Abstract: The existing literature on forecasting time series data is primarily based on univariate analysis and techniques such as Univariate Autoregressive (UAR), Univariate Moving Average (UMA), ...

Objective Patients with atrial fibrillation (AF) frequently have multiple comorbidities that increase the risk of hospitalisation and contribute to higher mortality. However, studies examining the ...

Background Despite anticoagulation, patients with atrial fibrillation (AF) experience persistent elevated cardiovascular risk ...

Objective We evaluated the long-term association between the COVID-19 pandemic and the language development of children aged ...

A look into how professional gait analysis helps runners improve form, prevent injuries, and maximize performance. Trump’s golf course takeover sparks outrage Woman suing Taylor Swift gets bad news ...

Many firms use a version of the SWOT analysis. This analysis looks at the internal strengths and weaknesses of a firm and develops strategies to improve them, while also focusing on the external ...

The AERCA algorithm performs robust root cause analysis in multivariate time series data by leveraging Granger causal discovery methods. This implementation in PyTorch facilitates experimentation on ...

Introduction: Immunosenescence is a dynamic process, where both genetic and environmental factors account for the substantial inter-individual variability. This paper integrates all the data on ...

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Recent advances in green chemistry have made multivariate experimental design popular in sample preparation development. This approach helps reduce the number of measurements and data for evaluation ...