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Preprints, versions of scientific manuscripts that precede peer review, are growing in popularity. They offer an opportunity to democratize and accelerate research, as they have no publication costs or a lengthy peer review process. Preprints are often later published in peer-reviewed venues, but these publications and the original preprints are frequently not linked in any way. To this end, we developed a tool, PreprintMatch, to find matches between preprints and their corresponding published papers, if they exist. This tool outperforms existing techniques to match preprints and papers, both on matching performance and speed. PreprintMatch was applied to search for matches between preprints (from bioRxiv and medRxiv), and PubMed. The preliminary nature of preprints offers a unique perspective into scientific projects at a relatively early stage, and with better matching between preprint and paper, we explored questions related to research inequity. We found that preprints from low income countries are published as peer-reviewed papers at a lower rate than high income countries (39.6% and 61.1%, respectively), and our data is consistent with previous work that cite a lack of resources, lack of stability, and policy choices to explain this discrepancy. Preprints from low income countries were also found to be published quicker (178 vs 203 days) and with less title, abstract, and author similarity to the published version compared to high income countries. Low income countries add more authors from the preprint to the published version than high income countries (0.42 authors vs 0.32, respectively), a practice that is significantly more frequent in China compared to similar countries. Finally, we find that some publishers publish work with authors from lower income countries more frequently than others. [ABSTRACT FROM AUTHOR]

Under the current rapid urbanization and industrialization in China, competition for production, living, and ecological spaces is becoming fierce. Improving production–living–ecological spaces (PLESs) has become a core issue in China's land‐space development strategy. In this study, the multiple linear logistic regression (MLLR)–multicriteria evaluation (MCE)–cellular automata (CA)–Markov hybrid model was used to predict the land‐use pattern for 2030, and the spatial conflict model was established based on landscape indices to measure the evolution characteristics of spatial conflicts. The results demonstrated that, from 2005 to 2030, the production–ecological space (PES) will have front‐runner status in Wuhan, followed by the ecological–production space (EPS). In 2005–2015, the living–production space (LPS) and PES occupied large amounts of EPS and ecological space (ES), especially in the main urban areas and near rivers, and the spatial transformation was the most significant. By 2030, there will be less transformation of each space type, but the conversion amount of LPS will still be relatively large. With the passage of time, the LPS will increase, the EPS will decrease slightly, and the ES will fluctuate. During the period 2005–2030, the conflict level will change gradually from seriously out of control to controllable, and the hot spot of PLES will show a scattering distribution. The cold spot area will be distributed mainly in the southern part of Wuhan, which will be expanded greatly by 2030. Our research indicates that analyzing the reasonable utilization of PLES is of great value by identifying spatial conflicts; differentiated sustainable development strategies should be formulated according to the future spatial and temporal patterns of PLES, which can help in scientific judgments on the spatial matching of land use and provide early warnings of spatial conflicts. Integr Environ Assess Manag 2022;18:1578–1596. © 2021 SETAC KEY POINTS: The exchange of production–living–ecological spaces can present the contradictory signals of regional spatial conflict.The results of conflict identification of PLES in time and space can provide a reference for future regional specific optimizing land allocation.The measurement model constructed based on the landscape pattern indices can reveal the identification of regional spatial conflicts. Over time, the PLES spatial conflict in Wuhan will be gradually improved. [ABSTRACT FROM AUTHOR]

Sea ice is the main cause of winter marine disasters in the Bohai Sea, which is an important sea area in China. The sea ice in the Bohai Sea is easily deformed by strong water currents and rapid temperature changes. Consequently, low spatial and temporal resolution data cannot meet the needs of high-precision monitoring of the sea ice in the Bohai Sea. The geostationary satellite Gaofen-4 (GF-4) has the unique advantages of a high temporal resolution (20 s) and a high spatial resolution (50 m) and can be used to monitor sea ice drift in the Bohai Sea. We evaluate a new algorithm that combines the scale-invariant feature transform (SIFT) technique and the normalized cross-correlation (NCC) method to obtain accurate sea ice drift information. The interclass separability method is used to determine the optimal band combination (OBC), and the results show that the OBC for distinguishing sea ice from sea water is B2×(B1× B5)−1. The proposed method improves noise immunity and calculation efficiency and considers the characteristics of ice blocks. The proposed algorithm can also obtain more correctly matched feature points than the NCC method and approximately 10 times more correct points than the SIFT technique. The mean absolute error (MAE) of the velocity obtained with the proposed algorithm is 0.1615 m s−1 lower than those obtained with the SIFT and NCC methods, and the root mean square error of the velocity determination of the three algorithms is very small. The MAE of the ice movement direction obtained with the proposed algorithm is 0.605° lower than those obtained with the SIFT and NCC methods, and the MAE of a second data set is 12.378° better than that of the SIFT method. The motion of sea ice obtained by the proposed algorithm is close to the actual movement of the sea ice in the Bohai Sea. [ABSTRACT FROM AUTHOR]

Aim: A better understanding of plant communities can be achieved by incorporating data of traits and dynamics into surveys. Wood density is a good predictor for growth and mortality in trees, but to date, no studies of lianas include all three. We examine how liana communities respond to environmental gradients and forest fragmentation in terms of abundance, diversity, size structure, mortality, relative growth rate and wood density, and tested how the latter three are related to each other in liana species. Location: Xishuangbanna, SW China. Methods: We repeated a survey of lianas (stems >0.5 cm diameter) in 47 plots, distributed in forest fragments of various size, on different bedrock (limestone or sandstone), over an elevational range and across different topographic elements (ridge, slope, valley). We gathered wood density data for 116 of 166 species, covering 90% of all surveyed stems. We also determined relative growth rate, mortality, stem diameter and basal area. Results: At the species level, liana mortality and relative growth rate were lower at higher wood density, and mortality was higher with greater relative growth rate. At the plot level, liana communities in valleys had high relative growth and mortality rates as well as high abundance and diversity. Forest on limestone hosted few species but more large‐stemmed liana individuals, and communities had higher wood density weighted by basal area. Liana abundance, relative growth rates and mortality were greater and average wood density lower towards fragment edges, but the explanatory power of these models was low. Conclusion: Habitat was the major factor shaping liana communities, whereas fragmentation was not an important predictor in our study. Resource‐rich environments such as valleys harbour diverse liana communities with high mortality and relative growth rates. This pattern matches earlier studies on survival/growth trade‐offs among plant species. The relationship between growth rates, mortality and wood density in lianas follows the same trade‐offs as found in trees. [ABSTRACT FROM AUTHOR]

Most previous studies have focused on the matching pattern of actual arable land and water resources in China. However, due to the lack of valid data about potential arable land resources, the matching pattern of potential arable land and water resources has seldom been investigated. In this paper, based on the potential arable land resources retrieved from remote sensing data, the matching degree of potential arable land resources with water resources in China was evaluated by using two indexes, i.e., water resource per unit area and Gini coefficient. First of all, according to the filter conditions suitable for crop cultivation, the spatial distribution of potential arable land area in China was retrieved from three indexes including DEM, slope and soil type. Then, the water resource per unit area of potential arable land in China were calculated, and the spatial matching degree of the raw water and soil resources in China was evaluated. Finally, we drew the Lorenz curve of water and land resources, calculated the Gini coefficient, and evaluated the overall matching pattern of water resource with potential arable land resource in China. The results showed that: 1) The potential arable land varied greatly in China that the area of potential arable land in northern China was much larger than that in southwestern China and east coastal regions. This indicated that in the future the potential arable land would be mainly located in the northern region. 2) There was a serious mismatching between the water resource and potential arable land resource in China. 3) The matching degree of water resource with potential arable land resource was extremely poor. About 80% of water resource served less than 23% of potential arable land in China. The Gini coefficient of water resource with arable land resource which was 0.712 in the whole country, was much higher than that of agricultural water use with farmland resources, which was 0.566. The matching degree of agricultural water and farmland resource was better than that of potential arable land and water resource, which was the result of human activities. This indicated that the Gini coefficient, calculated by the potential arable land without considering the water resource restriction, was much more suitable for expressing the matching degree of water and land resources. This study can provide scientific basis for us to take effective measures to adapt to the background matching pattern. [ABSTRACT FROM AUTHOR]

Remote transportation microwave sensor (RTMS) technology is being promoted for China's highways. The distance is about 2 to 5 kmbetween RTMSs,which leads tomissing data and data sparseness problems.These two problems seriously restrict the accuracy of travel time prediction. Aiming at the data-missing problem, based on traffic multimode characteristics, a tensor completion method is proposed to recover the lost RTMS speed and volume data. Aiming at the data sparseness problem, virtual sensor nodes are set up between real RTMS nodes, and the two-dimensional linear interpolation and piecewise method are applied to estimate the average travel time between two nodes. Next, compared with the traditional K-nearest neighbor method, an optimal KNN method is proposed for travel time prediction. optimization is made in three aspects. Firstly, the three original state vectors, that is, speed, volume, and time of the day, are subdivided into seven periods. Secondly, the traffic congestion level is added as a new state vector. Thirdly, the cross-validationmethod is used to calibrate the K value to improve the adaptability of the KNN algorithm. Based on the data collected from Jinggangao highway, all the algorithms are validated. The results show that the proposed method can improve data quality and prediction precision of travel time. [ABSTRACT FROM AUTHOR]

• The newly released GLDAS ET products from three LSMs are evaluated across China. • Three spatial evaluation methods are used to assess the simulated ET patterns. • The spatial evaluation provides valuable information to diagnose the model errors. • Noah has the overall best performance in matching the values and patterns of ET. Land surface models are important tools to represent and predict the spatiotemporal variability of evapotranspiration, which is a key variable in terrestrial water, energy and carbon cycles. However, evapotranspiration estimates from land surface models may suffer from various uncertainties in land surface modeling. Therefore, assessing the performance of evapotranspiration simulation plays a vital role in understanding the deficiencies of land surface modeling. Most of the evaluation studies of modeled evapotranspiration relied on comparisons with flux site observations (point scale) and water budget-derived evapotranspiration (basin scale), which have certain drawbacks and limitations. Thus, the evaluation results may be misleading for understanding the performance of land surface models on representing the spatial variability of evapotranspiration. In this study, a thorough spatial evaluation of the new and reprocessed Global Land Data Assimilation System evapotranspiration products is performed across China based on three bias-insensitive spatial evaluation methods, including the empirical orthogonal function analysis, the connectivity analysis and the fractions skill score. These evapotranspiration products were estimated from three land surface models, namely Noah, VIC and CLSM. The conventional evapotranspiration evaluation against eddy covariance measurements is also performed. The results show that all three products have consistent trends with the observed evapotranspiration series at both daily and monthly time scales. Noah and VIC have comparable performances in terms of different statistic metrics and outperform CLSM at both time scales. The spatial evaluation methods can provide additional valuable information to diagnose the model errors. VIC has the worst spatial performance during the warm months. Despite its inferior performance in late winter and early spring, Noah, overall, has the best spatial performance among the three. The gained insights of this study can help to improve the spatial performance of these models and further promote the system development. [ABSTRACT FROM AUTHOR]

In this article, we introduce and analyze an emerging polycentric governance model for addressing wastewater challenges in textile industrial parks in China. Unlike the conventional two-tier model with government and polluters only, the emerging model emphasizes multi-actor participation and presents new possibilities in enhancing the cost-effectiveness and alleviating the compliance deficiency. We introduce the model through a case study of the Binhai textile industrial park. The features of the model include levering the subject of monitoring, allowing collaborative wastewater treatment, employing the administrative power of local governing bodies, and engaging multiple local actors. To explain its formation, we adopt event sequence analysis, and point out the important role of local government as a facilitator and a regulator in the externally generated collaborative governance. We identified its success factors with pattern matching based on polycentric governance theories. We find the involvement of varied actors in the decision-making process of internal wastewater emission standard could lead to applicable policy making with high acceptance, which contributes to the environmental performance. To initiate the transition towards the new model, three conditions are required, including policy support from the government, the establishment of monitoring systems, and capacity building for actors. • Report of the emerging polycentric wastewater governance in industrial parks in China as a local governance innovation. • Analysis of the formation and effectiveness of the polycentric wastewater governance model. • Dissection of the real-world form and effects of the polycentric model with a case study. [ABSTRACT FROM AUTHOR]

• A novel hybrid prediction model based on pattern sequence-based matching method and extreme gradient boosting (XGBoost) is proposed for holiday short-term load forecasting. • The proposed methodology divides holiday short-term load forecasting problem into two independent prediction tasks (the predictions for proportional curve and daily extremum of electricity demand), which contributes to the law mining of holiday load characteristics. • Through the shape similarity measured by Euclidean distance, the forecasted proportional curve is obtained from pattern sequence-based matching method. • After holiday classification, daily extremum of electricity demand is obtained from XGBoost trained by samples with regard to holiday effect and other common effects. In short-term load forecast (STLF), forecasting holiday load is one of the most challenging problems. Aimed at this problem, a hybrid prediction model based on pattern sequence-based matching method and extreme gradient boosting (XGBoost) is presented. It divides holiday STLF problem into the predictions for proportional curve and daily extremum of electricity demand, which are relatively independent and relate to different factors. It is benefit for holiday STLF by task decomposing. Based on the shape similarity measured by Euclidean distance, the proportional curve is predicted by pattern sequence-based matching method. Daily extremum of electricity demand is predicted by XGBoost considering holiday classification. Finally, the predicted holiday load profile is synthesized from the above two prediction results with segment correction. The proposed methodology can analyze holiday load characteristics more effectively and get a higher prediction accuracy independent of sufficient data and expert experience. We evaluate our methodology with many algorithms on a real data set of one provincial capital city in eastern China. The results of case studies show that the proposed methodology gives much better forecasting accuracy with an average error 2.98% in holidays. [ABSTRACT FROM AUTHOR]