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Global climate action is urgent, with forest carbon stock critical for mitigating climate change, yet vulnerable to its impacts. However, the long-term dynamics of climate-driven forest carbon stock have not fully been expressed. Here, we introduce the Forest Carbon Stock Accumulated by Single Tree growth (FAST) framework and constructed a counterfactual scenario to isolate and quantify the climate-driven changes in forest total carbon stock for 1901–2022. Results show that breakpoints in climate-driven forest carbon stock occurred post-1970 are observed over 62% of the study areas, with Europe experiencing the latest, followed by Asia, and North America the earliest. Furthermore, we observe a prevailing increasing trend in climate-driven forest carbon stock, especially in post-breakpoints period (from 53% to 68%), indicating that climate changes have alleviated constraints on forest carbon storage capacity in most areas. FAST can be utilized for historical, current and future forest carbon stock estimation, providing scientific support for sustainable forest management decisions.

The agricultural production space, as where and how much each agricultural product grows, plays a vital role in meeting the increasing and diverse food demands. Previous studies on agricultural production patterns have predominantly centered on individual or specific crop types, using methods such as remote sensing or statistical metrological analysis. In this study, we characterize the agricultural production space (APS) by bipartite network connecting agricultural products and provinces, to reveal the relatedness between diverse agricultural products and the spatiotemporal characteristic of provincial production capabilities in China. The results show that core products are cereal, pork, melon, and pome fruit; meanwhile the milk, grape, and fiber crop show an upward trend in centrality, which is in line with diet structure changes in China over the past decades. The little changes in community components and structures of agricultural products and provinces reveal that agricultural production patterns in China are relatively stable. Additionally, identified provincial communities closely resemble China’s agricultural natural zones. Furthermore, the observed growth in production capabilities in North and Northeast China implies their potential focus areas for future agricultural production. Despite the superior production capabilities of southern provinces, recent years have witnessed a notable decline, warranting special attentions. The findings provide a comprehensive perspective for understanding the complex relationship of agricultural products’ relatedness, production capabilities and production patterns, which serve as a reference for the agricultural spatial optimization and agricultural sustainable development.

Food security as a national strategy, closely links to China's economic progress, people's livelihoods and social harmony. The balance between feed grain security and meat security remains a key unresolved issue that significantly impacts China's integral food security goal. In this study, we first apply food security index to demonstrate the efficacy of the self-sufficiency rate as an indicator for evaluating the food security situation, and then evaluate meat security and feed grain security with self-sufficiency rate, as well as explore their production challenges and successful long-term development experiences based on related agricultural data. We find that the burgeoning meat consumption has made feed grains a “rigid demand”, among which pork and soybean are critical parts. Pork consumption accounts for over 65% of total meat consumption in 2020, but its self-sufficient rate is waning. Moreover, the self-sufficiency rate of soybean is as low as 0.15, and a great unit yield gap between China and the United States. However, the current production patterns have already reached their attainable potential in production efficiency due to the declining growth rate of unit yield, which also pose challenges for improving production efficiency and domestic supply of soybean. According to the projection by FAO-OECD, the growth rate of unit yield of soybean will remain stagnant from 2020 to 2030. Furthermore, the percentage of medium-scale and large-scale pig farms is remarkably low, accounting for only 0.578% and 0.013%, respectively. Further analysis indicates that large-scale production in soybean and pig industry offers notable advantages in improving production efficiency and reducing feed consumption. Finally, based on factors such as soybean sowing area, regions with high pig slaughter and scale farms, farm size and the pig regional development planning, we propose the integration of soybean-pig in large-scale production in selected possible provinces, which has the potential to improve the production efficiency of soybean and pig, to strike a balance between feed grain security and meat security. This study has practical significance in optimizing the production space of feed grains and livestock products, to improve production capability and ensure China's food security.

The thermos-structural materials with high strength and high temperature water-oxygen corrosion resistance are urgently needed in aero-space fields. SiC whisker reinforced SiC matrix (SiCw/SiC) composites are the ideal candidate materials for their excellent mechanical properties and thermostability. However, the poor resistance to water-oxygen corrosion ability of SiC matrix limits their further application. In this work, a novel SiCw/SiC–Si–Y composites were first prepared by infiltrating silicon-yttrium (Si–Y) alloy into SiCw/C preform via reactive melt infiltration method. The as-obtained composites not only show the excellent mechanical properties (σF = 363 MPa, KⅠC = 9.14 MPa m1/2), and more importantly after water-oxygen corrosion of 20 h at such temperature as high as 1500 °C, the strength and toughness retention of the composites can reach 106.1% and 103.6%, respectively. The SiCw/SiC–Si–Y composites developed in this paper have a great potential as a new high-performance thermos-structural material for aero-space applications.

The fabrication of silicon nitride (Si3N4) fiber-reinforced boron nitride (BN) and Si3N4 matrix (Si3N4f/BN/Si3N4) composites with BN interphase through precursor infiltration and pyrolysis (PIP) process is reported. The effects of heating rate during PIP process on mechanical and dielectric properties of composites are analyzed. When the heating rate is 5 °C/min, the overall performance of Si3N4f/BN/Si3N4 composites is optimal, and the flexural strength and fracture toughness attain 138 MPa and 5.7 MPa m1/2, and the dielectric constant and dielectric loss are 3.90 and 0.008, respectively. The composites achieve a synergy combining structural with functional properties. The influence mechanism of the heating rate during PIP process on the Si3N4f/BN/Si3N4 composites disclose the following. First, the heating rate makes an effect on the carbon content in the Si3N4 matrix, and consequently the dielectric properties of the composites are changed. Second, at 8 °C/min, the modulus of Si3N4 matrix is the lowest due to the existence of nanopores. At 2 °C/min, the strong axial tensile stress results in severe cracking of the Si3N4 matrix, so the matrix modulus decrease. Third, the heating rate strongly affects the radial stress state at the interphase, which affects the interfacial shear strength of the composites. The matrix modulus and interfacial shear strength can be optimized at 5 °C/min to improve the mechanical properties of the composites.

The prediction of binding affinities between target proteins and small molecule drugs is essential for speeding up the drug research and design process. To attain precise and effective affinity prediction, computer-aided methods are employed in the drug discovery pipeline. In the last decade, a variety of computational methods has been developed, with deep learning being the most commonly used approach. We have gathered several deep learning methods and classified them into convolutional neural networks (CNNs), graph neural networks (GNNs), and Transformers for analysis and discussion. Initially, we conducted an analysis of the different deep learning methods, focusing on their feature construction and model architecture. We discussed the advantages and disadvantages of each model. Subsequently, we conducted experiments using four deep learning methods on the PDBbind v.2016 core set. We evaluated their prediction capabilities in various affinity intervals and statistically and visually analyzed the samples of correct and incorrect predictions for each model. Through visual analysis, we attempted to combine the strengths of the four models to improve the Root Mean Square Error (RMSE) of predicted affinities by 1.6% (reducing the absolute value to 1.101) and the Pearson Correlation Coefficient (R) by 2.9% (increasing the absolute value to 0.894) compared to the current state-of-the-art method. Lastly, we discussed the challenges faced by current deep learning methods in affinity prediction and proposed potential solutions to address these issues.

Understanding the response of vegetation spring phenology to climatic factors in arid regions is crucial for projecting the land-climate interactions of arid ecosystems under climate change. The Xinjiang region, an important part of northern China with complex climatic features, was selected as a case study. We investigated the impact of temperature, precipitation, and solar radiation on the start of the vegetation growing season (SOS) using a long-term series of the normalized difference vegetation index, climate data from 1982 to 2014, and the partial least squares regression (PLS) method. The primary findings were following: (1) the regional scaling of the mean SOS ranged from 95 to 113 day of year (DOY) and showed distinct spatial heterogeneity for different vegetation types. The relative percentage of the area that exhibited advanced SOS (65.80%) was larger than that of the area that exhibited delayed SOS (34.20%), with a regional advancing tendency of 0.19 days per year during the study period. Different vegetation types showed an advancing SOS, which shifted to delaying after 2005. (2) Temperature significantly affected the SOS for various vegetation types. Generally, the warming spring temperatures led to advanced SOS, whereas reduced warming in spring after 2005 reversed the SOS trends. The higher solar radiation occurring from late winter to spring also contributed to the advance in the SOS. Conversely, the increasing precipitation during this period caused a delay in the SOS. These results can provide a useful reference for improving the vegetation phenology models of arid regions.