Uncertainty in a mixed duopoly with quadratic costs

In this paper, we consider a mixed market with uncertain demand, involving one private firm and one public firm with quadratic costs. The model is a two-stage game in which players choose to make their output decisions either in stage 1 or stage 2. We assume that the demand is unknown until the end of the first stage. We compute the output levels at equilibrium in each possible role. We also determine ex-ante and ex-post firms’ payoff functions.

Incentive-based demand response programs designed by asset-light retailers for day-ahead market

Following the deregulation experience of retail electricity markets in most countries, the majority of the new entrants of the liberalized retail market were pure REP (retail electricity providers). These entities were subject to financial risks because of the unexpected price variations, price spikes, volatile loads and the potential for market power exertion by GENCO (generation companies). A REP can manage the market risks by employing the DR (demand response) programs and using its' generation and storage assets at the distribution network to serve the customers. The proposed model suggests how a REP with light physical assets, such as DG (distributed generation) units and ESS (energy storage systems), can survive in a competitive retail market. The paper discusses the effective risk management strategies for the REPs to deal with the uncertainties of the DAM (day-ahead market) and how to hedge the financial losses in the market. A two-stage stochastic programming problem is formulated. It aims to establish the financial incentive-based DR programs and the optimal dispatch of the DG units and ESSs. The uncertainty of the forecasted day-ahead load demand and electricity price is also taken into account with a scenario-based approach. The principal advantage of this model for REPs is reducing the risk of financial losses in DAMs, and the main benefit for the whole system is market power mitigation by virtually increasing the price elasticity of demand and reducing the peak demand.

Application of continuous wavelet transform to the analysis of the modulus of the fractional Fourier transform bands for resolving two component mixture

In this paper, the fractional Fourier transform (FrFT) is applied to the spectral bands of two component mixture containing oxfendazole and oxyclozanide to provide the multicomponent quantitative prediction of the related substances. With this aim in mind, the modulus of FrFT spectral bands are processed by the continuous Mexican Hat family of wavelets, being denoted by MEXH-CWT-MOFrFT. Four modulus sets are obtained for the parameter a of the FrFT going from 0.6 up to 0.9 in order to compare their effects upon the spectral and quantitative resolutions. Four linear regression plots for each substance were obtained by measuring the MEXH-CWT-MOFrFT amplitudes in the application of the MEXH family to the modulus of the FrFT. This new combined powerful tool is validated by analyzing the artificial samples of the related drugs, and it is applied to the quality control of the commercial veterinary samples.

Application of Continuous Wavelet Transform to the Analysis of the Modulus of the Fractional Fourier Transform Bands for Resolving Two Component Mixture

In this paper, the fractional Fourier transform (FrFT) is applied to the spectral bands of two component mixture containing oxfendazole and oxyclozanide to provide the multicomponent quantitative prediction of the related substances. With this aim in mind, the modulus of FrFT spectral bands are processed by the continuous Mexican Hat family of wavelets, being denoted by MEXH-CWT-MOFrFT. Four modulus sets are obtained for the parameter a of the FrFT going from 0.6 up to 0.9 in order to compare their effects upon the spectral and quantitative resolutions. Four linear regression plots for each substance were obtained by measuring the MEXH-CWT-MOFrFT amplitudes in the application of the MEXH family to the modulus of the FrFT. This new combined powerful tool is validated by analyzing the artificial samples of the related drugs, and it is applied to the quality control of the commercial veterinary samples.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

A comparison between thin film solar cells made from co-evaporated CuIn1-xGaxSe2using a one-stage process versus a three-stage process

Until this day, the most efficient Cu(In,Ga)Se2 thin film solar cells have been prepared using a rather complex growth process often referred to as three-stage or multistage. This family of processes is mainly characterized by a first step deposited with only In, Ga and Se flux to form a first layer. Cu is added in a second step until the film becomes slightly Cu-rich, where-after the film is converted to its final Cu-poor composition by a third stage, again with no or very little addition of Cu. In this paper, a comparison between solar cells prepared with the three-stage process and a one-stage/in-line process with the same composition, thickness, and solar cell stack is made. The one-stage process is easier to be used in an industrial scale and do not have Cu-rich transitions. The samples were analyzed using glow discharge optical emission spectroscopy, scanning electron microscopy, X-ray diffraction, current–voltage-temperature, capacitance-voltage, external quantum efficiency, transmission/reflection, and photoluminescence. It was concluded that in spite of differences in the texturing, morphology and Ga gradient, the electrical performance of the two types of samples is quite similar as demonstrated by the similar J–V behavior, quantum spectral response, and the estimated recombination losses.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Writing and living on the stage of history

This article studies the intercultural trajectory of a Portuguese female aristocrat of the eighteenth to nineteenth centuries. Her trajectory of intercultural transition from a Portuguese provincial lady into an independent owner of a sugar mill in tropical Bahia is documented through family letters, which provide a polyphonic representation of a movement of personal, family, and social transculturation over almost two decades. Maria Bárbara began her journey between cultures as a simple spectator-reader, progressively becoming a commentator-actor-protagonist-author in society, in politics, and in history. These letters function as a translation that is sometimes consecutive, other times simultaneous, of the events lived and witnessed. This concept of intercultural translation is based on the theories of Boaventura de Sousa Santos (2006, 2008), who argues that cultural differences imply that any comparison has to be made using procedures of proportion and correspondence which, taken as a whole, constitute the work of translation itself. These procedures construct approximations of the known to the unknown, of the strange to the familiar, of the ‘other’ to the ‘self’, categories which are always unstable. Likewise, this essay explores the unstable contexts of its object of study, with the purpose of understanding different rationalities and worldviews.

Elaboração e estruturação de um modelo de rating interno

Temos vindo a assistir nos últimos anos a uma evolução no que respeita à avaliação do risco de crédito. As constantes alterações de regulamentação bancária, que resultam dos Acordos de Basileia, têm vindo a impor novas normas que condicionam a quantidade e a qualidade do risco de crédito que as Instituições de Crédito podem assumir nos seus balanços. É de grande importância as Instituições de Crédito avaliarem o risco de crédito, as garantias e o custo de capital, pois têm um impacto direto na sua gestão nomeadamente quanto à afetação de recursos e proteção contra perdas. Desta forma, pretende-se com o presente trabalho elaborar e estruturar um modelo de rating interno através de técnicas estatísticas, assim como identificar as variáveis estatisticamente relevantes no modelo considerado. Foi delineada uma metodologia de investigação mista, considerando na primeira parte do trabalho uma pesquisa qualitativa e na segunda parte uma abordagem quantitativa. Através da análise documental, fez-se uma abordagem dos conceitos teóricos e da regulamentação que serve de base ao presente trabalho. No estudo de caso, o modelo de rating interno foi desenvolvido utilizando a técnica estatística designada de regressão linear múltipla. A amostra considerada foi obtida através da base de dados SABI e é constituída por cem empresas solventes, situadas na zona de Paredes, num horizonte temporal de 2011-2013. A nossa análise baseou-se em três cenários, correspondendo cada cenário aos dados de cada ano (2011, 2012 e 2013). Para validar os pressupostos do modelo foram efetuados testes estatísticos de Durbin Watson e o teste de significância - F (ANOVA). Por fim, para obtermos a classificação de rating de cada variável foi aplicada a técnica dos percentis. Pela análise dos três cenários considerados, verificou-se que o cenário dois foi o que obteve maior coeficiente de determinação. Verificou-se ainda que as variáveis independentes, rácio de liquidez geral, grau de cobertura do ativo total pelo fundo de maneio e rácio de endividamento global são estatisticamente relevantes.