Abstract
SEAS Shared Intelligence (SEAS SI) is a platform for algorithms sharing and execution developed under the scope of Smart Energy Aware Systems (SEAS) project to promote the intelligent management of smart grids and microgrids, by means of the shared usage of algorithms and tools, while ensuring code and intellectual protection. In this paper the platform goals and architecture are described, focusing on the recent achievement regarding the connection of distinct algorithms, which enables the execution of dynamic simulations using sequences of algorithms from distinct sources. A case study based on several SEAS SI available algorithms is presented with the objective of showing the advantages of the SEAS SI capability of supporting simulations based on sequences of algorithms. Namely, electricity market bid values are calculated by a metalearner, which is fed by market price forecasts using different methods, and by their respective forecasting errors. A case study presents some results to validate the presented work, through the simulation of the MIBEL electricity market using MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). © 2017 IEEE.

Abstract
This paper presents the specification of an architecture for self-organizing scheduling systems. The proposed architecture uses learning by observing the experts and interpretation of scheduling experience. The design of intelligent systems that learn with experts is a very hard and challenging domain because current systems are becoming more and more complex and subject to rapid changes. In this work, different areas as Intelligent and Adaptive Human-Machine Interfaces, Metacognition and Learning from Observation, Self-managed Systems, amongst others, are joint together resulting in a global fully integrated architecture for self-organizing scheduling systems.

Abstract
The energy consumption of the current building stock represents about 40% of the total final energy consumption in Europe. New gamification techniques may play a significant role in helping users adopt new and more energy efficient behaviours. This paper presents the advances achieved within the context of the EU-funded project EnerGAware - Energy Game for Awareness of energy efficiency in social housing communities. The main objective of the project, funded by the European Union under the Horizon2020 programme, is to reduce the energy consumption and carbon emissions in a sample of European social housing by changing the energy efficiency behaviour of the social tenants through the implementation of a serious game linked to the real energy use of the participants' homes. © 2017 IEEE.

Abstract
Background/Aim: This study aimed to evaluate the in vitro efficacy of carboplatin and piroxicam, both in isolation and combined, against T24 and 5637 human urinary bladder cancer cell lines. Materials and Methods: Cell viability, drug interaction, cell morphology, cell proliferation, apoptosis and autophagy were analyzed after 72 h of drug exposure. Statistical analysis was performed and values of p<0.05 were considered statistically significant. Results: Drug exposure in combination led to a significant reduction of cell viability comparatively to single-drug exposure. These combinations resulted in a synergistic interaction in the T24 (combination index for 50% effect (CI50)=0.65) and 5637 (CI50=0.17) cell lines. Notable increase of morphological alterations, a marked decrease of Ki-67 expression, a considerable enhancement of autophagic vacuoles and a minimal effect on apoptosis was observed in both cell lines treated with combined drugs. Conclusion: Data showed that in vitro combination of carboplatin and piroxicam produced a more potent antiproliferative effect when compared to single drugs.

Abstract

Abstract
Computational models of complex systems are usually elaborate and sensitive to implementation details, characteristics which often affect their verification and validation. Model replication is a possible solution to this issue. It avoids biases associated with the language or toolkit used to develop the original model, not only promoting its verification and validation, but also fostering the credibility of the underlying conceptual model. However, different model implementations must be compared to assess their equivalence. The problem is, given two or more implementations of a stochastic model, how to prove that they display similar behavior? In this paper, we present a model comparison technique, which uses principal component analysis to convert simulation output into a set of linearly uncorrelated statistical measures, analyzable in a consistent, model-independent fashion. It is appropriate for ascertaining distributional equivalence of a model replication with its original implementation. Besides model-independence, this technique has three other desirable properties: a) it automatically selects output features that best explain implementation differences; b) it does not depend on the distributional properties of simulation output; and, c) it simplifies the modelers' work, as it can be used directly on simulation outputs. The proposed technique is shown to produce similar results to the manual or empirical selection of output features when applied to a well-studied reference model.