Efeito da época de inoculação do vírus do mosaico das nervuras por Aphis gossypii Glover (Hemiptera: Aphididae) no desenvolvimento e na produção do algodoeiro

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Control of flexural waves on a beam using distributed vibration neutralisers

This paper is concerned with the use of distributed vibration neutralisers to control the transmission of flexural waves on a beam. Of particular interest is an array of beam-like neutralisers and a continuous plate-like neutraliser. General expressions for wave transmission and reflection metrics either side of the distributed neutralisers are derived. Based on transmission efficiency, the characteristics of multiple neutralisers are investigated in terms of the minimum transmission efficiency, the normalised bandwidth and the shape factor, allowing optimisation of their performance. Analytical results show that the band-stop property of the neutraliser array depends on various factors, including the neutraliser damping, mass, separation distance in the array and the moment arm of each neutraliser. Moreover, it is found that the particular attachment configuration of an uncoupled forcemoment-type neutraliser can be used to improve their overall performance. It is also shown that in the limit of many neutralisers in the array, the performance tends to that of a continuous neutraliser. © 2011 Elsevier Ltd.

An efficient Hopfield network to solve economic dispatch problems with transmission system representation

Economic dispatch (ED) problems have recently been solved by artificial neural network approaches. Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. The ability of neural networks to realize some complex non-linear function makes them attractive for system optimization. All ED models solved by neural approaches described in the literature fail to represent the transmission system. Therefore, such procedures may calculate dispatch policies, which do not take into account important active power constraints. Another drawback pointed out in the literature is that some of the neural approaches fail to converge efficiently toward feasible equilibrium points. A modified Hopfield approach designed to solve ED problems with transmission system representation is presented in this paper. The transmission system is represented through linear load flow equations and constraints on active power flows. The internal parameters of such modified Hopfield networks are computed using the valid-subspace technique. These parameters guarantee the network convergence to feasible equilibrium points, which represent the solution for the ED problem. Simulation results and a sensitivity analysis involving IEEE 14-bus test system are presented to illustrate efficiency of the proposed approach. (C) 2004 Elsevier Ltd. All rights reserved.

Constructive heuristic algorithm for the DC model in network transmission expansion planning

A novel constructive heuristic algorithm to the network expansion planning problem is presented the basic idea comes from Garver's work applied to the transportation model, nevertheless the proposed algorithm is for the DC model. Tests results with most known systems in the literature are carried out to show the efficiency of the method.

On the efficiency of an alternative codification for multiobjective service restoration in electric distribution networks

This paper proposes an alternative codification to solve the service restoration in electric power distribution networks using a SPEA2 multiobjective evolutionary algorithm, assuming the minimization of both the load not supplied and the number of switching operations involved in the restoration plan. Constrains as the line, power source and voltage drop limits in order to avoid the activation of protective devices are all included in the proposed algorithm. Experimental results have shown the convenience on considering these new representations in the sense of feasibility maintenance and also in the sense of better approximation to the Pareto set. ©2009 IEEE.

Domain reduction using GRASP construction phase for transmission expansion planning problem

This paper proposes a new strategy to reduce the combinatorial search space of a mixed integer linear programming (MILP) problem. The construction phase of greedy randomized adaptive search procedure (GRASP-CP) is employed to reduce the domain of the integer variables of the transportation model of the transmission expansion planning (TM-TEP) problem. This problem is a MILP and very difficult to solve specially for large scale systems. The branch and bound (BB) algorithm is used to solve the problem in both full and the reduced search space. The proposed method might be useful to reduce the search space of those kinds of MILP problems that a fast heuristic algorithm is available for finding local optimal solutions. The obtained results using some real test systems show the efficiency of the proposed method. © 2012 Springer-Verlag.

An enhanced genetic algorithm to solve the static and multistage transmission network expansion planning

An enhanced genetic algorithm (EGA) is applied to solve the long-term transmission expansion planning (LTTEP) problem. The following characteristics of the proposed EGA to solve the static and multistage LTTEP problem are presented, (1) generation of an initial population using fast, efficient heuristic algorithms, (2) better implementation of the local improvement phase and (3) efficient solution of linear programming problems (LPs). Critical comparative analysis is made between the proposed genetic algorithm and traditional genetic algorithms. Results using some known systems show that the proposed EGA presented higher efficiency in solving the static and multistage LTTEP problem, solving a smaller number of linear programming problems to find the optimal solutions and thus finding a better solution to the multistage LTTEP problem. Copyright © 2012 Luis A. Gallego et al.

Strategies to reduce the number of variables and the combinatorial search space of the multistage transmission expansion planning problem

This paper proposes strategies to reduce the number of variables and the combinatorial search space of the multistage transmission expansion planning problem (TEP). The concept of the binary numeral system (BNS) is used to reduce the number of binary and continuous variables related to the candidate transmission lines and network constraints that are connected with them. The construction phase of greedy randomized adaptive search procedure (GRASP-CP) and additional constraints, obtained from power flow equilibrium in an electric power system are employed for more reduction in search space. The multistage TEP problem is modeled like a mixed binary linear programming problem and solved using a commercial solver with a low computational time. The results of one test system and two real systems are presented in order to show the efficiency of the proposed solution technique. © 1969-2012 IEEE.

A transmission power self-optimization technique for wireless sensor networks

Wireless sensor networks (WSNs) are generally used to monitor hazardous events in inaccessible areas. Thus, on one hand, it is preferable to assure the adoption of the minimum transmission power in order to extend as much as possible the WSNs lifetime. On the other hand, it is crucial to guarantee that the transmitted data is correctly received by the other nodes. Thus, trading off power optimization and reliability insurance has become one of the most important concerns when dealing with modern systems based on WSN. In this context, we present a transmission power self-optimization (TPSO) technique for WSNs. The TPSO technique consists of an algorithm able to guarantee the connectivity as well as an equally high quality of service (QoS), concentrating on the WSNs efficiency (Ef), while optimizing the transmission power necessary for data communication. Thus, the main idea behind the proposed approach is to trade off WSNs Ef against energy consumption in an environment with inherent noise. Experimental results with different types of noise and electromagnetic interference (EMI) have been explored in order to demonstrate the effectiveness of the TPSO technique.

Ce0.97Cu0.03O2 Nanocatalysts Synthesized via Microwave-assisted Hydrothermal Method: Characterization and CO-PROX Catalytic Efficiency

In the work presented here, Ce0.97Cu0.03O2 nanoparticles were synthesized by a microwave-assisted hydrothermal method under different synthesis temperatures. The obtained nanoparticles were tested as catalysts in preferential oxidation of CO to obtain CO-free H2 (PROX reaction). The samples were characterized by X-ray diffraction, transmission electron microscopy (TEM), electron paramagnetic resonance spectroscopy (EPR) and temperature-programmed reduction (TPR). X-ray diffraction measurements detected the presence of pure cubic CeO2 for all synthesized samples. TEM images of the Ce0.97Cu0.03O2 nanoparticles revealed that samples synthesized at 80°C are composed mainly of nanospheres with an average size of 20 nm. The formation of some nanorods with an average diameter of 8 nm and 40 nm in length, and the size reduction of the nanoparticles from 20 to approximately 15 nm is observed with increasing synthesis temperature. EPR spectra indicated that copper is found well dispersed in sample synthesized at 160°C, located predominant in surface sites of ceria. For samples synthesized at 80 and 120°C, the species are less dispersed than in the other one, resulting in the formation of Cu2+−Cu2+ dimmers at the surface of ceria. TPR profiles presented two reduction peaks, one below 400°C attributed to the reduction of different copper species and a second peak around 800°C attributed to the reduction of Ce4+→ Ce3+ species located in the volume of the nanoparticles. The peak related to the reduction of copper species shifts to lower temperatures with increasing synthesis temperature, i.e., the sample synthesized at 160°C is more easily reduced than the ones synthesized at 120 and 80°C. The nanoparticles showed active as catalysts for the CO-PROX reaction. The microwave-assisted method revealed efficient for the synthesis of Ce0.97Cu0.03O2 nanoparticles with copper species selective for the CO-PROX reaction, which reaches CO conversions up to 92% for the sample synthesized at 160°C.