Despite data gaps in many countries, the burden of sickle cell disease, especially in west and central Africa, underscores the urgent need to scale up newborn and early childhood screening, ...

Background Early identification of patients at risk of heart failure (HF) provides opportunities for preventative management. Though models have been developed to predict HF incidence, their ...

Introduction The commonly used frequentist paradigm of null hypothesis statistics testing with its reliance on the p-value and the corresponding notion of ‘statistical significance’ has been under ...

Abstract: Visualizations play a critical role in validating and improving statistical models. However, the design space of model check visualizations is not well understood, making it difficult for ...

I’m going to go ahead and say it: what happens online doesn’t always necessarily square up with what’s actually going on in real life. Yes, we all know that Elon Musk’s political opinions are ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

In microbiome studies, addressing the unique characteristics of sequence data—such as compositionality, zero inflation, overdispersion, high dimensionality, and non-normality—is crucial for accurate ...

City Science, a rapidly expanding profession, is the scientific study and engineering of urban systems. It uses advanced technologies, big data, and complex systems physics to tackle challenges such ...

This research explores the dynamic responses of reinforced concrete (RC) slabs under high-velocity impacts. The study utilizes advanced numerical simulations to investigate the effects of varied ...

Every month, more than 100,000 employers report their payroll numbers to the US Bureau of Labor Statistics. Some transmit the data directly from internal payroll systems, others answer questions by ...