O'Hare Airport roadway traffic prediction via data fusion and Gaussian process regression

This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport area.Specifically,it proposes the ingestion of traffic datasets at different resolutions to build spatiotemporal models for predicting the distribution of traffic volume on the road network.Due to its good adaptability and flexibility for spatiotemporal data,the Gaussian process(GP)regression was employed to provide short-term forecasts using data collected by loop detectors(sensors)and supplemented by telematics data.The GP regression is used to make predictions of the distribution of the proportion of sensor data traffic volume represented by the telematics data for each location of the sensors.Consequently,the fitted GP model can be used to determine the approximate traffic distribution for a testing location outside of the training points.Policymakers in the transportation sector can find the results of this work helpful for making informed decisions relating to current and future transportation conditions in the area.

The Genome Architecture of the Copepod Eurytemora carolleeae-the Highly Invasive Atlantic Clade of the Eurytemoraaffinis Species Complex

Copepods are among the most abundant organisms on the planet and play critical functions in aquatic ecosystems.Among copepods,populations of the Eurytemora affinis species complex are numerically dominant in many coastal habitats and serve as food sources for major fisheries.Intriguingly,certain populations possess the unusual capacity to invade novel salinities on rapid time scales.Despite their ecological importance,high-quality genomic resources have been absent for calanoid copepods,limiting our ability to comprehensively dissect the genome architecture underlying the highly invasive and adaptive capacity of certain populations.Here,we present the first chromosome-level genome of a calanoid copepod,from the Atlantic clade(Eurytemora carolleeae)of the E.affinis species complex.This genome was assembled using high-coverage PacBio long-read and Hi-C sequences of an inbred line,generated through 30 generations of full-sib mating.This genome,consisting of 529.3 Mb(contig N50=4.2 Mb,scaffold N50=140.6 Mb),was anchored onto four chromosomes.Genome annotation predicted 20,262 protein-coding genes,of which ion transport-related gene families were substantially expanded based on comparative analyses of 12 additional arthropod genomes.Also,we found genome-wide signatures of historical gene body methylation of the ion transport-related genes and the significant clustering of these genes on each chromosome.This genome represents one of the most contiguous copepod genomes to date and is among the highest quality marine invertebrate genomes.As such,this genome provides an invaluable resource to help yield fundamental insights into the ability of this copepod to adapt to rapidly changing environments.

Dynamic mode decomposition of nonequilibrium electron-phonon dynamics:accelerating the firstprinciples real-time Boltzmann equation

Nonequilibrium dynamics governed by electron–phonon(e-ph)interactions plays a key role in electronic devices and spectroscopies and is central to understanding electronic excitations in materials.The real-time Boltzmann transport equation(rt-BTE)with collision processes computed from first principles can describe the coupled dynamics of electrons and atomic vibrations(phonons).Yet,a bottleneck of these simulations is the calculation of e–ph scattering integrals on dense momentum grids at each time step.Here we show a data-driven approach based on dynamic mode decomposition(DMD)that can accelerate the time propagation of the rt-BTE and identify dominant electronic processes.We apply this approach to two case studies,high-field charge transport and ultrafast excited electron relaxation.In both cases,simulating only a short time window of~10%of the dynamics suffices to predict the dynamics from initial excitation to steady state using DMD extrapolation.Analysis of the momentum-space modes extracted from DMD sheds light on the microscopic mechanisms governing electron relaxation to a steady state or equilibrium.The combination of accuracy and efficiency makes our DMD-based method a valuable tool for investigating ultrafast dynamics in a wide range of materials.