相关论文: Using Murphy-Good Plots to Interpret Field Emissio…

This work overviews a new, recent success of phase-field modelling: its application to predicting the evolution of the corrosion front and the associated structural integrity challenges. Despite its important implications for society,…

Even though the use of power electronics PE loads offers enhanced electrical energy conversion efficiency and control, they remain the primary sources of harmonics in grids. When diverse loads are connected in the distribution system, their…

The level structure of nuclei offers a large amount and variety of information to improve our knowledge of the strong interaction and of mesoscopic quantum systems. Gamma spectroscopy is a powerful tool to perform such studies: modern gamma…

Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals contributes to the understanding of neurological conditions and developing brain-computer…

Developing effective path representations has become increasingly essential across various fields within intelligent transportation. Although pre-trained path representation learning models have shown improved performance, they…

Strong-Field Electrodynamics (SFE) is Maxwell theory with a certain Lorentz-covariant Ohm's law which uses only the electromagnetic degrees of freedom. We show that SFE is {\it semi-dissipative}: while the dissipation rate of the…

Current theoretical description of field-induced electron emission remains mostly bounded by the classic Fowler-Nordheim (FN) framework developed nearly one century ago. For the emerging class of two-dimensional (2D) materials, many basic…

Recently, the Effective Field Theory (EFT) of perturbations on an arbitrary background metric with a timelike scalar profile was formulated in the context of scalar-tensor theories. Here, we generalize the dictionary between the…

Nonuniformly sampled signals are prevalent in real-world applications. However, estimating their power spectra from finite samples poses a significant challenge. The optimal solution-Bronez Generalized Prolate Spheroidal Sequence (GPSS) by…

Analytic mathematical models for the static spin ($G_-$) and density ($G_+$) local field factors for the uniform electron gas (UEG) as functions of wavevector and density are presented. These models closely fit recent quantum Monte Carlo…

Thin films of the manganite (La$_{1-y}$Pr$_y$)$_{1-x}$Ca$_x$MnO$_3$ exhibit dynamic phase coexistence with micrometer scale, fluid-like ferromagnetic metallic (FMM) regions interspersed in a charge-order insulating (COI) background. It has…

Field electron emission from an ultrathin multilayer planar cold cathode (UMPC) including quantum well structure has been both experimentally and theoretically investigated. We found that only tuning the energy levels of UMPC the field…

A new, much improved model of the Galactic Magnetic Field (GMF) is presented. We use the WMAP7 Galactic Synchrotron Emission map and more than forty thousand extragalactic rotation measures to constrain the parameters of the GMF model,…

We present a joint implementation of dynamical-mean-field theory (DMFT) with the pseudopotential plane-wave approach, via Wannier functions, for the determination of the electronic properties of strongly correlated materials. The scheme…

We discuss the conditions for an effective field theory (EFT) to give an adequate low-energy description of an underlying physics beyond the Standard Model (SM). Starting from the EFT where the SM is extended by dimension-6 operators,…

We present a simple analytic model of nanotube electro-optical emitters, along with improved experimental measurements using PMMA-passivated devices with reduced hysteresis. Both the ambipolar electrical characteristics and the motion of…

Interpretable machine learning has become a very active area of research due to the rising popularity of machine learning algorithms and their inherently challenging interpretability. Most work in this area has been focused on the…

Scientists and practitioners increasingly rely on machine learning to model data and draw conclusions. Compared to statistical modeling approaches, machine learning makes fewer explicit assumptions about data structures, such as linearity.…

This is a review of the status of efforts to model the large-scale Galactic magnetic field (GMF). Though important for a variety of astrophysical processes, the GMF remains poorly understood despite some interesting new tracers being used…

We applied a mean-field approach associated to Monte Carlo simulations in order to study the spin-1 ferromagnetic Blume-Capel model in the square and the linear lattice. This new technique, which we call MFT-MC, determines the molecular…