ABSTRACT
The purpose of the present study was to investigate the effects of different levels of a Chinese herbal medicine formulation combined with JM113 (CHM-JM113) on growth performance, antioxidant capacity, organ index, and intestinal health of AA broilers. The AA broiler chicks were randomly allocated to 5 treatments as follows: a basic diet for the control group, the basic diet supplemented with 0.25% CHM-JM113, 0.5% CHM-JM113, 1% CHM-JM113 and 2% CHM-JM113 for the treatment group, respectively. The results showed that the addition of CHM-JM113 to the diet significantly reduced the mortality (p < 0.01) and improved the European Broiler Index (EBI) (p < 0.05), whereas it had no significance on growth performance of AA broilers (p > 0.05). Comparing the control group, 0.5 and 1% CHM-JM113 group significantly improved the organ index of liver, spleen and bursa (p < 0.05). In terms of intestinal morphology and structure, the addition of different levels of CHM-JM113 increased VH and VH/CD ratio, decreased CD in the small intestine compared to the control group, with 1 and 2% of the additive dose being more effective (p < 0.05). Chinese herbal medicine and probiotics as natural antioxidants also significantly increased the content of SOD in serum of 21-day-old broilers (p < 0.01), and significantly decreased the content of MDA in serum (p < 0.01). At 42 days of age, the addition of 1 and 2% CHM-JM113 significantly increased the content of SOD (p < 0.01) and significantly decreased the content of MDA in the organism (p < 0.01), accompanied by a significant increase in T-AOC and CAT content. In the study of the effect of CHM-JM113 on intestinal immunity, compared with the control group, we found that 1% or 2% CHM-JM113 had a better effect on the expression of occludin and claudin-1 in the intestinal segments of broilers (p < 0.05). For the expression of GATA-3, 0.5% CHM-JM113 may have a better effect (p < 0.05). CHM-JM113 may be used as an antibiotic alternative in broiler production.
ABSTRACT
Coal seam drilling is a simple, economical, and effective measure commonly used to prevent and control rock burst. Following rock burst, coal exhibits significant dynamic characteristics under high strain-rate loading. Our purpose was to determine the physical processes associated with impact damage to drilled coal rock, and its mitigation mechanism. An impact test was carried out on prefabricated borehole coal specimens, and the impulse signals of the incident and transmission rods were monitored. The crack initiation, expansion, and penetration of coal specimens were video-recorded to determine the mechanical properties, crack expansion, damage modes, fragmentation, and energy dissipation characteristics of coal specimens containing different boreholes. The dynamic compressive strength of the coal specimens was significantly weakened by boreholes under high strain-rate loading; the dynamic compressive strength and the dynamic modulus of elasticity of coal rock showed a decreasing trend, with increasing numbers of boreholes and a rising and decreasing trend with increasing borehole spacing; the number and spacing of boreholes appeared to be design parameters that could weaken coal-rock material under high strain-rate loading; during the loading of coal and rock, initial cracks appeared and expanded in the tensile stress zone of the borehole side, while secondary cracks, which appeared perpendicular to the main crack, expanded and connected, destroying the specimen. As the number of boreholes increased, the fractal dimension (D) and transmission energy decreased, while the reflection energy increased. As the borehole spacing was increased, D decreased while the reflective energy ratio decreased and increased, and the transmissive energy ratio increased and decreased. Drilling under high strain modifies the mechanical properties of impact damaged coal rock.
ABSTRACT
In pursuit of high imaging quality, optical sparse aperture systems must correct piston errors quickly within a small range. In this paper, we modified the existing deep-learning piston detection method for the Golay-6 array, by using a more powerful single convolutional neural network based on ResNet-34 for feature extraction; another fully connected layer was added, on the basis of this network, to obtain the best results. The Double-defocused Sharpness Metric (DSM) was selected first, as a feature vector to enhance the model performance; the average RMSE of the five sub-apertures for valid detection in our study was only 0.015λ (9 nm). This modified method has higher detecting precision, and requires fewer training datasets with less training time. Compared to the conventional approach, this technique is more suitable for the piston sensing of complex configurations.