This study aimed to screen out the shrub species which can promote the seed germination and seedling growth of Amygdalus pedunculata Pall. and offer insight for ecological environment governance of the coal mines subsidence area in Mu Us Sandy Land, Yulin City of Shaanxi Province. The indoor bioassay method was used to study the aqueous leaf extracts from Amorpha fruticosa Linn., Hedysarum mongolicum Turez., Sabina vulgaris Ant., and Hippophae rhamnoides Linn. under different concentration gradients to examine seed germination, initial growth, and physiological and biochemical of two Amygdalus pedunculata varieties (YY-1 from Yuyang County (YY) and SM-6 from Shenmu County (SM), Shaanxi Province, China). The results showed that with aqueous leaf extracts concentrations at lower concentrations of 0.025 (T1) and 0.05 g·mL⁻¹ (T2) from A. fruticosa, H. mongolicum, and S. vulgaris significantly promoted seed germination and seedling growth of two A. pedunculata varieties. Moreover, H. rhamnoides aqueous leaf extracts had the strongest inhibitory effect on seed germination and seedling growth of A. pedunculata, and death occurred at concentrations of 0.15 (T4) and 0.20 g·mL⁻¹ (T5). The enzyme activity and chlorophyll content of the A. pedunculata leaves decreased with an increase in the aqueous leaf extracts concentration of the four shrubs; the change trend of malondialdehyde content was the opposite. Root activity of the A. pedunculata increased and then decreased. The H. mongolicum and S. vulgaris are the most suitable mixed tree species for YY-1, while H. mongolicum and A. fruticosa are the most suitable mixed tree species for SM-6 at a relatively low density with more security. The results provide a theoretical basis and technical support for the establishment of an artificial mixed forest of A. pedunculata in the coal mine subsidence area of Mu Us Sandy Land.

A greenhouse pot trial was conducted to investigate the effect of organic amendments combined with triple superphosphate on the bioavailability of heavy metals (HMs), Amorpha fruticosa growth and metal uptake from Pb-Zn mine tailings. Cattle manure compost (CMC), spent mushroom compost (SMC) and agricultural field soil (AFS) were applied to tailings at 5%, 10%, 20% and 30% w/w ratio, whereas sewage sludge (SS) and wood biochar (WB) were mixed at 2.5%, 5%, 10% and 20% w/w ratio. Triple superphosphate (TSP) was added to all the treatments at 4:1 (molar ratio). Amendments efficiently decreased DTPA-extracted Pb, Zn, Cd and Cu in treatments. Chlorophyll contents and shoot and root dry biomass significantly (p < 0.05) increased in the treatments of CMC (except T4 for chlorophyll b) and SMC, whereas treatments of SS (except T1 for chlorophyll a and b), WB and AFS (except T4 for chlorophyll a and b) did not show positive effects as compared to CK1. Bioconcentration factor (BCF) and translocation factor (TF) values in plant tissues were below 1 for most treatments. In amended treatments, soluble protein content increased, phenylalanine ammonialyase (PAL) and polyphenol oxidase (PPO) decreased, and catalase (CAT) activity showed varied results as compared to CK1 and CK2. Results suggested that A. fruticosa can be a potential metal phytostabilizer and use of CMC or SMC in combination with TSP are more effective than other combinations for the in situ stabilization of Pb-Zn mine tailings.

In the era of big data, high-performance rolling bearing fault diagnostic methods are of great significance to ensure manufacturing smoothly and avoid mass economic losses. A rolling bearing fault diagnostic CAR-SNN model based on Convolutional Block Attention Module (CBAM), Residual Network (ResNet), and Spiking Neural Networks (SNNs) is proposed based on deep learning (DL). The CAR-SNN model combines CBAM, ResNet, and SNNs to extract critical features while avoiding gradient vanishing or explosion phenomena. The ability of the CAR-SNN model to process spatio-temporal information is improved by using SNNs that incorporate spatio-temporal information simultaneously. The proposed model is used for the rolling bearing fault diagnosis, which datasets are from the experimental platforms of CWRU and Jiangnan University, with the highest diagnostic accuracy 99.995% and 99.18%, respectively. The experimental results validate the effectiveness and stability of the proposed model. The results of comparison experiments show that the CAR-SNN model has better fault diagnostic accuracy and stability than that of the CAR model and other comparison methods. The proposed data-driven rolling bearing fault diagnostic method based on the CAR-SNN model is easy to be implemented, and meeting the needs of high fault diagnostic accuracy in practical applications, and provides valuable input for fault diagnosis of rolling bearings.