Tool wear does great damage to product quality and machining efficiency. In this paper, a new tool condition monitoring (TCM) method based on a multi-sensor data fusion imaging and attention mechanism is proposed to indirectly measure and monitor tool wear. First, the multi-sensor signals collected during the operation of the machine tool are encoded and fused. A novel triangular matrix of angle summation method is proposed to fuse multi-sensor time-series signals at the data layer and to image them as two-dimensional images. The method used here effectively retains the detailed characteristics of the data, and also effectively retains the internal time relationship of the signal. In addition, a deep residual network with a convolution block attention module is used to extract the deep features from the encoded image and identify the wear stage of the tool. The proposed deep-learning network is then applied to TCM. It selects information in the channel and spatial domains based on its attention mechanism, and increases network depth by combining residual blocks to realize deep feature extraction. Compared with traditional machine learning methods, this end-to-end deep learning model does not rely on feature engineering, which requires a lot of expert knowledge and artificial experience. In our experimental study, a public data set was used to train the model, and the results showed that the accuracy of the model proposed in this paper was as high as 94%. To further verify its feasibility and effectiveness, the proposed model was also compared with other methods.

No abstract

In this paper, we investigated the effects of a diet with a moderate reduction of dietary crude protein (CP) level, supplemented with five crystalline amino acids (Lys, Met, Thr, Try, and Val), on the growth, metabolism, and fecal microbiota of Sushan nursery pigs. Seventy Sushan nursery pigs with an average body weight of 19.56 ± 0.24 kg were randomly allocated to two experimental dietary treatments: 18% CP (high protein; group HP), and 15% CP (low protein; group LP). We found that the differences in the two diets had no significant effect on the growth performance of Sushan nursery pigs. Nursery pigs on the 15% CP diet showed significantly improved protein, amino acid, and energy utilization. Furthermore, the LP diet cloud optimized the gut microflora composition to some extent. The functional structure of bacterial communities implied improved metabolic capabilities in group LP. Additionally, correlation analysis between fecal microbiota and metabolic profiles confirmed that the increase of beneficial bacterial in the feces was beneficial to the health and metabolism of the nursery pigs. In conclusion, a moderate reduction in the dietary protein level can improve growth and metabolism due to the improvement of intestinal microbiota in Sushan nursery pigs. This finding could provide useful reference data for the application of a different nutrition strategy in indigenous pig production.

No abstract

As global warming intensifies, emerging evidence has demonstrated high ambient temperature during pregnancy negatively affects maternal physiology with compromised pregnant outcomes; however, little is known about the roles of gut microbiota and its underlying mechanisms in this process. Here, for the first time, we explored the potential mechanisms of gut microbiota involved in the disrupted glycolipid metabolism via hepatic mitochondrial function. Our results indicate heat stress (HS) reduces fat and protein contents and serum levels of insulin and triglyceride (TG), while increases that of non-esterified fatty acid (NEFA), b-hydroxybutyric acid (B-HBA), creatinine and blood urea nitrogen (BUN) (P < 0.05). Additionally, HS downregulates both mitochondrial genes (mtDNA) and nuclear encoding mitochondrial functional genes with increasing serum levels of malondialdehyde (MDA) and 8hydroxydeoxyguanosine (8-OHdG) (P < 0.05). Regarding microbial response, HS boosts serum levels of lipopolysaccharide (LPS) (P < 0.05) and alters b-diversity (ANOSIM, P < 0.01), increasing the proportions of Escherichia-Shigella, Acinetobacter and Klebsiella (q < 0.05), while reducing that of Ruminiclostridium, Blautia, Lachnospiraceae_ NK4A136_group, Clostridium VadinBB60 and Muribaculaceae (q < 0.05). PICRUSt analysis predicts that HS upregulates 11 KEGG pathways, mainly including bile secretion and bacterial invasion of epithelial cells. The collective results suggest that microbial dysbiosis due to late gestational HS has strong associations with damaged hepatic mitochondrial function and disrupted metabolic profiles.