Procedure optimization of silicate rocks dissolution in open vessels for ICP-AES analysis.

The main variables found on procedure of the dissolution silicate rocks using acid dissolution in teflon open vessel for analysis of micro elements by ICP-AES has been determined. The results obtained for some samples showed strong dependence of the rock mineralogical composition, then it was recommended an alkaline fusion step after acid dissolution. The decomposition procedure use 20 mi of an acid mixture of HF:HNO3 in the proportion 3:1 for a fraction of 250 mg pulverized sample. The recommended temperatures were 60 degrees C for attack and 90 degrees C for acid volatilization. The fusion step with 50 mg LiBO2 at 1000 degrees C may be used if non-attacked residue is observed in the solution. The whole time was 6 h per sample. Nine types os silicate rocks that show mineralogical and chemical different compositions were chosen for obtaining the optimization of the variables. The elements used were Ce, Y, Yb and Zr. In addition, ultrassonic nebulization has been used. The percentual standard deviations obtained for five determinations were 0.7 and 1.4 for triplicate samples. The mineralogical and textural information from the petrographical analysis of the samples indicated the need of increasing the fusion step on the optimized procedure.

Evolutionary modeling of larval dispersal in blowflies using non-uniform cellular automata

When the food supply flnishes, or when the larvae of blowflies complete their development and migrate prior to the total removal of the larval substrate, they disperse to find adequate places for pupation, a process known as post-feeding larval dispersal. Based on experimental data of the Initial and final configuration of the dispersion, the reproduction of such spatio-temporal behavior is achieved here by means of the evolutionary search for cellular automata with a distinct transition rule associated with each cell, also known as a nonuniform cellular automata, and with two states per cell in the lattice. Two-dimensional regular lattices and multivalued states will be considered and a practical question is the necessity of discovering a proper set of transition rules. Given that the number of rules is related to the number of cells in the lattice, the search space is very large and an evolution strategy is then considered to optimize the parameters of the transition rules, with two transition rules per cell. As the parameters to be optimized admit a physical interpretation, the obtained computational model can be analyzed to raise some hypothetical explanation of the observed spatiotemporal behavior. © 2006 IEEE.

RAPD optimization for genetic studies with Citrulus lanatus and Sesamum indicum genotypes

The Random Amplified Polymorphic DNA (RAPD) technique is powerful for DNA polymorphism determinations and is widely used in research involving different organisms, but it is known that RAPD can be affected by many factors that may result in false positive bands and non-reproducible assays. In this study, we analyzed the effect of several factors such as DNA template, primer and Taq DNA polymerase concentrations to optimize and standardize the RAPD technique for further genetic studies with Citrulus lanattus and Sesamum indicum L. The best combination of DNA, Taq DNA polymerase enzyme and primer concentrations in RAPD amplification procedures for sesame and watermelon genotypes was established.

Optimization of satellite constellations in the moon

Nowadays, we return to live a period of lunar exploration. China, Japan and India heavily invest in missions to the moon, and then try to implement manned bases on this satellite. These bases must be installed in polar regions due to the apparent existence of water. Therefore, the study of the feasibility of satellite constellations for navigation, control and communication recovers importance. The Moon's gravitational potential and resonant movements due to the proximity to Earth as the Kozai-Lidov resonance, must be considered in addition to other perturbations of lesser magnitude. The usual satellite constellations provide, as a basic feature, continuous and global coverage of the Earth. With this goal, they are designed for the smallest number of objects possible to perform a specific task and this amount is directly related to the altitude of the orbits and visual abilities of the members of the constellation. However the problem is different when the area to be covered is reduced to a given zone. The required number of space objects can be reduced. Furthermore, depending on the mission requirements it may be not necessary to provide continuous coverage. Taking into account the possibility of setting up a constellation that covers a specific region of the Moon on a non-continuous base, in this study we seek a criterion of optimization related to the time between visits. The propagation of the orbits of objects in the constellation in conjunction with the coverage constraints, provide information on the periods of time in which points of the surface are covered by a satellite, and time intervals in which they are not. So we minimize the time between visits considering several sets of possible constellations and using genetic algorithms.

What is the importance of selecting features for non-technical losses identification?

Although non-technical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy has not attracted much attention in this context. In this paper, we focus on this problem applying a novel feature selection algorithm based on Particle Swarm Optimization and Optimum-Path Forest. The results demonstrated that this method can improve the classification accuracy of possible frauds up to 49% in some datasets composed by industrial and commercial profiles. © 2011 IEEE.

Self-optimization of dense wireless sensor networks based on simulated annealing

Wireless sensor network (WSN) Is a technology that can be used to monitor and actuate on environments in a non-intrusive way. The main difference from WSN and traditional sensor networks is the low dependability of WSN nodes. In this way, WSN solutions are based on a huge number of cheap tiny nodes that can present faults in hardware, software and wireless communication. The deployment of hundreds of nodes can overcome the low dependability of individual nodes, however this strategy introduces a lot of challenges regarding network management, real-time requirements and self-optimization. In this paper we present a simulated annealing approach that self-optimize large scale WSN. Simulation results indicate that our approach can achieve self-optimization characteristics in a dynamic WSN. © 2012 IEEE.

Identification and feature selection of non-technical losses for industrial consumers using the software WEKA

This work has as objectives the implementation of a intelligent computational tool to identify the non-technical losses and to select its most relevant features, considering information from the database with industrial consumers profiles of a power company. The solution to this problem is not trivial and not of regional character, the minimization of non-technical loss represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. This work presents using the WEKA software to the proposed objective, comparing various classification techniques and optimization through intelligent algorithms, this way, can be possible to automate applications on Smart Grids. © 2012 IEEE.

Optimization of the immobilization of sweet potato amylase using glutaraldehyde-agarose support. Characterization of the immobilized enzyme

A simplified procedure for the preparation of immobilized beta-amylase using non-purified extract from fresh sweet potato tubers is established in this paper, using differently activated agarose supports. Beta-amylase glutaraldehyde derivative was the preparation with best features, presenting improved temperature and pH stability and activity. The possibility of reusing the amylase was also shown, when this immobilized enzyme was fully active for five cycles of use. However, immobilization decreased enzyme activity to around 15%. This seems to be mainly due to diffusion limitations of the starch inside the pores of the biocatalyst particles. A fifteen-fold increase in the Km was noticed, while the decrease of Vmax was only 30% (10.1 U mg-1 protein and 7.03 U mg-1 protein for free and immobilized preparations, respectively). © 2013 Elsevier Ltd.

Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids. © 2013 IEEE.

A hybrid image restoration algorithm based on Projections onto Convex Sets and Harmony Search

Image restoration is a research field that attempts to recover a blurred and noisy image. Since it can be modeled as a linear system, we propose in this paper to use the meta-heuristics optimization algorithm Harmony Search (HS) to find out near-optimal solutions in a Projections Onto Convex Sets-based formulation to solve this problem. The experiments using HS and four of its variants have shown that we can obtain near-optimal and faster restored images than other evolutionary optimization approach. © 2013 IEEE.

Discovering promising regions to help global numerical optimization algorithms

We have developed an algorithm using a Design of Experiments technique for reduction of search-space in global optimization problems. Our approach is called Domain Optimization Algorithm. This approach can efficiently eliminate search-space regions with low probability of containing a global optimum. The Domain Optimization Algorithm approach is based on eliminating non-promising search-space regions, which are identifyed using simple models (linear) fitted to the data. Then, we run a global optimization algorithm starting its population inside the promising region. The proposed approach with this heuristic criterion of population initialization has shown relevant results for tests using hard benchmark functions.

A mixed-integer quadratically-constrained programming model for the distribution system expansion planning

This paper presents a mixed-integer quadratically-constrained programming (MIQCP) model to solve the distribution system expansion planning (DSEP) problem. The DSEP model considers the construction/reinforcement of substations, the construction/reconductoring of circuits, the allocation of fixed capacitors banks and the radial topology modification. As the DSEP problem is a very complex mixed-integer non-linear programming problem, it is convenient to reformulate it like a MIQCP problem; it is demonstrated that the proposed formulation represents the steady-state operation of a radial distribution system. The proposed MIQCP model is a convex formulation, which allows to find the optimal solution using optimization solvers. Test systems of 23 and 54 nodes and one real distribution system of 136 nodes were used to show the efficiency of the proposed model in comparison with other DSEP models available in the specialized literature. (C) 2014 Elsevier Ltd. All rights reserved.

Alocação ótima de chaves de interconexão nas redes de distribuição de energia elétrica

Pós-graduação em Engenharia Elétrica - FEIS

On the Newton method for solving fuzzy optimization problems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Net power optimization of an irreversible Otto cycle using ECOP and ecological function

This paper presents an analysis of an irreversible Otto cycle aiming to optimize the net power through ECOP and ecological function. The studied cycle operates between two thermal reservoirs of infinite thermal capacity, with internal irreversibilities derived from non-isentropic behavior of compression and expansion processes, irreversibilities from thermal resistance in heat exchangers and heat leakage from the high temperature reservoir to the low temperature reservoir. Analytical expressions are applied for the power outputs optimized by the ECOP, by the ecological function and by the maximum power criteria, in conjunction with a graphic analysis, in which some cycle operation parameters are analyzed for an increased comprehension of the effects of the irreversibilities in the optimized power.