Developing solidarity

Se presenta un estudio sobre la formación de la cultura solidaria en dos cooperativas brasileñas; una de ellas, compuesta por miembros de clases populares; la otra, por personas de clase social media con formación universitaria. Este trabajo trata de evidenciar, teórica y empíricamente, cómo la construcción de un emprendimiento colectivo – democrático y capaz de crear trabajo y riqueza – promueve la construcción de vínculos de solidaridad entre las personas. Esa ayuda mutua crea las bases para el cultivo y desarrollo de una cultura solidaria entre ellos, en nítido contraste con las determinaciones más amplias de la sociedad en que vivimos.This study presents the development of solidary culture in two Brazilian cooperatives: one formed by the popular social class and the other by the middle class who have a university degree. This work attemps to show, based on the theoretical and empirical data, how the formation of a collective venture, democratic and capable of work and income, promotes the creation of solidarity among people. This kind of mutual help forms the basis for the development of a cultural solidarity despite the major differences of contrasting societies

COVID-19 Pandemic Initial Effects on the Idiosyncratic Risk in Latin America

This work aims to estimate the idiosyncratic risk of Latin American economies and emerging economies using heteroscedastic conditional models to verify the impact of the Covid-19 pandemic on the risk associated with productive projects. The methodology used is based on the portfolio theory to estimate the idiosyncratic risk. The results highlight that Latin American economies are more susceptible to sanitary crises, such as the current pandemic, than emerging economies. The inability of emerging countries to generate the necessary savings to provide for their development imposes the need to attract resources for project financing and investment. Thus, determining the specific risk of Latin American countries is fundamental for international investors giving them another parameter when deciding on investment or financing on the continent. Originally, this work demonstrates how the sanitary crisis deriving from the Covid-19 pandemic affected the idiosyncratic or specific risk of Latin American economies using their capital market indicators. This study contributes to the assessment of Latin American economies specific risk or country risk at the beginning of the pandemic.Efectos iniciales de la Pandemia por COVID-19 sobre el riesgo idiosincrático en América Latina Este trabajo tiene como objetivo estimar el riesgo idiosincrásico de las economías latinoamericanas y las economías emergentes utilizando modelos condicionales heterocedásticos para verificar el impacto de la pandemia Covid-19 sobre el riesgo asociado a los proyectos productivos. La metodología utilizada se basa en la teoría de la cartera para estimar el riesgo idiosincrásico. Los resultados destacan que las economías latinoamericanas son más susceptibles a crisis sanitarias que las economías emergentes. La incapacidad de los países emergentes para generar los ahorros necesarios para su desarrollo, impone la necesidad de atraer recursos para la financiación e inversión de proyectos. Así, determinar el riesgo específico de los países latinoamericanos es fundamental para los inversores internacionales dándoles un parámetro más a la hora de decidir sobre inversión o financiación en el continente. De manera original, este trabajo demuestra cómo la crisis derivada de la pandemia Covid-19 afectó el riesgo idiosincrásico o específico de las economías latinoamericanas utilizando sus indicadores del mercado de capitales. Este estudio contribuye a la evaluación del riesgo específico o riesgo país de las economías latinoamericanas al inicio de la pandemia

The Relationship between Crude Oil Prices and Exchange Rates

Crude oil prices are influenced by several events that occur randomly, for example, the weather, the available stocks of oil, the economic growth, the variation in the industrial production, political or geopolitical aspects, exchange rate movements, and so on. Oil price volatility brings uncertainties for the world economy. Despite the difficulty in working with oil price time series, a lot of researches have been developing ways to better understand the stochastic process which represents oil prices movements. This work introduces an alternative methodology, with a Bayesian approach, for the construction of forecasting models to study the returns of oil prices. The methodology introduced here takes in consideration the violation of homoskedasticity and the occurrence of abnormal information, or the non-Gaussian distribution, in the construction of the price forecast models. Moreover, this work examines the relationship between crude oil prices and exchange rate through a cointegration test. The data used in this study consists of the daily closing exchange rate of US dollar to Euro, and oil prices of WTI, West Texas Intermediate, and Brent types, from January 2005 to March 2009. The results do not show the acceptance of cointegration hypothesis. With the presented models, it is possible to infer that the exchange rate is important to explain the oil barrel returns

Asymmetry between Gasoline and Crude Oil Prices in the Brazilian Economy and Some Selected Developed Economies

The objective of this work is to study the gasoline prices evolution and its relationship between crude oil prices in the international market through causality and cointegration tests and across regression models of asymmetric, specifically this work uses stochastic models with heteroskedasticity and error correction mechanisms when it is mandatory. To achieve the purpose the purpose of this work, the gasoline prices were collected in Brazil, the USA and in a selected sample of six European countries namely Belgium, France, Germany, Italy, the Netherlands and the United Kingdom markets. All results are comparing among the markets selected to observe country similarities. All prices information collected were converted into U.S. dollars per liter. The data covers the period from June 2006 to April 2013

The use of advanced signal processing and deep learning for pattern recognition in integrated metrics of quality performance: a smart grid application

Power quality (PQ) is not a new theme, but it should not be neglected in any way, as its performance parameters will reveal problems in the adequacy between the consumer equipment and the electrical grid. With the ongoing transformations in electrical power systems, characterized by the high penetration of renewable energy sources, the massive insertion of components based on power electronics in the network, and the decentralization of generation, these issues are becoming increasingly important. In Smart Grids, solutions are sought for more advanced solutions to solve PQ disturbances problems. Advanced signal processing plays an essential role in dealing with the network and supporting various applications within this context and Artificial Intelligence (AI), which has gained significant prominence to feed applications with innovative solutions in several areas. This research investigates the use of advanced signal processing and Deep Learning techniques for pattern recognition and classification of signals with PQ disorders. For this purpose, the Continuous Wavelet Transform with a filter bank is used to generate 2-D images with the time-frequency representation from signals with voltage disturbances. The work aims to use Convolutional Neural Networks (CNN) to classify this data according to the images’ distortion. In this implementation of AI, specific stages of design, training, validation, and testing were carried out for a model elaborated by the case file and a knowledge transfer technique with the pre-trained networks SqueezeNet, GoogleNet, and ResNet-50. The work was developed in the MATLAB/Simulink software, all signal processing stages, CNN design, simulation, and the investigated data generation. All steps have their objectives fulfilled, culminating in the excellent execution and development of the research. The results sought high precision for CNN de Scratch and ResNet-50 in classify the test set. The other two models obtained not-so-high accuracy, and the results are consistent when compared with different methodologies. Considerations about the results were pointed out. Finally, some conclusions were established and a philosophical reflection on the role of AI and advanced signal processing in electrical power systems.Agência 1Qualidade de Energia não é uma temática nova, porém de forma alguma deve ser negligenciada, pois seus parâmetros de performance indicam problemas na adequação entre o equipamento do consumidor e a rede elétrica. Com as transformações em andamento nos sistemas elétricos de potência, caracterizados pela alta penetração de fontes renováveis de energia, inserção massiva de componentes baseados em eletrônica de potência na rede e descentralização da geração, essas questões se tornam cada vez mais importantes. Nas Redes Inteligentes, busca-se soluções cada vez mais avançadas para solucionar questões dos distúrbios da Qualidade de Energia. Dentro desse contexto, o processamento avançado de sinais possui um papel importante para tratar as medições da rede e apoiar diversas aplicações. A Inteligência Artificial, tem ganhado grande destaque dar suporte para aplicações com soluções inovadoras em diversas áreas. Esta pesquisa tem como objetivo investigar o uso de processamento avançado de sinais e técnicas de Aprendizagem Profundo ("Deep Learning") para reconhecimento de padrões e classificação de sinais com distúrbios da Qualidade de Energia. Para este propósito, a Transformada Wavelet Contínua com um banco de filtros é usada para gerar imagens 2-D no domínio do tempo-frequência a partir de sinais com distúrbios de tensão. O trabalho visa utilizar Redes Neurais Convolucionais para classificar essas imagens de acordo com a respectiva distorção. Nesta implementação de Inteligência Artificial, etapas específicas de projeto, treinamento, validação e teste serão realizadas para um modelo elaborado pelo autor e também utilizando a técnica de transferência de conhecimento com as redes pré-treinadas SqueezeNet, GoogleNet, e ResNet-50. O trabalho foi desenvolvido no software MATLAB/Simulink, todas as etapas de processamento do sinal, projeto de modelos de classificação, simulação e geração dos dados investigados. Todas as etapas tiveram seus objetivos específicos cumpridos, culminando na boa execução e desenvolvimento da pesquisa. Os resultados obtidos mostraram alta precisão para "CNN de Scratch" e ResNet-50 em classificar o conjunto de testes. Os outros dois modelos obtiveram acurácias não tão altas, e os resultados se mostram consistentes ao comparar com outras metodologias. Considerações sobre os resultados foram apontadas. Por fim, algumas conclusões foram estabelecidas, assim como uma reflexão filosófica sobre o papel dos tópicos abordados para os sistemas elétricos de potência