With the increasing demands on energy and environmental domains, not only high oil production but also its accurate quantification has become one of the most important topics in academia and industry. This paper initially proposes a comprehensive workflow in which an integrated hierarchy–correlation model is used to thoroughly evaluate the influences of all relevant reservoir parameters on the ultimate oil recovery for water-flooding oil reservoirs. More specifically, the analytic hierarchy process, grey relation, and entropy weight are combined through the multiplicative weighting method to quantitatively describe the production parameters. Accordingly, novel multivariable linear and nonlinear correlations are developed to predict the production performance and validated through comparisons with numerical reservoir simulations. Seven factors, including five reservoir parameters, namely, permeability and its contrast, porosity, thickness, and saturation, and two production parameters, namely, the injection–production ratio and the operating pressure, have been identified as the most influential factors on recovery performances and thus are employed in the proposed correlations to predict the ultimate oil recovery factor. The results obtained by the proposed method are quite close to the real-time simulation data, while the accuracy is retained. The numerical results show that the recovery factors of water-flooding oil reservoirs are about 33.5–59.5%, and the corresponding linear and nonlinear correlation coefficients are 0.903 and 0.789, respectively. In comparison with the numerical simulation, the approximation error by the linear correlation is about 0.5%, which is lower than that of nonlinear correlation, for example, 12.3%. This study will be beneficial to analyze the reservoir-related parameters and provide a useful tool for rapid production performance evaluation of the water-flooding production scenario.
Emissions of reactive nitrogen as ammonia (NH3) and nitrogen oxides (NO x ), together with sulfur dioxide (SO2), contribute to formation of secondary PM2.5 in the atmosphere. Satellite observations of atmospheric NH3, NO2, and SO2 levels since the 2000s provide valuable information to constrain the spatial and temporal variability of their emissions. Here we present a bottom-up Chinese NH3 emission inventory combined with top-down estimates of Chinese NO x and SO2 emissions using ozone monitoring instrument satellite observations, aiming to quantify the interannual variations of reactive nitrogen emissions in China and their contributions to PM2.5 air pollution over 2005–2015. We find small interannual changes in the total Chinese anthropogenic NH3 emissions during 2005–2016 (12.0–13.3 Tg with over 85% from agricultural sources), but large interannual change in top-down Chinese NO x and SO2 emissions. Chinese NO x emissions peaked around 2011 and declined by 22% during 2011–2015, and Chinese SO2 emissions declined by 55% in 2015 relative to that in 2007. Using the GEOS-Chem chemical transport model simulations, we find that rising atmospheric NH3 levels in eastern China since 2011 as observed by infrared atmospheric sounding interferometer and atmospheric infrared sounder satellites are mainly driven by rapid reductions in SO2 emissions. The 2011–2015 Chinese NO x emission reductions have decreased regional annual mean PM2.5 by 2.3–3.8 μg m−3. Interannual PM2.5 changes due to NH3 emission changes are relatively small, but further control of agricultural NH3 emissions can be effective for PM2.5 pollution mitigation in eastern China.
This research sheds light on the link between social norms and economic development. It explores the determinants of inheriting the mother’s surname in China and its implications for children’s health status and education outcomes. It establishes that children whose mothers are younger, more educated, and from regions with a lower sex ratio are more likely to be named after their mother. Moreover, these children have superior health and education outcomes, reflecting predominantly the impact of women’s higher bargaining power on children’s human capital accumulation.
Abstract Aim Invertebrate-mediated dispersal has previously been proposed to promote angiosperm diversification and distribution. However, little is known about the specific impact of invertebrate-mediated dispersal on the biogeography and current distribution of plants. We aim to infer the influence of vespicochorous (hornet) and myrmecochorous (ant) dispersal on the historical biogeography of herbaceous monocot species. Location Southeast Asia, East Asia, Australia, North America. Taxon Family Stemonaceae. Method We sampled ca. 75% of the species diversity in Stemonaceae (28 out of 37 species), covering the entire distribution range of the family, to reconstruct the biogeographic history of this family. Using phylogenetic logistic regression analyses, we then tested the relationship between dispersal modes and geographic distributions. Results Stemonaceae originated on the Asian mainland during the late Cretaceous and then dispersed to North America, the western Malay Archipelago and eastern Malay Archipelago and Australia between the late Cretaceous and Pliocene. Geographical ranges of ant- versus hornet-dispersed Stemonaceae species are significantly different, with vespicochorous species having broader distribution ranges than myrmecochorous species. Main conclusions Invertebrate-mediated dispersal in Stemonaceae may promote narrow endemism and play an important role in shaping the current distribution of species. Most lineages dispersed by ants failed to cross biogeographic barriers and exhibit limited range expansion overland. Vespicochorous lineages were able to cross oceanic barriers and occupy larger continental areas and/or occur on oceanic islands.
<p id="p00015">Leaf is one of the important organs of plants that facilitates the exchange of water and air with the surrounding environment. The morphological variation of leaves directly affect the physiological and biochemical processes of plants, which also reflects the adaptive strategies of plants to obtain resources. By focusing on several leaf morphological traits, including leaf size, leaf shape, leaf margin (with or without teeth) and leaf type (i.e. single vs. compound leaf), here, we reviewed the relevant research progresses in this field. We summarized the ecological functions of leaf morphological traits, identified their geographical distribution patterns, and explored the underlying environmental drivers, potential ecological interactions, and their effects on ecosystem functioning. We found that the current studies exploring the distribution and determinants of leaf size and leaf margin states mainly focused on single or specific taxon in local regions. Studies have also explored the genetic mechanisms of leaf morphology development. Leaf traits trade off with other functional traits, and their spatial variation is driven by both temperature and water availability. Leaf morphological traits, especially leaf size, influence water and nutrient cycling, reflect the response of communities to climate change, and can be scaled up to predict ecosystem primary productivity. Further studies should pay attention to combine new approaches to obtain unbiased data with high coverage, to explore the long-term adaptive evolution of leaf morphology, and to generalize the scaling in leaf morphology and its effect on ecosystem functioning. Leaf provides an important perspective to understand how plants respond and adapt to environmental changes. Studying leaf morphological traits provides insight into species fitness, community dynamics and ecosystem functioning, and also improves our understanding of the research progresses made in related fields, including plant community ecology and functional biogeography.</p>
In this work, we develop a distributed least-square approximation (DLSA) method that is able to solve a large family of regression problems (e.g., linear regression, logistic regression, and Cox’s model) on a distributed system. By approximating the local objective function using a local quadratic form, we are able to obtain a combined estimator by taking a weighted average of local estimators. The resulting estimator is proved to be statistically as efficient as the global estimator. Moreover, it requires only one round of communication. We further conduct a shrinkage estimation based on the DLSA estimation using an adaptive Lasso approach. The solution can be easily obtained by using the LARS algorithm on the master node. It is theoretically shown that the resulting estimator possesses the oracle property and is selection consistent by using a newly designed distributed Bayesian information criterion. The finite sample performance and computational efficiency are further illustrated by an extensive numerical study and an airline dataset. The airline dataset is 52 GB in size. The entire methodology has been implemented in Python for a de-facto standard Spark system. The proposed DLSA algorithm on the Spark system takes 26 min to obtain a logistic regression estimator, which is more efficient and memory friendly than conventional methods. Supplementary materials for this article are available online.