We review the progress in the theory of one-dimensional (1D) Fermi liquids which has occurred over the past decade. The usual Fermi liquid theory based on a quasi-particle picture, breaks down in one dimension because of the Peierls divergence in the particlehole bubble producing anomalous dimensions of operators, and because of charge-spin separation. Both are related to the importance of scat...

More and more studies are based on digital microstructures of cement pastes obtained either by numerical modelling or by experiments. A comprehensive understanding of the their pore structures, therefore, becomes significant. In this study, the pore structure of a virtual cement paste (HYMO-1d) generated by cement hydration model HYMOSTRUC 3D is characterized. The pore structure of HYMO-1d is c...

The hybrid model of laser energy deposition is a combination of the geometrical-optics ray-tracing method with the one-dimensional (1D) solution of the Helmholtz wave equation in regions where the geometrical optics becomes inapplicable. We propose an improved version of this model, where a new physically consistent criterion for transition to the 1D wave optics is derived, and a special rescal...

Defocus of the reconstructed image of synthetic aperture radar (SAR) occurs in the presence of the phase error. In this work, a phase error correction method is proposed for compressed sensing (CS) radar imaging based on approximated observation. The proposed method has better image focusing ability with much less memory cost, compared to the conventional approaches, due to the inherent low mem...

Patient-specific one-dimensional (1D) blood flow modeling requires estimating model parameters from available clinical data, ideally acquired noninvasively. The larger the number of arterial segments in a distributed 1D model, the greater the number of input parameters that need to be estimated. We investigated the effect of a reduction in the number of arterial segments in a given distributed ...

With the record of highest market share mobile operating systems, Android system has become a prime target for cyber perpetrators as malicious applications are leveraged attack vectors to exploit systems. Machine learning detection solutions that have resort mostly rely on handcrafted features, process deemed be laborious and time-consuming. In this article, we employ deep learning-based model ...