Magnetization plateau in a two-dimensional multiple-spin exchange model

We study a multiple-spin exchange model on a triangular lattice, which is a possible model for low-density solid 3He films. Due to strong competitions between ferromagnetic three-spin exchange and antiferromagnetic four-spin one, the ground states are highly degenerate in the classical limit. At least 2^{L/2}-fold degeneracy exists on the L*L triangular lattice except for the SO(3) symmetry. In the magnetization process, we found a plateau at m/m_{sat}=1/2, in which the ground state is "uuud state" (a collinear state with four sublattices). The 1/2-plateau appears due to the strong four-spin exchange interaction. This plateau survives against both quantum and thermal fluctuations. Under a magnetic field which realizes the "uuud" ordered state, a phase transition occurs at a finite temperature. We predict that low-density solid 3He thin films may show the 1/2-plateau in the magnetization process. Experimental observation of the plateau will verify strength of the four-spin exchange. It is also discussed that this magnetization plateau can be understood as an insulating-conducting transition in a particle picture.Comment: 10 pages, RevTeX, 12 figures, added a reference and corrected typos, to be published in Phys.Rev.B (01 APR 99

Possible chiral phase transition in two-dimensional solid 3^3He

We study a spin system with two- and four-spin exchange interactions on the triangular lattice, which is a possible model for the nuclear magnetism of solid $^3$He layers. It is found that a novel spin structure with scalar chiral order appears if the four-spin interaction is dominant. Ground-state properties are studied using the spin-wave approximation. A phase transition concerning the scalar chirality occurs at a finite temperature, even though the dimensionality of the system is two and the interaction has isotropic spin symmetry. Critical properties of this transition are studied with Monte Carlo simulations in the classical limit.Comment: 4 pages, Revtex, 4 figures, to appear in Phys.Rev.Let

Informações sobre polinizadores em maracujazeiro no Vale do São Francisco.

Caracterização da região.; a cultura do maracujazeiro; socioeconômia; floração, morfologia e biologia floral; formação e desenvolvimento dos frutos; polinização do maracujajeiro; visitantes florais na região do Vale do São Francisco; características do comportamento das mamangavas; recomendações de manejo; oferta de recursos alimentares alternativos; oferta de locais para nidificação; alternativa para minimizar o impacto de pilhadores; sensibilização de produtores e técnicos.bitstream/CPATSA-2009-09/40644/1/SDC217.pd

Dimensionality effects in the LDOS of ferromagnetic hosts probed via STM: spin-polarized quantum beats and spin filtering

We theoretically investigate the local density of states (LDOS) probed by a STM tip of ferromagnetic metals hosting a single adatom and a subsurface impurity. We model the system via the two-impurity Anderson Hamiltonian. By using the equation of motion with the relevant Green functions, we derive analytical expressions for the LDOS of two host types: a surface and a quantum wire. The LDOS reveals Friedel-like oscillations and Fano interference as a function of the STM tip position. These oscillations strongly depend on the host dimension. Interestingly, we find that the spin-dependent Fermi wave numbers of the hosts give rise to spin-polarized quantum beats in the LDOS. While the LDOS for the metallic surface shows a damped beating pattern, it exhibits an opposite behavior in the quantum wire. Due to this absence of damping, the wire operates as a spatially resolved spin filter with a high efficiency.Comment: revised tex

Danos causados por Magnaporthe grisea em trigo.

bitstream/CNPT-2010/40567/1/p-co202.pd

A experiência da metodologia camponês a camponês em territórios de identidade rural no Nordeste do Brasil.

A pesquisa teve por objetivo a promoção da transição agroeocológica em territórios de identidade rural por meio do ajuste da metodologia ?campesino a campesino? visando à promoção do desenvolvimento sustentável e solidário nas condições do Nordeste do Brasil. Foram identificadas famílias agricultoras camponesas com perfil para inserção em redes de transição agroecológica, potenciais promotores de intercâmbios entre elas para a troca de experiências e saberes. O trabalho foi realizado no Território de identidade rural Sul Sergipano localizado nos tabuleiros costeiros do estado de Sergipe. A realização das ações resultou de uma parceria efetiva entre Colegiado Territorial, Empresa Brasileira de Pesquisa Agropecuária (Embrapa), Instituto Nacional de Colonização e Reforma Agrária (INCRA-SE), Movimento dos Trabalhadores Sem Terra (MST), Universidade Federal de Sergipe (UFS), Universidade Federal de Viçosa (UFV) e Centro de Formação e Assistência Comunitária (CFAC). Para a operacionalização do trabalho e ajuste da metodologia foram potencializadas as redes sociais de agroecologia existentes na região. Os resultados referem-se à identificação das principais experiências de transição agroecológica da região, a consolidação das redes existentes e a criação de formas inovadoras de intercâmbio de experiências potencializadas pela metodologia ajustada, então denominada ?camponês a camponês?. Os aprendizados referem-se à forma de construção coletiva necessária para a introdução de conhecimento agroecológico e, principalmente, de conceitos de maior complexidade, como agrofloresta sucessional e, a perspectiva de construção de uma nova abordagem para a extensão rural em agricultura familiar e camponesa em territórios de identidade rural

Analysis of growth form types and floristic composition due to past disturbance and plantation management in the SHIFT experimental area.

Em terra firme próximo a Manaus, Amazonas, está sendo executado um experimento com sistemas de policultivo de plantas úteis, estabelecido em um plantio de seringueira abandonado. São testadas diferentes combinações de plantas úteis em 90 parcelas e 5 blocos.bitstream/item/180857/1/Recuperacao-47-61.pd

Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models

Monitoring and controlling thermoelectric power plants (TPPs) operational parameters have become essential to ensure system reliability, especially in emergencies. Due to system complexity, operating parameters control is often performed based on technical know-how and simplified analytical models that can result in limited observations. An alternative to this task is using time series forecasting methods that seek to generalize system characteristics based on past information. However, the analysis of these techniques on large diesel/HFO engines used in Brazilian power plants under the dispatch regime has not yet been well-explored. Therefore, given the complex characteristics of engine fuel consumption during power generation, this work aimed to investigate patterns generalization abilities when linear and nonlinear univariate forecasting models are used on a representative database related to an engine-driven generator used in a TPP located in Pernambuco, Brazil. Fuel consumption predictions based on artificial neural networks were directly compared to XGBoost regressor adaptation to perform this task as an alternative with lower computational cost. AR and ARIMA linear models were applied as a benchmark, and the PSO optimizer was used as an alternative during model adjustment. In summary, it was possible to observe that AR and ARIMA-PSO had similar performances in operations and lower error distributions during full-load power output with normal error frequency distribution of −0.03 ± 3.55 and 0.03 ± 3.78 kg/h, respectively. Despite their similarities, ARIMA-PSO achieved better adherence in capturing load adjustment periods. On the other hand, the nonlinear approaches NAR and XGBoost showed significantly better performance, achieving mean absolute error reductions of 42.37% and 30.30%, respectively, when compared with the best linear model. XGBoost modeling was 8.7 times computationally faster than NAR during training. The nonlinear models were better at capturing disturbances related to fuel consumption ramp, shut-down, and sudden fluctuations steps, despite being inferior in forecasting at full-load, especially XGBoost due to its high sensitivity with slight fuel consumption variations