Search

One of the major barriers to hindering the sustainable development of the terrestrial environment is the desertification process, and revegetation is one of the most significant duties in anti-desertification. Desertification deteriorates land ecosystems through species decline, and remote sensing is becoming the most effective way to monitor desertification. Mu Us Sandy Land is the fifth largest desert and the representative area under manmade vegetation restorations in China. Therefore, it is essential to understand the spatiotemporal characteristics of artificial desert transformation for seeking the optimal revegetation location for future restoration planning. However, there are no previous studies focusing on exploring regular patterns between the spatial distribution of vegetation restoration and human-related geographical features. In this study, we use Landsat satellite data from 1986 to 2020 to achieve annual monitoring of vegetation change by a threshold segmentation method, and then use spatiotemporal analysis with Open Street Map (OSM) data to explore the spatiotemporal distribution pattern between vegetation occurrence and human-related features. We construct an artificial vegetation restoration suitability index (AVRSI) by considering human-related features and topographical factors, and we assess artificial suitability for vegetation restoration by mapping methods based on that index and the vegetation distribution pattern. The AVRSI can be commonly used for evaluating restoration suitability in Sandy areas and it is tested acceptable in Mu Us Sandy Land. Our results show during this period, the segmentation threshold and vegetation area of Mu Us Sandy Land increased at rates of 0.005/year and 264.11 km2/year, respectively. Typically, we found the artificial restoration vegetation suitability in Mu Us area spatially declines from southeast to northwest, but eventually increases in the most northwest region. This study reveals the revegetation process in Mu Us Sandy Land by figuring out its spatiotemporal vegetation change with human-related features and maps the artificial revegetation suitability. [ABSTRACT FROM AUTHOR]

Background: Mounting evidence suggests that sex differences exist in cognitive reserve (CR) for cognitively unimpaired (CU) elderly individuals. Global left frontal connectivity (gLFC connectivity) is a reliable neural substrate of CR.Objective: The purpose of this study was to explore sex differences in gLFC connectivity among CU elderly individuals.Methods: One hundred thirteen normal controls (NCs) (women = 66) and 132 individuals with subjective cognitive decline (SCD) (women = 92) were recruited from the Sino Longitudinal Study on Cognitive Decline (SILCODE) (data 1). Among them, 88 subjects underwent amyloid-β (Aβ) imaging, including 32 Aβ+ and 56 Aβ-subjects. Forty-six subjects underwent another rs-fMRI examination (data 2) to validate the repeatability of the calculation of gLFC connectivity, which was determined through seed-based functional connectivity between the LFC and voxels throughout the whole brain. Independent-sample t-tests were used to evaluate the sex differences in gLFC connectivity across different subgroups (NC versus SCD, Aβ+ versus Aβ-). Partial correlation analysis was used to calculate the correlations between gLFC connectivity and cognitive assessments.Results: Women exhibited lower gLFC connectivity in both the NC (p = 0.001) and SCD (p = 0.020) subgroups than men. Women also exhibited lower gLFC connectivity in both the Aβ-(p = 0.006) and Aβ+ (p = 0.025) groups. However, the significant difference disappeared in the Aβ+ group when considering the covariates of age, education, total intracranial volume, and APOE4-carrying status. In addition, gLFC connectivity values were negatively correlated with Geriatric Depression Scale scores in the SCD group (r = -0.176, p = 0.047).Conclusion: Women showed lower gLFC connectivity among CU elderly individuals. [ABSTRACT FROM AUTHOR]

Flood Detection and Service (FD&S) is of great significance to flood management and decision-making. But most of the state-of-art FD&S methods are weak in timeliness and extensibility, and could not reflect flood processes. To resolve the abovementioned problems, this paper proposed the process-based FD&S (PFD&S) method based on Sensor Web. PFD&S is a four-layer architecture the core components of which are the access adapter and process-based detection rules and it is capable of heterogeneous sensor access, flood detection, and flood phase adaptive services. A prototype was developed based on the PFD&S method. Two floods occurring in the Huanghan basin (Hubei, China, with the area of 5.04 × 104 km2) during July 2016, was selected as the case studies to validate the PFD&S method and prototype. The results demonstrated that the proposed PFD&S method and prototype could achieve the instant flood process detection and services in 2.7 min. • The PFD&S method was proposed to achieve the rapid flood process detection and flood phase adaptive services. • The PFD&S prototype was designed and implemented based on the PFD&S method. • The method and prototype were sensor web - based, with universality and extensibility. [ABSTRACT FROM AUTHOR]

The satellite-retrieved Aerosol Optical Depth (AOD) is widely used to estimate the concentrations and analyze the spatiotemporal pattern of Particulate Matter that is less than or equal to 2.5 microns (PM2.5), also providing a way for the related research of air pollution. Many studies generated PM2.5 concentration networks with resolutions of 3 km or 10 km. However, the relatively coarse resolution of the satellite AOD products make it difficult to determine the fine-scale characteristics of PM2.5 distributions that are important for urban air quality analysis. In addition, the composition and chemical properties of PM2.5 are relatively complex and might be affected by many factors, such as meteorological and land cover type factors. In this paper, an AOD product with a 1 km spatial resolution derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, the PM2.5 measurements from ground sites and the meteorological data as the auxiliary variable, are integrated into the Modified Support Vector Regression (MSVR) model that proposed in this paper to estimate the PM2.5 concentrations and analyze the spatiotemporal pattern of PM2.5. Considering the relatively small dataset and the somewhat complex relationship between the variables, we propose a Modified Support Vector Regression (MSVR) model that based on SVR to fit and estimate the PM2.5 concentrations in Hubei province of China. In this paper, we obtained Cross Correlation Coefficient (R²) of 0.74 for the regression of independent and dependent variables, and the conventional SVR model obtained R² of 0.60 as comparison. We think our MSVR model obtained relatively good performance in spite of many complex factors that might impact the accuracy. We then utilized the optimal MSVR model to perform the PM2.5 estimating, analyze their spatiotemporal patterns, and try to explain the possible reasons for these patterns. The results showed that the PM2.5 estimations retrieved from 1 km MAIAC AOD could reflect more detailed spatial distribution characteristics of PM2.5 and have higher accuracy than that from 3 km MODIS AOD. Therefore, the proposed MSVR model can be a better method for PM2.5 estimating, especially when the dataset is relatively small. [ABSTRACT FROM AUTHOR]

Accurate water-level prediction is of great significance to flood disaster monitoring. A genetic algorithm coupling a back-propagation neural network (GA-BPNN) has been adopted as a hybrid model to improve forecast performance. However, a traditional genetic algorithm can easily to fall into locally limited optimization and local convergence when facing a complex neural network. To deal with this problem, a novel method called an improved genetic algorithm (IGA) coupling a back-propagation neural network model (IGA-BPNN) is proposed with a variety of genetic strategies. The strategies are to supply a genetic population by a chaotic sequence, multi-type genetic strategies, adaptive dynamic probability adjustment and an attenuated genetic strategy. An experiment was tested to predict the water level in the middle and lower reaches of the Han River, China, with meteorological and hydrological data from 2010 to 2017. In the experiment, the IGA-BPNN, traditional GA-BPNN and an artificial neural network (ANN) were evaluated and compared using the root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE) coefficient and Pearson correlation coefficient (R) as the key indicators. The results showed that IGA-BPNN moderately correlates with the observed water level, outperforming the other two models on three indicators. The IGA-BPNN model can settle problems including the limited optimization effect and local convergence; it also improves the prediction accuracy and the model stability regardless of the scenario, i.e., sudden floods or a period of less rainfall. [ABSTRACT FROM AUTHOR]

Timely and active detecting and monitoring of a flood event are critical for a quick response, effective decision-making and disaster reduction. To achieve the purpose, this paper proposes an active service framework for flood monitoring based on Sensor Web services and an active model for the concrete implementation of the active service framework. The framework consists of two core components—active warning and active planning. The active warning component is based on a publish-subscribe mechanism implemented by the Sensor Event Service. The active planning component employs the Sensor Planning Service to control the execution of the schemes and models and plans the model input data. The active model, called SMDSA , defines the quantitative calculation method for five elements, scheme, model, data, sensor, and auxiliary information, as well as their associations. Experimental monitoring of the Liangzi Lake flood in the summer of 2010 is conducted to test the proposed framework and model. The results show that 1) the proposed active service framework is efficient for timely and automated flood monitoring. 2) The active model, SMDSA , is a quantitative calculation method used to monitor floods from manual intervention to automatic computation. 3) As much preliminary work as possible should be done to take full advantage of the active service framework and the active model. [ABSTRACT FROM AUTHOR]

Simultaneous/sequential occurrence of multiple extreme climate events has the tendency to inflict greater damage compared to isolated extreme climate events. The exploration of simultaneous or continuous extreme events holds profound implications for disaster risk prevention. Therefore, we identified the compound heatwaves and extreme precipitation events (CHP, extreme precipitation events following heatwaves) in China based on ERA5 data. Then, the individual extreme events and compound events were undertaken through event coincidence analysis. Furthermore, the atmospheric background of individual extreme events and compound extreme events was further investigated. The results show that the CHP events with an increasing trend mainly occurred in northern China and northeastern the Qinghai-Tibet Plateau (QTP). Notably, after the year 2000, the time interval of extreme precipitation in CHP events was significantly shortened. In July and August, the trigger coincidence rate of extreme precipitations by heatwaves occurred higher and extreme precipitation events following heatwaves accounted for 40%. Moreover, Extreme precipitation is more likely to occur after a longer duration and higher intensity heatwave in the QTP. Convective effective potential energy (CAPE) and relative humidity manifest a considerable increase during the heatwave duration, greatly augmenting the risk of extreme precipitation events. In addition, the frequency of CHP events is higher in northern and northeastern China during El Niño years. While it predominantly concentrates in the northeast region in La Niña years. • The time interval of extreme precipitation after the heatwave in the compound events became shorter after 2000. • Extreme precipitation is more likely to occur after a longer duration and higher intensity heatwave in the QTP. • Significantly increased CAPE and relative humidity during heatwave enhance the risk of extreme precipitation events. [ABSTRACT FROM AUTHOR]

Rapid population growth and urban sprawl create pressure on environmental resources along with a growing demand for physical resources in cities. The concept of a smart sustainable city proposes a framework to ensure citizens a better life and protect the environment to maintain sustainability through information and communication technologies. This paper constructed a sustainable smart city development indicator system using the International Telecommunication Union (ITU) maturity model and assessed the maturity level of 35 cities in China. Spectral clustering was adopted to recognize development patterns on three dimensions and 53 indicators. The results indicated four development patterns. Completing developing elements converged in comprehensive-development cities. Environmental-friendly cities enhanced environmental quality under government guidance supported by information and communications technology. Welfare cities showed good social welfare via the government's highly focused strategy but lacked economic support. Innovation, trade, capital investment, mental poverty, and poor living conditions characterized undeveloped cities. Practical implications are recommended for each type according to development differences to build a better smart sustainable city. [ABSTRACT FROM AUTHOR]

Background: Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown., Objective: In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry., Methods: A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied., Results: Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p < 0.001). The above results appeared in both the whole brain and DMN., Conclusion: There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.

The online public opinion is the sum of public views, attitudes and emotions spread on major public health emergencies through the Internet, which maps out the scope of influence and the disaster situation of public health events in real space. Based on the multi-source data of COVID-19 in the context of a global pandemic, this paper analyzes the propagation rules of disasters in the coupling of the spatial dimension of geographic reality and the dimension of network public opinion, and constructs a new gravity model-complex network-based geographic propagation model of the evolution chain of typical public health events. The strength of the model is that it quantifies the extent of the impact of the epidemic area on the surrounding area and the spread of the epidemic, constructing an interaction between the geographical reality dimension and online public opinion dimension. The results show that: The heterogeneity in the direction of social media discussions before and after the "closure" of Wuhan is evident, with the center of gravity clearly shifting across the Yangtze River and the cyclical changing in public sentiment; the network model based on the evolutionary chain has a significant community structure in geographic space, divided into seven regions with a modularity of 0.793; there are multiple key infection trigger nodes in the network, with a spatially polycentric infection distribution.