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Abstract Background Effective response and reducing the burden of family care for children with cancer is critical, and China currently lacks a specific assessment tool. Aims This study aimed to translate and validate the Caregiving Burden Scale for Family Caregivers of Children with Cancer (CBSFC-CC) and then test and implement the tool. Methods According to the Beaton cross-cultural debugging guide, preliminary Chinese version of CBSFC-CC scale was formed, which was suitable for Chinese language environment and clinical context. Exploratory factor analyses (EFA) and confirmatory factor analyses (CFA) were performed to verify structural validity. Convergent validity, discriminant validity and reliability were also conducted. Results A total of 529 family caregivers of children with cancer participated in the survey. EFA extracts and combines four factors and explained 65.80% of the total variation. CFA proved that all the goodness-of-fit indicators were acceptable. The Cronbach’s alpha of the Chinese version of CBSFC-CC was .96, and the test–retest reliability coefficient was .95. Four dimensions and 29 items were identified in the final Chinese version of CBSFC-CC. Conclusion The chinese version CBSFC-CC is scientifically reasonable and has good reliability and validity, which can be applied to the investigation of the nursing burden of family caregivers of children with cancer in China.

Objective: Elevated pesticide concentrations have been found in dust from homes with residents who use agricultural pesticides, but few studies have compared these concentrations to quantitative measures of their use. We evaluated household pesticide dust concentrations in relation to quantitative, active ingredient-specific metrics of agricultural pesticide use in the Biomarkers of Exposure and Effect in Agriculture Study. Methods: Participants provided vacuum dust samples (2013–2018) and information regarding recent (last 12 months) and lifetime pesticide use. Thirty-two pesticide analytes were measured in 295 dust samples from 213 participants; 54 had repeated measurements (median = 96 days between visits). We used mixed-effects quantile regression models to estimate relative differences in pesticide concentrations for recent and lifetime agricultural use (number of days, intensity-weighted days), recent home/garden use (yes/no), and household characteristics. Only household characteristics were examined for dacthal because of no use information. We calculated intraclass correlation coefficients (ICCs) to evaluate temporal variability. We report only descriptive statistics for pesticides with detection rates

Glyphosate is the most widely applied herbicide worldwide. Glyphosate biomonitoring data are limited for agricultural settings. We measured urinary glyphosate concentrations and assessed exposure determinants in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study. We selected four groups of BEEA participants based on self-reported pesticide exposure: recently exposed farmers with occupational glyphosate use in the last 7 days (n = 98), farmers with high lifetime glyphosate use (>80th percentile) but no use in the last 7 days (n = 70), farming controls with minimal lifetime use (n = 100), and nonfarming controls with no occupational pesticide exposures and no recent home/garden glyphosate use (n = 100). Glyphosate was quantified in first morning void urine using ion chromatography isotope-dilution tandem mass spectrometry. We estimated associations between urinary glyphosate concentrations and potential determinants using multivariable linear regression. Glyphosate was detected (≥0.2 µg/L) in urine of most farmers with recent (91 %) and high lifetime (93 %) use, as well as farming (88 %) and nonfarming (81 %) controls; geometric mean concentrations were 0.89, 0.59, 0.46, and 0.39 µg/L (0.79, 0.51, 0.42, and 0.37 µg/g creatinine), respectively. Compared with both control groups, urinary glyphosate concentrations were significantly elevated among recently exposed farmers (P

In this paper, four widely used temporal compositing algorithms, i.e. median, maximum NDVI, medoid, and weighted scoring-based algorithms, were evaluated for annual land cover classification using monthly Landsat time series data. Four study areas located in California, Texas, Kansas, and Minnesota, USA were selected for image compositing and land cover classification. Results indicated that images composited using weighted scoring-based algorithms have the best spatial fidelity compared to other three algorithms. In addition, the weighted scoring-based algorithms have superior classification accuracy, followed by median, maximum NDVI, and medoid in descending order. However, the median algorithm has a significant advantage in computational efficiency which was ∼70 times that of weighted scoring-based algorithms, and with overall classification accuracy just slightly lower (∼0.13% on average) than weighted scoring-based algorithms. Therefore, we recommended the weighted scoring-based compositing algorithms for small area land cover mapping, and median compositing algorithm for the land cover mapping of large area considering the balance between computational complexity and classification accuracy. The findings of this study provide insights into the performance difference between various compositing algorithms, and have potential uses for the selection of pixel-based image compositing technique adopted for land cover mapping based on Landsat time series data.

Savannas are widespread biomes with highly valued ecosystem services. To successfully manage savannas in the future, it is critical to better understand the long-term dynamics of their productivity and phenology. However, accurate large-scale gross primary productivity (GPP) estimation remains challenging because of the high spatial and seasonal variations in savanna GPP. China’s savanna ecosystems constitute only a small part of the world’s savanna ecosystems and are ecologically fragile. However, studies on GPP and phenological changes, while closely related to climate change, remain scarce. Therefore, we simulated savanna ecosystem GPP via a satellite-based vegetation photosynthesis model (VPM) with fine-resolution harmonized Landsat and Sentinel-2 (HLS) imagery and derived savanna phenophases from phenocam images. From 2015 to 2018, we compared the GPP from HLS VPM (GPPHLS-VPM) simulations and that from Moderate-Resolution Imaging Spectroradiometer (MODIS) VPM simulations (GPPMODIS-VPM) with GPP estimates from an eddy covariance (EC) flux tower (GPPEC) in Yuanjiang, China. Moreover, the consistency of the savanna ecosystem GPP was validated for a conventional MODIS product (MOD17A2). This study clearly revealed the potential of the HLS VPM for estimating savanna GPP. Compared with the MODIS VPM, the HLS VPM yielded more accurate GPP estimates with lower root-mean-square errors (RMSEs) and slopes closer to 1:1. Specifically, the annual RMSE values for the HLS VPM were 1.54 (2015), 2.65 (2016), 2.64 (2017), and 1.80 (2018), whereas those for the MODIS VPM were 3.04, 3.10, 2.62, and 2.49, respectively. The HLS VPM slopes were 1.12, 1.80, 1.65, and 1.27, indicating better agreement with the EC data than the MODIS VPM slopes of 2.04, 2.51, 2.14, and 1.54, respectively. Moreover, HLS VPM suitably indicated GPP dynamics during all phenophases, especially during the autumn green-down period. As the first study that simulates GPP involving HLS VPM and compares satellite-based and EC flux observations of the GPP in Chinese savanna ecosystems, our study enables better exploration of the Chinese savanna ecosystem GPP during different phenophases and more effective savanna management and conservation worldwide.

In this study, we conducted a numerical analysis of the flow characteristics and heat transfer mechanism of a Tesla valve. Our aim was to illustrate the improvement in heat transfer by presenting velocity clouds, temperature clouds, pressure clouds, velocity vectors, and local flow lines. The findings of our study indicate that the formation of different vortices at the inlet of the Tesla valve has varying effects on heat transfer and fluid flow in forward and reverse flow scenarios. During forward flow, longitudinal vortices are more likely to form, whereas transverse vortices tend to form during reverse flow. These vortices have the ability to disrupt the boundary layer and enhance heat transfer, resulting in improved heat transfer efficiency, albeit with a slight pressure drop. When the flow angle was increased from 40° to 80°, we observed a decrease in the Nusselt number (Nu) by 17.8 % and 8.1 % in forward and reverse flows, respectively. Conversely, the friction factor (f) increased by 22.6 % and 3.1 %, while the Dittus-Boelter factor (Dit) increased by 11.8 %. In addition, the extension of the lateral groove length from 10 cm to 18 cm resulted in a decrease of 7.5 % and 11.7 % in Nu for forward and reverse flows, respectively. Similarly, f decreased by 13.8 % and 9.1 %, while Dit decreased by 5.2 %. Moreover, mathematical equations were developed to describe the relationship between the lead angle, side channel length, Re, Nu, and f. These findings aim to offer valuable insights for the design and implementation of Tesla valves.

Deep learning technology has become one of the most important technologies in remote sensing land classification applications. Its powerful sample-learning and information-mining abilities promote the continuous improvement of classification accuracy. A large volume of high-quality and representative sample data is the premise for the successful application of deep learning technology. Conventional methods of obtaining samples through manual delineation or surface surveys require a great deal of manpower and material resources. Therefore, the inability to obtain sufficient and widely representative high-quality samples is one of the key factors limiting the application of deep learning technology. In this study, the method of generating sample data obtains high-confidence classification results from a variety of existing high-quality classification products as deep learning samples, which are then used to support the application of deep learning technology for land-cover classification. When the three global land-cover classification products, FROM-GLC-2015, GLC_FCS30-2015, and GlobeLand30, have the same type of discrimination, the sample is considered a high-confidence sample. Based on this, a large volume of sample data widely distributed around the world was obtained. Using the extracted samples, a random forest classifier was trained using multiple types of information from the Landsat data, and land-cover classification was achieved. Application experiments were conducted in several typical regions, and the classification results were verified. The results showed that the classification accuracy of random forests under the support of samples generated using the sample extraction method proposed in this article was considerably higher than that of the three land-cover classification products.

Rural collectively owned commercial construction land marketization (RCCCLM), a land policy that is still being piloted despite being allowed by law, has been regarded as an indispensable policy tool that can meet the land demand for rural development. This study aims to analyze the typical modes and key paths of RCCCLM for rural transformation theoretically and practically. We developed an innovative theoretical framework of “Situation-Structure-Implementation-Outcome” to illustrate the relationship between RCCCLM and rural transformation. By tracing the process of the impact of RCCCLM on rural transformation in typical areas, this study fills the gap in existing research that has not yet explored the theoretical mechanisms between RCCCLM and rural transformation. The results show that a combination of such factors as geographical location and rural land use status brings about three major ways of RCCCLM, namely, local marketization, adjustive marketization and consolidation-based marketization, to promote rural transformation. The influencing mechanism of RCCCLM on rural transformation mainly concerns rights granting, rural land use structure reshaping, the transformation of farmers’ livelihood and improvement in rural governance capacity. Rural transformation follows a basic rule and phased characteristics of evolving from single function to multiple functions, and RCCCLM displays a differentiated driving effect on the evolution of rural functions in different stages of rural development. These findings will provide new insights into rural land use and rural transformation in China and other developing countries and help solve the bottlenecks in rural transformation.

Under low-temperature conditions in winter, asphalt pavement is prone to cracking, icing and other distresses, which affect its safety and comfort. Therefore, by incorporating phase-change materials into asphalt and conducting relevant performance studies, the aim is to alleviate low-temperature distress and regulate road surface temperature and expand the application of phase-change materials in asphalt pavement. We mixed the selected phase-change materials with different dosages into the matrix asphalt to prepare phase-change temperature-regulating asphalt and tested the four basic indicators: road performance, latent heat characteristics, temperature-regulating performance, and rheological properties of phase-change asphalt and its mixture. The research results indicate that with the increase in phase-change material content, the penetration, softening point, ductility, and dynamic viscosity of phase-change high-viscosity asphalt gradually increase. Under the constant temperature test conditions of −2.5 °C and −5 °C, the surface icing speed of asphalt binder specimens mixed with phase-change materials is slower than that of specimens without phase-change materials. Adding phase-change materials can improve the high-temperature and low-temperature PG grading of high-viscosity asphalt, effectively improving its high-temperature rutting resistance and low-temperature cracking performance. According to the temperature regulation test results, phase-change temperature-regulating asphalt has a certain regulating effect on temperature under low-temperature conditions, which can slow down the cooling rate of asphalt, reduce the thermal conductivity of permeable asphalt mixture by more than 50%, increase the temperature regulation rate by more than 30%, and improve the ice-melting and snow-melting ability by more than 20%. Phase-change materials have almost no effect on the porosity of permeable asphalt mixtures and can effectively improve the water stability, low-temperature crack resistance, and antiflying performance of permeable asphalt mixtures. Their Marshall stability and rutting stability decrease, but still meet the requirements of the specifications. Applying phase-change materials to permeable asphalt pavement can automatically adjust the temperature of the pavement, reduce the cooling rate of the asphalt pavement during cooling, alleviate the problem of snow and ice accumulation on the asphalt pavement in winter, and thereby improve the performance of permeable asphalt pavement against freeze–thaw cycles.

The complicated multielectron and multiphase electrocatalytic sulfur reduction reaction (SRR) occurring in the Li–S battery is demonstrated, which strongly influences the performances of this battery chemistry. Effective candidates for SRR are often based on heteroatom‐doped carbon‐based electrocatalysts. However, the electrocatalytic sulfur reduction activity of these catalysts is so far insufficiently explored. Herein, a series of graphene doped with nonmetal elements (nitrogen, phosphorus, and sulfur) are designed and synthesized. It is shown that nitrogen‐doped graphene has a superior SRR catalytic activity with highest electrochemical reversibility and best electrochemical kinetics for the liquid–solid two‐phase conversion from long‐chain soluble Li2Sx (4 ≤ x ≤ 8) and the solid‐state Li2S2 to Li2S conversion. The considerably improved kinetics of the liquid–solid and solid–solid phases conversion reduces the continued accumulation of lithium polysulfides in electrolyte and the passivation of the electrode, thus resulting in a significant improvement in electrochemical performance of Li–S cells. Density‐functional theory calculations demonstrates that the highest SRR performance of N/G is originated from the strongest adsorption of the sulfur species and lowest energy barriers for Li2S decomposition among three doped graphene samples. This study is believed to guide the design of efficient electrocatalysts to exceed the performance of the benchmark for Li–S battery.

The high-quality development of new agricultural business entities (NABEs) is an important driving force for realizing rural revitalization and accelerating the modernization of agriculture and rural areas. The main purpose of the study is to investigate the spatial distribution pattern, aggregation scales, development mechanism, and internal differences of various types of NABEs in different regions. It provides targeted ideas for alleviating regional differences in the development of NABEs in different agricultural regions. Kernel density estimation, nearest neighbor distance analysis, Tyson’s polygon coefficient of variation, and Ripley’s K function are used to study the spatial and temporal evolution, spatial aggregation, and scale divergence of various types of NABEs, and Pearson correlation analysis is incorporated to explore the specific factors affecting the development of various types of NABEs. The study results: First, family farms are the most widely distributed, and agricultural enterprises are the most sparsely distributed, being distributed “more in the southeast and less in the northwest” in all three categories. Second, the strongest aggregation scales of different NABEs are increasing, and the strongest aggregation scales of agricultural enterprises are larger than those of family farms and cooperatives in all agricultural areas. Third, the development of specialized farmers’ cooperatives (abbreviated as ‘cooperatives’) is more constrained by traditional agricultural inputs and is a kind of agricultural input-oriented development. Family farms are more constrained by the living standards of rural residents in the region and are a kind of rural economy-oriented development. Agricultural enterprises are more subject to the economic level of the region, which is a kind of market economy-oriented development. Finally, in the process of developing NABEs, regional differences should be emphasized, and a small number of agriculturally leading enterprises and model cooperatives should drive a large number of small-scale family farms and smallholder farmers in order to become a characteristic path for China’s agricultural development.

The usage of flame retardants in flammable polymers has been an effective way to protect both lives and material goods from accidental fires. Phosphorus flame retardants have the potential to be follow-on flame retardants after halogenated variants, because of their low toxicity, high efficiency and compatibility. Recently, the emerging allotrope of phosphorus, two-dimensional black phosphorus, as a flame retardant has been developed. To further understand its performance in flame-retardant efficiency among phosphorus flame retardants, in this work, we built model materials to compare the flame-retardant performances of few-layer black phosphorus, red phosphorus nanoparticles, and triphenyl phosphate as flame-retardant additives in cellulose and polyacrylonitrile. Aside from the superior flame retardancy in polyacrylonitrile, few-layer black phosphorus in cellulose showed the superior flame-retardant efficiency in self-extinguishing, ~1.8 and ~4.4 times that of red phosphorus nanoparticles and triphenyl phosphate with similar lateral size and mass load (2.5~4.8 wt%), respectively. The char layer in cellulose coated with the few-layer black phosphorus after combustion was more continuous and smoother than that with red phosphorus nanoparticles, triphenyl phosphate and blank, and the amount of residues of cellulose coated with the few-layer black phosphorus in thermogravimetric analysis were 10 wt%, 14 wt% and 14 wt% more than that with red phosphorus nanoparticles, triphenyl phosphate and blank, respectively. In addition, although exothermic reactions, the combustion enthalpy changes in the few-layer black phosphorus (−127.1 kJ mol−1) are one third of that of red phosphorus nanoparticles (−381.3 kJ mol−1). Based on a joint thermodynamic, spectroscopic, and microscopic analysis, the superior flame retardancy of the few-layer black phosphorus was attributed to superior combustion reaction suppression from the two-dimensional structure and thermal nature of the few-layer black phosphorus.

Lake systems are of fundamental significance for the ecological environment and social development. Driven by climatic changes and human activities, the hydrological situation of rivers and lakes has been altered considerably, and it has directly affected the reliability of irrigation and water supply in lake areas. In the case of the Yangtze River–Dongting Lake area, the quasi-equilibrium of their hydrological states and water balance have been affected by the operation of the Three Gorges Project that has led to irrigation and water supply problems across the lake area. Ever since, the available water supply in the Dongting Lake area has decreased by 31.5% and 47.7% during the months of September and October, and the overall water level in the lake area has dropped by 0.8–2.0 m. The proportion of domestic and agricultural irrigation water demands to available water supply in the Dongting Lake area is very small, and the decline of water level is the main reason for the lack of water supply in Dongting Lake area, Although the total amount of water in the lake area is still above the water supply threshold, an obvious decreasing trend has been observed in the fulfillment of the demand for both irrigation and water supply in urban and rural regions. Additionally, the fulfillment of secured water supply and irrigation requirements has respectively dropped from 95% to 78% before the operation of the Three Gorges Project to 82% and 46% after it. Meanwhile, the total water shortage increased from 57.683 to 143.607 million cubic meters. By advancing or postponing impoundment schedules, the impact of the Three Gorges Reservoir on irrigation and water supply in the lake area during September and October can be alleviated, and the water supply capacity can be respectively increased by approximately 1.3% and 1.4%. The highlights of this study include the quantification of the impacts of the operation of large-scale water conservancy projects at the level at which the requirements on irrigation and water supply in the lake area are satisfied, and the prediction of the trends for future evolvement. In addition, corresponding strategies that provide a technical reference for the comprehensive regulation and control over water resources and the security of water supply in the river basin are discussed.

Fine-scale mapping of inland water is important for regional or global ecosystem monitoring; however, due to the spectral heterogeneity of inland water, large-scale dynamic mapping of these water bodies is usually challenging. In this article, we propose a water change tracking (WCT) algorithm, that uses only optical data, for accurate inland water mapping. Simultaneously, time-series of Landsat imagery are used as an example for illustrating the effectiveness of the method. First, time-series of Minimum Normalized Water Score (MNWS) images and corresponding `bad-pixel' (cloud, ice and snow) masks were developed using time-series of Landsat imagery. Secondly, based on the MNWS time-series stack and `bad-pixel' masks, time-series trajectories were formed to track changes in water bodies. A 30-m Dynamic Inland Water Body Map (DIWBM) of China was then produced to test the robustness of the WCT algorithm. Eight test sites were selected, and the water detection algorithms used in the Global 1 arc-second Water Body Map (G1WBM) and the function of mask (FMASK) were applied to our data to provide a comparison with the DIWBM results. The results indicated that the DIWBM gave a better performance of 96.20% against 85.91% for the G1WBM results and against 76.36% for the FMASK results. The DIWBM achieved user's accuracies of 98.36% and 92.53%, and producer's accuracies of 97.45% and 95.39% for permanent and temporary water, respectively. The proposed method was also better at identifying small water bodies. It is concluded, therefore, that the WCT algorithm is a promising method for large-scale dynamic inland water mapping.

The lithium–sulfur battery is one of the next-generation rechargeable battery candidates due to its high theoretical energy density and low cost. However, the sluggish conversion kinetics of soluble lithium polysulfides into insoluble Li2S2/Li2S leads to low sulfur utilization, retarded rate responses, and rapid capacity decay. Here, we enhance the sulfur reduction kinetics by designing and synthesizing a lamellar-structured NiFeLDH and reduced graphene oxide (rGO) composite. The assembly of a two-dimensional NiFeLDH with rGO, which has high conductivity and electrocatalytic activity, significantly enhances the electrochemical steps of sulfur reduction. The S@NiFeLDH/rGO cathode delivers an initial discharge capacity of 1014 mAh g−1 at 0.2 C and a capacity of 930 mAh g−1 after 100 cycles at 0.2 C. Even at a high current density of 1 C, the S@NiFeLDH/rGO could maintain a high capacity of 554 mAh g−1 after 400 cycles.

As a country with a vast area and complex terrain, the differentiation between paddy field and dryland under different topographic gradients in China is difficult. Based on a land-use grid data set with an accuracy of 1 km, this study applied the Topographic Potential Index and used land-use transition matrices and landscape analysis to compare the change in dryland and paddy field in China from 1990 to 2020 at different elevations, slopes, and slope aspects. The results indicate that paddy field and dryland were mostly distributed in areas with better photothermal conditions. However, in recent years, the paddy field and dryland on the “sunny” slope decreased. Specifically, the area of paddy field and dryland on the southeast, south, and southwest slopes decreased, while they increased on the northwest, north, and northeast slopes. From 1990 to 2020, land conversion among paddy field, dryland, and other land use was mostly concentrated in the third ladder (

The heat transfer and flow characteristics have been numerically investigated in the cross-combined dimple tube with the k-ε model, and the enhancement model based on composite-shape surface technologies is established for the first time. The results indicate that the heat transfer index, friction factor and performance evaluation criteria increase tremendously by comparing with the single ellipsoidal dimple tube at considered operating conditions. In particular, the heat transfer enhancement rises by 18.8–48.3%, and an average of 24.8%, when compared with the traditional single ellipsoidal tube. 3D simulation is carried out to elaborate the influence mechanism of dimples on the thermal performance and concludes that dimples can further enhance heat transfer through changing temperature distribution and increasing the gradient of the core region at the dimple section. The concave surface profile can disturb steady flow and boundary layer development to enhance the mixture of hot fluid and cold fluid. Additionally, the effects of geometric parameters on thermal and hydraulic performances are discussed to reveal that the larger quantity and volume of dimples lead to a better comprehensive thermo-hydraulic performance.

With the popularity of location-based social networks, location prediction has become an important task and has gained significant attention in recent years. However, how to use massive trajectory data and spatial-temporal context information effectively to mine the user’s mobility pattern and predict the users’ next location is still unresolved. In this paper, we propose a novel network named STSAN (spatial-temporal self-attention network), which can integrate spatial-temporal information with the self-attention for location prediction. In STSAN, we design a trajectory attention module to learn users’ dynamic trajectory representation, which includes three modules: location attention, which captures the location sequential transitions with self-attention; spatial attention, which captures user’s preference for geographic location; and temporal attention, which captures the user temporal activity preference. Finally, extensive experiments on four real-world check-ins datasets are designed to verify the effectiveness of our proposed method. Experimental results show that spatial-temporal information can effectively improve the performance of the model. Our method STSAN gains about 39.8% Acc@1 and 4.4% APR improvements against the strongest baseline on New York City dataset.

Strong convection nowcasting has been gaining importance in regional security, economic development, and water resource management. Rainfall nowcasting with strong timeliness needs to effectively forecast the intensity of rainfall in a local region in the short term. The forecast performance of traditional methods is limited. In this paper, a rainfall nowcasting model based on the Convolutional Long Short-Term Memory (ConvLSTM) is proposed. Combined reflectance (CR) and the retrieved wind field are selected as the key factors for prediction. The model considers the influence of water vapor content, transport, and change on rainfall by measuring CR and the retrieved wind field. Forecast results are compared to different models and different input data schemes. The analysis shows that the CSI scores of this method can reach about 0.8, which is 28% higher than that of the optical flow method. The ConvLSTM structure with spatial analysis and time memory can greatly enhance the predictive ability of the model, and the addition of wind field data also improves the forecasting performance of the model. Therefore, the idea of forecasting rainfall on the basis of water vapor content and water vapor transport is practical and effective, and the accuracy of a forecast can be improved by using ConvLSTM network to extract spatiotemporal features.

In the rapid process of urbanization in China, arable land resources are faced with dual challenges in terms of quantity and quality. Starting with the change in the coupling coordination relationship between the input and output on arable land, this study applies an evaluation model of the degree of coupling coordination between the input and output (D_CCIO) on arable land and deeply analyzes the recessive transition mechanism and internal differences in arable land use modes in 31 provinces on mainland China. The results show that the total amount and the amount per unit area of the input and output on arable land in China have presented different spatio-temporal trends, along with the mismatched movement of the spatial barycenter. Although the D_CCIO on arable land increases slowly as a whole, 31 provinces show different recessive transition mechanisms of arable land use, which is hidden in the internal changes in the input–output structure. The results of this study highlight the different recessive transition patterns of arable land use in different provinces of China, which points to the outlook for higher technical input, optimized planting structure, and the coordination of human-land relationships.

Land-cover plays an important role in the Earth’s energy balance, the hydrological cycle, and the carbon cycle. Therefore, it is important to evaluate the current global land-cover (GLC) products and to understand the differences between these products so that they can be used effectively in different applications. In this study, three 30-m GLC products, namely GlobeLand30-2010, GLC_FCS30-2015, and FROM_GLC30-2015, were evaluated in terms of areal consistency and spatial consistency using the Land Use/Cover Area frame statistical Survey (LUCAS) reference dataset over the European Union (EU). Given the limitations of the traditional confusion matrix used in accuracy assessment, we adjusted the confusion matrices from sample counts by accounting for the class proportions of the map and reported the standard errors of the descriptive accuracy measures in the accuracy assessment. The results revealed the following. (1) The overall accuracy of the GlobeLand30-2010 product was the highest at 88.90 ± 0.68%; this was followed by GLC_FCS30-2015 (84.33 ± 0.80%) and FROM_GLC2015 (65.31 ± 1.0%). (2) The consistency between the GLC_FCS30-2015 and GlobeLand30-2010 is higher than the consistency between other products, with an area correlation coefficient of 0.930 and a proportion of consistent pixels of 52.41%, respectively. (3) Across the area of the EU, the dominant land-cover types such as forest and cropland are the most consistent across the three products, whereas the spatial consistency for bare land, grassland, shrubland, and wetland is relatively low. (4) The proportion of pixels for which the consistency is low accounts for less than 16.17% of pixels, whereas the proportion of pixels for which the consistency is high accounts for about 39.12%. The disagreement between these products primarily occurs in transitional zones with mixed land cover types or in mountain areas. Overall, the GlobeLand30 and GLC-FCS30 products were found to be the most consistent and to have good classification accuracy in the EU, with the disagreement between the three 30-m GLC products mainly occurring in heterogeneous regions.

Percentile features derived from Landsat time-series data are widely adopted in land-cover classification. However, the temporal distribution of Landsat valid observations is highly uneven across different pixels due to the gaps resulting from clouds, cloud shadows, snow, and the scan line corrector (SLC)-off problem. In addition, when applying percentile features, land-cover change in time-series data is usually not considered. In this paper, an improved percentile called the time-series model (TSM)-adjusted percentile is proposed for land-cover classification based on Landsat data. The Landsat data were first modeled using three different time-series models, and the land-cover changes were continuously monitored using the continuous change detection (CCD) algorithm. The TSM-adjusted percentiles for stable pixels were then derived from the synthetic time-series data without gaps. Finally, the TSM-adjusted percentiles were used for generating supervised random forest classifications. The proposed methods were implemented on Landsat time-series data of three study areas. The classification results were compared with those obtained using the original percentiles derived from the original time-series data with gaps. The results show that the land-cover classifications obtained using the proposed TSM-adjusted percentiles have significantly higher overall accuracies than those obtained using the original percentiles. The proposed method was more effective for forest types with obvious phenological characteristics and with fewer valid observations. In addition, it was also robust to the training data sampling strategy. Overall, the methods proposed in this work can provide accurate characterization of land cover and improve the overall classification accuracy based on such metrics. The findings are promising for percentile-based land cover classification using Landsat time series data, especially in the areas with frequent cloud coverage.

Accurate cloud detection using medium-resolution multispectral satellite imagery (such as Landsat and Sentinel data) is always difficult due to the complex land surfaces, diverse cloud types, and limited number of available spectral bands, especially in the case of images without thermal bands. In this paper, a novel classification extension-based cloud detection (CECD) method was proposed for masking clouds in the medium-resolution images. The new method does not rely on thermal bands and can be used for masking clouds in different types of medium-resolution satellite imagery. First, with the support of low-resolution satellite imagery with short revisit periods, cloud and non-cloud pixels were identified in the resampled low-resolution version of the medium-resolution cloudy image. Then, based on the identified cloud and non-cloud pixels and the resampled cloudy image, training samples were automatically collected to develop a random forest (RF) classifier. Finally, the developed RF classifier was extended to the corresponding medium-resolution cloudy image to generate an accurate cloud mask. The CECD method was applied to Landsat-8 and Sentinel-2 imagery to test the performance for different satellite images, and the well-known function of mask (FMASK) method was employed for comparison with our method. The results indicate that CECD is more accurate at detecting clouds in Landsat-8 and Sentinel-2 imagery, giving an average F-measure value of 97.65% and 97.11% for Landsat-8 and Sentinel-2 imagery, respectively, as against corresponding results of 90.80% and 88.47% for FMASK. It is concluded, therefore, that the proposed CECD algorithm is an effective cloud-classification algorithm that can be applied to the medium-resolution optical satellite imagery.

Fine resolution land cover information is a vital foundation of Earth science. In this paper, a novel SPECLib-based operational method is presented for the classification of multi-temporal Landsat imagery using reflectance spectra from the spatial-temporal spectral library (SPECLib) for 30 m land-cover mapping for the whole of China. Firstly, using the European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product and the MODIS Version 6 Nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) product (MCD43A4), a global SPECLib with a spatial resolution of 158.85 km (equivalent to 1.43° at the equator) and a temporal resolution of eight days was developed in the sinusoidal projection. Then, the Landsat datacube covering the whole of China was developed using all available observations of Landsat OLI imagery in 2015. Thirdly, the multi-temporal random forest method based on SPECLib was presented to produce an annual land-cover map with 22 land-cover types using the Landsat datacube. Finally, the annual China land-cover map was validated by two different validation systems using approximately 11,000 interpretation points. The mapping results achieved the overall accuracy of 71.3% and 80.7% and the kappa coefficient of 0.664 and 0.757 for the level-2 validation system (19 land-cover types) and the level-1 validation system (nine land-cover types), respectively. Therefore, the case study in China indicates that the proposed SPECLib method is an operational and accurate method for regional/global fine land-cover mapping at a spatial resolution of 30 m.

The fraction of absorbed photosynthetically active radiation by vegetation (FAPAR) is a key variable in describing the light absorption ability of the vegetation canopy. Most global FAPAR products, such as MCD15A2H and GEOV1, correspond to FAPAR under black-sky conditions at the satellite overpass time only. In this paper, we aim to produce both the global white-sky and black-sky FAPAR products based on the moderate resolution imaging spectroradiometer (MODIS) visible (VIS) albedo, leaf area index (LAI), and clumping index (CI) products. Firstly, a non-linear spectral mixture model (NSM) was designed to retrieve the soil visible (VIS) albedo. The global soil VIS albedo and its dynamics were successfully mapped at a resolution of 500 m using the MCD43A3 VIS albedo product and the MCD15A2H LAI product. Secondly, a method based on the energy balance residual (EBR) principle was presented to retrieve the white-sky and black-sky FAPAR using the MODIS broadband VIS albedo (white-sky and black-sky) product (MCD43A3), the LAI product (MCD15A2H) and CI products. Finally, the two EBR FAPAR products were compared with the MCD15A2H and Geoland2/BioPar version 1 (GEOV1) black-sky FAPAR products. A comparison of the results indicates that these FAPAR products show similar spatial and seasonal patterns. Direct validation using FAPAR observations from the Validation of Land European Remote sensing Instrument (VALERI) project demonstrates that the EBR black-sky FAPAR product was more accurate and had a lower bias (R2 = 0.917, RMSE = 0.088, and bias = −2.8 %) than MCD15A2H (R2 = 0.901, RMSE = 0.096, and bias = 7.6 % ) and GEOV1 (R2 = 0.868, RMSE = 0.105, and bias = 6.1%).

Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE), Bayes’ theorem and expectation-maximization (EM) algorithm are applied to quantitatively estimate the individual error-rate and “ground truth” according to multiple participants’ assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring “ground truth” according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image.

Due to the detection sensitivity provided by the high nuclear spin polarization, hyperpolarized 129Xe has been employed in animal and human magnetic resonance imaging (MRI). However, during the accumulation-thawing process of hyperpolarized 129Xe, multiple factors lead to the relaxation of spin polarization and would hinder the wider application of 129Xe. In this research, the spin relaxation mechanism of hyperpolarized 129Xe during the accumulation-thawing process is investigated by both theoretical model analysis and experimental measurements. Meanwhile, the stability of the homebuilt device for the accumulation-thawing is measured. Our results demonstrate that the thawing mode and the cold trap material significantly affect the polarization loss; the automatic device is very stable during long-term operation and shows a high degree of automation, resulting in a polarization recovery ratio of 85.6% ± 4.7%. This research greatly helps to improve the efficiency of hyperpolarized 129Xe in animal and human MRI.

Sulfur redox reactions render lithium-sulfur (Li-S) batteries with an energy density of > 500 Wh kg-1 but suffer a low practical capacity and fast capacity fade due to sluggish sulfur redox reaction (SRR) kinetics, which lies in the complex reaction process that involves a series of reaction intermediates and proceeds via a cascade reaction. Here, we present a Pt-Cu dual-atom catalyst (Pt/Cu-NG) as an electrocatalyst for sulfur redox reactions. Pt/Cu-NG enabled the rapid conversion of soluble polysulfide intermediates into insoluble Li2S2/Li2S, and consequently, it prevented the accumulation and shuttling of lithium polysulfides, thus outperforming the corresponding singleatom catalysts (SACs) with individual Pt or Cu sites. Operando X-ray absorption spectroscopy and density functional theory calculations revealed that a synergistic effect between the paired Pt and Cu atoms modifies the electronic structure of the Pt site through d-orbital interactions, resulting in an optimal moderate interaction of the metal atom with the different sulfide species. This optimal interaction enhanced charge transfer kinetics and promoted sulfur redox reactions. Our work thus provides important insights on the atomic scale into the synergistic effects operative in dual-atom catalysts and will thus pave the way to electrocatalysts with enhanced efficiency for high-performance Li-S batteries. [ABSTRACT FROM AUTHOR]

Objective: Unsafe medication practices and medication errors are a major cause of harm in healthcare systems around the world. This study aimed to explore the factors that influence the risk of medication and provide medication risk evaluation model for adults in Shanxi province, China. Methods: The data was obtained from the provincial questionnaire from May to December 2022, relying on the random distribution of questionnaires and online questionnaires by four hospitals in Shanxi Province. Multiple linear regression analysis was used to explore the factors affecting the KAP score of residents. Univariate and multivariate logistic regression was used to determine the independent risk factors, and the nomogram was verified by receiver operating characteristic curve, calibration and decision curve analysis. Results: A total of 3,388 questionnaires were collected, including 3,272 valid questionnaires. The average scores of drugs KAP were 63.2 ± 23.04, 33.05 ± 9.60, 23.67 ± 6.75 and 33.16 ± 10.87, respectively. On the evaluation criteria of the questionnaire, knowledge was scored "fair", attitude and practice were scored "good". Sex, monthly income, place of residence, insurance status, education level, and employment were regarded as independent risk factors for medication and a nomogram was established by them. Conclusion: Males, low-income, and low-educated people are important factors affecting the risk of medication. The application of the model can help residents understand the risk of their own medication behavior and reduce the harm of medication. [ABSTRACT FROM AUTHOR]

The existing market of public charging pile services for electric vehicle (EV) users has occupied a particular market share. However, instead of solely focusing on pre-planning the construction of charging piles, it is crucial to address the shortcomings of the existing charging pile service and develop effective marketing strategies. This approach can help optimize the utilization of charging pile resources and minimize wastage. In this study, we explore EV users' comments on the public charging pile service and adopt a natural language pre-training model to classify comments for extracting positive and negative comments. For these two types of comments respectively, we construct the text-to-knowledge to mine the keywords from multiple dimensions. We further excavate the words correlated with the keywords by utilizing dependency parsing to create relational dependency graphs. Taken together, we identify key factors influencing EV user satisfaction or dissatisfaction and uncover the relationships among these factors. These insights provide valuable information for charging pile operators to develop targeted marketing strategies and improvement plans for the existing public charging pile resources, ultimately enhancing the overall user experience. [ABSTRACT FROM AUTHOR]