Transmission network expansion planning considering repowering and reconfiguration

Transmission expansion planning (TEP) is a classic problem in electric power systems. In current optimization models used to approach the TEP problem, new transmission lines and two-winding transformers are commonly used as the only candidate solutions. However, in practice, planners have resorted to non-conventional solutions such as network reconfiguration and/or repowering of existing network assets (lines or transformers). These types of non-conventional solutions are currently not included in the classic mathematical models of the TEP problem. This paper presents the modeling of necessary equations, using linear expressions, in order to include non-conventional candidate solutions in the disjunctive linear model of the TEP problem. The resulting model is a mixed integer linear programming problem, which guarantees convergence to the optimal solution by means of available classical optimization tools. The proposed model is implemented in the AMPL modeling language and is solved using CPLEX optimizer. The Garver test system, IEEE 24-busbar system, and a Colombian system are used to demonstrate that the utilization of non-conventional candidate solutions can reduce investment costs of the TEP problem. (C) 2015 Elsevier Ltd. All rights reserved.

Characterization of the nonlinear behavior of a F-16 aircraft using discrete-time Volterra series

The linearity assumption in the structural dynamics analysis is a severe practical limitation. Further, in the investigation of mechanisms presented in fighter aircrafts, as for instance aeroelastic nonlinearity, friction or gaps in wing-load-payload mounting interfaces, is mandatory to use a nonlinear analysis technique. Among different approaches that can be used to this matter, the Volterra theory is an interesting strategy, since it is a generalization of the linear convolution. It represents the response of a nonlinear system as a sum of linear and nonlinear components. Thus, this paper aims to use the discrete-time version of Volterra series expanded with Kautz filters to characterize the nonlinear dynamics of a F-16 aircraft. To illustrate the approach, it is identified and characterized a non-parametric model using the data obtained during a ground vibration test performed in a F-16 wing-to-payload mounting interfaces. Several amplitude inputs applied in two shakers are used to show softening nonlinearities presented in the acceleration data. The results obtained in the analysis have shown the capability of the Volterra series to give some insight about the nonlinear dynamics of the F-16 mounting interfaces. The biggest advantage of this approach is to separate the linear and nonlinear contributions through the multiple convolutions through the Volterra kernels.

Análise de falhas por fadiga num rotor de hidrogerador vertical via método dos elementos finitos

Pós-graduação em Engenharia Mecânica - FEG

A dama e o unicórnio: uma leitura iconográfica

This paper embraces the tapestry, from Cluny Museum: La Dame a la Licorne. There are in two types of analyses: One is a diachronic vew and also a sincrhonic, which guide its pratice. In the tapestry La Dame a la Licorne at the Cluny Museum a reading based over the myths and symbols was the main line. \The first analyses, diachronic covers the temporality of the tapestry production, the second one, synchronic, describes the level of history, religion and myths envolved. This work consists in a process of actualization and an effort to bring closely the discussion over tapestry, using methods applied to non-verbal readings.

Identidade visual e sua relação com a linguagem não verbal

The visual identity is based on a semantic relationship of several signs that make up a coherent system. A bimédia language formed by text and image complement to create an understandable message. This study aims the use of non-verbal communication in the corporate visual identity design project, contextualizing the role of the designer as mediator for informational corporate message to their audiences.

Um processo para utilizar a tecnologia de impressão 3D na construção de instrumentos didáticos para o ensino de Ciências

Pós-graduação em Educação para a Ciência - FC

Segmentação interativa de imagens utilizando competição e cooperação entre partículas

Pós-graduação em Ciência da Computação - IBILCE

Comportamentos de crianças do espectro do autismo com seus pares no contexto de educação musical

O Transtorno do espectro do autismo (TEA) é marcado por prejuízos nas áreas de interação social, comunicação, comportamento e processamento sensorial. Aspectos relacionados a prejuízos no repertório de interação social, bem como estratégias para torná-la mais adequada têm sido amplamente estudados. Dentre estas estratégias, as que utilizam música têm recebido atenção. O presente estudo tem como objetivo investigar os benefícios da educação musical ao desenvolvimento da interação social de crianças com seus pares, focando-se na qualidade e na frequência da apresentação de tais comportamentos. Participaram duas crianças com TEA, com idades de cinco e seis anos, em aulas de percussão em grupo. Os instrumentos utilizados foram a Ficha de dados sociodemográficos e de desenvolvimento, para traçar os perfis dos participantes; e o Protocolo de observação de comportamentos de crianças com TEA com seus pares, para a análise comportamental, durante oito aulas/percussão (240 minutos). Os resultados sugerem que ambos apresentaram tendência ao aumento de iniciativas e respostas espontâneas e à diminuição de comportamentos não funcionais. Verificou-se a ocorrência do uso de estereotipias para tentativas de/e interações, embora esporadicamente. Destacaram-se os papéis do contexto, dos perfis das crianças, e do manejo comportamental por adultos, na promoção de interações.

Active Learning to Maximize Area Under the ROC Curve

In active learning, a machine learning algorithmis given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal is then to judiciously choose which examples in U to have labeled in order to optimize some performance criterion, e.g. classification accuracy. We study how active learning affects AUC. We examine two existing algorithms from the literature and present our own active learning algorithms designed to maximize the AUC of the hypothesis. One of our algorithms was consistently the top performer, and Closest Sampling from the literature often came in second behind it. When good posterior probability estimates were available, our heuristics were by far the best.

Bandit-Based Algorithms for Budgeted Learning

We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ labels but has to pay for each attribute that is specified. This learning model is appropriate in many areas, including medical applications. We present new algorithms for choosing which attributes to purchase of which examples in the budgeted learning model based on algorithms for the multi-armed bandit problem. All of our approaches outperformed the current state of the art. Furthermore, we present a new means for selecting an example to purchase after the attribute is selected, instead of selecting an example uniformly at random, which is typically done. Our new example selection method improved performance of all the algorithms we tested, both ours and those in the literature.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Anti-Aversive Effects of Cannabidiol on Innate Fear-Induced Behaviors Evoked by an Ethological Model of Panic Attacks Based on a Prey vs the Wild Snake Epicrates cenchria crassus Confrontation Paradigm

Several pharmacological targets have been proposed as modulators of panic-like reactions. However, interest should be given to other potential therapeutic neurochemical agents. Recent attention has been given to the potential anxiolytic properties of cannabidiol, because of its complex actions on the endocannabinoid system together with its effects on other neurotransmitter systems. The aim of this study was to investigate the effects of cannabidiol on innate fear-related behaviors evoked by a prey vs predator paradigm. Male Swiss mice were submitted to habituation in an arena containing a burrow and subsequently pre-treated with intraperitoneal administrations of vehicle or cannabidiol. A constrictor snake was placed inside the arena, and defensive and non-defensive behaviors were recorded. Cannabidiol caused a clear anti-aversive effect, decreasing explosive escape and defensive immobility behaviors outside and inside the burrow. These results show that cannabidiol modulates defensive behaviors evoked by the presence of threatening stimuli, even in a potentially safe environment following a fear response, suggesting a panicolytic effect. Neuropsychopharmacology (2012) 37, 412-421; doi:10.1038/npp.2011.188; published online 14 September 2011

Determination of trace elements in bovine semen samples by inductively coupled plasma mass spectrometry and data mining techniques for identification of bovine class

The reproductive performance of cattle may be influenced by several factors, but mineral imbalances are crucial in terms of direct effects on reproduction. Several studies have shown that elements such as calcium, copper, iron, magnesium, selenium, and zinc are essential for reproduction and can prevent oxidative stress. However, toxic elements such as lead, nickel, and arsenic can have adverse effects on reproduction. In this paper, we applied a simple and fast method of multi-element analysis to bovine semen samples from Zebu and European classes used in reproduction programs and artificial insemination. Samples were analyzed by inductively coupled plasma spectrometry (ICP-MS) using aqueous medium calibration and the samples were diluted in a proportion of 1:50 in a solution containing 0.01% (vol/vol) Triton X-100 and 0.5% (vol/vol) nitric acid. Rhodium, iridium, and yttrium were used as the internal standards for ICP-MS analysis. To develop a reliable method of tracing the class of bovine semen, we used data mining techniques that make it possible to classify unknown samples after checking the differentiation of known-class samples. Based on the determination of 15 elements in 41 samples of bovine semen, 3 machine-learning tools for classification were applied to determine cattle class. Our results demonstrate the potential of support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) chemometric tools to identify cattle class. Moreover, the selection tools made it possible to reduce the number of chemical elements needed from 15 to just 8.

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

COMMUNICATION STRATEGIES USED BY HEALTH CARE PROFESSIONALS IN PROVIDING PALLIATIVE CARE TO PATIENTS

The objective of this study is to verify the relevance and utilization of communication strategies in palliative care. This is a multicenter qualitative study using a questionnaire, performed from August of 2008 to July of 2009 with 303 health care professionals who worked with patients receiving palliative care. Data were subjected to descriptive statistical analysis. Most participants (57.7%) were unable to state at least one verbal communication strategy, and only 15.2% were able to describe five signs or non-verbal communication strategies. The verbal strategies most commonly mentioned were those related to answering questions about the disease/treatment. Among the non-verbal strategies used, the most common were affective touch, looking, smiling, physical proximity, and careful listening. Though professionals have assigned a high degree of importance to communication in palliative care, they showed poor knowledge regarding communication strategies. Final considerations include the necessity of training professionals to communicate effectively in palliative care.