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Nonstationary calculation of sediment load is an important basis for reservoir operation and management. The Jinsha River Basin (JRB) is the most important sediment-producing section of the Yangtze River, and the main source of sediment into the Three Gorges Reservoir (TGR). In this study, three sediment transport nonstationary analysis models were constructed through the Generalized Additive Models for Location, Scale and Shape (GAMLSS) model, namely Mode0, Mode1 and Mode2. The study found that the correlation coefficients between sediment load with runoff and rainfall are 0.78 and 0.73, respectively. Runoff and precipitation can be used as covariates for the nonstationary analysis of sediment transport. The optimal edge distribution of Mode0, Mode1, and Mode2 is Gumbel, Weibull, and Logistic, respectively. Mode2 can accurately describe the nonstationary change of sediment load, and Mode1 can basically reflect the nonstationary change of sediment load. While Mode0 is a consistent model, which cannot reflect the nonstationary change of sediment load over time. Mode2 has a good ability to simulate the quantile changes of sediment load during the training period and the test period, while Mode1 has a weak generalization ability. Mode0 cannot reflect inconsistent changes, and the simulation result is a fixed value. This study provides an important reference for the nonstationary analysis of sediment load under inconsistency conditions in the JRB. [ABSTRACT FROM AUTHOR]

A facile and efficient method to synthesize thioethers from sulfonyl chlorides and benzylic trimethylammonium salts is presented. This protocol provides a simple and direct approach to realize the reductive coupling reaction of readily available sulfonyl chlorides with benzyl ammonium salts in the presence of triphenylphosphine along with base via C−N cleavage of benzyl ammonium salts. This reaction features mild conditions and good substrate compatibility to afford thioethers in moderate to good yields. [ABSTRACT FROM AUTHOR]

Focusing on the problems of complicated sample processing and long detection time of traditional soil composition testing in mining areas, this paper proposes a rapid soil classification method of grid search and cross validation (GSCV) quadratic optimization-seeking network combined with magnetic field enhanced laser-induced breakdown spectroscopy (MF-LIBS), which can quickly and accurately classify standard soil samples from 10 different mining areas. First, the effects of different magnetic field strengths on the enhancement factor, signal-to-noise ratio (SNR) and plasma electron temperature of the characteristic spectral lines were investigated. Next, 5400 12 248-dimensional spectral data under the 0.98 T magnetic field constraint were preprocessed using principal component analysis (PCA). Then the back propagation (BP) network was optimized by combining the sparrow search algorithm (SSA) external optimization network with the genetic algorithm (GA) internal optimization network. Finally, the hyperparameters of the optimal network architecture are refined by GSCV for secondary optimization. By comparing with the unoptimized network, the results show that the classification accuracy of GSCV-SSA-GA-BP is 0.99997, the precision is 0.99977, the recall is 0.99981, and the F1-score is 0.99979. The proposed technique of GSCV quadratic optimization network combined with MF-LIBS soil classification can not only play an important role in soil research, but also provide a new scheme for rapid detection and high-precision qualitative analysis of soil components. [ABSTRACT FROM AUTHOR]

Due to the large climatic and geographical differences, the occurrence time of maximum 1-day precipitation (RM1day) in different zones of the Huaihe River Bain (HRB) is different. In this study, in order to analyze the characteristics of precipitation encounters, we used the mixture of von Mises distributions and Copula functions to analyze the occurrence time distribution, interval time distribution, and joint encounter probability of RM1day in the HRB. We divide the upstream area of HRB into Zone1, the midstream area into Zone2-1 and Zone2-2, and the downstream area into Zone3 and Zone4. The results show that the RM1day of Zone1, Zone2-1, Zone2-2, Zone3, and Zone4 are mainly concentrated on July 13, July 9, July 6, July 19, and July 14, respectively. The probability appearing RM1day of Zone1 first to Zone2-1, Zone1 first to Zone2-2, Zone2-2 first to Zone2-1, and Zone4 first to Zone3 is 50.84%, 40.0%, 49.1%, and 42.4%, respectively. It shows that most of the RM1day occurred in the southern sites of the HRB earlier than the northern sites. Zone1 and Zone2-2, Zone1 and Zone2-1, Zone2-2 and Zone2-1, and Zone4 and Zone3 have the highest encounter probability of RM1day in July, with 0.34%, 0.56%, 0.37%, and 0.42%, respectively. Therefore, we need to pay more focus to the risk of extreme precipitation encounter in July. This study provides an important reference for flood control and anti-logging in the HRB. [ABSTRACT FROM AUTHOR]

This study investigated the interfacial characteristics of tungsten-particle-reinforced Zr-based bulk-metallic-glass composites (Wp/Zr-BMGs) with varying tungsten-particle sizes. To this end, Wp/Zr-BMGs with three different Wp sizes were fabricated using spark plasma sintering. Subsequently, the microstructures and interfacial structures of the Wp/Zr-BMGs were extensively examined, and the mechanical properties of the microzone at the Wp/Zr-BMG interface were evaluated using a nanoindentation method. The results revealed that the interfaces of Wp/Zr-BMGs, irrespective of the Wp size, exhibited dissolution-diffusion characteristics. Moreover, the thickness of the interface diffusion layer was positively correlated to the size of Wp. The addition of Wp enhanced the elastic modulus and hardness of Zr-BMGs at the interface, as these effects are inversely related to the Wp size. Furthermore, this study established a relationship between the interfacial mechanical properties and the interfacial characteristics of particle-reinforced bulk-metallic-glass composites. Thus, this study can serve as a guide for future research in the field of Wp/Zr-BMGs and similar particle-reinforced composites. [ABSTRACT FROM AUTHOR]

Depression harms older adults' physical health and quality of life, especially for those with disabilities. This study aims to investigate the epidemiological characteristics of depressive symptoms in older adults and influencing factors involving neuroticism in China. A multistage random sampling method was adopted to recruit older adults. We conducted a multiple mediating analysis to explore how overall family health and perceived social support affect the relationship between neuroticism and depressive symptoms in older adults. A total of 1122 older adults were included in this study, and 529 individuals reported suffering from depressive symptoms (47.1 %). Mediation analysis results indicated that neuroticism had a direct predictive effect on depressive symptoms in older adults. Family health and perceived social support partly mediated the relationship between neuroticism and depressive symptoms in older adults. Moreover, depressive symptoms in older adults with high neuroticism was directly affected by decreasing family health and indirectly and cumulatively aggravated by the mediation of lower perceived social support. The cross-sectional study design limits the conclusion about causation and directionality. Family health and perceived social support can prevent depressive symptoms in older adults with high neuroticism. • Neuroticism and depressive symptoms affect older adults' quality of life. • Neuroticism has a direct predictive effect on depressive symptoms in older adults. • Family health & perceived social support partly mediate neuroticism & depressive symptoms. • Poor family health affects depressive symptoms in older adults with high neuroticism. • Low perceived social support indirectly & cumulatively aggravates depressive symptoms. [ABSTRACT FROM AUTHOR]

With the intensification of global warming, extreme precipitation events occur frequently all over the world. Extreme precipitation has brought huge challenges to the development of human society and economy, and it is urgent to strengthen the analysis of the causes of extreme precipitation. In this study, we first extracted precipitation events from the precipitation time series from 1960 to 2018 in the Huaihe River Basin (HRB) and then extracted extreme precipitation events based on precipitation amount and precipitation intensity. The results show that extreme precipitation in the HRB has an increasing trend after 2000, although the increasing trend is not obvious, and the uncertainty of the occurrence of extreme precipitation events had increased. Extreme precipitation mainly had a strong correlation with the surface air temperature in the North Pacific, Indian Ocean, and Qinghai–Tibet Plateau. The El Niño year and the year of abnormal circulation contribute to the production of extreme precipitation in the HRB. In the years when extremely heavy precipitation occurred in the HRB, the subtropical high was northerly. Although most of the water vapor is brought from the Pacific by the East Asian monsoon, the water vapor that causes extreme precipitation in the HRB mainly comes from the Indian Ocean. Through multivariate wavelet coherence (MWC) analysis, annual precipitation is greatly affected by multiple climate variables, while extreme precipitation is greatly affected by a single climate variable. This study provides an important reference for the analysis and prediction of extreme precipitation. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibet Plateau (TP) is one of the most sensitive areas to climate change, and its ecological environment changes directly or indirectly reflect the global climate change trend. The snow cover ratio (SCR) is one of the important indicators reflecting the climate and environmental changes on the TP. The daily remote sensing data of snow cover on the TP from 2003 to 2014 were used to study the spatiotemporal distribution of snow cover on the TP. This dataset is generated based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) product. The snowmelt days were extracted using the moving average method. The results have shown that the average snowmelt day on the TP starts on the 103rd day and ends on the 223rd day of a year, and the snowmelt duration has a downward trend. Snow is mainly distributed in the Nyainqentanglha Mountains, Karakoram Mountains and Himalayas. The SCR in summer has a downward trend, while in autumn has a rising trend. This shows that the difference in SCR during the year has enlarged, increasing the risk of snowmelt floods. The SCR is highly correlated with temperature, but weakly correlated with precipitation. Using long-term snow cover remote sensing data, the distribution of glacier coverage on the TP can be roughly extracted, of which the glacier area accounts for about 1.1% of the total area of the TP. This study provides an important reference for understanding the spatiotemporal changes of snow cover in the TP. [ABSTRACT FROM AUTHOR]

China is one of the countries most severely affected by extreme precipitation, and it is urgent to carry out relevant research on the spatiotemporal distribution of extreme precipitation in China. Compared with single-day precipitation events, heavy precipitation events often last for multiple days and have greater potential disaster impacts, such as floods and mudslides. Therefore, in this study, we focus on continuous precipitation (CP) events for analysis. In order to determine the most probable duration of continuous heavy precipitation events, we innovatively adopted two time-frequency variation methods, namely Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT). Then through Detrended Fluctuation Analysis (DFA) and multifractal DFA (MF-DFA) methods, extreme CP thresholds are extracted according to the fluctuation characteristics of the CP series. The results show that the average duration of heavy precipitation in the Pearl River Basin (PRB) area was the longest (up to 7.7 days), followed by the Continental Basin (CB) area (7.6 days). The Haihe River Basin (HRB) area had the shortest duration of heavy precipitation (5.0 days), followed by the Huaihe River Basin (HURB) area (5.9 days). The extreme CP threshold in the Southeast Basin (SEB) area was the largest (112.17 mm), followed by the PRB area (108.22 mm). The extreme CP threshold in the CB area was the smallest (20.17 mm), followed by the Yellow River Basin (YRB) area (44.31 mm). Through mutation and trend testing to evaluate the changing state of the extreme CP time series, it was found that, with one exception (HRB), the average extreme CP after the mutation in the watershed areas was larger than that before the mutation, and eight watersheds showed an upward trend in extreme CP. This suggests that most watershed areas in China are at risk of extreme CP increases. This study can provide an important reference for the analysis of extreme CP in China. • We exploited the time-frequency variation to determine the duration of heavy precipitation. • We utilized DFA and MF-DFA to extract extreme continuous precipitation thresholds. • The spatiotemporal distribution of extreme continuous precipitation was analyzed. • The mutation and trend characteristics of extreme continuous precipitation were also analyzed. [ABSTRACT FROM AUTHOR]

Runoff prediction plays an important guiding role in water resources planning and management, flood and drought prevention. As the Hanjiang River Basin (HRB) is the water source of the middle line of the South-to-North Water Diversion Project, it has higher requirements for water resources accurate prediction. In order to analyze the prediction capabilities of different prediction methods for the HRB runoff, this study constructed 12 prediction models to simulate and predict the runoff of four hydrological stations in the HRB. Furthermore, the Markov Chain Monte Carlo (MCMC) method was used to analyze the transition probability of runoff from low-to-high (high-to-low). The results showed that the runoff of four hydrological stations in the HRB all showed a downward trend, and most of the sudden changes occurred in the 1980 s. The smoother the runoff changes, the easier it is to make accurate prediction. Among the 12 models, the quadric spline Markov forecasting model (QS-MFM), moving average Markov forecasting model (MA-MFM), Markov forecasting model (MFM), deep neural networks (DNN), and cubic exponential smoothing (CES) methods have stronger generalization ability and can more accurately predict the runoff of the HRB. The average relative error during the validation period is 0.27, 0.28, 0.33, 0.34 and 0.39, respectively. The logistic model can accurately simulate the change of runoff status in the HRB. The wet threshold of Baihe (BH), Huanglongtan (HLT), Huangjiagang (HJG), and Huangzhuang (HZ) is 819.9 m3/s, 207.4 m3/s, 1313.9 m3/s and 1681.7 m3/s, and the dry threshold is 480.4 m3/s, 130.6. m3/s, 817.8 m3/s and 1083.4 m3/s, respectively. The MCMC method can accurately estimate the parameters of the logistic model, and the low-high (high-low) runoff transition probability model constructed in the HRB can accurately calculate the low to high (high to low) runoff transition probability. [ABSTRACT FROM AUTHOR]

The Hanjiang River is the source of water for the Middle Route of the South-to-North Water Diversion Project. So a reasonable evaluation on water resources future changes in the Hanjiang River Basin (HRB) is essential to ensure its water safety. In this study, we firstly evaluated the China meteorological forcing dataset (CMFD), and then used it to measure the applicability of the eight global climate models (GCMs). Finally, the empirical orthogonal function (EOF) and clustering algorithms were used to analyze the spatial distribution status of precipitation in the HRB. The results showed that the BCC-CSM2-MR model has the best effect, followed by the IPSL-CM6A-LR model. In the SSP126, SSP245, SSP370, and SSP585 scenarios, total precipitation in the HRB all shows an increasing trend. However, the difference in the spatial distribution of precipitation intensified, for the upstream and downstream tended to be wetter, while the middle reaches had less precipitation. In the future climate scenarios, the areas with more precipitation (Class 1) increased significantly, while the areas with medium precipitation (Class 3) decreased significantly, which will enlarge the precipitation difference between regions in the HRB. The increase in the total amount of precipitation, coupled with the increase in the spatial heterogeneity of precipitation, will make the HRB more vulnerable to flooding disasters. Therefore, the water resources management measures in the HRB should be strengthened in the future. [ABSTRACT FROM AUTHOR]

• We used the GAMLSS model to compare the effects of different explanatory variables on the annual maximum runoff. • We compared the simulation differences between the stationary model and the nonstationary model. • We analyzed the effects of single and double explanatory variables on the annual maximum runoff. Due to the impact of climate change and human activities, flood disasters have occurred frequently in the Hanjiang River Basin (HRB) in recent years, and the stationary behavior of its flood fluctuation has been disrupted. In this study, we used Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to analyze the nonstationary changes of annual maximum runoff in the HRB and its influencing factors. It has also been complemented with a stationary model for comparative analysis. We found that the annual maximum runoff has a significant decreasing trend. The top four factors influencing this runoff change are the local changes in precipitation and temperature, and the regional climate oscillations as reflected by the North Atlantic Oscillation index (NAO) and the Pacific North American Index (PNA). The nonstationary model had better simulation effect than the stationary model. The stationary model could not reflect the impact of explanatory variables on annual maximum runoff, while the nonstationary model could well analyze the impact of single and dual explanatory variables. The results of this study provide some new insights in support of flood prevention and control in the HRB. [ABSTRACT FROM AUTHOR]

Coal is one of our most important energy sources. Because of differences in coal-forming year and coal seam quality, the elemental compositions of coal vary among different mining areas, directly affecting coal combustion efficiency and pollution emission. To improve the accuracy of coal classification, laser-induced breakdown spectroscopy (LIBS) combined with a support vector machine (SVM) algorithm model based on principal component analysis (PCA) is proposed to identify and classify coal from 10 different regions. The samples were first penetrated using LIBS, and spectral data were collected. The spectral data were then normalized to improve their signal-to-noise ratio. Finally, the PCA-SVM model is established, the model parameters are evaluated using the grid search method, and the spectral feature classification performance is determined via cross-validation. The results show that the classification accuracy of the PCA-SVM model can reach 98.52%. To verify the performance of this classification model, it was compared with those of five other common classification models and proved that PCA-SVM has great potential for coal quality identification and classification, thus providing a new data analysis and processing solution for coal quality identification and classification in industrial applications. [Display omitted] • Efficient coal quality identification and classification method is proposed. • Method is based on laser-induced breakdown spectroscopy combined with SVM and PCA. • Method is experimentally verified using coals from different mining areas. • Results of proposed method are in good agreement with assays from independent lab. • Performance of proposed method is verified against those of other ML-based methods. [ABSTRACT FROM AUTHOR]

In the context of climate change and human activities, the global sea level is facing a rising trend, which poses serious challenges to the ecological environment of coastal areas. In this study, we selected the monthly mean sea level (MSL) time series of 9 stations in the coastal areas of China as the research object. First, we analyzed the spatiotemporal distribution characteristics of the monthly MSL in the coastal areas of China. Secondly, we analyzed the ability of ensemble empirical mode decomposition (EEMD) to decompose the monthly MSL series. Finally, we choose three machine learning models, namely Back Propagation (BP), K-Nearest Neighbor (KNN), and Long Short-Term Memory (LSTM) neural network models to compare model prediction effect between single machine learning models with machine learning models combined with EEMD. The results show that except for the YANTAI (YT) station, which showed an insignificant downward trend, the monthly MSL of other stations showed an upward trend, indicating that the coastal areas of China are facing the risk of sea level rise. EEMD can effectively reduce the complexity of the original monthly MSL time series, and different intrinsic mode functions (IMFs) reflect changes in monthly MSL at different frequencies. Comparing the single machine learning model and the machine learning model combined with EEMD, it is found that the simulation effect of the machine learning model combined with EEMD is better than that of the single model. The model with the best prediction effect on monthly MSL in the coastal areas of China is LSTM-EEMD, followed by KNN-EEMD. This study provides an important reference for systematically understanding sea level changes and selecting an appropriate monthly MSL prediction model in the coastal areas of China. • We obtained the state of sea level change in coastal areas of China. • The sea level sequences were decomposed by using EEMD. • We constructed multiple neural network models incorporating mode decomposition. • We obtained the most robust model for sea level simulations. [ABSTRACT FROM AUTHOR]

Uncertainty about the occurrence of extreme precipitation events has increased significantly under global climate change. The Hanjiang River Basin (HRB) is located at the junction of the north and the south of China, and extreme precipitation events occur frequently due to the impact of climate change. Extreme precipitation is the leading factor causing flood disasters in the HRB, and effective forecasting of extreme precipitation is of great significance for preventing flood disasters. In this study, we take full advantage of the uncertainty information in THORPEX Interactive Grand Global Ensemble (TIGGE) ensemble forecasts datasets and perform statistical postprocessing through the Bayesian model averaging (BMA) method. Then, the extreme precipitation index (EPI) is constructed using the cumulative distribution functions (CDFs) of historical model precipitation and the forecast precipitation. The research results show that the constructed EPI index can well reflect the occurrence intensity of precipitation in the HRB. According to the Spearman correlation coefficient, the CMA model has the best effect, followed by the control forecast (CF) model, and the JMA model has the worst effect. As indicated by the area under curve (AUC) indicator at the 25 mm precipitation threshold, the forecast effect of CMA model is still the best, followed by the CF model, and the third is the control forecast and perturbed forecast (CFPF) model. The comprehensive effect of the JMA model is significantly worse than other models. Under the precipitation threshold of 50 mm, the prediction effect of the NCEP model is the best, followed by the ECMWF model, and the third is the CF model. The least effective is also the JMA model, followed by the UKMO model. Under the precipitation threshold of 25 mm, the prediction effect of all models is better than that of the precipitation threshold of 50 mm. The prediction stability of NCEP and ECMWF models is the strongest, and the effects of CF and CFPF models integrating multiple models are in the middle. This study provides an important reference for the theory and application of extreme precipitation forecasting in the HRB. • We used the BMA to obtain the probability density function of the integrated precipitation forecast. • We constructed extreme precipitation index using cumulative distribution functions. • The forecasting ability of EPI for 25 mm and 50 mm precipitation thresholds was measured. [ABSTRACT FROM AUTHOR]

• A novel score-based attack is proposed to deceive the DNN models. • Using substitute and target model to evaluate the word importance. • Synonym is used for generating adversarial samples. • Adversarial training can help to enhance the robustness of DNN models. Deep neural networks (DNNs) are vulnerable to adversarial attacks, in which a small perturbation to samples can cause misclassification. However, how to select important words for textual attack models is a big challenge. Therefore, in this paper, an innovative score-based attack model is proposed to solve the important words selection problem for textual attack models. To this end, the generation of semantically adversarial examples in this model is adopted to mislead a text classification model. Then, this model integrates the self-attention mechanism and confidence probabilities for the selection of the important words. Moreover, an alternative model similar to the transfer attack is introduced to reflect the correlation degree of words inside the texts. Finally, adversarial training experimental results demonstrate the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

PM2.5 is an important indicator reflecting changes in air quality. In recent years, affected by climate change and human activities, the problem of environmental pollution has become more and more prominent. In this study, the PM2.5 data from 2000 to 2018 obtained by satellite remote sensing inversion algorithm were selected to analyze the temporal and spatial distribution of PM2.5 in China. The results show that the areas with higher PM2.5 concentrations were mainly in the North China, the Sichuan Basin, and the Tarim Basin. The areas with a significant increase in PM2.5 were mainly in the Northeast China, while the areas with a significant decrease were mainly in the Sichuan Basin and southeastern Gansu. The change of PM2.5 in southern China was not significantly correlated with the change of population and economy, while PM2.5 in Northeast China increases with the increase of population and economy. In 2000, 2005, 2010, and 2015, the proportion of the population polluted by PM2.5 was 8.65%, 7.2%, 22.99%, and 9.75%, respectively. The year with the highest percentage (37.63%) of population when air quality reached EXCELLENT was 2015. When the PM2.5 spatial cluster number was six, it can better reflect the PM2.5 spatial distribution state. The places with large changes in PM2.5 spatial clustering were mainly in the Northeast China, Sichuan Basin, and Tarim Basin, which were also areas with large changes in PM2.5. This study provides an important reference for atmospheric environmental monitoring and protection. • The temporal and spatial variation patterns of PM2.5 in China from 2000 to 2018 were obtained. • The relationship between PM2.5 and population and GDP was obtained. • The change rule of the population affected by PM2.5 was obtained. • We used cluster analysis method to obtain PM2.5 spatial clustering status. [ABSTRACT FROM AUTHOR]

• The main factor that affects NDVI in most areas of China is surface soil moisture (SSM), followed by temperature, while in arid areas root zone soil moisture (RZSM) and profile soil moisture (PSM) are the main influencing factors. • The climate zones of China can be roughly divided into northern, southern and northwestern inland areas, and there are transitional zones between different zones. • Deep learning can simulate NDVI well, and the Nash efficiency coefficients during training and verification periods are 0.893 and 0.833, respectively. It shows that these selected influencing factors can simulate and predict NDVI changes well. • The environmental drift areas discovered by concept drift detection are mainly in the Heilongjiang River Basin in Northeastern China, Yunnan, Sichuan, and Southern Tibet in Southwestern China, and Gansu, Qinghai and Xinjiang in Northwestern China. Normalized Difference Vegetation Index (NDVI) is an important indicator reflecting the state of regional climate and environment, which is affected by precipitation, temperature, soil water content, and so on. This study analyzed the influence of different factors on NDVI through the spearman correlation coefficient (SCC) and standard regression coefficient (SRC), carried out climate zoning in China through principal component analysis (PCA) and cluster analysis, simulated the NDVI value through deep learning algorithm, and analyzed the spatial mutation positions of different factors through concept drift detection. The research results show that the main factor that affects NDVI in most areas of China is surface soil moisture (SSM), followed by temperature, while in arid areas root zone soil moisture (RZSM) and profile soil moisture (PSM) are the main influencing factors. The climate zones of China can be roughly divided into northern, southern and northwestern inland areas, while there are transitional zones between different main zones. The deep learning algorithm can simulate the NDVI value very well, and the Nash efficiency coefficients (NSE) during training and verification periods are 0.893 and 0.833, respectively. However, the details of some areas are rough. This shows that the selected impact factors can basically determine the status of NDVI, and these impact factors can be used to predict the changes in NDVI. The environmental drift areas discovered by concept drift detection are mainly in the Heilongjiang River Basin in Northeastern China, Yunnan, Sichuan, and Southern Tibet in Southwestern China, and Gansu, Qinghai and Xinjiang in Northwestern China. This study provides an important reference for the analysis of environmental regional zoning, climate change detection and simulation. [ABSTRACT FROM AUTHOR]

Precipitation in China has a sharply uneven spatial-temporal distribution, which has caused serious droughts and heavy floods in China. Using statistic and K-means clustering methods, this study systematically assessed the distribution characteristics of precipitation in China from four aspects, including the spatial-temporal distribution of precipitation, the impact of typhoons on precipitation, precipitation clustering, and precipitation extreme value distribution. The results indicates that southern China has more precipitation but it varies widely in different periods during the year. While in the north and northwest inland areas, the amount of precipitation and precipitation fluctuation during the year are very small. Light precipitation is the main form of precipitation in China, reaching 1.146 × 106 mm, but the average precipitation per event is only 1.8 mm. Precipitation decreases as elevation increases, and high-precipitation areas in China are concentrated in 106°E to 119°E, 18°N to 27°N. Typhoon is the main reason for extreme precipitation in China, and the precipitation caused by typhoon in northern areas is especially more likely to cause floods. Through cluster analysis, dividing the precipitation into 8 categories can basically represent the difference in precipitation distribution of China. Precipitation of the same level is distributed in a belt, decreasing from the southeast coast to the northwest inland. Through distribution fitting, Gamma distribution is the most suitable for fitting extreme precipitation distribution. Extreme precipitation cycle of 100 days, 200 days, 300 days, 400 days and 500 days are respectively 252.4 mm, 285.9 mm, 305.3 mm, 319 mm and 329.7 mm, which can reasonably reflect the characteristics of extreme precipitation. Consequently, this study improves the understanding of the spatial-temporal distribution of precipitation in China. • Light precipitation volume reaches 1.146 × 106 mm, but the average precipitation per event is 1.8 mm. • High-precipitation areas in China are concentrated in 106°E to 119°E, 18°N to 27°N. • Dividing the precipitation into 8 categories can basically represent the difference in precipitation distribution of China. • Extreme precipitation of 100 days, 200 days, 300 days, 400 days and 500 days show an increasing trend. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to construct a standardized health risk assessment index system (HRAIS) under the guidance of general health and facilitate the family doctors to carry out chronic disease management. Methods: Available chronic disease surveillance systems and indexes were reviewed to identify potential indexes. The Delphi method was performed to establish the HRAIS, and the analytic hierarchy process was used to calculate the index weight. Results: HRAIS included four first-level indexes and 38 second-level indexes. The authority coefficient was 0.86. The Kendall's W for the two rounds of Delphi consultation were 0.202 and 0.210 (p < 0.001). The weights of the first-level indexes from high to low were physiological health (0.409), psychosocial health (0.290), health-related behaviors (0.205), and environment (0.097). Thus, HRAIS is a multi-dimension and multi-index tool, which can be used as a guideline for family doctors in early screening, early intervention, and classified management of main chronic diseases. [ABSTRACT FROM AUTHOR]

Sub-health can have an impact on the physical condition of the young adults, and this study aimed to investigate the influencing factors of sub-health related to the Big Five personality in Chinese young adults. A multi-stage random sampling method was used to survey the Chinese young adults. A moderated mediation analysis was conducted to investigate how sleep quality and family health influenced the relationship between diverse personalities and sub-health in young adults. A total of 6165 young adults were included in this study. The results of the mediation analysis indicated that sleep quality partially mediated the relationship between neurotic, extraversion, agreeableness, and conscientiousness of young adults and sub-health. Family health played a moderating role between the sleep quality and agreeableness of young adults. The cross-sectional study limits conclusions about causal relationships between factors. Family health and sleep quality can influence sub-health in Chinese young adults with different personality traits. • Sleep quality and family health influence the sub-health of Chinese young adults. • Sleep quality played the mediating role between personality and sub-health. • The mediation role of sleep quality was moderated by family health in the relationship between young adults' agreeableness and their sub-health. [ABSTRACT FROM AUTHOR]

• Exploring the influence of tungsten particles (W p) on the crystallisation behaviour of Zr 55 Cu 30 Al 10 Ni 5 amorphous alloy. • Observing differences in the structural characteristics, crystallisation process, and thermal stability of Zr 55 Cu 30 Al 10 Ni 5 and W p /Zr 55 Cu 30 Al 10 Ni 5. • The nucleation and growth process of W p /Zr 55 Cu 30 Al 10 Ni 5 is mainly controlled by diffusion, and the nucleation rate decreased with time. • W particles increase internal viscosity, hinder the growth process of grains and result in the higher crystallization activation energy. The influence of W particles (W p) addition on the crystallization behaviour was studied by comparing crystallization processes of Zr 55 Cu 30 Al 10 Ni 5 amorphous alloy and W p /Zr 55 Cu 30 Al 10 Ni 5 amorphous composites by DSC, In-situ XRD and SEM/EDS. Results showed that W p addition increased T g , decreased T x , T p , and ∆T , resulting in a decrease in the thermal stability of the amorphous alloy. W p could play a role of non-spontaneous nucleation and promoted the crystallization process. At the same time, W p increased the internal viscosity, hindered the growth process of grains and led to a higher crystallization-activation energy. The nucleation growth process was mainly controlled by diffusion, and the nucleation rate decreased with increasing time. The crystallization products are primarily comprised the Zr 2 Cu phase, accompanied by a small amount of the Zr 6 Ni 8 Al 15 phase as well as some unknown phases. [ABSTRACT FROM AUTHOR]

[Display omitted] • A completely new 3DPM, Portland cement clinker, was developed. • The rheological properties, extrudability, and buildability of clinker-based 3DPM were investigated. • The maximum output length is 70 mm, and there are a total of 17 printed layers when it was the best ratio. This paper presents the experimental results concerning the mix design and 3D printing correlation performance of a completely new printing material, Portland cement clinker (Pcc). According to the results, the cement-sand ratio is 0.6–1.0, the water-cement ratio is 0.34–0.4, and the setting time of Pcc paste is approximately 5 min. When it increases, the rheology (G', G″, |η*|, torque) of Pcc paste also increases. Furthermore, when the cement-sand ratio and water-cement ratio increase, the maximum flexural strength and compressive strength of the mortar at 28 days are 7.93 MPa and 44.76 MPa, respectively. The ultimate swelling degree of the mortar increases from 18 cm to 22 cm, the final slump increases from 4.0 cm to 4.5 cm. When the ratio is D-2, the extrusion length is 70 mm, and the maximum number of printing layers is 17 layers. According to the SEM image analysis of the sample, the hydration products primarily include hydrated calcium silicate, calcium hydroxide, and others, which is supported by the XRD test results. In addition, the reaction model of Pcc is proposed. The results of Pcc hydration reaction analysis indicate that adding a water-lowering agent can slow the cement hydration reaction. [ABSTRACT FROM AUTHOR]

Artificial pancreas (AP) is an important treatment for patients with Type 1 diabetes (T1D). The control algorithm adopted in an AP system determines its reliability and accuracy. The generalized predictive control (GPC) is a representative adaptive control algorithm and has been widely applied to AP systems. However, we found that the traditional GPC controller does not work well for adolescents with T1D because of their high‐fluctuating blood glucose and high insulin resistance. Here, we propose an improved GPC algorithm with an adaptive reference glucose trajectory and an adaptive softening factor. The slopes of the reference trajectory and the value of softening factor are calculated real‐time on the basis of the blood glucose concentration (BGC) variations. In silico testing was done using the US Food and Drug Administration (FDA) approved virtual patient software T1D mellitus. The BGC trace and density of 20 patient‐subjects (10 adults and 10 adolescents) were recorded. Results showed that the average BGC percentage within the target regions (70–180 mg/dL) of the tests with adaptive reference glucose trajectory and softening factor for adolescents (0.93 ± 0.07) was significantly higher than that of the traditional GPC algorithm tests (0.88 ± 0.11), suggesting that the control quality of the blood glucose of adolescents is significantly improved with our GPC algorithm. Therefore, our improved GPC controller is effective and should have a good applicability in AP systems. [ABSTRACT FROM AUTHOR]

• Aspiration-based exit option in prisoners dilemma game can promote cooperation. • In structured population, the intermediate aspiration level promotes cooperation, because defectors around the cooperative cluster exit the game, which weakens the exploitation of cooperators, and help the cooperators at the edge of the cooperative cluster to expand to inactive nodes or the defectors through network reciprocity. • The combined effect of aspiration level and dilemma strength affects the active state of players. Cooperation is promoted in the structured population when individual has the exit option and it is found that aspiration driven updating rules can also promote cooperation to a certain extent. The right to exit gives individuals the opportunity to get rid of exploitation, and the aspiration is the self-evaluation of individuals. Stimulated by these, we study the effect of aspiration driven exit on cooperation in the spatial prisoner dilemma game. In the game, the aspiration level and payoff of interactive determine the individual's state. When the individual gets more than aspiration level by interacting with other players, the individual will continue to participate in the game as an active player. On the contrary, when the payoff is lower than the aspiration level, individual exits the game as an inactive player. At the same time, inactive player who exits from the game will get random payoff and pay a certain cost. The promotion of cooperative behavior mainly depends on the aspiration level of players. An appropriate level of aspiration will lead to a high cooperation, which is based on the formation of cooperative clusters. These cooperators can get rid of the invasion of defectors, even if there is a great temptation of defection, this is because the inactive defectors who exit have cut off the interaction between the cooperative cluster and the defectors. Our research provides a feasible way to solve the social dilemma and will stimulate further application. [ABSTRACT FROM AUTHOR]

• Tripartite evolutionary game model between AIFS is constructed. • AIFS stakeholder's strategy is analyzed in the process of WRT and CET. • The studying case is Dongjiang river basin of Guangdong, south China. • The synergies between WRT and CET are asymmetric. • Individual behavior do not necessarily conform to the expectations of the market Water scarcity and carbon emissions are two of the most pressing issues facing the world. Water rights trading (WRT) and carbon emissions trading (CET) can alleviate pressure on resources and the environment and promote sustainable development. However, the relationship between water scarcity and carbon emissions results in a complex water-carbon nexus between sectors. To explore the mechanisms of interaction among related sectors in the context of WRT and CET, this study built a tripartite evolutionary game model that includes the agriculture, industry, and forestry sectors (AIFSs) to simulate the strategic evolution process and interactions between stakeholders based on replicated dynamic equations. Four cities in the Dongjiang River Basin (DRB) in southern China were analyzed to quantify the influence of key parameters on the strategic evolution process and reveal the relationship between sectors under the effect of WRT and CET. The results indicated that There is a significant impact of agricultural net benefits in the DRB on the choice of AIFS strategy, which in turn leads to better WRT that promotes the mutual coupling of AIFS strategies. In addition, strategy coupling brings higher relative returns and risks to the game system, and multiple markets enable AIFS to hedge losses that may result from single market failures in the mutual game. Furthermore, AIFS' behavioral strategies are not necessarily aligned with market expectations, which has implications for us as we guide the development of WRT and CET. [ABSTRACT FROM AUTHOR]

Sensitive glucose detection enables indirect blood glucose sensing through easily accessible biofluids such as saliva and sweat. In this work, silver coated gold nanorods (Au@Ag NRs) were synthesized and used to prepare plasmonic substrate for surface-enhanced Raman spectroscopy (SERS) to leverage highly sensitive detection of glucose for quantitative analysis. By synthetically manipulating of gold NRs and the outer silver shell, the size and aspect ratio of Au@Ag NRs were optimized, and the plasmon resonance wavelength was tuned to approximately the excitation wavelength. 4-Mercaptophenyl-boronic acid (4-MPBA) and 4-Cyanophenylboronic acid (4-CPBA) were used as primary and secondary receptors respectively to specifically capture glucose molecules. The distinct Raman peak at 2226 cm −1 of the cyano group in 4-CPBA was used as a signal reporter for glucose sensing. It is located in a biological silent region (1800–2800 cm −1 ), thus offering specific sensing of glucose, without the interference of other endogenous molecules. Our results showed that the SERS substrate was long-term stable. Glucose in urine solution with additive glucose was quantitatively and specifically determined, with the detection limit down to 10 −8 M. Further experiments using urine from mild diabetes shows positive results, demonstrating the feasibility of clinical use. [ABSTRACT FROM AUTHOR]

Preparation of large size, few-layered (FL) 2D materials is one of the challenging problems in material science. In this study, an anisotropic etching method to prepare FL (5–7 layer) MoO 3 with width of tens of microns and length exceeding 100 μm on SiO 2 was developed. Anisotropic etching along [1 0 0] direction was found to be accelerated in high concentration KOH solution. The first few layer on SiO 2 surface kept intact in the first few seconds. The transformation of MoO 6 octahedron to MoO 4 tetrahedron was suggested to be due to the weak bonding of Mo–O(2) along [1 0 0] direction, while the etching speed of the first few layer was slowed down by the interfacial interaction. It’s shown that the surface defects were strongly affected by the etching time, and thus the electrical properties could be controlled. [ABSTRACT FROM AUTHOR]

• Standardized River Stage Index (SRSI) is proposed to measure drought. • SRSI is calculated using satellite altimetry without in-situ observations. • SRSI captures variation in drought of river water and identifies drought events. • SRSI reveals time lags in drought propagation. The lack of in-situ hydrological observations, especially in remote and developing areas, is one of the main challenges of drought monitoring. This paper proposes a novel drought index, the Standardized River Stage Index (SRSI), based on data retrieved from satellite altimetry missions. The novelties of the SRSI are that it can quantitatively monitor drought of river water deficits at multiple timescales and that it does not require in-situ observations. The Mekong River is used as the case study to demonstrate and validate the SRSI calculated from the multi-mission satellite altimetry data (Envisat and Jason-2/3). The log-logistic distribution is best fitted for SRSI calculation for the highest goodness-of-fit for river stages for the Mekong River. The comparison with the Standardized Streamflow Index (SSI) from the in-situ discharge observations shows that the SRSI can reasonably capture the drought of river water deficit. It is effective in identifying the timing and intensity of severe drought events, such as the 2019 Thailand drought. The SRSI is also compared with the Standardized Precipitation Evapotranspiration Index and the GRACE Drought Index in revealing drought features such that the river water deficit of Mekong River has time lags of 2 to 3 months for precipitation deficits and water storage deficits. Hence, the SRSI is more suitable for monitoring drought of river water deficit, because it directly links to the streamflow via physical stage-discharge relationship. Overall, the SRSI provides an effective indicator of drought of river water deficit when in-situ observations are unavailable. It can serve in the real-time drought early warning in remote and developing areas. [ABSTRACT FROM AUTHOR]

• Standardized River Stage Index (SRSI) is proposed to measure drought. • SRSI is calculated using satellite altimetry without in-situ observations. • SRSI captures variation in drought of river water and identifies drought events. • SRSI reveals time lags in drought propagation. The lack of in-situ hydrological observations, especially in remote and developing areas, is one of the main challenges of drought monitoring. This paper proposes a novel drought index, the Standardized River Stage Index (SRSI), based on data retrieved from satellite altimetry missions. The novelties of the SRSI are that it can quantitatively monitor drought of river water deficits at multiple timescales and that it does not require in-situ observations. The Mekong River is used as the case study to demonstrate and validate the SRSI calculated from the multi-mission satellite altimetry data (Envisat and Jason-2/3). The log-logistic distribution is best fitted for SRSI calculation for the highest goodness-of-fit for river stages for the Mekong River. The comparison with the Standardized Streamflow Index (SSI) from the in-situ discharge observations shows that the SRSI can reasonably capture the drought of river water deficit. It is effective in identifying the timing and intensity of severe drought events, such as the 2019 Thailand drought. The SRSI is also compared with the Standardized Precipitation Evapotranspiration Index and the GRACE Drought Index in revealing drought features such that the river water deficit of Mekong River has time lags of 2 to 3 months for precipitation deficits and water storage deficits. Hence, the SRSI is more suitable for monitoring drought of river water deficit, because it directly links to the streamflow via physical stage-discharge relationship. Overall, the SRSI provides an effective indicator of drought of river water deficit when in-situ observations are unavailable. It can serve in the real-time drought early warning in remote and developing areas. [ABSTRACT FROM AUTHOR]

• TVF-EMD method combined with machine learning is developed to predict precipitation. • The differences of different decomposition methods are compared systematically. • The sensitivity of TVF-EMD-ENN model to precipitation prediction is studied. • Climate indices are used to explain the poor prediction performance. In recent years, due to the influence of human activities and changing climate conditions, precipitation time series have had increasing non-stationarity and randomness. Therefore, the scientific and accurate prediction of precipitation has become increasingly difficult. To improve precipitation prediction accuracy, nonlinear precipitation data was decomposed into several subcomponents via three decomposition methods (time varying filtering based empirical mode decomposition (TVF-EMD), wavelet transform (WT), and complementary ensemble empirical mode decomposition (CEEMD)). Then, the Elman neural network (ENN) was used to construct TVF-EMD-ENN, WT-ENN, and CEEMD-ENN models to predict the subcomponents of precipitation. Finally, these three models were applied to the monthly and daily precipitation series from different meteorological stations. The results showed that TVF-EMD-ENN made relatively stable predictions for monthly and daily precipitation, and was better than WT-ENN, CEEMD-ENN, and other models. Predictions for Turpan station were generally poor, likely due to the discontinuous resonance periods and abnormal resonance periods between precipitation and climate indices. Overall, the predictions by TVF-EMD-ENN were better, which was clearly reflected in the variance contribution rates (VCR) of high-frequency subcomponents, which were lower than those of WT and CEEMD. Parameter sensitivities of TVF-EMD indicated that the bandwidth threshold (BT) and the b-spline order (BSO) had a strong influence on model performance, and that BT and BSO should remain within 0.15–0.50 and 15–65, respectively. This study has provided a new approach to precipitation prediction that is better equipped to handle non-stationarity and randomness. [ABSTRACT FROM AUTHOR]

• Dual-functional coating was prepared via a one-step mussel-inspired chemistry. • This non-leachable coating effectively killed drug-resistant bacteria on contact. • Pre-osteoblast proliferation and hydroxyapatite formation on it were promoted. Prevention of infection and promotion of osteoconduction are the key factors to achieve long-term success in orthopaedic implants. In this work, an effective antimicrobial and osteoconductive coating is facilely prepared by the cross-linking of ε-poly-L-lysine (EPL) and catechol (abbreviated as EPLC) via a one-step mussel-inspired chemistry. A series of EPLC paints were prepared by catechol (15 mM) and varying concentrations of EPL (30, 40, and 50 mM) and named as EPLC1, EPLC2, and EPLC3. The coatings were easy to paint on implants. EPLC3 with the highest EPL concentration exhibited the most potent antimicrobial activity (greater than 99.99%) against both Gram-negative bacteria Escherichia coli (E. coli) and Gram-positive "superbug" methicillin-resistant Staphylococcus aureus (MRSA), and inhibited the biofilm formation of MRSA up to one week. Furthermore, the EPLC3 painted Ti implant was capable of reducing more than 3-log bacterial burden in a rodent subcutaneous infection model infected by MRSA. More interestingly, EPLC3 coating increased the osteoconductivity of Ti surfaces by promoting mouse pre-osteoblast MC3T3-E1 adhesion and proliferation. And the EPLC3 painted Ti surfaces could induce hydroxyapatite (HAp) formation after incubation in simulating human body fluids (SBF) within two weeks. Overall, the mussel-inspired EPLC paint can be easily applied to orthopaedic implants, and endowed Ti with antimicrobial activity and promoted its osteoconductivity, which will benefit the long-term success of Ti implants. [ABSTRACT FROM AUTHOR]