Application of the Preisach and Jiles–Atherton models to the simulation of hysteresis in soft magnetic alloys

his article describes the advances in unification of model descriptions of hysteresis in magnetic materials and demonstrates the equivalence of two widely accepted models, the Preisach (PM) and Jiles–Atherton (JA) models. Recently it was shown that starting from general energy relations, the JA equation for a loop branch can be derived from PM. The unified approach is here applied to the interpretation of magnetization measured in nonoriented Si–Fe steels with variable grain size ⟨s⟩, and also in as-cast and annealed Fe amorphous alloys. In the case of NO Fe–Si, the modeling parameter k defined by the volume density of pinning centers is such that k≈A+B/⟨s⟩, where the parameters A and B are related to magnetocrystalline anisotropy and grain texture. The value of k in the amorphous alloys can be used to estimate the microstructural correlation length playing the role of effective grain size in these materials

Environmental performance rating and disclosure - China's green-watch program

This paper describes a new incentive-based pollution control program in China in which the environmental performance of firms is rated and reported to the public. Firms are rated from best to worst using five colors-green, blue, yellow, red, and black-and the ratings are disseminated to the public through the media. The impact has been substantial, suggesting that public disclosure provides a significant incentive for firms to improve their environmental performance. The paper focuses on the experience of the first two disclosure programs, in Zhenjiang, Jiangsu Province and Hohhot, Inner Mongolia. Successful implementation of these programs at two very different levels of economic and institutional development suggests that public disclosure should be feasible in most of China. The Zhenjiang and Hohhot experiences have also shown that performance disclosure can significantly reduce pollution, even in settings where environmental nongovernmental organizations are not very active and there is no formal channel for public participation in environmental regulation.Environmental Economics&Policies,Public Health Promotion,Decentralization,Water and Industry,Health Monitoring&Evaluation,Environmental Economics&Policies,Water and Industry,Health Monitoring&Evaluation,National Governance,Health Economics&Finance

Domain-wall motion in random potential and hysteresis modeling

Two different approaches to hysteresis modeling are compared using a common ground based on energy relations, defined in terms of dissipated and stored energy. Using the Preisach model and assuming that magnetization is mainly due to domain-wall motion, one can derive the expression of magnetization along a major loop typical of the Jiles–Atherton model and then extend its validity to cases where mean-field effects and reversible contributions are present

Event-driven simulations of a plastic, spiking neural network

We consider a fully-connected network of leaky integrate-and-fire neurons with spike-timing-dependent plasticity. The plasticity is controlled by a parameter representing the expected weight of a synapse between neurons that are firing randomly with the same mean frequency. For low values of the plasticity parameter, the activities of the system are dominated by noise, while large values of the plasticity parameter lead to self-sustaining activity in the network. We perform event-driven simulations on finite-size networks with up to 128 neurons to find the stationary synaptic weight conformations for different values of the plasticity parameter. In both the low and high activity regimes, the synaptic weights are narrowly distributed around the plasticity parameter value consistent with the predictions of mean-field theory. However, the distribution broadens in the transition region between the two regimes, representing emergent network structures. Using a pseudophysical approach for visualization, we show that the emergent structures are of "path" or "hub" type, observed at different values of the plasticity parameter in the transition region.Comment: 9 pages, 6 figure

Substrate-Dependent Modulation of SIRT2 by a Fluorescent Probe, 1-Aminoanthracene

Sirtuin isoform 2 (SIRT2) is an enzyme that catalyzes the removal of acyl groups from lysine residues. SIRT2’s catalytic domain has a hydrophobic tunnel where its substrate acyl groups bind. Here, we report that the fluorescent probe 1-aminoanthracene (AMA) binds within SIRT2’s hydrophobic tunnel in a substrate-dependent manner. AMA’s interaction with SIRT2 was characterized by its enhanced fluorescence upon protein binding (\u3e10-fold). AMA interacted weakly with SIRT2 alone in solution (Kd = 37 μM). However, when SIRT2 was equilibrated with a decanoylated peptide substrate, AMA’s affinity for SIRT2 was enhanced ∼10-fold (Kd = 4μM). The peptide’s decanoyl chain and AMA co-occupied SIRT2’s hydrophobic tunnel when bound to the protein. In contrast, binding of AMA to SIRT2 was competitive with a myristoylated substrate whose longer acyl chain occluded the entire tunnel. AMA competitively inhibited SIRT2 demyristoylase activity with an IC50 of 21 μM, which was significantly more potent than its inhibition of other deacylase activities. Finally, binding and structural analysis suggests that the AMA binding site in SIRT2’s hydrophobic tunnel was structurally stabilized when SIRT2 interacted with a decanoylated or 4-oxononanoylated substrate, but AMA’s binding site was less stable when SIRT2 was bound to an acetylated substrate. Our use of AMA to explore changes in SIRT2’s hydrophobic tunnel that are induced by interactions with specific has implications for developing ligands that modulate SIRT2’s substrate specificity

Cosmological parameters sigma_8, the baryon density, and the UV background intensity from a calibrated measurement of H I Lyman-alpha absorption at z = 1.9

We identify a concordant model for the intergalactic medium (IGM) at redshift z=1.9 that uses popular values for cosmological and astrophysical parameters and accounts for all baryons with an uncertainty of 6%. We have measured the amount of absorption, DA, in the Ly-alpha forest at redshift 1.9 in spectra of 77 QSO from the Kast spectrograph. We calibrated the continuum fits with realistic artificial spectra, and we found that averaged over all 77 QSOs the mean continuum level is within 1-2% of the correct value. Absorption from all lines in the Ly-alpha forest at z=1.9 removes DA=15.1 +/- 0.7% of the flux between 1070 and 1170 (rest) Angstroms. This is the first measurement using many QSOs at this z, and the first calibrated measurement at any redshift. Metal lines absorb 2.3 +/- 0.5%, and LLS absorb 1.0 +/- 0.4% leaving 11.8 +/- 1.0% from the lower density bulk of the IGM. Averaging over Delta z=0.1 or 154 Mpc, the dispersion is 6.1 +/- 0.3% including LLS and metal lines, or 3.9 (+0.5, -0.7)% for the lower density IGM alone, consistent with the usual description of large scale structure. LLS and metal lines are major contributors to the variation in the mean flux, and they make the flux field significantly non-Gaussian. We find that a hydrodynamic simulation on a 1024 cubed grid in a 75.7 Mpc box reproduces the observed DA from the low density IGM with parameters values H_o=71 km/s/Mpc, Omega_Lambda=0.73, Omega_m=0.27, Omega_b=0.044, sigma_8=0.9 and a UV background that has an ionization rate that is 1.08 +/- 0.08 times the prediction by Madau, Haardt & Rees (1999).Comment: Submitted to Ap

Analog Gated Recurrent Neural Network for Detecting Chewing Events

We present a novel gated recurrent neural network to detect when a person is chewing on food. We implemented the neural network as a custom analog integrated circuit in a 0.18 um CMOS technology. The neural network was trained on 6.4 hours of data collected from a contact microphone that was mounted on volunteers' mastoid bones. When tested on 1.6 hours of previously-unseen data, the neural network identified chewing events at a 24-second time resolution. It achieved a recall of 91% and an F1-score of 94% while consuming 1.1 uW of power. A system for detecting whole eating episodes -- like meals and snacks -- that is based on the novel analog neural network consumes an estimated 18.8uW of power.Comment: 11 pages, 16 figure