization capability. HPA detected that the impervious surface abundance (28.07%), vegetation abundance (20.80%), and cumulate precipitation (16.25%) are the dominant drivers of waterlogging. This result suggests that priority should be given to controlling these three factors to mitigate the risk of waterlogging. It is interesting to note that under different urbanization and rainfall scenarios, the urban waterlogging susceptibility has a considerable variation. The watershed spatial location and watershed characteristics are relevant aspects to be considered in identifying and assessing waterlogging susceptibility, which provides original insights that urban waterlogging mitigation strategies should be developed according to different local conditions and future scenarios
Three pheromone-binding proteins help segregation between two Helicoverpa species utilizing the same pheromone components
No full textSoil properties, clay minerals and genesis of montane soils in Chilan region, northern Taiwan.
No full textHow to develop site-specific waterlogging mitigation strategies? Understanding the spatial heterogeneous driving forces of urban waterlogging
No full textUrban waterlogging seriously threatens urban sustainable development and human life. The effects of various landscape elements on urban waterlogging have been extensively documented. However, less attention is deserved to the spatial heterogeneity effects of urban landscape elements on urban waterlogging. The spatial pattern of dominant driving forces and how the interactive effects of landscape elements affect urban waterlogging with different environmental configurations have not been well examined. These shortcomings have hindered the development of target-specific urban waterlogging mitigation strategies. To shed some light on this topic, an innovative method that integrated the boruta algorithm, cubist regression tree, and geographical detector model is presented to investigate the spatial heterogeneous mechanisms of urban waterlogging and map the waterlogging dominant driving forces with different local conditions. The results show that the boruta algorithm proposed in this study introduces shadow variables as a benchmark, thus enabling an unbiased and stable selection of representative waterlogging driving factors based on local conditions. By comparing with two other commonly used regression methods (global regression model, spatial lag model), the cubist regression tree divides the urban waterlogging space into multiple homogeneous subgroups to quantify the spatial non-stationarity relationship and spatially explicit the local driving forces in Guangzhou and Shenzhen, with the adjusted R2 of 0.79 and 0.88. The geographical detector model denotes that waterlogging magnitude within different subgroups is affected by different dominant factors. Even for the same dominant factor, its contribution to waterlogging varies considerably in different subgroups. The independent contribution of the dominant factor in Guangzhou was 23.28%–57.82%, while in Shenzhen it ranged from 25.95% to 53.59%. In addition to the dominant factor of each subgroup, it is noteworthy that in some subgroups the combined effect of different representative factors on waterlogging is significantly stronger than the contribution of their dominant factors. In view of this, urban planners and local authorities need to comprehensively consider the interaction effect between representative factors, which develop urban waterlogging mitigation strategies that integrate multiple factors. The results from this study extend our scientific understanding of the site-specific mechanism of urban waterlogging, which facilitates the implementation of more targeted and effective mitigation strategies, rather than a “one-size-fits-all” policy
Representative soils selected from arable and slope soils in Taiwan and their database establishment.
No full textBimetal defects boost efficient photocatalytic H2O2 in-situ production of Cu1-xCo2-yO4-z for contaminant degradation
No full textTargeted design of metal defects in photocatalysts helps to realize stable and efficient wastewater treatment. Herein, a novel spinel Cu1-xCo2-yO4-z with bimetal defects is precisely designed by a simple temperature programmed reduction method in hydrogen atmosphere. The co-existence of metallic defects leads to defects distortion forming polarization effect, which can significantly accelerate the separation of photogenerated carriers. By optimizing the band structure, Cu1-xCo2-yO4-z induced in-situ H2O2 production (190 μmol L−1·h−1 with sacrificial agent and 78 μmol L−1·h−1 without sacrificial agent) to boost the generation of hydroxyl radical, and the photodegradation activity was 7.8 and 8.4 times for MB and Cr(VI) as that of CuCo2O4, respectively. Bimetal defects achieve high performance through in-situ production of H2O2 and purification of contaminants, providing novel strategies for the applications of spinel photocatalysts in environmental purification.No Full Tex
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