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The detection of external fire in coal mines and the reliable identification of initial fire are of great significance for improving the level of coal mine fire detection. It is also an important direction of intelligent mine construction in the future. In order to improve the speed, precision and real-time of coal mine external fire detection, a coal mine external fire detection method based on edge intelligence is proposed. The feature scale of the backbone network of the YOLOv5s model is improved. The model can fully learn the shallow features and improve the small target detection performance. At the same time, an adaptive attention module is added on the basis of the original feature pyramid network (FPN) to improve the detection precision of the model. There are problems of image detection error and missed detection caused by poor light conditions, more dust and camera shooting angle in the underground mine. In order to solve the above problems, the YOLOv5s-as model is constructed by using multi-sensor auxiliary detection and weighting fusion identification of video detection information and multi-sensor detection information through dynamic weighting algorithm. The YOLOv5s-as model is transplanted to the intelligent edge processor, and lightweight processing is carried out to realize the deployment of edge intelligent devices. The experimental results show that the reasoning time of the YOLOv5s-as model is slightly longer than that of the YOLOv5s-a model without sensor information fusion reasoning, but mean value of average precision when the intersection over union is 0.5 (mAP@0.5) is increased by 7.24%. Compared with the YOLOv5s model before transplantation, the mAP@0.5 of the YOLOv5s-as model transplanted to the intelligent edge processor and subjected to lightweight processing increased by 15.04%. For small target fire sources, SSD 300, SSD 512 and YOLOv5s models cannot identify them. The YOLOv5s-a and YOLOv5s-as models can detect small target fire sources with good adaptability. When using the edge processing method, the response period of YOLOv5s as model is 238 ms, which is 38.66% shorter than the centralized processing method.

This study was conducted to investigate the relationship between changes in intestinal aquaporins (AQPs) in piglets fed diets with different protein levels and nutritional diarrhoea in piglets. Briefly, 96 weaned piglets were randomly divided into four groups fed diets with crude protein (CP) levels of 18%, 20%, 22% and 24%. The small intestines and colons of the weaned piglets were collected, and several experiments were conducted. In the small intestine, AQP4 protein expression was higher in weaned piglets fed the higher‐CP diets (22% and 24% CP) than in those fed the 20% CP diet except at 72 h (p < 0.01). At 72 h, the AQP4 protein expression in the small intestine was lower in the 18% group than in the other three groups (p < 0.01). Under 20% CP feeding, AQP2, AQP4 and AQP9 protein expression in the colons of piglets peaked at certain time points. The AQP2 and AQP4 mRNA levels in the colon and the AQP4 and AQP4 mRNA levels in the distal colon were approximately consistent with the protein expression levels. However, the AQP9 mRNA content in the colon was highest in the 18% group, and the AQP2 mRNA content in the distal colon was significantly higher in the 24% group than in the 20% group. AQP2 and AQP4 were expressed mainly around columnar cells in the upper part of the smooth colonic intestinal villi, and AQP9 was expressed mainly on columnar cells and goblet cells in the colonic mucosa. In conclusion, 20% CP is beneficial to the normal expression of AQP4 in the small intestine, AQP2, AQP4 and AQP9 in the colon of weaned piglets, which in turn maintains the balance of intestinal water absorption and secretion in piglets. [ABSTRACT FROM AUTHOR]