中国劳动关系学院

This study systematically investigates the explosion characteristics of over 40 coal samples with varying metamorphic degrees using a standardized 20-liter spherical vessel. Equivalence ratios derived from ultimate analysis were calculated, while proximate analysis and microstructural characterization techniques were integrated to evaluate macro-micro parameter changes pre-and post-explosion. Results demonstrate that Pmax and (dP/dt)max reach peak values at a & Fcy; of exactly 1, with medium-low rank coals exhibiting higher magnitudes. Delta Vad shows positive correlation with explosion intensity, whereas Delta FCad demonstrates negative correlation. Carbon and oxygen contents in ultimate analysis both correlates significantly with explosion intensity. Post-explosion analyses reveal oxygen consumption and CO2 concentration positively correlating with Pmax, while CO/CO2 ratios show inverse correlation. Microstructural evidence indicates substantial consumption of functional groups and increased disorder in low-rank coals after explosion, contrasted by enhanced blast resistance in high-rank coals due to their ordered aromatic structures. These findings provide theoretical foundations for coal mine safety protocols and suppression technology development.

Pea protein, characterized by high nutritional value and a rich amino acid profile, constitutes a promising source for the development of salty peptides. In this study, hydrolysates of pea protein were fractionated by ultrafiltration and gel permeation chromatography, and peptides were identified using LC-MS/MS. Ten peptides were selected based on docking energy, frequency of occurrence of saltiness-related amino acid sequences, and hydrophobic amino acid content. Sensory evaluation analysis further showed that FDDKVR exhibited the highest saltiness intensity (4.25) and RSDPQNPF increased the saltiness of a 3 mg/mL NaCl solution by 136%. Molecular docking analyses indicated that hydrogen bonding was the primary interaction between salty peptides and TMC4 receptor, primarily involving arginine and alanine residues. 3D-QSAR model was established to elucidate the structure-activity relationship of peptides. Molecular dynamics simulations further elucidate the hydrogen bond interactions. These findings provide insights into the development of salt-reducing formulations and the application of pea protein.

Climate physical risk exerts complex nonlinear effects on agricultural carbon footprint pressure (ACFP), yet the underlying cost and innovation mechanisms remain insufficiently examined. Using balanced panel data from 30 Chinese provinces over the period 2000-2023, this study empirically investigates the nonlinear impact of the Climate Physical Risk Index (CPRI) on ACFP. The findings reveal a statistically significant inverted U-shaped relationship, with a turning point at CPRI = 50.15307. Below this threshold, climate physical risk intensifies ACFP, reflecting a cost effect; above it, risk mitigates ACFP, indicating an innovation effect. Mechanism analysis shows that on the left side of the inflection point, green finance moderates the cost-induced increase in ACFP, whereas on the right side, digital inclusive finance reduces ACFP through a chain mediation pathway involving agricultural digital innovation. Moreover, the inverted U-shaped relationship is more pronounced in provinces northwest of the Hu Line and under high climate policy uncertainty. The study concludes by proposing targeted policy measures, including the deployment of differentiated financial instruments and the incorporation of ACFP into local performance evaluation systems, to promote a coordinated governance framework for pollution reduction, carbon mitigation, and climate adaptation in agriculture.

To address the limitations of the Artificial Rabbits Optimization (ARO) algorithm in solving complex high-dimensional and multimodal optimization problems-such as insufficient convergence guidance, weak balance between exploration and exploitation, and lack of adaptive search control-this paper proposes a novel dual-strategy enhanced ARO algorithm, termed DS-ARO. The proposed method integrates three complementary mechanisms: (1) a convergence-diversity balanced mutation strategy based on current-to-pbest/1 to simultaneously enhance convergence pressure and population diversity; (2) an adaptive elite-guided search mechanism to improve the reliability of search directions and convergence stability through elite learning and adaptive perturbation control; and (3) a success-rate-based strategy selection mechanism to dynamically adjust the usage probability of different search strategies according to real-time optimization performance. To comprehensively evaluate the effectiveness of the proposed algorithm, extensive experiments are conducted on the CEC2017 (30-dimensional) and CEC2022 (10- and 20-dimensional) benchmark test suites, and compared with several state-of-the-art metaheuristic algorithms. Statistical analyses, including the Friedman test and Wilcoxon signed-rank test, are further employed to verify the significance of performance improvements. In addition, ablation studies are performed to quantify the contribution of each proposed strategy. The experimental results demonstrate that DS-ARO consistently achieves superior optimization accuracy, faster convergence speed, and stronger robustness compared with the competing algorithms. Furthermore, the proposed DS-ARO is applied to the hyperparameter optimization of the K-Nearest Neighbors (KNN) model for corporate bankruptcy prediction. Experimental results on the Wieslaw financial dataset show that the DS-ARO-KNN model outperforms traditional KNN and several mainstream machine learning models, achieving higher accuracy, precision, recall, and F1-score, while effectively reducing misclassification risks.