Investigation of Relation between Solar Activity and Earthquakes with Deep Learning Method

Solar activity (SA) has been hypothesized to be a trigger of earthquakes, although it is not as intuitively associated as other potential triggers such as tidal stress, rainfall, and the building of artificial water reservoirs. Here, we investigate the relation between SA and global earthquake numbers (GEN) by using a deep learning method to test the hypothesis. We use the daily data of GEN and SA (1996/01/01-2019/12/31) to construct a temporal convolution network (TCN). From the computational results, we confirm that the TCN captures the relation between SA and earthquakes with magnitudes from 4.0 to 4.9. We also find that the TCN achieves better fitting and prediction performance compared with previous work.

Exome-wide variation in a diverse barley panel reveals genetic associations with ten agronomic traits in Eastern landraces

Barley(Hordeum vulgare ssp.vulgare)is one of the first crops to be domesticated and is adapted to a wide range of environments.Worldwide barley germplasm collections possess valuable allelic variations that could further improve barley productivity.Although barley genomics has offered a global picture of allelic variation among varieties and its association with various agronomic traits,polymorphisms from East Asian varieties remain scarce.In this study,we analyze exome polymorphisms in a panel of 274 barley varieties collected worldwide,including 137 varieties from East Asian countries and Ethiopia.We reveal the underlying population structure and conduct genome-wide association studies for 10 agronomic traits.Moreover,we examin genome-wide associations for traits related to grain size such as awn length and glume length.Our results demonstrate the value of diverse barley germplasm panels containing Eastern varieties,highlighting their distinct genomic signatures relative to Western subpopulations.