Considering the propagation of an SH wave at a corrugated interface between a monoclinic layer and heterogeneous half-space in the presence of initial stress. The inhomogeneity in the half-space is the causation of an exponential function of depth. Whittaker’s function is employed to find the half-space solution. The dispersion relation has been established in closed form. The special cases are discussed, and the classical Love wave equation is one of the special cases. The influence of nonhomogeneity parameter, coupling parameter, and depth of irregularity on the phase velocity was studied.
Acoustical properties of the sea bottom can be described using geoacoustic (GA) models. Most existing propagation models use GA parameters as the bottom properties. It is difficult to obtain GA parameters for a layered bottom because of inter parameter coupling. These problems can be solved by inverting the model-independent reflective parameters P and Q. For a multilayered bottom, a sound field computation model, RamPQ, is developed using the mapping of GA and (P, Q) spaces. The mean square error of the transmission loss in numerical simulations and experimental data for low-frequency sound propagation are employed to validate RamPQ and demonstrate the performance of the model.
In the course of further studies of phase transitions in martensites [Driver, Salje, Howard, Lampronti, Ding & Carpenter (2020), Phys. Rev. B, 102, 014105], errors were uncovered in a few entries in Table 3 of the paper by Carpenter & Howard [(2018), Acta Cryst. B74, 560–573]. The required corrections are given here.
AbstractSummaryRecently we developed scCapsNet, an interpretable deep learning cell type classifier for single cell RNA sequencing data, based on capsule network. However, the running process of scCapsNet is not fully automatic, in which a manual intervention is required for getting the final results. Here we present scCapsNet-mask, an updated version of scCapsNet that utilizes a mask to fully automate the running process of scCapsNet. scCapsNet-mask could constrain the internal parameter coupling coefficients and result in a one to one correspondence between the primary capsule and type capsule. Based on those bijective mapping between primary capsule and type capsule, the model could automatically extract the cell type related genes according to weight matrix connecting input and primary capsule, without a need for manual inspection of the relationship between primary capsules and type capsules. The scCapsNet-mask is evaluated on two single cell RNA sequence datasets. The results show that scCapsNet-mask not only retains the merits of the original scCapsNet with high classification accuracy and high interpretability, but also has the virtue of automatic processing.
Effect of Initial Stress on an SH Wave in a Monoclinic Layer over a Heterogeneous Monoclinic Half-Space