The δ expansion and the principle of minimal sensitivity

The δ-expansion is a nonperturbative approach for field theoretic models which combines the techniques of perturbation theory and the variational principle. Different ways of implementing the principle of minimal sensitivity to the δ-expansion produce in general different results for observables. For illustration we use the Nambu-Jona-Lasinio model for chiral symmetry restoration at finite density and compare results with those obtained with the Hartree-Fock approximation.

Optimized delta expansion for relativistic nuclear models: Renormalization of vacuum fluctuations

We use the optimized linear δ expansion and functional methods to study vacuum contributions in nuclear matter up to the lowest non-trivial order which includes exchange terms. We show that well known results (MFT, RHA and HF) can be easily reproduced when appropriate limits are taken. Neglecting vacuum contributions we explicitly show that the δ expansion goes beyond the traditional loop approximation previously used to study two loop vacuum contributions in nuclear matter. We then evaluate and renormalize vacuum exchange contributions showing that they are numerically very large, as predicted by the ordinary loop approximation.

Free expansion of attractive and repulsive Bose-Einstein condensed vortex states

A numerical study of the time-dependent Gross-Pitaevskii equation for an axially symmetric trap to obtain insight into the free expansion of vortex states of BEC is presented. As such, the ratio of vortex-core radius to radia rms radius xc/xrms(<1) is found to play an interesting role in the free expansion of condensed vortex states. the larger this ratio, the more prominent is the vortex core and the easier is the possibility of experimental detection of vortex states.

A constructive heuristic algorithm to Short Term Transmission Network Expansion Planning

In this paper a method for solving the Short Term Transmission Network Expansion Planning (STTNEP) problem is presented. The STTNEP is a very complex mixed integer nonlinear programming problem that presents a combinatorial explosion in the search space. In this work we present a constructive heuristic algorithm to find a solution of the STTNEP of excellent quality. In each step of the algorithm a sensitivity index is used to add a circuit (transmission line or transformer) to the system. This sensitivity index is obtained solving the STTNEP problem considering as a continuous variable the number of circuits to be added (relaxed problem). The relaxed problem is a large and complex nonlinear programming and was solved through an interior points method that uses a combination of the multiple predictor corrector and multiple centrality corrections methods, both belonging to the family of higher order interior points method (HOIPM). Tests were carried out using a modified Carver system and the results presented show the good performance of both the constructive heuristic algorithm to solve the STTNEP problem and the HOIPM used in each step.

A branch-and-bound algorithm for the multi-stage transmission expansion planning

This work presents a branch-and-bound algorithm to solve the multi-stage transmission expansion planning problem. The well known transportation model is employed, nevertheless the algorithm can be extended to hybrid models or to more complex ones such as the DC model. Tests with a realistic power system were carried out in order to show the performance of the algorithm for the expansion plan executed for different time frames. © 2005 IEEE.

Transmission network expansion planning in full open market considering security constraints

This paper presents a mathematical model and a methodology to solve the transmission network expansion planning problem with security constraints in full competitive market, assuming that all generation programming plans present in the system operation are known. The methodology let us find an optimal transmission network expansion plan that allows the power system to operate adequately in each one of the generation programming plans specified in the full competitive market case, including a single contingency situation with generation rescheduling using the security (n-1) criterion. In this context, the centralized expansion planning with security constraints and the expansion planning in full competitive market are subsets of the proposal presented in this paper. The model provides a solution using a genetic algorithm designed to efficiently solve the reliable expansion planning in full competitive market. The results obtained for several known systems from the literature show the excellent performance of the proposed methodology.

Periodontal effects of rapid maxillary expansion with tooth-tissue-borne and tooth-borne expanders: A computed tomography evaluation

Introduction: The force delivered during rapid maxillary expansion (RME) produces areas of compression on the periodontal ligament of the supporting teeth. The resulting alveolar bone resorption can lead to unwanted tooth movement in the same direction. The purpose of this study was to evaluate periodontal changes by means of computed tomography after RME with tooth-tissue-borne and tooth-borne expanders. Methods: The sample comprised 8 girls, 11 to 14 years old, with Class I or II malocclusions with unilateral or bilateral posterior crossbites Four girls were treated with tooth-tissue-borne Haas-type expanders, and 4 were treated with tooth-borne Hyrax expanders. The appliances were activated up to the full 7-mm capacity of the expansion screw. Spiral CT scans were taken before expansion and after the 3-month retention period when the expander was removed. One-millimeter thick axial sections were exposed parallel to the palatal plane, comprising the dentoalveolar area and the base of the maxilla up to the inferior third of the nasal cavity. Multiplanar reconstruction was used to measure buccal and lingual bone plate thickness and buccal alveolar bone crest level by means of the computerized method. Results and Conclusions: RME reduced the buccal bone plate thickness of supporting teeth 0.6 to 0.9 mm and increased the lingual bone plate thickness 0.8 to 1.3 mm. The increase in lingual bone plate thickness of the maxillary posterior teeth was greater in the tooth-borne expansion group than in the tooth-tissue-borne group. RME induced bone dehiscences on the anchorage teeth's buccal aspect (7.1 ± 4.6 mm at the first premolars and 3.8 ± 4.4 mm at the mesiobuccal area of the first molars), especially in subjects with thinner buccal bone plates. The tooth-borne expander produced greater reduction of first premolar buccal alveolar bone crest level than did the tooth-tissue-borne expander. © 2006 American Association of Orthodontists.

Genetic algorithm of chu and beasley for static and multistage transmission expansion planning

In this paper the genetic algorithm of Chu and Beasley (GACB) is applied to solve the static and multistage transmission expansion planning problem. The characteristics of the GACB, and some modifications that were done, to efficiently solve the problem described above are also presented. Results using some known systems show that the GACB is very efficient. To validate the GACB, we compare the results achieved using it with the results using other meta-heuristics like tabu-search, simulated annealing, extended genetic algorithm and hibrid algorithms. © 2006 IEEE.

Post expansion evaluation of the midpalatal suture in children submitted to rapid palatal expansion: A CT study

The aim of this prospective study was to evalute the midpalatal suture in children submitted to rapid palatal expansion, at the end of the retention stage, with CT scans. The sample was comprised of 17 children aged between 5 years 2 months and 10 years 5 months. The tomographic images showed that the midpalatal suture was completely ossified from the anterior nasal spine area to the posterior nasal spine area at the end of the retention phase, that is, 8 to 9 months post-expansion.

Branch and bound algorithm for transmission network expansion planning using DC model

This paper presents an algorithm to solve the network transmission system expansion planning problem using the DC model which is a mixed non-linear integer programming problem. The major feature of this work is the use of a Branch-and-Bound (B&B) algorithm to directly solve mixed non-linear integer problems. An efficient interior point method is used to solve the non-linear programming problem at each node of the B&B tree. Tests with several known systems are presented to illustrate the performance of the proposed method. ©2007 IEEE.

Heuristic algorithm to solve the short term transmission network expansion planning

In this paper, a method for solving the short term transmission network expansion planning problem is presented. This is a very complex mixed integer nonlinear programming problem that presents a combinatorial explosion in the search space. In order to And a solution of excellent quality for this problem, a constructive heuristic algorithm is presented in this paper. In each step of the algorithm, a sensitivity index is used to add a circuit (transmission line or transformer) or a capacitor bank (fixed or variable) to the system. This sensitivity index is obtained solving the problem considering the numbers of circuits and capacitors banks to be added (relaxed problem), as continuous variables. The relaxed problem is a large and complex nonlinear programming and was solved through a higher order interior point method. The paper shows results of several tests that were performed using three well-known electric energy systems in order to show the possibility and the advantages of using the AC model. ©2007 IEEE.

Transmission network expansion planning considering uncertainty in generation and demand

This paper presents a mathematical model and a methodology to solve a transmission network expansion planning problem considering uncertainty in demand and generation. The methodology used to solve the problem, finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with uncertainty. The model presented results in an optimization problem that is solved using a specialized genetic algorithm. The results obtained for known systems from the literature show that cheaper plans can be found satisfying the uncertainty in demand and generation. ©2008 IEEE.

Transmission network expansion planning considering multiple generation scenarios

This paper presents a mathematical model and a methodology to solve a transmission network expansion planning problem considering open access. The methodology finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with multiples generation scenarios. The model presented is solved using a specialized genetic algorithm. The methodology is tested in a system from the literature. ©2008 IEEE.