A magnetic type integral operator which is stable till extremely low frequencies

We introduce a new magnetic field integral equation that does not suffer from low-frequency numerical cancelations even for extremely low frequencies (10-40Hz). The new equation is obtained by symmetrizing the standard MFIE with its dual equation and by using appropriately chosen quasi-Helmholtz projectors. When compared to mixed discretized MFIEs, the new equation maintains the favorable properties of mixed discretizations even without using high precision integration rules. Numerical results confirm the theoretical developments and show the effectiveness of the new scheme

Low frequency stability of the mixed discretization of the MFIE

Recently, a novel discretization for the magnetic field integral equation (MFIE) was presented. This discretization involves both Rao-Wilton-Glisson (RWG) basis functions and Buffa-Christiansen (BC) basis functions and is dubbed `mixed'. The scheme conforms to the functional spaces most natural to electromagnetics and thus can be expected to yield more accurate results. In this contribution, this intuition is corroborated by an analysis of the low frequency behavior of the classical and mixed discretizations of the MFIE. It is proved that the mixed discretization of the MFIE yields accurate results at very low frequencies whereas the classical discretization breaks down, as was already discussed extensively in literature

A functional variant in the Stearoyl-CoA desaturase gene promoter enhances fatty acid desaturation in pork

There is growing public concern about reducing saturated fat intake. Stearoyl-CoA desaturase (SCD) is the lipogenic enzyme responsible for the biosynthesis of oleic acid (18:1) by desaturating stearic acid (18:0). Here we describe a total of 18 mutations in the promoter and 3′ non-coding region of the pig SCD gene and provide evidence that allele T at AY487830:g.2228T>C in the promoter region enhances fat desaturation (the ratio 18:1/18:0 in muscle increases from 3.78 to 4.43 in opposite homozygotes) without affecting fat content (18:0+18:1, intramuscular fat content, and backfat thickness). No mutations that could affect the functionality of the protein were found in the coding region. First, we proved in a purebred Duroc line that the C-T-A haplotype of the 3 single nucleotide polymorphisms (SNPs) (g.2108C>T; g.2228T>C; g.2281A>G) of the promoter region was additively associated to enhanced 18:1/18:0 both in muscle and subcutaneous fat, but not in liver. We show that this association was consistent over a 10-year period of overlapping generations and, in line with these results, that the C-T-A haplotype displayed greater SCD mRNA expression in muscle. The effect of this haplotype was validated both internally, by comparing opposite homozygote siblings, and externally, by using experimental Duroc-based crossbreds. Second, the g.2281A>G and the g.2108C>T SNPs were excluded as causative mutations using new and previously published data, restricting the causality to g.2228T>C SNP, the last source of genetic variation within the haplotype. This mutation is positioned in the core sequence of several putative transcription factor binding sites, so that there are several plausible mechanisms by which allele T enhances 18:1/18:0 and, consequently, the proportion of monounsaturated to saturated fat.This research was supported by grants from the Spanish Ministry of Science and Innovation (AGL2009-09779 and AGL2012-33529). RRF is recipient of a PhD scholarship from the Spanish Ministry of Science and Innovation (BES-2010-034607). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of manuscript

Cone-Shaped Space Target Inertia Characteristics Identification by Deep Learning With Compressed Dataset

An effective method for identifying inertia characteristics of cone-shaped space target based on deep learning is proposed. The inertia ratio is determined by the time-varying scattering fields from the cone-shaped targets. The multistatic method is introduced to reduce the evaluation time of time-varying scattering fields. The micro-Doppler spectrogram (MDS) dataset is constructed by the time-frequency analysis with numerical simulation method, point scattering model, and experimental tests. The compressed dataset is further achieved by truncated singular value decomposition (SVD). Finally, the micromotion parameter identification model is constructed to identify the inertia ratio for the cone-shaped space target. The interaction loss function and the feedforward denoising convolutional neural networks (DnCNNs) are employed to improve the identification accuracy. Parameters identification of the precession frequency, precession angle, spin frequency, and inertia ratio with both simulation and experiment datasets demonstrate the validity of the proposed method