Eigenvalue analyses for non-transposed three-phase transmission line considering non-implicit ground wires

This paper presents a method for analyzing electromagnetic transients using real transformation matrices in three-phase systems considering the presence of ground wires. So, for the Z and Y matrices that represent the transmission line, the characteristics of ground wires are not implied in the values related to the phases. A first approach uses a real transformation matrix for the entire frequency range considered in this case. This transformation matrix is an approximation to the exact transformation matrix. For those elements related to the phases of the considered system, the transformation matrix is composed of the elements of Clarke's matrix. In part related to the ground wires, the elements of the transformation matrix must establish a relationship with the elements of the phases considering the establishment of a single homopolar reference in the mode domain. In the case of three-phase lines with the presence of two ground wires, it is unable to get the full diagonalization of the matrices Z and Y in the mode domain. This leads to the second proposal for the composition of real transformation matrix: obtain such transformation matrix from the multiplication of two real and constant matrices. In this case, the inclusion of a second matrix had the objective to minimize errors from the first proposal for the composition of the transformation matrix mentioned. © 2012 IEEE.

The fiscal impact of trade liberalization and commodity price fluctuation: the case of Dominican Republic, 1980-1998

Includes bibliography

Wave equation depth migration using complex Padé approximation

Propomos um novo método de migração em profundidade baseado na solução da equação da onda com densidade constante no domínio da freqüência. Uma aproximação de Padé complexa é usada para aproximar o operador de evolução aplicado na extrapolação do campo de ondas. Esse método reduz as imprecisões e instabilidades devido às ondas evanescentes e produz imagens com menos ruídos numéricos que aquelas obtidas usando-se a aproximação de Padé real para o operador exponencial, principalmente em meios com fortes variações de velocidades. Testes em dados de afastamento nulo do modelo de sal SEG/EAGE e nos dados de tiro comum 2-D Marmousi foram realizados. Os resultados obtidos mostram que o método de migração proposto consegue lidar com fortes variações laterais e também tem uma boa resposta para refletores com mergulhos íngremes. Os resultados foram comparados àqueles resultados obtidos com os métodos split-step Fourier (SSF), phase shift plus interpolarion (PSPI) e Fourier diferenças-finitas (FFD).

Migração FD 3D em profundidade usando aproximação de Padé complexa

A implementação convencional do método de migração por diferenças finitas 3D, usa a técnica de splitting inline e crossline para melhorar a eficiência computacional deste algoritmo. Esta abordagem torna o algoritmo eficiente computacionalmente, porém cria anisotropia numérica. Esta anisotropia numérica por sua vez, pode levar a falsos posicionamentos de refletores inclinados, especialmente refletores com grandes ângulos de mergulho. Neste trabalho, como objetivo de evitar o surgimento da anisotropia numérica, implementamos o operador de extrapolação do campo de onda para baixo sem usar a técnica splitting inline e crossline no domínio frequência-espaço via método de diferenças finitas implícito, usando a aproximação de Padé complexa. Comparamos a performance do algoritmo iterativo Bi-gradiente conjugado estabilizado (Bi-CGSTAB) com o multifrontal massively parallel solver (MUMPS) para resolver o sistema linear oriundo do método de migração por diferenças finitas. Verifica-se que usando a expansão de Padé complexa ao invés da expansão de Padé real, o algoritmo iterativo Bi-CGSTAB fica mais eficientes computacionalmente, ou seja, a expansão de Padé complexa atua como um precondicionador para este algoritmo iterativo. Como consequência, o algoritmo iterativo Bi-CGSTAB é bem mais eficiente computacionalmente que o MUMPS para resolver o sistema linear quando usado apenas um termo da expansão de Padé complexa. Para aproximações de grandes ângulos, métodos diretos são necessários. Para validar e avaliar as propriedades desses algoritmos de migração, usamos o modelo de sal SEG/EAGE para calcular a sua resposta ao impulso.

Migração FFD 3D em profundidade usando aproximação de Padé complexa

Implementações dos métodos de migração diferença finita e Fourier (FFD) usam fatoração direcional para acelerar a performance e economizar custo computacional. Entretanto essa técnica introduz anisotropia numérica que podem erroneamente posicionar os refletores em mergulho ao longo das direções em que o não foi aplicado a fatoração no operador de migração. Implementamos a migração FFD 3D, sem usar a técnica do fatoração direcional, no domínio da frequência usando aproximação de Padé complexa. Essa aproximação elimina a anisotropia numérica ao preço de maior custo computacional buscando a solução do campo de onda para um sistema linear de banda larga. Experimentos numéricos, tanto no modelo homogêneo e heterogêneo, mostram que a técnica da fatoração direcional produz notáveis erros de posicionamento dos refletores em meios com forte variação lateral de velocidade. Comparamos a performance de resolução do algoritmo de FFD usando o método iterativo gradiente biconjugado estabilizado (BICGSTAB) e o multifrontal massively parallel direct solver (MUMPS). Mostrando que a aproximação de Padé complexa é um eficiente precondicionador para o BICGSTAB, reduzindo o número de iterações em relação a aproximação de Padé real. O método iterativo BICGSTAB é mais eficiente que o método direto MUMPS, quando usamos apenas um termo da expansão de Padé complexa. Para maior ângulo de abertura do operador, mais termos da série são requeridos no operador de migração, e neste caso, a performance do método direto é mais eficiente. A validação do algoritmo e as propriedades da evolução computacional foram avaliadas para a resposta ao impulso do modelo de sal SEG/EAGE.

Finite-difference calculation of donor energy levels in a spherical quantum dot subject to a magnetic field

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Tracking the implicit self using event-related potentials

Negative biases in implicit self-evaluation are thought to be detrimental to subjective well-being and have been linked to various psychological disorders, including depression. An understanding of the neural processes underlying implicit self-evaluation in healthy subjects could provide a basis for the investigation of negative biases in depressed patients, the development of differential psychotherapeutic interventions, and the estimation of relapse risk in remitted patients. We thus studied the brain processes linked to implicit self-evaluation in 25 healthy subjects using event-related potential (ERP) recording during a self-relevant Implicit Association Test (sIAT). Consistent with a positive implicit self-evaluation in healthy subjects, they responded significantly faster to the congruent (self-positive mapping) than to the incongruent sIAT condition (self-negative mapping). Our main finding was a topographical ERP difference in a time window between 600 and 700 ms, whereas no significant differences between congruent and incongruent conditions were observed in earlier time windows. This suggests that biases in implicit self-evaluation are reflected only indirectly, in the additional recruitment of control processes needed to override the positive implicit self-evaluation of healthy subjects in the incongruent sIAT condition. Brain activations linked to these control processes can thus serve as an indirect measure for estimating biases in implicit self-evaluation. The sIAT paradigm, combined with ERP, could therefore permit the tracking of the neural processes underlying implicit self-evaluation in depressed patients during psychotherapy.

Fitting 3D morphable models using implicit representations

We consider the problem of approximating the 3D scan of a real object through an affine combination of examples. Common approaches depend either on the explicit estimation of point-to-point correspondences or on 2-dimensional projections of the target mesh; both present drawbacks. We follow an approach similar to [IF03] by representing the target via an implicit function, whose values at the vertices of the approximation are used to define a robust cost function. The problem is approached in two steps, by approximating first a coarse implicit representation of the whole target, and then finer, local ones; the local approximations are then merged together with a Poisson-based method. We report the results of applying our method on a subset of 3D scans from the Face Recognition Grand Challenge v.1.0.

Unresolved Problems with the "I", the "A" and the "T": Logical and Psychometric Critique of the Implicit Association Test (IAT)

The Implicit Association Test (IAT) had already gained the status of a prominent assessment procedure before its psychometric properties and underlying task structure were understood. The present critique addresses five major problems that arise when the IAT is used for diagnostic inferences: (1) the asymmetry of causal and diagnostic inferences; (2) the viability of the underlying association model; (3) the lack of a testable model underlying IAT-based inferences; (4) the difficulties of interpreting difference scores; and (5) the susceptibility of the IAT to deliberate faking and strategic processing. Based on a theoretical reflection of these issues, and a comprehensive survey of published IAT studies, it is concluded that a number of uncontrolled factors can produce (or reduce) significant IAT scores independently of the personality attribute that is supposed to be captured by the IAT procedure.

Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.

This paper presents some ideas about a new neural network architecture that can be compared to a Taylor analysis when dealing with patterns. Such architecture is based on lineal activation functions with an axo-axonic architecture. A biological axo-axonic connection between two neurons is defined as the weight in a connection in given by the output of another third neuron. This idea can be implemented in the so called Enhanced Neural Networks in which two Multilayer Perceptrons are used; the first one will output the weights that the second MLP uses to computed the desired output. This kind of neural network has universal approximation properties even with lineal activation functions. There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get optimum solution. The latter ones are based on genetic models, that is, individuals can die and new individuals are created combining information of alive one; or are based on molecular/celular behaviour passing information from one structure to another. A swarm-based model is applied to obtain the Neural Network, training the net with a Particle Swarm algorithm.

Neurite, a finite difference large scale parallel program for the simulation of the electrical signal propagation in neurites under mechanical loading

With the growing body of research on traumatic brain injury and spinal cord injury, computational neuroscience has recently focused its modeling efforts on neuronal functional deficits following mechanical loading. However, in most of these efforts, cell damage is generally only characterized by purely mechanistic criteria, function of quantities such as stress, strain or their corresponding rates. The modeling of functional deficits in neurites as a consequence of macroscopic mechanical insults has been rarely explored. In particular, a quantitative mechanically based model of electrophysiological impairment in neuronal cells has only very recently been proposed (Jerusalem et al., 2013). In this paper, we present the implementation details of Neurite: the finite difference parallel program used in this reference. Following the application of a macroscopic strain at a given strain rate produced by a mechanical insult, Neurite is able to simulate the resulting neuronal electrical signal propagation, and thus the corresponding functional deficits. The simulation of the coupled mechanical and electrophysiological behaviors requires computational expensive calculations that increase in complexity as the network of the simulated cells grows. The solvers implemented in Neurite-explicit and implicit-were therefore parallelized using graphics processing units in order to reduce the burden of the simulation costs of large scale scenarios. Cable Theory and Hodgkin-Huxley models were implemented to account for the electrophysiological passive and active regions of a neurite, respectively, whereas a coupled mechanical model accounting for the neurite mechanical behavior within its surrounding medium was adopted as a link between lectrophysiology and mechanics (Jerusalem et al., 2013). This paper provides the details of the parallel implementation of Neurite, along with three different application examples: a long myelinated axon, a segmented dendritic tree, and a damaged axon. The capabilities of the program to deal with large scale scenarios, segmented neuronal structures, and functional deficits under mechanical loading are specifically highlighted.

Comparison of implicit and explicit FSI coupling strategies in cardiovascular system = Comparación de las estrategias implícitas y explícitas de acoplamiento FSI en el sistema cardiovascular

Comparación de los esquemas de integración temporal explícito e implícito, en la simulación del flujo sanguíneo y su interacción con la pared arterial. There are two major strategies in FSI coupling techniques: implicit and explicit. The general difference between these methodologies is how many times the data is exchanged between the fluid and solid domains at each FSI time-step. In both coupling strategies, the pressure values coming from fluid domain calculations at each time-step are exported to the solid domain, and consequently, the solid domain is analyzed with these imported forces. In contrast to the explicit coupling, in the implicit approach the fluid and solid domain’s data is exchanged several times until the convergence is achieved. Although this method may boost the numerical stabilization, it increases the computational cost due to the extra data exchanges. In cardiovascular simulations, depending on the analysis objectives, one may choose an explicit or implicit approach. In the current work, the advantage of an explicit coupling strategy is highlighted when simulation of pulsatile blood flow in elastic arteries is desired.

Difference Blindness vs. Bias Awareness: Why Law Firms with the Best of Intentions Have Failed to Create Diverse Partnerships

This Article uses the example of BigLaw firms to explore the challenges that many elite organizations face in providing equal opportunity to their workers. Despite good intentions and the investment of significant resources, large law firms have been consistently unable to deliver diverse partnership structures - especially in more senior positions of power. Building on implicit and institutional bias scholarship and on successful approaches described in the organizational behavior literature, we argue that a significant barrier to systemic diversity at the law firm partnership level has been, paradoxically, the insistence on difference blindness standards that seek to evaluate each person on their individual merit. While powerful in dismantling intentional discrimination, these standards rely on an assumption that lawyers are, and have the power to act as, atomistic individuals - a dangerous assumption that has been disproven consistently by the literature establishing the continuing and powerful influence of implicit and institutional bias. Accordingly, difference blindness, which holds all lawyers accountable to seemingly neutral standards, disproportionately disadvantages diverse populations and normalizes the dominance of certain actors - here, white men - by creating the illusion that success or failure depends upon individual rather than structural constraints. In contrast, we argue that a bias awareness approach that encourages identity awareness and a relational framework is a more promising way to promote equality, equity, and inclusion.

Intracranial EEG correlates of implicit relational inference within the hippocampus

Drawing inferences from past experiences enables adaptive behavior in future situations. Inference has been shown to depend on hippocampal processes. Usually, inference is considered a deliberate and effortful mental act which happens during retrieval, and requires the focus of our awareness. Recent fMRI studies hint at the possibility that some forms of hippocampus-dependent inference can also occur during encoding and possibly also outside of awareness. Here, we sought to further explore the feasibility of hippocampal implicit inference, and specifically address the temporal evolution of implicit inference using intracranial EEG. Presurgical epilepsy patients with hippocampal depth electrodes viewed a sequence of word pairs, and judged the semantic fit between two words in each pair. Some of the word pairs entailed a common word (e.g.,‘winter - red’, ‘red - cat’) such that an indirect relation was established in following word pairs (e.g, ‘winter - cat’). The behavioral results suggested that drawing inference implicitly from past experience is feasible because indirect relations seemed to foster ‘fit’ judgments while the absence of indirect relations fostered 'do not fit' judgments, even though the participants were unaware of the indirect relations. A event-related potential (ERP) difference emerging 400 ms post-stimulus was evident in the hippocampus during encoding, suggesting that indirect relations were already established automatically during encoding of the overlapping word pairs. Further ERP differences emerged later post-stimulus (1500 ms), were modulated by the participants' responses and were evident during encoding and test. Furthermore, response-locked ERP effects were evident at test. These ERP effects could hence be a correlate of the interaction of implicit memory with decision-making. Together, the data map out a time-course in which the hippocampus automatically integrates memories from discrete but related episodes to implicitly influence future decision making.

From ideal to real:a longitudinal study of the role of implicit leadership theories on leader-member exchanges and employee outcomes

The results of the present longitudinal study demonstrate the importance of implicit leadership theories (ILTs) for the quality of leader-member exchanges (LMX) and employees' organizational commitment, job satisfaction, and well-being. Results based on a sample of 439 employees who completed the study questionnaires at 2 time points showed that the closer employees perceived their actual manager's profile to be to the ILTs they endorsed, the better the quality of LMX. Results also indicated that the implicit-explicit leadership traits difference had indirect effects on employee attitudes and well-being. These findings were consistent across employee groups that differed in terms of job demand and the duration of manager-employee relation, but not in terms of motivation. Furthermore, crossed-lagged modeling analyses of the longitudinal data explored the possibility of reciprocal effects between implicit-explicit leadership traits difference and LMX and provided support for the initially hypothesized direction of causal effects.