The Helfrich theory of membrane bending, supported by molecular dynamics simulations, is a promising approach for evaluating ...

This paper offers practical advice on how to improve statistical power in randomized experiments through choices and actions researchers can take at the design, implementation, and analysis stages. At ...

Deep research’ AI agents combine large language models with sophisticated reasoning frameworks to conduct in-depth, ...

Aim This study evaluates if characteristics (eg, location, size, volume) of clusters of defects on an initial visual field ...

Traditional clustering methods often fail when faced with complex, non-linear data patterns. This is where density-based clustering comes into play.

To solve this problem, a grid-based clustering algorithm via load analysis for IIoT is presented in this paper. First, the network load is quantitatively analyzed and then a load model is constructed.

This paper presents a density- and grid- based (DGB) clustering method for categorizing data with arbitrary shapes and noise. As most of the conventional clustering approaches work only with ...

Both hard and fuzzy clustering methods utilize cluster validity indices to determine the optimal number of clusters and evaluate clustering quality (Oyelade et al., 2016; Khorram et al., 2021). There ...

Novel machine learning-based cluster analysis method that leverages target material property New cluster analysis technique for grouping materials based on both basic features and targeted ...

And finally, cluster analysis was performed on the transformed z-vectors. This allowed the researchers to categorize more than 1,000 oxides into material groups based on their basic features like ...

First, a comprehensive cluster partition index-based cluster partition method is proposed, which involves the indexes such as electrical distance, power balance of the cluster, and cluster size.