Abstract
The incredible numbers associated with social network sites makes technology a very attractive element in the eyes of organizations. Despite this, the existing scholarly literature does not demonstrate sufficient knowledge on how firms should adopt and use these technologies. With this lack in mind, a study was conducted aiming to understand what might be the determinants with the most influence on the SNS adoption process at firm level. the study was performed making use of a mixed methodology approach. In order to achieve an initial list of variables that might have a significant level of relative importance (RI) to the adoption of SNS, a Delphi study was designed and executed through the inclusion of 25 experts in the IT/IS area. From the Delphi results, a proposal for an adoption model that characterized the adoption of SNS at firm level was designed and validated through an empirical study. This empirical approach revealed that the proposed model explained 65% of variation in SNS adoption at firm level. The active involvement of top management, the alignment of the SNS plan with the firm's business plan, the existence of competitive pressures, and the use of SNS for gaining competitive advantages are the determinants with the most influence on technology adoption by firms.

Abstract
O uso das Tecnologias da Informação envolve uma crescente geração de resíduos eletrônicos, pois devido à rápida inovação tecnológica estes, em um curto espaço temporal, tornam-se obsoletos e têm seu ciclo de vida reduzido. Este artigo apresenta o desenvolvimento de modelos de simulação para avaliação dos impactos ambientais e financeiros ocasionados pela extensão do ciclo de vida de computadores pessoais por meio de seu reuso e remanufatura. Para a modelagem do sistema, foi utilizada a metodologia de Dinâmica de Sistemas mediante uso do simulador Vensim. Para a experimentação, verificação e validação do modelo, foram definidos dois cenários: otimista, com altas taxas de reutilização do resíduo; e moderado, com taxas menores de reutilização. Na concepção do modelo, optou-se pela construção deste em submodelos, de modo a facilitar a sua experimentação e análise. Assim, foram desenvolvidos três submodelos, os quais foram denominados: Aquisição/Descarte, PC Remanufaturado e Avaliação dos Benefícios. 

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This chapter presents the final version of the procedural modelling methodology which works with an ontology-based grammar. Its procedural modelling process supports the generation of virtual buildings delimited by non-convex shapes, with divisions constrained by a variable number of delimiting wall segments. Furthermore, an experimental graph-based stochastic process that bridges with this methodology to enable the production of virtual random buildings is covered. © The Author(s) 2016.

Abstract
The growing integration of renewable energy in power systems demands for adequate planning of generation systems not only to meet long-term capacity requirements hut also to cope with sudden capacity shortages that can occur during system operation. As a matter of fact, system operators must schedule an adequate amount of operational reserve to avoid capacity deficits which can be caused by, for instance, overestimating the wind power that will be available. The framework proposed for the long-term assessment of operational reserve relies on the Nave forecasting method to produce wind power forecasts for the next hour. This forecasting model is simple and widely used to obtain short-term forecasts. However, it has been shown that regression models, such as the Autoregressive Integrated Moving Average (ARIMA) model, can outperform the Naive model even for forecasting horizons of up to 1 hour. This paper investigates the differences in the risk indices obtained for the long-term operational reserve when using the Naive and the ARIMA forecasting models. The objective is to assess the impact of the forecasting error in the long-term operational reserve risk indices. Experiments using the Sequential Monte Carlo Simulation (SMCS) method were carried out on a modified version of the IEEE RTS 79 test system that includes wind and hydro power variability. A sensitivity analysis was also performed taking into account several wind power integration scenarios and two different merit orders for scheduling generating units.