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Vegetation restoration is an effective measure to cope with global climate change and promote soil carbon sequestration. However, during vegetation restoration, the turnover and properties of carbon within various aggregates change. The effects of plant source carbon input on surface soil and subsurface soil may be different. Thus, the characteristics of carbon components in aggregates are affected. Therefore, the research object of this study is the Robinia pseudoacacia forest located in 16–47a of the Loess Plateau, and compared with farmland. The change characteristics of organic carbon functional groups in 0–20 cm, 20–40 cm, and 40–60 cm soil layers were analyzed by Fourier near infrared spectroscopy, and the relationship between the chemical structure of organic carbon and the content of organic carbon components in soil aggregates was clarified, and the mechanism affecting the distribution of organic carbon components in soil aggregates was revealed in the process of vegetation restoration. The results show the following: (1) The stability of surface aggregates is sensitive, while that of deep aggregates is weak. Vegetation restoration increased the surface soil organic carbon content by 1.97~3.78 g·kg−1. (2) After vegetation restoration, the relative contents of polysaccharide functional groups in >0.25 mm aggregates were significantly reduced, while the relative contents of aromatic and aliphatic functional groups of organic carbon were significantly increased. The opposite is true for aggregates smaller than 0.25 mm. (3) With the increase in soil depth, the effect of litter on organic carbon gradually decreased, while the effect of root input on the accumulation of inert carbon in deep soil was more lasting. [ABSTRACT FROM AUTHOR]

Cereal + legume rotation is an integrated system that facilitates soil fertility and sustainable agricultural production. However, research on the management compatibility affecting soil physico-chemical properties yields overall agro-ecosystem sustainability, but profitability is lacking, especially under straw retention and potential reductions in fertilizer application. An 11-year field experiment investigated three treatments: no straw retention + traditional mineral fertilization (TNS), straw retention + traditional mineral fertilization (TS), and straw retention + reduced mineral fertilization (DS). Compared with TNS, TS significantly improved soil physico-chemical properties, including macro-aggregates (R > 0.25 mm), porosity, field water capacity (FWC), soil organic carbon (SOC) storage, total nitrogen storage, microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) by 17.3%, 3.2%, 13.0%, 5.5%, 3.2%, 15.5%, and 13.8%, respectively. TS also significantly increased total (wheat + soybean) yields (TYs), economic profits, and emergy sustainability index (ESI) by 15.8%, 25.0%, 3.7 times that of TNS, respectively. Surprisingly, compared with TS, DS further significantly improved R > 0.25 mm, porosity, FWC, SOC storage, MBC, MBN, TY, economic profits, and ESI by 11.4%, 1.5%, 6.1%, 3.0%, 10.6%, 7.2%, 5.7%, 11.1%, and 36.5%, respectively. Overall, retaining straw with reduced fertilization enhances soil properties, yields, and emergy sustainability in wheat–soybean rotation systems. [ABSTRACT FROM AUTHOR]

In deep-sea mixed transmission mining systems, the mixed transmission hard pipe plays an important role in mineral transport. To speed up the process of connecting the hard pipe, a quick disassembly and assembly system was designed. By model analysis, the key focus of the rapid disassembly and assembly system was the clamp connector. On the basis of vibration theory and finite-element simulations, the connector underwent static and vibration analysis, and the fatigue life was predicted. Finally, laboratory and sea test tests were conducted on the rapid disassembly and assembly system. The quick disassembly and assembly system can realise quick disassembly and assembly under the target working conditions and maintained smooth operation during two 24-h cycle tests. [ABSTRACT FROM AUTHOR]

The appearance of tea leaves is the most intuitive factor in assessing their quality and is an important indicator of the price. The study utilizes machine vision and hyperspectral image technology to develop a rapid method for assessing the appearance quality of seven grades of Dianhong Congou black tea samples based on shape, color, texture, and feature fusion. Using the shape metrics of length, width, perimeter, narrowness, area, and rectangularity, 88 texture values, and nine color values as features, the effectiveness of intelligent recognition algorithms such as support vector machine, random forest, and least squares support vector machine (LSSVM) in classifying single features (shape, color, and texture) and fused data models of the categories of Dianhong tea samples was explored. The results showed that the accuracy of the feature fusion-based appearance quality evaluation models was all higher than the modeling results of the corresponding single features, and the accuracy of the LSSVM model based on the fused data was 100%. This study showed that feature data fusion reflects the external information of tea leaves more comprehensively and that intelligent classification algorithms combined with image analysis techniques were an effective strategy for the identification of regional black tea. [ABSTRACT FROM AUTHOR]