Design and test of the key components for a combing-type tobacco harvester

The input of exogenous organic matter could affect the transformation of soil carbon (C) and nitrogen (N), and their C- and N-priming effects (CPE and NPE) play a key role in the balance of soil C and N. However, little is known about how the interaction effect between straw and straw biochar regulates CPE and NPE. Therefore, we conducted a 90-day incubation experiment, which included five treatments: no straw and straw biochar (CK), 1.5% straw (S), 0.53% straw biochar (B), 1.5% straw + 0.53% straw biochar (SB), and 1.5% straw + 1.06% straw biochar (SB1). Our findings revealed that cumulative soil CO₂ emissions were increased by 95.52–216.53% through the short-term input of exogenous organic matter input; however, this trend gradually weakened with decreasing dissolved organic C (DOC) content. The cumulative NPE generated by the addition of exogenous organic matter was much smaller than the cumulative CPE. Under the B and S treatments, the cumulative CPE and NPE were negative throughout the entire incubation period. The SB treatment remarkably boosted the microbial biomass nitrogen (MBN) content; however, the SB1 treatment was more effective in inhibiting soil C and N mineralization processes than SB treatment. Moreover, the cumulative CPE and NPE were mainly regulated by N. We conclude that the combination of straw and straw biochar preferentially stimulated soil C mineralization, but that this effect decreased with time, which may be due to the consumption of labile DOC caused by the initial positive CPE, while soil N mineralization had a lag effect.

Accurate recognition of pig behavior is important for animal health monitoring and precision livestock farming. However, fine-grained behavior recognition in group-housed pigs is challenging because of individual occlusion, large posture variation, and subtle visual differences among similar behaviors. Here, an improved YOLOv8n-based model, EPP-YOLOv8n, was developed for fine-grained pig behavior recognition. An efficient multi-scale attention module was inserted after the spatial pyramid pooling-fast module (SPPF) to enhance behavior-related feature extraction. A behavior-factorized asymmetric head (BFA-Head) was designed with scale-adaptive mixed convolution to reduce detection-head complexity, while posture- and interaction-assisted supervision was incorporated to guide fine-grained feature learning. A partial-channel mixed convolution module (P5-selective PLC-Mix) was deployed within the Backbone P5 C2f module to reduce redundant computation, followed by task-aware structured pruning with recovery training for further model compression. On a dataset comprising 2713 images and 30,695 annotated instances after training-set augmentation, EPP-YOLOv8n achieved 98.13% precision, 94.50% recall, and 89.07% mean average precision at intersection over union (IoU) thresholds from 0.50 to 0.95 (mAP@0.5:0.95), representing increases of 2.05, 1.52, and 1.64 percentage points, respectively, over YOLOv8n. Parameter count and billion floating-point operations (GFLOPs) were reduced by 14.59% and 9.70%, respectively. These results indicate that the proposed model can improve fine-grained behavior recognition while reducing model complexity, supporting automated behavior monitoring in group-housed pigs.

Numerical simulation and experimental study of the extrusion process for rice straw powder based on EDEM