The fluid film between piston and cylinder works simultaneously as bearing and lubricating functions, its thickness determines the lubrication efficiency and performance of an axial piston pump. Because of the thickness is usually micron level, and its change in the whole cycle is difficult to observe directly. In order to reveal it particularly, this paper present a mathematical model which considering structure, piston kinematics and a dynamic piston load to predict the film thickness in a cycle. The minimum thickness is used as an index to evaluate lubrication performance and its variation are studied under different load pressure, rotating speed and length ratio between cylinder and piston. A special test rig with four displacement sensors placed through the cylinder surface allows measuring the film thickness at these points. The model was verified by comparing the numerical results with measurements taken on the test rig. Results indicate that: (1) The mathematical model could effectively predict the film thickness trend and the position of the minimum thickness; (2) The minimum thickness in pressure phase is greater than in suction phase, and increases with the increase of load pressure and decreases with the increase of rotation speed, the influence of load pressure on film thickness is much greater than that of rotational speed. (3) Increasing the length ratio reduces the piston eccentric and raises the minimum film thickness, and in order to obtain the better lubrication, the length ratio should be greater than 0.5.
Brain fatigue is often associated with inattention, mental retardation, prolonged reaction time, decreased work efficiency, increased error rate, and other problems. In addition to the accumulation of fatigue, brain fatigue has become one of the important factors that harm our mental health. Therefore, it is of great significance to explore the practical and accurate brain fatigue detection method, especially for quantitative brain fatigue evaluation. In this study, a biomedical signal of ballistocardiogram (BCG), which does not require direct contact with human body, was collected by optical fiber sensor cushion during the whole process of cognitive tasks for 20 subjects. The heart rate variability (HRV) was calculated based on BCG signal. Machine learning classification model was built based on random forest to quantify and recognize brain fatigue. The results showed that: Firstly, the heart rate obtained from BCG signal was consistent with the result displayed by the medical equipment, and the absolute difference was less than 3 beats/min, and the mean error is 1.30 ± 0.81 beats/min; secondly, the random forest classifier for brain fatigue evaluation based on HRV can effectively identify the state of brain fatigue, with an accuracy rate of 96.54%; finally, the correlation between HRV and the accuracy was analyzed, and the correlation coefficient was as high as 0.98, which indicates that the accuracy can be used as an indicator for quantitative brain fatigue evaluation during the whole task. The results suggested that the brain fatigue quantification evaluation method based on the optical fiber sensor cushion and machine learning can carry out real-time brain fatigue detection on the human brain without disturbance, reduce the risk of human accidents in human–machine interaction systems, and improve mental health among the office and driving personnel.
Driving fatigue is the main cause of traffic accidents, which seriously affects people’s life and property safety. Many researchers have applied electroencephalogram (EEG) signals for driving fatigue detection to reduce negative effects. The main challenges are the practicality and accuracy of the EEG-based driving fatigue detection method when it is applied on the real road. In our previous study, we attempted to improve the practicality of fatigue detection based on the proposed non-hair-bearing (NHB) montage with fewer EEG channels, but the recognition accuracy was only 76.47% with the random forest (RF) model. In order to improve the accuracy with NHB montage, this study proposed an improved transformer architecture for one-dimensional feature vector classification based on introducing the Gated Linear Unit (GLU) in the Attention sub-block and Feed-Forward Networks (FFN) sub-block of a transformer, called GLU-Oneformer. Moreover, we constructed an NHB-EEG-based feature set, including the same EEG features (power ratio, approximate entropy, and mutual information (MI)) in our previous study, and the lateralization features of the power ratio and approximate entropy based on the strategy of brain lateralization. The results indicated that our GLU-Oneformer method significantly improved the recognition performance and achieved an accuracy of 86.97%. Our framework demonstrated that the combination of the NHB montage and the proposed GLU-Oneformer model could well support driving fatigue detection.
Background: Geese are conventionally considered to be herbivorous, which could also be raised with concentrate feeding diets without green grass because of the similar gastrointestinal tract with other poultry. However, the geese gut microbiota profiles and their interactions with epithelial cells are still of limited study. Flavonoids were well-documented to shape gut microbiota and promote epithelial barrier functions individually or cooperatively with other metabolites. Therefore, in the present study, honeycomb flavonoids (HF) were supplemented to investigate the effects on growth performances, intestinal development, and gut microbiome of geese.Material and Methods: A total of 400 1-day-old male lion-head geese with similar birth weight (82.6 ± 1.4 g) were randomly divided into five treatments: the control treatment (CON) and the HF supplementation treatments, HF was supplemented arithmetically to increase from 0.25 to 1%. Growth performance, carcass performances, and intestines' development parameters were measured to determine the optimum supplement. Junction proteins including ZO-1 and ZO-2 and cecal microbiota were investigated to demonstrate the regulatory effects of HF on both microbiota and intestinal epithelium.Results: Results showed that 0.5% of HF supplement had superior growth performance, carcass performance, and the total parameters of gastrointestinal development to other treatments. Further research showed that tight junction proteins including ZO-1 and ZO-2 significantly up-regulated, while Firmicutes and some probiotics including Clostridiales, Streptococcus, Lachnoclostridium, and Bifidobacterium, remarkably proliferated after HF supplement. In conclusion, HF supplement in concentrate-diet feeding geese effectively increased the growth performances by regulating the gut microbiota to increase the probiotic abundance to promote the nutrient digestibility and fortify the epithelial development and barrier functions to facilitate the nutrient absorption and utilization.
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