Purpose: The purpose of this paper is to examine the effect of application of a heat pipe in an aspect of hydrostatic thrust bearings on thermal balance and deformation and the role of this application in increasing the rotating speed of a workbench. Design/methodology/approach: Numerical simulations of oil film temperature field, the temperature field and thermal deformation of the bearing’s workbench and base were performed by finite element analysis (FEA) software for both the traditional hydrostatic thrust bearings and the heat pipe ones. Findings: Oil pad and workbench of the hydrostatic thrust bearings are fabricated with a heat pipe cooling structure, which can take away most of the heat generated by shearing of the oil film, control the temperature rise and thermal deformation of the hydrostatic thrust bearing effectively, avoid the dry friction phenomenon and finally improve the processing quality of equipment. Originality/value: The heat pipe hydrostatic thrust bearings could control the temperature rise and thermal deformation of the hydrostatic thrust bearing effectively, avoid the dry friction phenomenon and improve the processing quality of equipment.

Tomato leaf diseases seriously affect tomato production, and early image-based screening is important for assisted diagnosis under greenhouse or semi-controlled cultivation conditions. Traditional manual inspection methods are often inefficient and highly subjective. To address these limitations, this study proposes a lightweight tomato leaf disease detection model named HDR-YOLO, which integrates high-level semantic guidance and dynamic upsampling. First, a High-level Semantic Feature Pyramid Network (HSFPN) is introduced to reconstruct the feature fusion neck. This modification significantly reduces the parameter count while enhancing the fusion of semantic features. Second, a dynamic upsampling operator (DySample) replaces traditional upsampling techniques to minimize information loss during feature map expansion. Finally, Receptive Field Coordinate Attention Convolution (RFCAConv) is incorporated into the critical feature fusion path. This enhances spatial coordinate perception, thereby improving the recognition accuracy for diseases with complex textures, such as early blight. Experimental results demonstrate that HDR-YOLO achieves a mean average precision at an Intersection over Union of 0.5 (mAP(0.5%)) of 79.5% on the test dataset. Compared to the baseline YOLOv12n model, the proposed method increases mAP(0.5%) by 2.2% while reducing the number of parameters by approximately 29.5% and computational complexity by approximately 14.3%. HDR-YOLO achieves efficient and accurate detection, providing a lightweight image-based framework for tomato leaf disease screening and assisted diagnosis under greenhouse or semi-controlled cultivation conditions.