Publications by authors named "Ming Zhao Liu"
Insect-inspired passive wing collision recovery in flapping wing microrobots.
Bioinspir Biomim
January 2025
Flying insects have developed two distinct adaptive strategies to minimize wing damage during collisions. One strategy includes an elastic joint at the leading edge, which is evident in wasps and beetles, while another strategy features an adaptive and deformable leading edge, as seen in bumblebees and honeybees. Inspired by the latter, a novel approach has been developed for improving collision recovery in micro aerial vehicles (MAVs) by mimicking the principle of stiffness anisotropy present in the leading edges of these insects.
View Article and Find Full Text PDFBackground: Patients with airway stenosis (AS) are associated with considerable morbidity and mortality after lung transplantation (LTx). This study aims to develop and validate machine learning (ML) models to predict AS requiring clinical intervention in patients after LTx.
Methods: Patients who underwent LTx between January 2017 and December 2019 were reviewed.
Article Synopsis
- Research highlighted a lack of effective prognostic tools for predicting survival in lung transplant recipients (LTx) despite known prognostic factors.
- The study aimed to create and validate a new survival prediction model using a machine learning approach called random survival forests (RSF).
- Results indicated the RSF model provided significant improvements in predictive accuracy compared to traditional Cox regression, with a high integrated area under the curve (iAUC) score, demonstrating its potential as a valuable resource for clinical decision-making in lung transplantation.