6,662 research outputs found
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
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