Abstract

Abstract
Hybridisation is a systematic process along which the characteristic features of hybrid logic, both at the syntactic and the semantic levels, are developed on top of an arbitrary logic framed as an institution. In a series of papers this process has been detailed and taken as a basis for a specification methodology for reconfigurable systems. The present paper extends this work by showing how a proof calculus (in both a Hilbert and a tableau based format) for the hybridised version of a logic can be systematically generated from a proof calculus for the latter. Such developments provide the basis for a complete proof theory for hybrid(ised) logics, and thus pave the way to the development of (dedicated) proof support.

Abstract
In this paper the nonparametric identification of state-space linear parameter-varying models with dynamic mapping between the scheduling signal and the model matrices is considered. Indeed, we are particularly interested on the problem of estimating a model using data generated from an LPV system with static dependence, which is however represented on a different state-basis from the one considered by the estimator.

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
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.