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Articles by Xiaowei Ye
Exploring the mechanisms by which the microbiome influences muscle development during early growth in pigs through holo-omics analyses
Abstract The gut microbiota is intricately linked to host phenotypes, yet its influence on host early growth and development, especially regarding the development of meat quality traits remains poorly understood. Here, we applied integrative multi-omics analyses of the gut microbiome, metabolome, and transcriptomes of muscle and colon tissues in Jinhua pigs during the rapid growth stage.
[2405.04823] Counting Cohesive Subgraphs with Hereditary Properties
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Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
Abstract : The inspection of bridge structural cracks is essential to the structural safety evaluation and could provide reference for preventive maintenance. The traditional bridge structure inspection methods rely heavily on trained engineers with professional equipment. While such kind of way could provide reliable crack inspection data, the enormous amount of existing bridges waiting for inspection challenges the efficiency of these methods.
Modification and Evaluation of Attention-Based Deep Neural Network for Structural Crack Detection
1. Introduction With the development of China’s economy and the continuously expanding investment in infrastructure, the number of large structures such as bridges and buildings has increased [1,2,3]. Some buildings are in a long-term state of overload, corrosion, etc., and are susceptible to functional barriers under the overlapping impact of natural disasters, resulting in serious accidents [4,5].
Identification and Tracking of Vehicles between Multiple Cameras on Bridges Using a YOLOv4 and OSNet-Based Method
Abstract : The estimation of vehicle loads is a rising research hotspot in bridge structure health monitoring (SHM). Traditional methods, such as the bridge weight-in-motion system (BWIM), are widely used but they fail to record the locations of vehicles on the bridges. Computer vision-based approaches are promising ways for vehicle tracking on bridges.
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