IntroductionThe experiment was conducted to evaluate the effects of Ganoderma lingzhi culture (GLC) as a fermented feed on growth performance, serum biochemical profile, meat quality, and intestinal morphology and microbiota in Sanhuang broilers. In addition, the association between gut bacteria and metabolites was investigated via untargeted metabolomic analysis.MethodsA total of 192 Sanhuang broilers (112 days old) with an initial body weight of 1.62 ± 0.19 kg were randomly allocated to four treatments, six replicate pens per treatment with 8 broilers per pen. The four treatments contain a control diet (corn-soybean meal basal diet, CON), a positive control diet (basal diet + 75 mg/kg chlortetracycline, PCON), and the experimental diets supplemented with 1.5 and 3% of GLC, respectively. The trial includes phase 1 (day 1–28) and phase 2 (day 29–56).ResultsThe results showed that broilers in PCON and GLC-added treatments showed a lower FCR (P < 0.05) in phase 2 and overall period and a higher ADG (P < 0.05) in phase 2. On day 56, the concentrations of serum SOD (P < 0.05), and HDL (P < 0.05) and cecal SCFA contents (P < 0.05) were increased in broilers fed GLC diets. Broilers fed GLC also showed a higher microbiota diversity and an elevated abundance of SCFA-related bacteria in the caecum. The association between intestinal bacteria and metabolites was investigated via correlation analysis. The differential metabolites in the caecum, such as L-beta-aspartyl-L-aspartic acid and nicotinamide riboside, were identified.ConclusionIn summary, dietary GCL supplementation could increase growth performance to some extent. Moreover, GLC might benefit broilers' health by improving serum HDL content, antioxidant status, SCFAs contents, bacterial diversity, and probiotic proliferation in the caecum.
As the train speeds up, the damage caused by the collisions between trains and foreign objects are becoming more and more severe. Therefore, it is of great significance to monitor the intrusion of foreign objects in the track environment. In this paper, transfer learning is introduced into Mask-RCNN deep learning model. And the data set of rail image is used to train the model, which improves the effect of rail segmentation. The trained model is used to segment the rail in the picture, and the rail vulnerable area is divided based on the segmentation results. The sliding window ORB feature matching algorithm is used to calculate the similarity of vulnerable area. The detection of foreign objects in the area where is easy to invade is realized, and the detection reliability is improved. Experiments represent that this method has high accuracy, strong practicability, good robustness and universality.
Because the fault data of rail transit switch machine are difficult to obtain and the site fault is difficult to reproduce, it is difficult to diagnose or predict the switch machine. In this paper, the power fault data of S700K switch machine is divided into creeping fault and mutation fault, and a simulation data generator for generating massive fault data is developed. The generators involve the synthesis of minority over-sampling techniques and generative confrontation networks. Finally, the long-term memory neural network is used to predict the generated gradual fault data to verify the authenticity and reliability of the simulation data generator. The experimental results show that the generated data can predict the future power trend of the switch machine, which proves the authenticity and feasibility of the simulation data generator.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.