Shangqiu Normal University

ABSTRACT Agricultural ammonia (NH 3 ) emissions impair nitrogen use efficiency and exacerbate environmental pollution. While most research has focused on suppressing NH 3 volatilisation through nitrogen source modification, the synergistic role of matching fertiliser types with crop genetic traits remains largely overlooked. Here, we examined the potential of combining controlled‐release urea (CRU) with maize varietal selection to mitigate NH 3 emissions in summer maize on the North China Plain, integrating data synthesis with a two‐year field experiment (2021–2022). Results indicated an average NH 3 volatilisation rate of 10.35%, strongly governed by nitrogen rate, fertiliser type and maize variety. Field trials evaluated four nitrogen treatments at 180 kg N ha −1 —N180U (100% conventional urea), N180C1 (one‐third CRU + two‐thirds conventional urea), N180C2 (two‐thirds CRU + one‐third conventional urea) and N180C (100% CRU)—applied to an early‐maturing (JNK728) and a late‐maturing (ZD958) variety. The results show that compared with other treatments, N180C1 and N180C2 increased the grain yield of JNK728 by 10%–17.4% and ZD958 by 5.5%–24.5% over two growing seasons. Relative to conventional urea (N180U), N180C1 reduced NH 3 emissions by 23.52%–23.56% and enhanced agricultural sustainability by 154.11% for JNK728, whereas N180C2 achieved reductions of 23.62%–27.83% in NH 3 emissions and a 162.63% improvement in sustainability for ZD958. The structural equation model reveals the underlying mechanism: This fertilisation strategy tailored for different crop varieties not only reduces the peak concentration of ammonium nitrogen in the soil but also promotes root growth, enhances nitrogen absorption efficiency and ultimately reduces ammonia volatilisation losses. Our findings establish that coupling CRU with variety‐specific management forms a potent two‐dimensional plant–nitrogen strategy, effectively curtailing NH 3 emissions while advancing the productivity and sustainability of maize systems.

Surface deformation from fault displacement threatens buried pipelines crossing fault zones. Previous studies often idealized fault displacement as a quasi-static load, neglecting dynamic rupture effects. This study investigates the dynamic fault displacement effects on buried High-Density Polyethylene (HDPE) pipelines, specifically for the case where the pipeline is perpendicular to the fault trace. A finite element model incorporating a visco-elastic artificial boundary was established and validated against shaking table tests. Dynamic fault dislocation was simulated via an equivalent load input method. Results show a strong correlation between the displacement pulse period and fault displacement magnitude. As displacement increases, the pipeline enters a plastic state, causing significant cross-sectional deformation and amplifying dynamic effects. Under combined short pulse periods and large dislocations, localized deformation is exacerbated by high-strain-rate material embrittlement and concentrated energy dissipation, impairing safety. Transient peak displacements during dynamic dislocation significantly increase plastic strain. Increasing pipeline wall thickness enhances resistance to dynamic pulses. Compared to soft clay, hard clay sites provide stronger constraints, amplifying the pulse period's influence. Sandy soil sites demonstrate superior resistance to the dynamic effects of fault displacement. The dip angle of the reverse fault has a significant influence on the pipeline's response under dynamic fault displacement loading.

Abstract Objective To compare the performance of 2.5D deep learning (DL) with multi-instance learning (MIL), 2D DL, and radiomics models in predicting axillary lymph node (ALN) metastasis in breast cancer (BC) patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods In this study, 732 patients from two independent institutions who underwent preoperative DCE-MRI were included. Based on the primary tumor region, we developed and compared four single-modality prediction models: a radiomics model, a 2D DL model, and two 2.5D DL-MIL models using different feature aggregation strategies. A stacking model was subsequently constructed by integrating the optimal single-modality models. The models’ performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Decision Curve Analysis. Results The stacking model achieved the highest predictive performance, with AUCs of 0.962 (95% CI: 0.946–0.977) in the training set, 0.885 (95% CI: 0.837–0.933) in the internal validation set, and 0.890 (95% CI: 0.840–0.939) in the external validation set. It demonstrated high specificity (1.000 and 0.968 in the internal and external validation sets, respectively) and provided a significant net clinical benefit. The 2.5D DL-MIL models significantly outperformed the 2D DL and radiomics models. Furthermore, the stacking model maintained robust performance across key clinical subgroups defined by age, tumor size, and BI-RADS category. Conclusion The 2.5D DL-MIL-based stacking model provides an accurate, non-invasive tool for preoperative prediction of ALN metastasis in BC. Its high specificity holds promise for reducing unnecessary sentinel lymph node biopsies, thereby aiding in personalized surgical planning.