Objectives To examine associations between The Daily Mile, a school-based active mile intervention, and pupils’ physical ...

Michael O. Lawanson, a Nigerian data scientist at the University of Arkansas, United States, is at the forefront of global ...

Objective To examine whether a multicomponent commercial fitness app with very small (‘micro’) financial incentives (FI) ...

Researchers at National University of Singapore used multiple interpretable machine learning methods to predict traffic congestion in in Alameda ...

The strong role of socioeconomic factors underscores the limits of purely spatial or technical solutions. While predictive models can identify where risk concentrates, addressing why it does so ...

This is an important contribution that largely confirms prior evidence that word recognition - a cornerstone of development - improves across early childhood and is related to vocabulary growth. This ...

A research team shows that phenomic prediction, which integrates full multispectral and thermal information rather than ...

By training statistical and machine-learning models to predict expert visual scores, the study demonstrates that phenomics can match or outperform ...

Objective To characterise the age-related impact of organ damage patterns on health-related quality of life (HRQoL) in ...

This study examined the relationship between the Monetary Policy Rate (MPR) and inflation across five continents from 2014 to 2023 using both Frequentist and Bayesian Linear Mixed Models (LMM). It ...

A research team has developed a low-cost, high-throughput phenotyping platform that continuously measures plant transpiration, enabling real-time monitoring of drought response.

Explainable AI plays a central role in validating model behavior. Using established explainability techniques, the study examines which financial variables drive fraud predictions. The results show a ...