Extensive segment floating will result in segment dislocation, crack, and leakage, posing significant risks of engineering accidents. It is important to control the segment floating based on adjusting shield operational parameters finely. A knowledge-based intelligence method designed for controlling segment floating is proposed in this study. Leveraging prior knowledge in segment floating, the framework of the intelligence method is constructed. This framework consists of a segment floating prediction model along with two auxiliary models. The segment floating prediction model considers the spatial and temporal characteristics of the shield operational parameters, including the early activation of the shield excavation parameters and the hysteretic nature of tail grouting parameters. The segment floating prediction model is the basis of the knowledge-based intelligence method. A multi-ring optimization strategy is designed to solve the conflict between the optimization results of adjacent rings. The case study shows that the segment floating prediction model has high prediction accuracy due to consideration of the spatial and temporal characteristics of the shield operational parameters. Considering the performance and computation cost, the optimal parameter configuration is figured out.

A new paper published in Chemistry of Materials puts artificial intelligence and machine learning on the task of finding new, powerful electrolytes for designing next-generation batteries for electric vehicles, phones, laptops and grid-scale energy storage. The paper from the University of Chicago Pritzker School of Molecular Engineering outlines a new framework for finding molecules that maximize three components that make an ideal battery electrolyte – ionic conductivity, oxidative stability and Coulombic efficiency.

Getting a timely diagnosis of autism spectrum disorder is a major challenge, but new research out of York University shows that how young adults, and potentially children, grasp objects could offer a simpler way to diagnose someone on the autism spectrum. The team, part of an international collaboration, used machine learning to analyze naturalistic hand movements – specifically, finger motions during grasping – in autistic and non-autistic individuals.

Endemic to China’s Yangtze River, the Yangtze finless porpoise is known for its intelligence and charismatic appearance; it looks like it has a perpetual smile on its face. To track how this critically endangered porpoise’s habitat range has changed over time, a team of biodiversity and conservation experts compiled 724 ancient Chinese poems referencing the porpoise from historic collections across China. Publishing in the Cell Press journal Current Biology on May 5, their results show that the porpoise’s range has decreased by at least 65% over the past 1,400 years, with the majority of this decline occurring in the past century.  

Summary:

Researchers developed an AI tool called ulrb to identify rare microbes, significantly improving biodiversity assessments and ecological research.

The open-source software, created through international collaboration, is versatile and applicable to both microbial and non-microbial data, enhancing studies on climate change and ecosystem health.