StairNetV3: Depth-aware Stair Modeling using Deep Learning

Vision-based stair perception can help autonomous mobile robots deal with the challenge of climbing stairs, especially in unfamiliar environments. To address the problem that current monocular vision methods are difficult to model stairs accurately without depth information, this paper proposes a depth-aware stair modeling method for monocular vision. Specifically, we take the extraction of stair geometric features and the prediction of depth images as joint tasks in a convolutional neural network (CNN), with the designed information propagation architecture, we can achieve effective supervision for stair geometric feature learning by depth information. In addition, to complete the stair modeling, we take the convex lines, concave lines, tread surfaces and riser surfaces as stair geometric features and apply Gaussian kernels to enable the network to predict contextual information within the stair lines. Combined with the depth information obtained by depth sensors, we propose a stair point cloud reconstruction method that can quickly get point clouds belonging to the stair step surfaces. Experiments on our dataset show that our method has a significant improvement over the previous best monocular vision method, with an intersection over union (IOU) increase of 3.4 %, and the lightweight version has a fast detection speed and can meet the requirements of most real-time applications. Our dataset is available at https://data.mendeley.com/datasets/6kffmjt7g2/1

Process Monitoring and Fault Diagnosis for Shell Rolling Production of Seamless Tube

Continuous rolling production process of seamless tube has many characteristics, including multiperiod and strong nonlinearity, and quickly changing dynamic characteristics. It is difficult to build its mechanism model. In this paper we divide production data into several subperiods by -means clustering algorithm combined with production process; then we establish a continuous rolling production monitoring and fault diagnosis model based on multistage MPCA method. Simulation experiments show that the rolling production process monitoring and fault diagnosis model based on multistage MPCA method is effective, and it has a good real-time performance, high reliability, and precision

Investigating the Short-Term Effects of Cold Stress on Metabolite Responses and Metabolic Pathways in Inner-Mongolia Sanhe Cattle

Inner-Mongolia Sanhe cattle are well-adapted to low-temperature conditions, but the metabolic mechanisms underlying their climatic resilience are still unknown. Based on the 1H Nuclear Magnetic Resonance platform, 41 metabolites were identified and quantified in the serum of 10 heifers under thermal neutrality (5 °C), and subsequent exposure to hyper-cold temperature (−32 °C) for 3 h. Subsequently, 28 metabolites were pre-filtrated, and they provided better performance in multivariate analysis than that of using 41 metabolites. This indicated the need for pre-filtering of the metabolome data in a paired experimental design. In response to the cold exposure challenge, 19 metabolites associated with cold stress response were identified, mainly enriched in “aminoacyl-tRNA biosynthesis” and “valine, leucine, and isoleucine degradation”. A further integration of metabolome and gene expression highlighted the functional roles of the DLD(dihydrolipoamide dehydrogenase), WARS (tryptophanyl-tRNA synthetase), and RARS(arginyl-tRNA synthetase) genes in metabolic pathways of valine and leucine. Furthermore, the essential regulations of SLC30A6 (solute carrier family 30 (zinc transporter), member 6) in metabolic transportation for propionate, acetate, valine, and leucine under severe cold exposure were observed. Our findings presented a comprehensive characterization of the serum metabolome of Inner-Mongolia Sanhe cattle, and contributed to a better understanding of the crucial roles of regulations in metabolites and metabolic pathways during cold stress events in cattle

Genetic Polymorphism and mRNA Expression Studies Reveal IL6R and LEPR Gene Associations with Reproductive Traits in Chinese Holsteins

Genetic selection of milk yield traits alters the energy distribution of high producing cows, resulting in gene-induced negative energy balance, and consequently, poor body condition scores and reduced reproductive performances. Here, we investigated two metabolic-syndrome pathway genes, IL6R (Interleukin 6 receptor) and LEPR (Leptin receptor), for their polymorphism effects on reproductive performance in dairy cows, by applying polymorphism association analyses in 1588 Chinese Holstein cows (at population level) and gene expression analyses in granulosa cells isolated from eight cows (at cell level). Among the six single nucleotide polymorphisms we examined (two SNPs for IL6R and four SNPs for LEPR), five were significantly associated with at least one reproductive trait, including female fertility traits covering both the ability to recycle after calving and the ability to conceive and keep pregnancy when inseminated properly, as well as calving traits. Notably, the identified variant SNP g.80143337A/C in LEPR is a missense variant. The role of IL6R and LEPR in cattle reproduction were further confirmed by observed differences in relative gene expression levels amongst granulosa cells with different developmental stages. Collectively, the functional validation of IL6R and LEPR performed in this study improved our understanding of cattle reproduction while providing important molecular markers for genetic selection of reproductive traits in high-yielding dairy cattle