MODELAGEM E SIMULAÇÃO DE SISTEMA LOGÍSTICO DE DISTRIBUIÇÃO DE CARNE DE FRANGO.

O sistema logístico para distribuição de produtos acabados caracteriza-se pela integração dos serviços de comunicação, transporte e financeiros com a finalidade de atender às demandas do consumidor final. Estima-se que no estado do Espírito Santo, o consumo de carne de frango seja de 44,4 quilos per capita por ano. Para atender a esta demanda, o estado conta com matadouros-frigoríficos distribuídos pelo seu território, bem como, com a participação de outras empresas localizadas no país. Em sistemas de transportes, são característicos Problemas de Roteamento de Veículos (VRP), que precisam ser estudados, caracterizados e otimizados, normalmente, através de rotinas computacionais, que permitem avaliar maior quantidade de variáveis. O presente trabalho teve por objetivo caracterizar um VRP de um matadouro-frigorífico da região do Sul do Espírito Santo e desenvolver um aplicativo computacional que seja suporte para os gestores de logística, servindo para avaliar e propor rotas, e analisar parâmetros logísticos do processo de distribuição de produtos. No desenvolvimento do aplicativo computacional foi necessário caracterizar o sistema logístico da empresa, coletar e analisar os dados das operações logísticas, desenvolver as rotinas computacionais que representassem o sistema em estudo, verificar a confiabilidade dos resultados fornecidos pelo aplicativo, validá-lo e então, poder realizar as experimentações. O aplicativo desenvolvido permitiu reproduzir dados do sistema estudado e avaliar rotas segundo parâmetros logísticos. Pode-se concluir que o aplicativo computacional desenvolvido é útil aos gestores de logística, permitindo a avaliação das rotas praticadas e de novas configurações de rotas

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

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

Estudo sobre o comportamento de cultivares de milho na região semi-árida do Estado de Sergipe.

bitstream/item/80184/1/CPATC-COM.-TEC.-16-84.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

Brazilian embryo industry in context: pitfalls, lessons, and expectations for the future.

Proceedings of the 31st Annual Meeting of the Brazilian Embryo Technology Society (SBTE); Cabo de Santo Agostinho, PE, Brazil, August 17th to 19th, 2017. Abstract

SIMULATION AND EXPERIMENTAL EVALUATION OF AN INNOVATIVE ROTARY COMPRESSOR WITH VARIABLE SPEED DISPLACERS

This paper describes preliminary studies of a new rotary compressor with variable speed displacers. Two displacers rotate concentrically, in an annular space, at variable and phased angular velocities, thus creating two variable-volume compression spaces between them. The displacers are individually driven by two concentric shafts. An innovative driving mechanism imposes phased variable angular speeds to the shafts and, consequently, to the displacers, thus providing a volume variation in the gas compression spaces. The driving mechanism also offers a convenient way of capacity control, from zero to 100%, at constant electric motor speed. A mathematical model simulating the performance was developed. A traditional simulation model for positive displacement compressors was employed, where mass and energy conservation equations, in differential form, were applied to the control volumes (two compression spaces). Uniformly distributed thermodynamic properties of gas, varying with time, were assumed for each control volume. Equations describing the volume variation with time, the gas to cylinder wall heat transfer and gas flow through valve ports and leakage passages were also employed. The resulting mathematical model was a system of ordinary differential equations, the numerical integration of which provides the time-history of pressure and temperature of the gas inside the compression chambers. A prototype was also constructed and tested. First performance results are presented, showing the compressor behaviour under different operational conditions

Spin-Wave Theory of the Multiple-Spin Exchange Model on a Triangular Lattice in a Magnetic Field : 3-Sublattice Structures

We study the spin wave in the S=1/2 multiple-spin exchange model on a triangular lattice in a magnetic field within the linear spin-wave theory. We take only two-, three- and four-spin exchange interactions into account and restrict ourselves to the region where a coplanar three-sublattice state is the mean-field ground state. We found that the Y-shape ground state survives quantum fluctuations and the phase transition to a phase with a 6-sublattice structure occurs with softening of the spin wave. We estimated the quantum corrections to the ground state sublattice magnetizations due to zero-point spin-wave fluctuations.Comment: 8 pages, 20 figure

Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis

The planning of efficient policies based on forecasting electricity demand is essential to guarantee the continuity of energy supply for consumers. Some techniques for forecasting electricity demand have used specific procedures to define input variables, which can be particular to each case study. However, the definition of independent and casual variables is still an issue to be explored. There is a lack of models that could help the selection of independent variables, based on correlate criteria and level of importance integrated with artificial networks, which could directly impact the forecasting quality. This work presents a model that integrates a multi-criteria approach which provides the selection of relevant independent variables and artificial neural networks to forecast the electricity demand in countries. It provides to consider the particularities of each application. To demonstrate the applicability of the model a time series of electricity consumption from a southern region of Brazil was used. The dependent inputs used by the neural networks were selected using a traditional method called Wrapper. As a result of this application, with the multi-criteria ELECTRE I method was possible to recognize temperature and average evaporation as explanatory variables. When the variables selected by the multi-criteria approach were included in the predictive models, were observed more consistent results together with artificial neural networks, better than the traditional linear models. The Radial Basis Function Networks and Extreme Learning Machines stood out as potential techniques to be used integrated with a multi-criteria method to better perform the forecasting