Experimental measurement and numerical simulation are two typical methods to monitor the
strength variation of structures. However, the former method is difficult to lay sufficient …

Identifying ligand-binding sites on the protein surface is a crucial step in the structure-based
drug design. Although multiple techniques have been proposed, including those using …

Background Students' subjective feelings during learning construct their diverse and
complex educational experience, and are essential to self-definition and learning quality, yet …

Fracture modeling of metallic alloys with microscopic pores relies on multiscale damage
simulations which typically ignore the manufacturing-induced spatial variabilities in porosity …

Graph Convolutional Networks (GCNs) are powerful representation learning methods for
non-Euclidean data. Compared with the Euclidean data, labeling the non-Euclidean data is …

This study uses mobility data in the context of 2017 Hurricane Harvey in Harris County to
examine the impact of flooding on access to dialysis centers. We examined access …

This paper explored the method of clustering. Two main categories of algorithms will be
used, namely k-means and Gaussian Mixture Model clustering. We will look at algorithms …

Purpose Disease risk prediction poses a significant and growing challenge in the medical
field. While researchers have increasingly utilised machine learning (ML) algorithms to …

Concurrent multiscale damage models are often used to quantify the impacts of
manufacturing-induced micro-porosity on the damage response of macroscopic metallic …

Recent advances in microblog content summarization has primarily viewed this task in the
context of traditional multi-document summarization techniques where a microblog post or …