Andreev tunneling through a double quantum-dot system coupled to a ferromagnet and a superconductor: effects of mean field electronic correlations

We study the transport properties of a hybrid nanostructure composed of a ferromagnet, two quantum dots, and a superconductor connected in series. By using the non-equilibrium Green's function approach, we have calculated the electric current, the differential conductance and the transmittance for energies within the superconductor gap. In this regime, the mechanism of charge transmission is the Andreev reflection, which allows for a control of the current through the ferromagnet polarization. We have also included interdot and intradot interactions, and have analyzed their influence through a mean field approximation. In the presence of interactions, Coulomb blockade tend to localized the electrons at the double-dot system, leading to an asymmetric pattern for the density of states at the dots, and thus reducing the transmission probability through the device. In particular, for non-zero polarization, the intradot interaction splits the spin degeneracy, reducing the maximum value of the current due to different spin-up and spin-down densities of states. Negative differential conductance (NDC) appears for some regions of the voltage bias, as a result of the interplay of the Andreev scattering with electronic correlations. By applying a gate voltage at the dots, one can tune the effect, changing the voltage region where this novel phenomenon appears. This mechanism to control the current may be of importance in technological applications.Comment: 12 pages, 11 figure

Arranjos e populações de plantas de milho e feijão no sistema consorciado em Sergipe.

bitstream/item/69094/1/CPATC-COM.-TEC.-02-79.pd

Thermal Effective Lagrangian of Static Gravitational Fields

We compute the effective Lagrangian of static gravitational fields interacting with thermal fields. Our approach employs the usual imaginary time formalism as well as the equivalence between the static and space-time independent external gravitational fields. This allows to obtain a closed form expression for the thermal effective Lagrangian in $d$ space-time dimensions.Comment: Accepted for publication in the Physical Review

High-resolution abundance analysis of HD 140283

HD 140283 is a reference subgiant that is metal poor and confirmed to be a very old star. The abundances of this type of old star can constrain the nature and nucleosynthesis processes that occurred in its (even older) progenitors. The present study may shed light on nucleosynthesis processes yielding heavy elements early in the Galaxy. A detailed abundance analysis of a high-quality spectrum is carried out, with the intent of providing a reference on stellar lines and abundances of a very old, metal-poor subgiant. We aim to derive abundances from most available and measurable spectral lines. The analysis is carried out using high-resolution (R = 81 000) and high signal-to-noise ratio (800 < S/N/pixel < 3400) spectrum, in the wavelength range 3700 - 10475, obtained with a seven-hour exposure time, using the ESPaDOnS at the CFHT. The calculations in LTE were performed with the OSMARCS 1D atmospheric model and the spectrum synthesis code Turbospectrum, while the analysis in NLTE is based on the MULTI code. We present LTE abundances for 26 elements, and NLTE calculations for the species C I, O I, Na I, Mg I, Al I, K I, Ca I, Sr II, and Ba II lines. The abundance analysis provided an extensive line list suitable for metal-poor subgiant stars. The results for Li, CNO, alpha-, and iron peak elements are in good agreement with literature. The newly NLTE Ba abundance, along with a NLTE Eu correction and a 3D Ba correction from literature, leads to [Eu/Ba] = +0.59 +/- 0.18. This result confirms a dominant r-process contribution, possibly together with a very small contribution from the main s-process, to the neutron-capture elements in HD 140283. Overabundances of the lighter heavy elements and the high abundances derived for Ba, La, and Ce favour the operation of the weak r-process in HD 140283.Comment: 34 pages, 27 figure

Composição química e aminogramas de cultivares de milho em melhoramento genético.

bitstream/item/118240/1/composicao-quimica-e-aminogramas-milho.pd

The graphene sheet versus the 2DEG: a relativistic Fano spin-filter via STM and AFM tips

We explore theoretically the density of states (LDOS) probed by an STM tip of 2D systems hosting an adatom and a subsurface impurity,both capacitively coupled to AFM tips and traversed by antiparallel magnetic fields. Two kinds of setups are analyzed, a monolayer of graphene and a two-dimensional electron gas (2DEG). The AFM tips set the impurity levels at the Fermi energy, where two contrasting behaviors emerge: the Fano factor for the graphene diverges, while in the 2DEG it approaches zero. As result, the spin-degeneracy of the LDOS is lifted exclusively in the graphene system, in particular for the asymmetric regime of Fano interference. The aftermath of this limit is a counterintuitive phenomenon, which consists of a dominant Fano factor due to the subsurface impurity even with a stronger STM-adatom coupling. Thus we find a full polarized conductance, achievable just by displacing vertically the position of the STM tip. To the best knowledge, our work is the first to propose the Fano effect as the mechanism to filter spins in graphene. This feature arises from the massless Dirac electrons within the band structure and allows us to employ the graphene host as a relativistic Fano spin-filter

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

VELOCITY PROFILE VISUALIZATION OF WATER NATURAL PERCOLATION IN A POROUS MEDIUM

This paper aims to show the profile and the behavior of the velocity of the water flow through a porous medium composed of clay and sand aggregated by burning in an oil furnace. The work models mathematics based on the Navier-Stokes differential equation, which represents the behavior of the water velocity flow in porous medium taking into account parameters of a low velocity laminar flow, increased load loss value and Number of Reynolds > 1. Physical phenomena such as porosity, permeability, particles arrangement, radius and wet perimeter are considered in the equation. The study shows the three-dimensional profile of the water percolation velocity which, originated from the capillary phenomenon, causes a sum of the tensions of increased values able to produce cracks in the medium structure. And, differently from filtration phenomenon, which overcomes the capillarity of the medium by the gravitational force or by efforts applied aiming to increase the flow velocity, the natural percolation opposes to the gravity and to the surrounding pressure moving slowly, reaching the flow at 30 and 40 centimeters depending on the permeability of the porous medium