Rice-crayfish farming system promote subsoil microbial residual carbon accumulation and stabilization by mediating microbial metabolism process.

Rice-crayfish farming systems (RCs) can help mitigate climate change by enhancing soil organic carbon (SOC) sequestration. However, the mechanisms that govern the responses of microbial residues carbon (MRC), a key component of SOC, in RCs are not fully understood. We conducted a 6-year field experiment comparing RCs and rice monoculture systems (RMs). Specifically, we explored how MRC formation and stabilization differ between the two systems and how those differences are linked to changes in the metabolic processes of microbes. Results showed that MRC levels in RCs were 5.2 % and 40.0 % higher in the topsoil and subsoil, respectively, compared to RMs, indicating depth-dependent effects. Notably, MRC accumulation and stabilization in RCs were promoted through a cascade of processes of dissolved organic carbon (DOC) accessibility-microbial metabolism-mineral protection. In addition, the mechanism of MRC accumulation in subsoil differed between the two systems. Specifically, RMs improved accessibility of DOC by reducing humification and aromaticity of subsoil DOC, which helped microbes access to resources at lower cost. This decreased the respiration rate of microbes, thereby increasing microbial carbon pump (MCP) efficiency and thus promoting MRC accumulation. By contrast, the crayfish in RCs facilitated carbon exchange between topsoil and subsoil through their burrowing behaviors. This increased carbon allocation for microbial metabolism in the subsoil, supporting a larger microbial population and thus enhancing the MCP capacity, while reducing MRC re-decomposition via enhanced mineral protection, further increasing subsoil MRC accumulation. That is, MRC accumulation in the subsoil of RCs was predominantly driven by microbial population numbers (MCP capacity) whereas that of RMs was mostly driven by microbial anabolic efficacy (MCP efficiency). Our findings reveal a key mechanism by which RCs promoted soil MRC accumulation and stabilization, highlighting the potential role of DOC accessibility-microbial metabolism-mineral protection pathway in regulating MRC accumulation and stabilization.

Intelligent Fault Diagnosis of Rolling Bearings Based on a Complete Frequency Range Feature Extraction and Combined Feature Selection Methodology.

The utilization of multiscale entropy methods to characterize vibration signals has proven to be promising in intelligent diagnosis of mechanical equipment. However, in the current multiscale entropy methods, only the information in the low-frequency range is utilized and the information in the high-frequency range is discarded. In order to take full advantage of the information, in this paper, a fault feature extraction method utilizing the bidirectional composite coarse-graining process with fuzzy dispersion entropy is proposed. To avoid the redundancy of the full frequency range feature information, the Random Forest algorithm combined with the Maximum Relevance Minimum Redundancy algorithm is applied to feature selection. Together with the K-nearest neighbor classifier, a rolling bearing intelligent diagnosis framework is constructed. The effectiveness of the proposed framework is evaluated by a numerical simulation and two experimental examples. The validation results demonstrate that the extracted features by the proposed method are highly sensitive to the bearing health conditions compared with hierarchical fuzzy dispersion entropy, composite multiscale fuzzy dispersion entropy, multiscale fuzzy dispersion entropy, multiscale dispersion entropy, multiscale permutation entropy, and multiscale sample entropy. In addition, the proposed method is able to identify the fault categories and health states of rolling bearings simultaneously. The proposed damage detection methodology provides a new and better framework for intelligent fault diagnosis of rolling bearings in rotating machinery.

Genome-Wide Association Study of Xian Rice Grain Shape and Weight in Different Environments.

Drought is one of the key environmental factors affecting the growth and yield potential of rice. Grain shape, on the other hand, is an important factor determining the appearance, quality, and yield of rice grains. Here, we re-sequenced 275 Xian accessions and then conducted a genome-wide association study (GWAS) on six agronomic traits with the 404,411 single nucleotide polymorphisms (SNPs) derived by the best linear unbiased prediction (BLUP) for each trait. Under two years of drought stress (DS) and normal water (NW) treatments, a total of 16 QTLs associated with rice grain shape and grain weight were detected on chromosomes 1, 2, 3, 4, 5, 7, 8, 11, and 12. In addition, these QTLs were analyzed by haplotype analysis and functional annotation, and one clone (GSN1) and five new candidate genes were identified in the candidate interval. The findings provide important genetic information for the molecular improvement of grain shape and weight in rice.