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Soil organic carbon (C) and aggregates are the important components of soil fertility and the foundation of sustainable agriculture. The storage and protection of SOC in aggregates is widely regarded as the material basis of soil organic C accumulation. However, current understanding of soil aggregate and its associated organic C is insufficient to elucidate the regulation mechanism of soil organic C. A nine-year field experiment including chemical fertilizer (FR) and organic manure (OM) treatments was set up in the eastern plain of Funiu Mountain, central China. Using chemical analysis, physical sieving as well as nuclear magnetic resonance (NMR) methods, we mainly probed into the response of soil organic C concentration and composition, and C functional groups, water-stable aggregates to different treatments. Furthermore, scanning electronic microscopy (SEM) and partial least square structural equation modelling (PLS-SEM) was conducted to characterise the different size aggregates and to analyse the mechanism of soil organic C accumulation and stabilisation at aggregate scales. After nine years of farming, OM treatment substantially increased soil organic C content (by 3.77 g kg−1) and significantly enhanced the formation of macro-aggregates (> 250 μm), while FR had no significant influence on soil organic C. At the aggregate scale, the amounts of soil organic C, C physical fractions (particulate and mineral-associated organic C), total nitrogen and microbial biomass carbon associated in macro-aggregates (> 250 μm) were obviously higher than that in micro-aggregates and silt + clay fraction, and OM treatment greatly increased the accumulation of soil organic C and its components in macro-aggregates. Moreover, microbial biomass carbon (MBC) amounts in aggregates were remarkably increased (27–116%) by the application of OM. And MBC had a positively effect on the physical fractions of SOC but not on the C chemical structure within aggregates. The present study indicated that soil organic C accumulation mainly rely on macro-aggregates (> 250 μm). Intra–particulate organic carbon (POC) and mineral-associated organic carbon (MOC) within macro-aggregates played an important role in soil organic C accumulation. Meanwhile, soil microbes were a driving force for the accumulation of soil organic C physical fractions (POC and MOC). We concluded that OM treatment accelerated the synergistic process between organic C sequestration and soil aggregation, and showed great potential to increase soil organic C accumulation. [ABSTRACT FROM AUTHOR]

• The application of reduced tillage plus organic manure promoted soil aggregation. • SOC and its active fractions were preferentially fixed in macroaggregates. • Reduced tillage plus organic manure improved the quantity and quality of SOC. Studying the impact of tillage and fertilization on soil organic carbon (SOC) content and aggregate is essential to define better strategies for improving soil structure and SOC sequestration. A field experiment was conducted for 9 years to investigate effects of different tillage and fertilization management practices on SOC and its active fractions [labile organic carbon (LOC), dissolved organic carbon (DOC) and microbial biomass carbon (MBC)] within the bulk soil and in soil aggregates under rice (Oryza sativa L.)–wheat (Triticum aestivum L.)-based cropping systems. Five treatments were included: conventional tillage with no fertilizer (CT), conventional tillage with chemical fertilizer (CTFR), reduced tillage with chemical fertilizer (RTFR), conventional tillage with organic manure (CTOM), and reduced tillage with organic manure (RTOM). The results showed that the organic manured plots (CTOM and RTOM) significantly increased the concentrations of SOC and active carbon fractions (LOC, DOC and MBC) within the bulk soil and soil macroaggregates, and increased the mass proportion of macroaggregates (>250 μm) compared with chemical fertilizer treatments (CTFR and RTFR) or CT. The highest SOC concentration and macroaggregates (>250 μm) were observed in CTOM. The increased SOC, especially active carbon fractions, was mainly concentrated in soil macroaggregates (>250 μm). Soil aggregates were dominated by macroaggregates (>250 μm), which had higher MBC and lower PAOC (the percentage of slow plus passive carbon fractions) after 9 years of RTOM treatment than after CT or CRFR treatment. The results indicated that RTOM played a positive role in promoting the formation of the soil macroaggregate structure. By studying the responses of SOC and water-stable aggregate contents to different tillage and fertilization managements, the continual application of organic manure may be feasible and the periodic organic manure addition may be combined with reduced tillage practice to produce an integrated soil management strategy for the paddy–upland rotation region in Middle China. [ABSTRACT FROM AUTHOR]

In recent decades, glaciers in the southeastern Tibetan Plateau (SETP) have been rapidly melting and showing a large scale of glacier mass loss. Due to the lack of large-scale, high-resolution, and high-precision observations, knowledge on the spatial distribution of the glacier mass balance and the response to climate change is limited in this region. We propose a TanDEM-X bi-static InSAR (Interferometric Synthetic Aperture Radar) algorithm with a non-local mean filter method and difference strategy, to improve the precision of glacier surface elevation change detection. Moreover, we improved the glacier mass balance estimation algorithm with a correction method for multi-source system errors and an uncertainty evaluation method based on error propagation theory to reduce the uncertainty of estimations. We used 13 pairs of TanDEM-X bi-static InSAR images to obtain the glacier mass balance data for the entire SETP. The total area of glaciers monitored was 5821 km2 and the total number of glaciers monitored was 2321; the glacier surface elevation change rate was −0.505 ± 0.005 m/yr, and the glacier mass balance estimation was −454.5 ± 13.1 mm w.eq. during 2000–2014. Additionally, we analyzed the spatial distribution of the glacier mass balance within the SETP using the sub-watershed analysis method. The results showed that the mass loss rate had a decreasing trend from the southeast to the northwest. Furthermore, the temperature change and the glacier mass loss rate showed a positive correlation from the southeast to the northwest in this region. This study greatly advances our understanding of the regularities of glacier dynamics in this region, and can provide scientific support for major national goals such as the rational utilization of surrounding water resources and construction of important transportation projects. [ABSTRACT FROM AUTHOR]

Credit scoring systems have seen revolutionary development in recent decades, with many classification algorithms being proposed. However, with the increase in the data volume, the performance of traditional algorithms tends to encounter bottlenecks. Although deep learning methods have advantages in handling big data, they are not commonly applied in credit scoring. As one of the most frequently used methods in deep learning, convolutional neural network (CNN) use convolutional kernels as feature extraction tools and has been very successful in tasks related to images or text. This is because image and text data naturally have a structural characteristic called spatial local correlation, which means that the pixels or tokens covered by the same convolutional kernel are highly correlated, and they can be jointly processed to extract meaningful feature representations. However, the tabular data used for credit scoring do not naturally have such a characteristic. The main contribution of this paper is to propose a novel end-to-end soft reordering one-dimensional CNN (SR-1D-CNN), which can adaptively reorganize the original tabular data and make them more conducive to CNN learning. Several real-world credit scoring datasets of different sizes are used for a comprehensive comparison with traditional machine learning classifiers and other deep learning methods. The experimental results demonstrate that the soft reordering mechanism can effectively improve the classification effect of the CNN for tabular data. With the increase in the data scale, the proposed approach obtains superior results to those of other benchmark models. [ABSTRACT FROM AUTHOR]

Annual mass balance is an important reflection of glacier status that is also very sensitive to climate fluctuations. However, there is no effective and universal albedo-based method for the reconstruction of annual mass balance due to the scarcity of field observations. Here, we present an improved albedo–mass balance (IAMB) method to estimate annual glacier surface mass balance series using remote sensing techniques. The averaged glacier-wide albedo derived with the MODImLab algorithm during the summer season provides an effective proxy of the annual mass change. Defined as the variation in the albedo as a function of elevation change, the altitude–albedo gradient ( ∂ z / ∂ α ) can be obtained from a glacier digital elevation model (DEM) and optical images. The Chhota Shigri glacier situated in the western Himalayas was selected to test and assess the accuracy of this method over the period from 2003 to 2014. Reconstructed annual mass budgets correlated well with those from the observed records, with an average difference and root mean square error (RMSE) of −0.75 mm w.e. a−1 and 274.91 mm w.e. a−1, respectively, indicating that the IAMB method holds promise for glacier mass change monitoring. This study provides a new technique for annual mass balance estimation that can be applied to glaciers with no or few mass balance observations. [ABSTRACT FROM AUTHOR]

Background and aims: This paper was primarily devoted to understand the interactions of soil aggregates, organic carbon (C) and carbon cycle enzymes in aggregates under different fertilization managements, aiming to identify the effects of organic and inorganic fertilizer amendments on soil organic C accumulation and the activities of carbon cycle enzymes within aggregates in Vertisol. Methods: A Vertisol soil following 4-year compost and inorganic fertilizer amendments, i.e. no fertilizer (CK), mineral fertilizer (FR) and 60% compost N plus 40% fertilizer N (FRM), was collected to identify the dynamics of organic C, enzymes activities and their associations with macroaggregation using aggregate fractionation techniques. Results: The organic C content in all FR and FRM treatments was 8.24–41.15% higher than that in CK. An increased amounts of carbon cycle enzymes in aggregates or 0–20 cm bulk soil were also observed in FRM plots. Compared to FR, FRM significantly strengthened the structural stability of macroaggregates and the intimate connection between enzyme activities and macroaggregates. Conclusions: As a recommended measure, supplementation with organic manure such as compost strengthened the process of mutual promotion between carbon cycle enzymes and macroaggregates, and the synergistic effect would be highly beneficial to soil organic C sequestration. [ABSTRACT FROM AUTHOR]

Corporate financial distress prediction research has been ongoing for more than half a century, during which many models have emerged, among which ensemble learning algorithms are the most accurate. Most of the state-of-the-art methods of recent years are based on gradient boosted decision trees. However, most of them do not consider using feature importance for feature selection, and a few of them use the feature importance method with bias, which may not reflect the true importance of features. To solve this problem, a heuristic algorithm based on permutation importance (PIMP) is proposed to modify the biased feature importance measure in this paper. This method ranks and filters the features used by machine learning models, which not only improves accuracy but also makes the results more interpretable. Based on financial data from 4,167 listed companies in China between 2001 and 2019, the experiment shows that compared with using the random forest (RF) wrapper method alone, the bias in feature importance is indeed corrected by combining the PIMP method. After the redundant features are removed, the performance of most machine learning models is improved. The PIMP method is a promising addition to the existing financial distress prediction methods. Moreover, compared with traditional statistical learning models and other machine learning models, the proposed PIMP-XGBoost offers higher prediction accuracy and clearer interpretation, making it suitable for commercial use. • The model Combines a corrected feature selection measure and XGBoost. • Permutation importance can correct the bias of feature importance. • The model is validated on Chinese listed companies datasets over five metrics. • The model is proved to outperform several benchmark techniques. • The feature importance and partial dependence plot enhance model interpretation. [ABSTRACT FROM AUTHOR]