Maximum penetration of microgeneration photovoltaic panels in distribution network

In smart grids context, the distributed generation units based in renewable resources, play an important rule. The photovoltaic solar units are a technology in evolution and their prices decrease significantly in recent years due to the high penetration of this technology in the low voltage and medium voltage networks supported by governmental policies and incentives. This paper proposes a methodology to determine the maximum penetration of photovoltaic units in a distribution network. The paper presents a case study, with four different scenarios, that considers a 32-bus medium voltage distribution network and the inclusion storage units.

Mathematical aspects of the Heisenberg uncertainty principle within local fractional Fourier analysis

In this paper, we discuss the mathematical aspects of the Heisenberg uncertainty principle within local fractional Fourier analysis. The Schrödinger equation and Heisenberg uncertainty principles are structured within local fractional operators.

Self-similarity principle: the reduced description of randomness

A new general fitting method based on the Self-Similar (SS) organization of random sequences is presented. The proposed analytical function helps to fit the response of many complex systems when their recorded data form a self-similar curve. The verified SS principle opens new possibilities for the fitting of economical, meteorological and other complex data when the mathematical model is absent but the reduced description in terms of some universal set of the fitting parameters is necessary. This fitting function is verified on economical (price of a commodity versus time) and weather (the Earth’s mean temperature surface data versus time) and for these nontrivial cases it becomes possible to receive a very good fit of initial data set. The general conditions of application of this fitting method describing the response of many complex systems and the forecast possibilities are discussed.

Maximum-revenue tariff under Bertrand duopoly with unknown costs

This paper considers an international trade under Bertrand model with differentiated products and with unknown production costs. The home government imposes a specific import tariff per unit of imports from the foreign firm. We prove that this tariff is decreasing in the expected production costs of the foreign firm and increasing in the production costs of the home firm. Furthermore, it is increasing in the degree of product substitutability. We also show that an increase in the tariff results in both firms increasing their prices, an increase in both expected sales and expected profits for the home firm, and a decrease in both expected sales and expected profits for the foreign firm.

Maximum-revenue tariff with different roles in a price-setting competition

Published also at Lecture Notes in Engineering and Computer Science

Simple laccase-based biosensor for formetanate hydrochloride quantification in fruits

This work describes the development of an electrochemical enzymatic biosensor for quantification of the pesticide formetanate hydrochloride (FMT). It is based on a gold electrode modified with electrodeposited gold nanoparticles and laccase. The principle behind its development relies on FMT's capacity to inhibit the laccase catalytic reaction that occurs in the presence of phenolic substrates. The optimum values for the relevant experimental variables such as gold nanoparticles electrochemical deposition (at − 0.2 V for 100 s), laccase immobilization (via glutaraldehyde cross-linking), laccase concentration (12.4 mg/mL), substrate selection and concentration (5.83×10−5 M of aminophenol), pH (5.0), buffer (Britton–Robinson), and square-wave voltammetric parameters were determined. The developed biosensor was successfully applied to FMT determination in mango and grapes. The attained limit of detection was 9.5×10−8 ± 9.5×10−10 M (0.02 ± 2.6×10−4 mg/kg on a fresh fruit weight basis). Recoveries for the five tested spiking levels ranged from 95.5 ± 2.9 (grapes) to 108.6 ± 2.5% (mango). The results indicated that the proposed device presents suitable characteristics in terms of sensitivity (20.58 ± 0.49 A/μM), linearity (9.43×10−7 to 1.13×10−5 M), accuracy, repeatability (RSD of 1.4%), reproducibility (RSD of 1.8%) and stability (19 days) for testing of compliance with established maximum residue limits of FMT in fruits and vegetables.

Processos Colaborativos e de Comunicação

Esta tese descreve um projeto desenvolvido na sua maioria durante o segundo semestre do ano letivo 2013/2014 nas instalações da Bosch Security Systems ST em Ovar, uma empresa do Grupo Bosch. Este projeto resultou de uma cooperação existente entre a Avans University of Applied Sciences (AUAS) e o Instituto Superior de Engenharia do Porto (ISEP), bem como entre a fábrica Bosch ST Ovar e o Centro de Desenvolvimento Bosch situado em Eindhoven, Holanda. O objetivo é o estudo dos processos colaborativos de comunicação que se realizam entre a R&D (Research and Development/Centro de Desenvolvimento) e a MP (Manufacturing Plant/Fábrica). O sucesso das organizações empresariais só é possível se se alcançar a satisfação das expectativas do mercado. Assim, para o sucesso de uma empresa, temos de ter em conta a sua eficiência para conseguir obter o máximo rendimento da sua missão, que só é possível através de comunicações e de processos produtivos/serviços que acrescentem valor ao bem que disponibilizam. Tendo sempre em vista este princípio, o Grupo Bosch tem vindo a adotar metodologias com o intuito de eliminar os desperdícios existentes nos seus negócios aumentando o máximo possível os seus rendimentos. O desafio foi encontrar problemas ou dificuldades que levam com que a comunicação não seja realizada eficazmente, o que origina, na maioria dos casos, atrasos nos projetos realizados nesta parceria. Com a orientação do diretor técnico da fábrica e observando o trabalho realizado por 2 MBI’s (Manufacturing Business Interface), iniciou-se a compreensão do meio e os métodos de trabalho da empresa, a forma como é que é feita a parceria com os centros de desenvolvimento, como também as responsabilidades e funções de cada um. O estudo desenvolvido teve como base a metodologia Soft Systems Methodology com envolvimento de um questionário submetido aos colaboradores Bosch, realizando-se no final as sugestões de melhoria. Este relatório apresenta assim o estudo dos processos colaborativos e de comunicação entre a fábrica e o centro de desenvolvimento.

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.

Mechanical properties of chemically modified portuguese pinewood

To turn wood into a construction material with enhanced properties, many methods of chemical modification have been developed in the last few decades. In this work, mechanical properties of pine wood were chemically modified, compared and evaluated. Maritime pine wood (Pinus pinaster) was modified with four chemical processes: 1,3-dimethylol-4,5- dihydroxyethyleneurea, N-methylol melamine formaldehyde, tetra-alkoxysilane and wax. The following mechanical properties were assessed experimentally: Modulus of elasticity measured statically, stiffness stabilization efficiency in different climates (30 and 87% of relative humidity), modulus of rupture, work maximum load, impact bending strength, compression, tensile and shear strength at indoor conditions (65% of relative humidity). In both types of active principle of modification, cell wall or lumen fill, no significant changes on the bending stiffness (modulus of elasticity) were found. In the remaining properties analysed significant changes in the modified wood-material took place compared to unmodified wood control: - Cell wall modification was the most effective method to achieve high stiffness stabilization efficiency (up to 60%) and also increased compression strength (up to 230%). However, modulus of rupture, tensile, shear and the impact bending strength were reduced by both resins, but in a varying extent, where the N-methylol melamine formaldehyde endured less reduction than 1,3-dimethylol-4,5-dihydroxyethyleneurea resin. In the latter, reduction up to 60% can take place. - In the lumen fill modification: tetra-alkoxysilane has no effect in the mechanical properties. Although, a slight increase in shear strength parallel to the grain was found. Wax specimens have shown a slight increase in bending strength, compression, tensile and shear strength as well as in the absorption energy capacity.

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.