931 resultados para Radial channel
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Este trabalho desenvolve um novo "canal de Confiança" da política fiscal e caracteriza a política ótima quando esse canal é levado em consideração. Para esse objetivo, utilizamos um modelo estático com (i) concorrência monopolística, (ii) custos de ajustamento fixos para investir, (iii) complementaridade estratégica devido a informação imperfeita com respeito a produtividade agregada, e (iv) bens privados como substitutos imperfeitos de bens privados. Este arcabouço acomoda a possibilidade de falhas de coordenação nos investimentos, mas apresenta um equilíbrio único. Mostramos que a política fiscal tem efeitos importantes na coordenação. Um aumento dos gastos do governo leva a uma maior demanda por bens privados. Mais importante, este também afeta as expectativas de ordem superior com relação a demanda das demais firmas, que amplifica os efeitos do aumento inicial da demanda devido a complementaridade estratégica nas decisões de investimento. Como as demais firmas estão se deparam com uma demanda maior, espera-se que estas invistam mais, que por sua vez, aumenta a demanda individual de cada firma, que aumenta os incentivos a investir. Denominamos isto como o "canal de confiança" da política fiscal. Sob a ameaça de falhas de coordenação, a política fiscal ótima prescreve produzir além do ponto em que o benefício marginal resultante do consumo de bens públicos é igual ao custo marginal desses bens. Este benefício adicional vem do fato de que a política fiscal pode ampliar a coordenação dos investimentos.
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The onset of the financial crisis in 2008 and the European sovereign crisis in 2010 renewed the interest of macroeconomists on the role played by credit in business cycle fluctuations. The purpose of the present work is to present empirical evidence on the monetary policy transmission mechanism in Brazil with a special eye on the role played by the credit channel, using different econometric techniques. It is comprised by three articles. The first one presents a review of the literature of financial frictions, with a focus on the overlaps between credit activity and the monetary policy. It highlights how the sharp disruptions in the financial markets spurred central banks in developed and emerging nations to deploy of a broad set of non conventional tools to overcome the damage on financial intermediation. A chapter is dedicated to the challenge face by the policymaking in emerging markets and Brazil in particular in the highly integrated global capital market. This second article investigates the implications of the credit channel of the monetary policy transmission mechanism in the case of Brazil, using a structural FAVAR (SFAVAR) approach. The term “structural” comes from the estimation strategy, which generates factors that have a clear economic interpretation. The results show that unexpected shocks in the proxies for the external finance premium and the credit volume produce large and persistent fluctuations in inflation and economic activity – accounting for more than 30% of the error forecast variance of the latter in a three-year horizon. Counterfactual simulations demonstrate that the credit channel amplified the economic contraction in Brazil during the acute phase of the global financial crisis in the last quarter of 2008, thus gave an important impulse to the recovery period that followed. In the third articles, I make use of Bayesian estimation of a classical neo-Keynesian DSGE model, incorporating the financial accelerator channel developed by Bernanke, Gertler and Gilchrist (1999). The results present evidences in line to those already seen in the previous article: disturbances on the external finance premium – represented here by credit spreads – trigger significant responses on the aggregate demand and inflation and monetary policy shocks are amplified by the financial accelerator mechanism. Keywords: Macroeconomics, Monetary Policy, Credit Channel, Financial Accelerator, FAVAR, DSGE, Bayesian Econometrics
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Robust Monetary Policy with the Consumption - Wealth Channel
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In the contemporary societies, many children are drawn to digital media, using it in ways that were initially unfathomable. Changing digital habits among young children have been affiliated to the rapid development, witnessed in the technological field. Prevalently, new forms of technology are being developed and ingrained into young children’s day-to-day activities. The emergence of new forms of technology has in turn prompted significant changes in digital and media consumption particularly, among young children. Changes in media and digital consumption have in turn instigated linear transition in the analogue media industries. This has resulted in analogue media networks working towards digitalizing their industries in a manner that will befit changing digital habits among young children. This report aims at establishing and analyzing the different ways in which children’s digital habits have changed and revolutionized. To achieve this, the report will critically examine the existing scope of knowledge, with reference to changing digital habits among young audiences. Further, the report also aims at establishing the manner in which children television networks have adapted to the changing digital habits among young audiences. To achieve this, the report will focus on two children television networks, Disney channel, and Nickelodeon. After which, a comparative analysis will be conducted to establish the changes made by each of these television channels, with the aim of adapting to the new digital habits among children.
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Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.
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The study aimed to evaluate the radial profile and the uniformity of water distribution of sprinkler manufactured by the company NaanDanJain, model 427 1/2 '' M and nozzle with 2.8 mm of internal diameter, operating at pressures of 150, 200, 300 and 400 kPa and five positions of the deflector (0, 20, 50, 80 and 100%). For the determination of the parameters evaluated, the grid method was used and with the help of computer application CATCH 3D, overlapping layers of water depths was calculated with ten spacing. The results show that the deflector adjustment influences the radius of wetness and the distribution profile while the uniformity of water application showed as an important mechanism, since it permits different behavior for the sprinkler, ensuring wide track of utilization of the equipment.
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The present study evaluated the origin, distribution and ramification of the radial nerves were studied in 30 adult domestic cats. The sample included 15 females and 15 males of unknown breed. The specimens were fixed in 10% formaldehyde solution. The radial nerve showed many fascicles from the origin also your ramification in superficial and deep branches. Radial nerves were observed to originate, in 16 cases (26.7%), from the ventral branch of the sixth cervical spinal nerve; in 60 cases (100%), from the ventral branch of the seventh cervical spinal nerve; in 60 cases (100%), from the ventral branch of the eight cervical nerve and in 60 cases (100%), from the ventral branch of the first thoracic nerve. The radial nerves branched out, in all of the animals studied (100.0%), to the tensor fasciae antebrachii, long, accessory, medial and lateral heads of the triceps branchii and anconeus muscles. The radial nerve emits of 14 to 25 nervous branches in this region. However, the branch of the sixth cervical spinal nerve and the nervous fascicles reveal significant differences (p <= 0.05), respectively, in or with relation to sex of the animals and the studied region.
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The reference intervals for biochemical variables and red blood cell indices of healthy intensively bred channel catfish Ictalurus punctatus were determined. The blood variables were determined using standardized clinical methods. The reference intervals (25th and 75th percentiles) were established using a non-parametric method. Reference intervals for plasma glucose, serum total protein, sodium, potassium, calcium, magnesium, chloride concentration, primary and secondary red blood cell indices were established. The haematological and biochemical reference intervals established may allow important clinical decisions about channel catfish. (c) 2007 the Authors Journal compilation (C) 2007 the Fisheries Society of the British Isles.
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Este trabalho apresenta um levantamento dos problemas associados à influência da observabilidade e da visualização radial no projeto de sistemas de monitoramento para redes de grande magnitude e complexidade. Além disso, se propõe a apresentar soluções para parte desses problemas. Através da utilização da Teoria de Redes Complexas, são abordadas duas questões: (i) a localização e a quantidade de nós necessários para garantir uma aquisição de dados capaz de representar o estado da rede de forma efetiva e (ii) a elaboração de um modelo de visualização das informações da rede capaz de ampliar a capacidade de inferência e de entendimento de suas propriedades. A tese estabelece limites teóricos a estas questões e apresenta um estudo sobre a complexidade do monitoramento eficaz, eficiente e escalável de redes
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This dissertation dea1s with the active magnetic suspension controI system of an induction bearingIess motor configured with split windings. It analyses a dynamic modeI for the radial magnetic forces actuating on the rotor. From that, it proposes a new approach for the composition of the currents imposed to the machine's stator. It shows the tests accomplished with a prototype, proving the usefulness of the new actuating structure for the radial positioning controI. Finnaly, it points out the importance of adapting this structure to well-known rotational controI techniques, continuing this kind of equipment research, which is carried out at Federal University of Rio Grande do Norte since 2000
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This study aims to seek a more viable alternative for the calculation of differences in images of stereo vision, using a factor that reduces heel the amount of points that are considered on the captured image, and a network neural-based radial basis functions to interpolate the results. The objective to be achieved is to produce an approximate picture of disparities using algorithms with low computational cost, unlike the classical algorithms
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The present work describes the use of a mathematical tool to solve problems arising from control theory, including the identification, analysis of the phase portrait and stability, as well as the temporal evolution of the plant s current induction motor. The system identification is an area of mathematical modeling that has as its objective the study of techniques which can determine a dynamic model in representing a real system. The tool used in the identification and analysis of nonlinear dynamical system is the Radial Basis Function (RBF). The process or plant that is used has a mathematical model unknown, but belongs to a particular class that contains an internal dynamics that can be modeled.Will be presented as contributions to the analysis of asymptotic stability of the RBF. The identification using radial basis function is demonstrated through computer simulations from a real data set obtained from the plant
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An alternative nonlinear technique for decoupling and control is presented. This technique is based on a RBF (Radial Basis Functions) neural network and it is applied to the synchronous generator model. The synchronous generator is a coupled system, in other words, a change at one input variable of the system, changes more than one output. The RBF network will perform the decoupling, separating the control of the following outputs variables: the load angle and flux linkage in the field winding. This technique does not require knowledge of the system parameters and, due the nature of radial basis functions, it shows itself stable to parametric uncertainties, disturbances and simpler when it is applied in control. The RBF decoupler is designed in this work for decouple a nonlinear MIMO system with two inputs and two outputs. The weights between hidden and output layer are modified online, using an adaptive law in real time. The adaptive law is developed by Lyapunov s Method. A decoupling adaptive controller uses the errors between system outputs and model outputs, and filtered outputs of the system to produce control signals. The RBF network forces each outputs of generator to behave like reference model. When the RBF approaches adequately control signals, the system decoupling is achieved. A mathematical proof and analysis are showed. Simulations are presented to show the performance and robustness of the RBF network
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The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques.