Research on Mine-Personnel Helmet Detection Based on Multi-Strategy-Improved YOLOv11.

In the complex environment of fully mechanized mining faces, the current object detection algorithms face significant challenges in achieving optimal accuracy and real-time detection of mine personnel and safety helmets. This difficulty arises from factors such as uneven lighting conditions and equipment obstructions, which often lead to missed detections. Consequently, these limitations pose a considerable challenge to effective mine safety management.

Automatic Reproduction of Natural Head Position in Orthognathic Surgery Using a Geometric Deep Learning Network.

: Accurate determination of the natural head position (NHP) is essential in orthognathic surgery for optimal surgical planning and improved patient outcomes. However, traditional methods encounter reproducibility issues and rely on external devices or patient cooperation, potentially leading to inaccuracies in the surgical plan. : To address these limitations, we developed a geometric deep learning network (NHP-Net) to automatically reproduce NHP from CT scans.

In silico characterization of defense system hotspots in Acinetobacter spp.

The bacteria-phage arm race drives the evolution of diverse bacterial defenses. This study identifies and characterizes the defense hotspots in Acinetobacter baumannii using a reference-free approach. Among 4383 high-quality genomes, we found a total of 17,430 phage defense systems and with 54.

Chorionic-based intrahepatic cholestasis in pregnancy on perinatal outcome in twin pregnancies.

This study investigates the impact of twin intrahepatic cholestasis in pregnancy (ICP) in different chorionicity scenarios on pregnancy outcome and risk factors. This retrospective study was designed to investigate the association between ICP and pregnancy outcomes and associated risk factors. Logistic regression analysis was used to verify the correlation between ICP and pregnancy outcome and the associated risk factors with the risk of ICP.