The Effectiveness of eHMI Displays on Pedestrian-Autonomous Vehicle Interaction in Mixed-Traffic Environments.

External human-machine interfaces (eHMIs) serve as communication bridges between autonomous vehicles (AVs) and road users, ensuring that vehicles convey information clearly to those around them. While their potential has been explored in one-to-one contexts, the effectiveness of eHMIs in complex, real-world scenarios with multiple pedestrians remains relatively unexplored. Addressing this gap, our study provides an in-depth evaluation of how various eHMI displays affect pedestrian behavior.

Redefining biomaterial biocompatibility: challenges for artificial intelligence and text mining.

The surge in 'Big data' has significantly influenced biomaterials research and development, with vast data volumes emerging from clinical trials, scientific literature, electronic health records, and other sources. Biocompatibility is essential in developing safe medical devices and biomaterials to perform as intended without provoking adverse reactions. Therefore, establishing an artificial intelligence (AI)-driven biocompatibility definition has become decisive for automating data extraction and profiling safety effectiveness.

Biomaterials text mining: A hands-on comparative study of methods on polydioxanone biocompatibility.

Scientific information extraction is fundamental for research and innovation, but is currently mostly a manual, time-consuming process. Text Mining tools (TMTs) enable automated, accurate and quick information extraction from text, but there is little precedent of their use in the biomaterials field. Here, we compare the ability of various TMTs to extract useful information from biomaterials abstracts.

DEBBIE: The Open Access Database of Experimental Scaffolds and Biomaterials Built Using an Automated Text Mining Pipeline.

Biomaterials research output has experienced an exponential increase over the last three decades. The majority of research is published in the form of scientific articles and is therefore available as unstructured text, making it a challenging input for computational processing. Computational tools are becoming essential to overcome this information overload.

Correction to: In Vivo Evaluation of Mg-5%Zn-2%Nd Alloy as an Innovative Biodegradable Implant Material.

The article In Vivo Evaluation of Mg-5%Zn-2%Nd Alloy as an Innovative Biodegradable Implant Materialwritten by Elkaiam et al. was originally published electronically on the publisher's internet portal (currently SpringerLink) on September 17, 2019 with open access. With the author(s)' decision to step back from Open Choice, the copyright of the article changed on October 3, 2019 to Biomedical Engineering Society 2019 and the article is forthwith distributed under the terms of copyright.