Wing polymorphism and dispersal of Scaptocoris carvalhoi (Hemiptera : Cydnidae)

Functional wing polymorphism is commonly observed it) insects, and it may confer an important adaptive value to populations that bear this trait, because it allows dispersal and the location to more favorable habitats for their survival and reproduction. According to the oogenesis-flight syndrome theory, such wing polymorphism may imply differences in the locomotion Capacity of individuals, which is a factor induced by adverse environmental conditions during muscle development in immatures. Scaptocoris carvalhoi Becker (Hemiptera: Cydnidae) is an important agriculture pest in Brazil, and it has burrowing habits. The adults swarm in the beginning of the rainy season after a prolonged drought period in the Brazilian cerrado region. In these swarms, part of the population leaves the soil, performing long flights until locations with more abundant vegetation. In this study, we characterized wing polymorphism in S. carvalhoi, this being the first description in a species of Cydnidae. Brachypterous and macropterous males and females were observed, which showed positive and significant correlations between body length and hindwing length. Macropterous individuals demonstrated greater locomotion capacity than brachypterous individuals. In addition, only long-winged adults could fly, showing wing mobility and flight reaction. The increased number of macropterous individuals inside the soil during the swarming season and in the beginning of the rainy period suggests that wing polymorphism in this population occurs in seasonal cycles and that factors related to the scarcity of rains influence the development of immatures and the formation of polymorphic adults.

The ultrastructure and function of the silk-producing basitarsus in the Hilarini (Diptera : Ernpididae)

The tribe Hilarini (Diptera: Empididae), commonly known as dance flies, can be recognised by their swollen silk-producing prothoracic basitarsus, a male secondary sexual characteristic. The ultrastructure and function of the silk-producing basitarsus from one undescribed morphospecies of Hilarini, 'Hilarempis 20', is presented. Male H. 20 collect small parcels of diatomaceous algae from the surface of freshwater creeks that they bind with silk produced by the gland in the basitarsus. The gift is then presented to females in a nearby swarm, composed predominately of females. The basitarsus houses approximately 12 pairs of class III dermal glandular units that congregate on the ventral side of the cavity. Each gland cell has a large extracellular lumen where secretion accumulates. The lumen drains to the outside via a conducting canal encompassed by a canal cell and a duct extending through the shaft of a specialised secretory spine. The secretory spines lie in pairs in a ventral groove that runs the length of the basitarsus. A comparison of the basitarsal secretory spines with sensilla on the basitarsi of non gland-bearing legs of males, and with non gland-bearing prothoracic. basitarsi of females, suggests that the glandular units are derived from contact chemosensory sensilla. (C) 2003 Elsevier Ltd. All rights reserved.

Um algoritmo PSO híbrido para planejamento de caminhos em navegação de veículos utilizando A*

Utilizar robôs autônomos capazes de planejar o seu caminho é um desafio que atrai vários pesquisadores na área de navegação de robôs. Neste contexto, este trabalho tem como objetivo implementar um algoritmo PSO híbrido para o planejamento de caminhos em ambientes estáticos para veículos holonômicos e não holonômicos. O algoritmo proposto possui duas fases: a primeira utiliza o algoritmo A* para encontrar uma trajetória inicial viável que o algoritmo PSO otimiza na segunda fase. Por fim, uma fase de pós planejamento pode ser aplicada no caminho a fim de adaptá-lo às restrições cinemáticas do veículo não holonômico. O modelo Ackerman foi considerado para os experimentos. O ambiente de simulação de robótica CARMEN (Carnegie Mellon Robot Navigation Toolkit) foi utilizado para realização de todos os experimentos computacionais considerando cinco instâncias de mapas geradas artificialmente com obstáculos. O desempenho do algoritmo desenvolvido, A*PSO, foi comparado com os algoritmos A*, PSO convencional e A* Estado Híbrido. A análise dos resultados indicou que o algoritmo A*PSO híbrido desenvolvido superou em qualidade de solução o PSO convencional. Apesar de ter encontrado melhores soluções em 40% das instâncias quando comparado com o A*, o A*PSO apresentou trajetórias com menos pontos de guinada. Investigando os resultados obtidos para o modelo não holonômico, o A*PSO obteve caminhos maiores entretanto mais suaves e seguros.

A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

Magma flow, exsolution processes and rock metasomatism in the Great Messejana-Plasencia dyke (Iberian Peninsula)

Magma flow in dykes is still not well understood; some reported magnetic fabrics are contradictory and the potential effects of exsolution and metasomatism processes on the magnetic properties are issues open to debate. Therefore, a long dyke made of segments with different thickness, which record distinct degrees of metasomatism, the Messejana-Plasencia dyke (MPD), was studied. Oriented dolerite samples were collected along several cross-sections and characterized by means of microscopy and magnetic analyses. The results obtained show that the effects of metasomatism on rock mineralogy are important, and that the metasomatic processes can greatly influence anisotropy degree and mean susceptibility only when rocks are strongly affected by metasomatism. Petrography, scanning electron microscopy (SEM) and bulk magnetic analyses show a high-temperature oxidation-exsolution event, experienced by the very early Ti-spinels, during the early stages of magma cooling, which was mostly observed in central domains of the thick dyke segments. Exsolution reduced the grain size of the magnetic carrier (multidomain to single domain transformation), thus producing composite fabrics involving inverse fabrics. These are likely responsible for a significant number of the 'abnormal' fabrics, which make the interpretation of magma flow much more complex. By choosing to use only the 'normal' fabric for magma flow determination, we have reduced by 50 per cent the number of relevant sites. In these sites, the imbrication angle of the magnetic foliation relative to dyke wall strongly suggests flow with end-members indicating vertical-dominated flow (seven sites) and horizontal-dominated flow (three sites).

Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. (C) 2012 Elsevier Ltd. All rights reserved.

Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn.

An optimal scheduling problem in distribution networks considering V2G

This paper addresses the problem of energy resource scheduling. An aggregator will manage all distributed resources connected to its distribution network, including distributed generation based on renewable energy resources, demand response, storage systems, and electrical gridable vehicles. The use of gridable vehicles will have a significant impact on power systems management, especially in distribution networks. Therefore, the inclusion of vehicles in the optimal scheduling problem will be very important in future network management. The proposed particle swarm optimization approach is compared with a reference methodology based on mixed integer non-linear programming, implemented in GAMS, to evaluate the effectiveness of the proposed methodology. The paper includes a case study that consider a 32 bus distribution network with 66 distributed generators, 32 loads and 50 electric vehicles.

LMP based bid formation for virtual power players operating in smart grids

Power system organization has gone through huge changes in the recent years. Significant increase in distributed generation (DG) and operation in the scope of liberalized markets are two relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased importance, and is being seen as a paradigm able to support power system requirements for the future. This paper proposes a computational architecture to support day-ahead Virtual Power Player (VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation module. Due to the involved problems characteristics, the implementation of this architecture requires the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource scheduling. The paper presents a case study that considers a 33 bus distribution network that includes 67 distributed generators, 32 loads and 9 storage units.

Computational intelligence applications for future power systems

Power system planning, control and operation require an adequate use of existing resources as to increase system efficiency. The use of optimal solutions in power systems allows huge savings stressing the need of adequate optimization and control methods. These must be able to solve the envisaged optimization problems in time scales compatible with operational requirements. Power systems are complex, uncertain and changing environments that make the use of traditional optimization methodologies impracticable in most real situations. Computational intelligence methods present good characteristics to address this kind of problems and have already proved to be efficient for very diverse power system optimization problems. Evolutionary computation, fuzzy systems, swarm intelligence, artificial immune systems, neural networks, and hybrid approaches are presently seen as the most adequate methodologies to address several planning, control and operation problems in power systems. Future power systems, with intensive use of distributed generation and electricity market liberalization increase power systems complexity and bring huge challenges to the forefront of the power industry. Decentralized intelligence and decision making requires more effective optimization and control techniques techniques so that the involved players can make the most adequate use of existing resources in the new context. The application of computational intelligence methods to deal with several problems of future power systems is presented in this chapter. Four different applications are presented to illustrate the promises of computational intelligence, and illustrate their potentials.

Technical and economic resources management in smart grids using heuristic optimization methods

Intensive use of Distributed Generation (DG) represents a change in the paradigm of power systems operation making small-scale energy generation and storage decision making relevant for the whole system. This paradigm led to the concept of smart grid for which an efficient management, both in technical and economic terms, should be assured. This paper presents a new approach to solve the economic dispatch in smart grids. The proposed methodology for resource management involves two stages. The first one considers fuzzy set theory to define the natural resources range forecast as well as the load forecast. The second stage uses heuristic optimization to determine the economic dispatch considering the generation forecast, storage management and demand response

Reactive power compensation by EPSO technique

This paper presents a methodology to address reactive power compensation using Evolutionary Particle Swarm Optimization (EPSO) technique programmed in the MATLAB environment. The main objective is to find the best operation point minimizing power losses with reactive power compensation, subjected to all operational constraints, namely full AC power flow equations, active and reactive power generation constraints. The methodology has been tested with the IEEE 14 bus test system demonstrating the ability and effectiveness of the proposed approach to handle the reactive power compensation problem.

Optimal methodology for renewable energy dispatching in islanded operation

The management of energy resources for islanded operation is of crucial importance for the successful use of renewable energy sources. A Virtual Power Producer (VPP) can optimally operate the resources taking into account the maintenance, operation and load control considering all the involved cost. This paper presents the methodology approach to formulate and solve the problem of determining the optimal resource allocation applied to a real case study in Budapest Tech’s. The problem is formulated as a mixed-integer linear programming model (MILP) and solved by a deterministic optimization technique CPLEX-based implemented in General Algebraic Modeling Systems (GAMS). The problem has also been solved by Evolutionary Particle Swarm Optimization (EPSO). The obtained results are presented and compared.

Comparison between deterministic and meta-heuristic methods applied to ancillary services dispatch

This paper proposes two meta-heuristics (Genetic Algorithm and Evolutionary Particle Swarm Optimization) for solving a 15 bid-based case of Ancillary Services Dispatch in an Electricity Market. A Linear Programming approach is also included for comparison purposes. A test case based on the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is used to demonstrate that the use of meta-heuristics is suitable for solving this kind of optimization problem. Faster execution times and lower computational resources requirements are the most relevant advantages of the used meta-heuristics when compared with the Linear Programming approach.