Sources of methane (CH 4 ) become highly variable for countries undergoing a heightened period of development due to both human activity and climate change. An urgent need therefore exists to budget key sources of CH 4 , such as wetlands (rice paddies and natural wetlands) and lakes (including reservoirs and ponds), which are sensitive to these changes. For this study, references in relation to CH 4 emissions from rice paddies, natural wetlands, and lakes in China were first reviewed and then reestimated based on the review itself. Total emissions from the three CH 4 sources were 11. ing reservoirs and ponds). Plentiful water and warm conditions, as well as its large rice paddy area make rice paddies in southeastern China the greatest overall source of CH 4 , accounting for approximately 55% of total paddy emissions. Natural wetland estimates were slightly higher than the other estimates owing to the higher CH 4 emissions recorded within Qinghai-Tibetan Plateau peatlands. Total CH 4 emissions from lakes were estimated for the first time by this study, with three quarters from the littoral zone and one quarter from lake surfaces. Rice paddies, natural wetlands, and lakes are not constant sources of CH 4 , but decreasing ones influenced by anthropogenic activity and climate change. A new progress-based model used in conjunction with more observations through model-data fusion approach could help obtain better estimates and insights with regard to CH 4 emissions deriving from wetlands and lakes in China.

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Compared to the common selective laser sintering (SLS) manufacturing method, fused deposition modeling (FDM) seems to be an economical and efficient three-dimensional (3D) printing method for high temperature polymer materials in medical applications. In this work, a customized FDM system was developed for polyether-ether-ketone (PEEK) materials printing. The effects of printing speed, layer thickness, printing temperature and filling ratio on tensile properties were analyzed by the orthogonal test of four factors and three levels. Optimal tensile properties of the PEEK specimens were observed at a printing speed of 60 mm/s, layer thickness of 0.2 mm, temperature of 370 °C and filling ratio of 40%. Furthermore, the impact and bending tests were conducted under optimized conditions and the results demonstrated that the printed PEEK specimens have appropriate mechanical properties.

Forest productivity may be determined not only by biodiversity but also by environmental factors and stand structure attributes. However, the relative importance of these factors in determining productivity is still controversial for subtropical forests. Based on a large dataset from 600 permanent forest inventory plots across subtropical China, we examined the relationship between biodiversity and forest productivity and tested whether stand structural attributes (stand density in terms of trees per ha, age and tree size) and environmental factors (climate and site conditions) had larger effects on productivity. Furthermore, we quantified the relative importance of environmental factors, stand structure and diversity in determining forest productivity. Diversity, together with stand structure and site conditions, regulated the variability in forest productivity. The relationship between diversity and forest productivity did not vary along environmental gradients. Stand density and age were more important modulators of forest productivity than diversity. Synthesis. Diversity had significant and positive effects on productivity in species‐rich subtropical forests, but the effects of stand density and age were also important. Our work highlights that while biodiversity conservation is often important, the regulation of stand structure can be even more important to maintain high productivity in subtropical forests.

A brief summary of recent finite element simulation results for the friction stir welding process is presented. The focus of the present study is on the characterisation of material flow around the rotating tool pin. Material flow patterns predicted by finite element simulations are found to compare favourably with experimental observations. The simulation results also reveal that material particles tend to pass and move behind the rotating pin from the trailing side, rather than both sides, of the pin. Possible variations in the material flow pattern due to variations in process parameters are discussed.

Plants interact to the seasonality of their environments, and changes in plant phenology have long been regarded as sensitive indicators of climatic change. Plant phenology modeling has been shown to be the simplest and most useful tool to assess phenol–climate shifts. Temperature, solar radiation, and water availability are assumed to be the key factors that control plant phenology. Statistical, mechanistic, and theoretical approaches have often been used for the parameterization of plant phenology models. The statistical approaches correlate the timing of phenological events to environmental factors or heat unit accumulations. The approaches have the simplified calculation procedures, correct phenological mechanism assumptions, but limited applications and predictive abilities. The mechanistic approaches describe plant phenology with the known or assumed “cause–effect relationships” between biological processes and key driving variables. The mechanistic approaches have the improved parameter processes, realistic assumptions, broad applications, and effective predictions. The theoretical approaches assume cost–benefit tradeoff strategies in trees. These methods are capable of capturing and quantifying the potential impacts and consequences of global climate change and human activity. However, certain limitations still exist related to our understanding of phenological mechanisms in relation to (1) interactions between plants and their specific climates, (2) the integration of both field observational and remote sensing data with plant phenology models across taxa and ecosystem type, (3) amplitude clarification of scale-related sensitivity to global climate change, and (4) improvements in parameterization processes and the overall reduction of modeling uncertainties to forecast impacts of future climate change on plant phenological dynamics. To improve our capacity in the prediction of the amplitude of plant phenological responses with regard to both structural and functional sensitivity to future global climate change, it is important to refine modeling methodologies by applying long-term and large-scale observational data. It is equally important to consider other less used but critical factors (such as heredity, pests, and anthropogenic drivers), apply advanced model parameterization and data assimilation techniques, incorporate process-based plant phenology models as a dynamic component into global vegetation dynamic models, and test plant phenology models against long-term ground observations and high-resolution satellite data across different spatial and temporal scales.

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