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We employed density functional theory to investigate Co-N4/Gra single-atom catalysts, focusing on the modification of axial ligand (X=-F, -CN and -C3H4N2) and examined the influence of ligand field and electric potential on eNORR. Our results demonstrate that Co-N4[X]/Gra exhibit favorable thermodynamic adsorption (Δ G *NO < 0) and remarkable activation activity toward NO molecules. Co-N4/Gra demonstrates a limiting potential of −0.13 V with the potential-determining step (PDS) of *NH3 → * + NH3(g). Notably, under the introduction of ligands, Co-N4[F]/Gra achieves the most favorable overpotential of −0.07 V, superior to most of NORR electrocatalysts, and meanwhile shows an exceptional selectivity of 99.99% for eNORR compared to competitive hydrogen evolution reaction. Under the solvent environment, Co-N4/Gra features the triggering potentials of −0.33 V for a pH of 1, −0.71 V for a pH of 7, and −1.10 V vs RHE for a pH of 13, respectively. Additionally, the potential reduces the desorption energy and adsorption energy of NH3, facilitating the accessibility of the NH3 desorption step (PDS) and promoting NO reduction. Thus, under realistic conditions, the Co ion's active site displays variable valence electrons and forms a dynamic interplay with the ligand field effect, rendering a crucial effect on the adsorption of reaction species during the eNORR. [ABSTRACT FROM AUTHOR]

The effects of different levels of fermented soybean residue on the growth performance, nutrient apparent digestibility, and meat quality of finishing pigs were investigated in the experiment, and the feasibility of fermented soybean residue as a substitute for some basic feed in finishing pigs was determined. A total of 160 healthy 'Duroc x Landrace x Yorkshire' finishing pigs with similar body weight were randomly divided into four groups with four replicates per group and 10 pigs per replicate. The finishing pigs in each group were fed diets containing 0 (control group), 2.5%, 5.0%, and 10.0% fermented soybean residue, respectively. The pre-feeding period was one week and the formal experiment period was 60 days. The results showed that the final weight and average daily gain of finishing pigs in the 5.0% fermented soybean residue group were higher than those in the control group (P<0.05), and the ratio of feed to gain was lower than that in the control group (P<0.05). Compared with the control group, the apparent digestibility of dry matter and crude protein of finishing pigs in the 5.0% fermented soybean residue group was significantly increased (P<0.05), and the apparent digestibility of crude fiber of fattening pigs in the 2.5%, 5.0%, and 10.0% fermented soybean residue groups was significantly increased (P<0.05). The live weight before slaughter, carcass weight and slaughter rate of finishing pigs in the 5.0% fermented soybean residue group were significantly higher than those in the control group (P<0.05), and the backfat thickness of finishing pigs in the 5.0% and 10.0% fermented soybean residue groups was significantly lower than that in the control group (P<0.05). Compared with the control group, the pH24 h value and meat color a* value of longissimus dorsi muscle in the 5.0% and 10.0% fermented soybean residue groups were significantly increased (P<0.05), the L* value was significantly decreased (P<0.05), and the drip loss of longissimus dorsi muscle in the 5.0% fermented soybean residue group was significantly decreased (P<0.05). There was no significant effect of fermented soybean residue on nutrient composition of longissimus dorsi muscle of finishing pigs (P>0.05). The study indicates that feeding proper amount of fermented soybean residue can improve the growth performance, nutrient apparent digestibility, slaughter performance and meat quality of finishing pigs. The optimal supplemental level of fermented soybean residue is 5.0%. [ABSTRACT FROM AUTHOR]

To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation (BP) neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based on the BP neural network. It was trained by Automatic Dependent Surveillance – Broadcast trajectory from Qingdao to Beijing and used to predict the flight trajectory at future moments. In this paper, the model is evaluated by the common measurement index such as maximum absolute error, mean absolute error and root mean square error. It also gives an analysis and comparison of the predicted over-point time, the predicted over-point altitude, the actual over-point time and the actual over-point altitude. The results indicate that the predicted 4D trajectory is close to the real flight data, and the time error at the crossing point is no more than 1 min and the altitude error at the crossing point is no more than 50 m, which is of high accuracy.