Emergent Antiferromagnetism Out Of The Hidden-order" State In Uru2si2: High Magnetic Field Nuclear Magnetic Resonance To 40 T."

Very high field (29)Si-NMR measurements using a fully (29)Si-enriched URu(2)Si(2) single crystal were carried out in order to microscopically investigate the hidden order (HO) state and adjacent magnetic phases in the high field limit. At the lowest measured temperature of 0.4 K, a clear anomaly reflecting a Fermi surface instability near 22 T inside the HO state is detected by the (29)Si shift, (29)K(c). Moreover, a strong enhancement of (29)K(c) develops near a critical field H(c) ≃ 35.6 T, and the ^{29}Si-NMR signal disappears suddenly at H(c), indicating the total suppression of the HO state. Nevertheless, a weak and shifted (29)Si-NMR signal reappears for fields higher than H(c) at 4.2 K, providing evidence for a magnetic structure within the magnetic phase caused by the Ising-type anisotropy of the uranium ordered moments.

Beam-energy Dependence Of Charge Separation Along The Magnetic Field In Au+au Collisions At Rhic.

Local parity-odd domains are theorized to form inside a quark-gluon plasma which has been produced in high-energy heavy-ion collisions. The local parity-odd domains manifest themselves as charge separation along the magnetic field axis via the chiral magnetic effect. The experimental observation of charge separation has previously been reported for heavy-ion collisions at the top RHIC energies. In this Letter, we present the results of the beam-energy dependence of the charge correlations in Au+Au collisions at midrapidity for center-of-mass energies of 7.7, 11.5, 19.6, 27, 39, and 62.4 GeV from the STAR experiment. After background subtraction, the signal gradually reduces with decreased beam energy and tends to vanish by 7.7 GeV. This implies the dominance of hadronic interactions over partonic ones at lower collision energies.

Uso de redes neurais artificiais na identificação de vocalização de suínos

Animal welfare has been an important research topic in animal production mainly in its ways of assessment. Vocalization is found to be an interesting tool for evaluating welfare as it provides data in a non-invasive way as well as it allows easy automation of process. The present research had as objective the implementation of an algorithm based on artificial neural network that had the potential of identifying vocalization related to welfare pattern indicatives. The research was done in two parts, the first was the development of the algorithm, and the second its validation with data from the field. Previous records allowed the development of the algorithm from behaviors observed in sows housed in farrowing cages. Matlab® software was used for implementing the network. It was selected a retropropagation gradient algorithm for training the network with the following stop criteria: maximum of 5,000 interactions or error quadratic addition smaller than 0.1. Validation was done with sows and piglets housed in commercial farm. Among the usual behaviors the ones that deserved enhancement were: the feed dispute at farrowing and the eventual risk of involuntary aggression between the piglets or between those and the sow. The algorithm was able to identify through the noise intensity the inherent risk situation of piglets welfare reduction.

Desenvolvimento de ferramentas de análise de desempenho de atletas em 3 diferentes modalidades : Handebol, Provas Combinadas e Salto com Vara

Universidade Estadual de Campinas. Faculdade de Educação Física