Abstract
There is a generalized lack of clear definitions and estimations as to what represents cultural activities and creative industries. This paper critically reviews the growing corpus of literature on approaches to the measurement of creative industries and presents a detailed mapping of the creative sectors according to relevant industry- based methodologies. Using a unique official database, Quadros de Pessoal/ Matched Employer-Employee Dataset, which includes over 3 million workers, we found that, for Portugal, depending on the approach used, the size of creative industries differs considerably, ranging from 2.5% (DCMS model) to 4.6% (WIPO copyright model). We further propose a distinct industry-based approach focusing on core creative industries. Accordingly, core creative industries represent 3.5% of Portuguese employment, in which ‘software publishing’/‘computer consultancy’ (1.0%), ‘publishing’ (1.0%) and ‘advertising and marketing’ (0.4%) are the most relevant sub-segments. © 2015 Taylor & Francis.

Abstract
This paper offers an efficient and robust concept for a decentralized bad data processing, able to supply in real-time a power system state estimator with a repaired measurement set. Corrupted measurement vectors are funneled through a denoising auto-associative neural network in order to project the biased vector back to the data manifold learned during an offline training process. In order to improve accuracy, a maximum similarity with the solution manifold, measured with Correntropy, is searched for by a meta-heuristic. The extreme robustness and scalability of the process is demonstrated in multiple characteristic case studies.

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During many years, battery models have been proposed with different levels of accuracy and complexity. In some cases, simple low-order aggregated battery pack models may be more appropriate and feasible than complex physic-chemical or high-order multi-cell battery pack models. For example: in early stages of the system design process, in non-focused battery applications, or whenever low configuration effort or low computational complexity is a requirement. The latter may be the case of Electrical Equivalent Circuit Models (EECM) suitable for energy optimization purposes at a system level in the context of energy management or sizing problem of energy storage systems. In this paper, an improved parametrization method for Li-ion linear static EECMs based on the so called concept of direct current resistance (DCR) is presented. By drawing on a DCR-based parametrization, the influence of both diffusion polarization effects and changing of Open-Circuit Voltage (OCV) are virtually excluded on the estimation of the battery's inner resistance. This results in a parametrization that only accounts for pure ohmic and charge transfer effects, which may be beneficial, since these effects dominate the battery dynamic power response in the range of interest of many applications, including electro-mobility. Model validation and performance evaluation is achieved in simulations by comparison with other low and high order EECM battery models over a dynamic driving profile. Significant improvements in terms of terminal voltage and power losses estimation may be achieved by a DCR-based parametrization, which in its simplest form may only require one short pulse characterization test within a relatively wide range of SoCs and currents. Experimental data from a 53 Ah Li-ion pouch cell produced by Kokam (Type SLPB 120216216) with Nickel Manganese Cobalt oxide (NMC) cathode material is used.