Rapid and accurate multi-class fault diagnosis is essential for high-end equipment because different fault categories require different maintenance responses. This study aims to develop a lightweight and discriminative diagnostic framework that can identify multiple fault categories from non-stationary vibration signals while reducing redundant computation. High-frequency vibration signals provide direct condition information, but long sequences, noise, nonlinear dynamics, and non-stationary behavior make raw-signal classification unreliable. From an entropy-based information-processing perspective, the key issue is to separate informative fault modes from redundant fluctuations and enlarge inter-class distinctions in the probabilistic decision space. This study proposes Fastformer, an integrated framework for vibration-based fault identification. Empirical Mode Decomposition first converts each signal into Intrinsic Mode Functions to reduce modal mixing and preserve fault-related oscillatory components. The resulting components are processed by an encoder-oriented Q/K/V dot-product scoring mechanism, which constructs compact spatiotemporal embeddings without adopting a complete Transformer architecture. Validation-guided pruning removes low-contribution attention responses, while a Margin-Enhanced Fault Softmax classifier optimized with a cross-entropy-based objective strengthens category separation. By combining stable decomposition, lightweight attention scoring, pruning, and probabilistic margin learning, Fastformer achieves faster and more stable convergence. On the XJTU-SpurGear dataset, Fastformer obtains precision, recall, F1-score, and AUC values of 1.000. Additional validation on the HUST bearing dataset further shows that Fastformer achieves the best overall performance among the compared methods, with an AUC value of 0.9596.

High-Standard Farmland Construction (HSFC) is widely expected to support both food security and low-carbon agriculture in China, yet prevailing assessments treat agricultural carbon performance (ACP) as a single aggregate, obscuring the structural asymmetry between emission mitigation and carbon sequestration. This study examines whether HSFC simultaneously reduces agricultural emissions and enhances carbon sequestration. Using a city-level panel of the Yangtze River Economic Belt (2007–2022), this study treats HSFC as a quasi-experiment and decomposes ACP into carbon sequestration performance (CSP) and carbon mitigation performance (CMP). The results show that HSFC robustly improves overall ACP and CSP, while its effect on CMP is statistically insignificant. Further decomposition indicates that HSFC promotes ACP through both efficiency change and technological change, though these gains are concentrated primarily in the sequestration dimension. Mechanism analysis shows that farmland scale management improves both efficiency and technology adoption, whereas agricultural socialized services promote both channels with a particularly pronounced effect on technological progress; neither channel, however, delivers a strong mitigation effect. In addition, the CSP-enhancing effect is robust across mechanization levels and in major grain-producing areas, whereas it is statistically absent in the upstream reaches characterized by fragmented terrain, and no significant CMP effect emerges in any subgroup. These findings provide new evidence on the climate consequences of farmland policies and offer implications for better aligning food security with agricultural decarbonization.