This study addresses a key question in developmental cognitive neuroscience by identifying early neural correlates of variability in language learning and showing how syllable tracking and word ...

The research fills a gap in standardized guidance for lipidomics/metabolomics data analysis, focusing on transparency and reproducibility using R and Python. The approach offers modular, interoperable ...

A global team led by Michal Holčapek, professor of analytical chemistry at the Faculty of Chemical Technology, UPCE, Pardubice (Czech Republic), and Jakub Idkowiak, a research associate from KU Leuven ...

ABSTRACT: This paper investigates the application of machine learning techniques to optimize complex spray-drying operations in manufacturing environments. Using a mixed-methods approach that combines ...

Introduction: City-scale rainfall prediction is crucial for various essential services, such as transportation, supply chain logistics, and leisure activities, as well as for preventing risks ...

Abstract: Recently, deep learning network is introduced to terrain segmentation application in polarimetric synthetic aperture radar (PolSAR) images and has achieved remarkable performance. However, ...

1 Department of Otolaryngology Head and Neck Surgery, Hunan Provincial People's Hospital (First Affiliated Hospital of Hunan Normal University), Changsha, China 2 State Key Laboratory of Cognitive ...

ABSTRACT: In this paper, we investigate the convergence of the generalized Bregman alternating direction method of multipliers (ADMM) for solving nonconvex separable problems with linear constraints.

Abstract: Statistical learning is the cognitive ability to rapidly identify structure and meaning in unfamiliar streams of sensory experience, even in the absence of feedback. Despite extensive ...

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