Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …

Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …

Climate change is expected to impact the functioning of the entire Earth system. However,
detecting changes in ecosystem dynamics and attributing such change to anthropogenic …

Spatially resolved genomic technologies have allowed us to study the physical organization
of cells and tissues, and promise an understanding of local interactions between cells …

Western Eurasia witnessed several large-scale human migrations during the Holocene,,,–.
Here, to investigate the cross-continental effects of these migrations, we shotgun-sequenced …

With the prevalence of Intelligent Transportation Systems (ITS), massive sensors are
deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several …

The integrated nested Laplace approximation (INLA) is a recent computational method that
can fit Bayesian models in a fraction of the time required by typical Markov chain Monte …

Purpose There are growing signs that the COVID‐19 virus has started to spread to rural
areas and can impact the rural health care system that is already stretched and lacks …

Text mining is an emerging topic that advances the review of academic literature. This paper
presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power …

Geostatistical data—spatially referenced observations related to some continuous spatial
phenomenon—are ubiquitous in ecology and can reveal ecological processes and inform …