Iso-nitrogenous and iso-lipidic diets containing 0%, 3%, 6%, 9%, and 12% hydrolyzed porcine mucosa (namely, HPM0, HPM3, HPM6, HPM9, and HPM12) were prepared to evaluate their effects on the growth performance, muscle nutrition composition, texture property, and gene expression related to muscle growth of hybrid groupers ( Epinephelus fuscoguttatus ♀ × Epinephelus lanceolatus ♂). Groupers were fed to apparent satiation at 08:00 and 16:00 every day for a total of 56 days. It was found that the weight gain percentage in the HPM0, HPM3, and HPM6 groups did not differ ( P > 0.05). The cooking loss and drip loss of the dorsal muscle in the HPM3 group were lower than those in the HPM6 and HPM9 groups ( P < 0.05). The hardness and chewiness of the dorsal muscle in the HPM3 group were higher than those in the HPM0, HPM9, and HPM12 groups ( P < 0.05). The gumminess in the HPM3 group was higher than that in the HPM9 and HPM12 groups ( P < 0.05). The total essential amino acid content of the dorsal muscle in the HPM12 group was higher than that in the HPM0 group ( P < 0.05). The contents of total n-3 polyunsaturated fatty acid and total n-3 highly unsaturated fatty acid, as well as the ratio of n-3/n-6 polyunsaturated fatty acid in the dorsal muscle was higher in the HPM0 group than in all other groups ( P < 0.05). The relative expressions of gene myogenic factor 5, myocyte enhancer factor 2c, myocyte enhancer factor 2a, myosin heavy chain, transforming growth factor-beta 1 ( TGF - β1 ), and follistatin ( FST ) were the highest in the dorsal muscle of the HPM3 group. The results indicated that the growth performance of hybrid grouper fed a diet with 6% HPM and 27% fish meal was as good as that of the HPM0 group. When fish ingested a diet containing 3% HPM, the expression of genes TGF-β1 and FST involved in muscle growth were upregulated, and then the muscle quality related to hardness and chewiness were improved. An appropriate amount of HPM could be better used in grouper feed.

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The self-activated phosphors without any luminescent dopants, usually displaying excellent optical properties, such as high oscillator strength, large stokes shift and strong luminescence efficiency, have been widely investigated by researchers...

In an open-pit mine in Xinjiang, part of the stripped area is covered by burnt rock. Due to the low strength and fragility of burnt rock, dust is more easily generated during blasting. Taking the mining area as the research background, the mechanical property parameters of burnt rock were tested, and the blasting parameter design of on-site operation was understood. The blasting numerical simulation of burnt rock step was carried out by using a numerical simulation software (LS-DYNA). From the angle of stress on rock, the stress cloud and stress curve of numerical simulation are analyzed, and it is concluded that the fundamental reason for the large dust production in blasting operation is that the burnt rock is crushed excessively after the action of explosion wave, and the explosive energy is too large, which is converted into kinetic energy to drive the dust to escape. In order to improve the utilization rate of explosives and reduce the output of blasting dust, the original blasting parameters were optimized as 8-m hole spacing, 6.5-m row spacing, 0.21-kg/m³ unit explosive consumption, 1-m interval charge, and 55-ms short-delay blasting through numerical simulation and orthogonal experiment. In the mining area, the measures of dustproof and dust reduction by blasting protection blanket and dust absorption cotton are adopted. Combined with the optimized blasting parameters, the field test proves that the dust removal efficiency is up to 82.4%.

Aiming at the complex nonlinear relationship among factors affecting blasting fragmentation, the input weight and hidden layer threshold of ELM (extreme learning machine) were optimized by gray wolf optimizer (GWO) and the prediction model of GWO-ELM blasting fragmentation was established. Taking No. 2 open-pit coal mine of Dananhu as an example, seven factors including the rock tensile strength, compressive strength, hole spacing, row spacing, minimum resistance line, super depth, and specific charge are selected as the input factors of the prediction model. The average size of blasting fragmentation X50 is selected as the output factor of the prediction model and compared with the results of PSO-ELM and ELM. The results show that MAPE of GWO-ELM, PSO-ELM, and ELM are 1.78%, 5.40%, and 10.90%, respectively; their RMSE are 0.007, 0.022, and 0.045, respectively. The ELM model optimized by the gray wolf optimizer is more accurate and has stronger data fitting ability than PSO-ELM and ELM models, and the prediction accuracy of GWO-ELM is much higher than that of PSO-ELM and ELM.