Nuclear binding energy in holographic QCD

Saturation of the nuclear binding energy is one of the most important properties of atomic nuclei. We derive the saturation in holographic QCD, by building a shell-model-like mean-field nuclear potential from the nuclear density profile obtained in a holographic multibaryon effective theory. The numerically estimated binding energy is close to the experimental value

Pinning field representation using play hysterons for stress-dependent domain-structure model

© 2019 To predict the stress-dependent magnetization properties of silicon steel using a multiscale magnetization model called assembled domain structure model, pinning field models are developed using the play model. The hysteretic property of pinning field is identified from measured BH loops under stress-free condition. From the unidirectional hysteretic property, the distribution of the play hysterons is determined via an identification method that uses scalar and vector play models under the assumption of 2D or 3D distribution of crystal orientations. The loss properties of non-oriented silicon steel under compressive and tensile stresses are predicted successfully using an energy minimization process without parameter fitting to the stress-dependent measurement results

String is a double slit

We perform imaging of a fundamental string from string scattering amplitudes, and show that its image is a double slit

Anisotropic Vector Play Model and its Application in Magnetization Analysis

An anisotropic vector play model was developed by the superposition of scalar play models. An analytical identification method was derived for a uniaxially anisotropic term. Computed BH loops accurately reconstructed the measured anisotropic hysteretic characteristics of non-oriented (NO) silicon steel sheet. Its application to magnetization analysis by a physical magnetization model using multi-domain particles enhanced the prediction accuracy of the stress-dependent loss property

Estimation of Continental-Basin-Scale Sublimation in the Lena River Basin, Siberia

The Lena River basin in Siberia produces one of the largest river inflows into the Arctic Ocean. One of the most important sources of runoff to the river is spring snowmelt and therefore snow ablation processes have great importance for this basin. In this study, we simulated these processes with fine resolution at basin scale using MicroMet/SnowModel and SnowAssim. To assimilate snow water equivalent (SWE) data in SnowAssim, we used routine daily snow depth data and Sturm’s method. Following the verification of this method for SWE estimation in the basin, we evaluated the impact of snow data assimilation on basin-scale snow ablation. Through validation against MODIS snow coverage data and in situ snow survey observations, we found that SnowAssim could not improve on the original simulation by MicroMet/SnowModel because of estimation errors within the SWE data. Vegetation and accumulated snowfall control the spatial distribution of sublimation and we established that sublimation has an important effect on snow ablation. We found that the ratio of sublimation to snowfall in forests was around 26% and that interannual variation of sublimation modulated spring river runoff

Model Order Reduction of Cage Induction Motor With Skewed Rotor Slots Using Multiport Cauer Ladder Network Method

A method for efficiently deriving a reduced-order model of a cage induction motor (IM) with skewed rotor slots is proposed based on the multiport Cauer ladder network (CLN) method. This article presents several formulations of the multiport CLN method for the skewed rotor, in which the continuity of the bar currents and the space harmonics included in the air-gap flux density waveform are treated differently. The effectiveness of the developed methods was verified from the viewpoints of computational accuracy and cost through application to a practical cage IM with skewed rotor slots