European legislation and implementation measures in the management of construction and demolition waste.

The intense activity in the construction sector during the last decade has generated huge volumes of construction and demolition (C&D) waste. In average, Europe has generated around 890 million tonnes of construction and demolition waste per year. Although now the activity has entered in a phase of decline, due to the change of the economic cycle, we don’t have to forget all the problems caused by such waste, or rather, by their management which is still far from achieving the overall target of 70% for C&D waste --excludes soil and stones not containing dangerous substances-- should be recycled in the EU Countries by 2020 (Waste Framework Directive). But in fact, the reality is that only 50% of the C&D waste generated in EU is recycled and 40% of it corresponds to the recycling of soil and stones not containing dangerous substances. Aware of this situation, the European Countries are implementing national policies as well as different measures to prevent the waste that can be avoidable and to promote measures to increase recycling and recovering. In this aspect, this article gives an overview of the amount of C&D waste generated in European countries, as well as the amount of this waste that is being recycled and the different measures that European countries have applied to solve this situation.

Legal Aspects which Implement Good Practice Measures in the Management of Construction and Demolition Waste

The construction industry, one of the most important ones in the development of a country, generates unavoidable impacts on the environment. The social demand towards greater respect for the environment is a high and general outcry. Therefore, the construction industry needs to reduce the impact it produces. Proper waste management is not enough; we must take a further step in environmental management, where new measures need to be introduced for the prevention at source, such as good practices to promote recycling. Following the amendment of the legal frame applicable to Construction and Demolition Waste (C&D waste), important developments have been incorporated in European and International laws, aiming to promote the culture of reusing and recycling. This change of mindset, that is progressively taking place in society, is allowing for the consideration of C&D waste no longer as an unusable waste, but as a reusable material. The main objective of the work presented in this paper is to enhance C&D waste management systems through the development of preventive measures during the construction process. These measures concern all the agents intervening in the construction process as only the personal implication of all of them can ensure an efficient management of the C&D waste generated. Finally, a model based on preventive measures achieves organizational cohesion between the different stages of the construction process, as well as promoting the conservation of raw materials through the use and waste minimization. All of these in order to achieve a C&D waste management system, whose primary goal is zero waste generation

Some geometrical methods for constructing contradiction measures on Atanassov's intuitionistic fuzzy sets

Trillas et al. (1999, Soft computing, 3 (4), 197–199) and Trillas and Cubillo (1999, On non-contradictory input/output couples in Zadeh's CRI proceeding, 28–32) introduced the study of contradiction in the framework of fuzzy logic because of the significance of avoiding contradictory outputs in inference processes. Later, the study of contradiction in the framework of Atanassov's intuitionistic fuzzy sets (A-IFSs) was initiated by Cubillo and Castiñeira (2004, Contradiction in intuitionistic fuzzy sets proceeding, 2180–2186). The axiomatic definition of contradiction measure was stated in Castiñeira and Cubillo (2009, International journal of intelligent systems, 24, 863–888). Likewise, the concept of continuity of these measures was formalized through several axioms. To be precise, they defined continuity when the sets ‘are increasing’, denominated continuity from below, and continuity when the sets ‘are decreasing’, or continuity from above. The aim of this paper is to provide some geometrical construction methods for obtaining contradiction measures in the framework of A-IFSs and to study what continuity properties these measures satisfy. Furthermore, we show the geometrical interpretations motivating the measures.

Supplementarity measures on fuzzy sets.

In this paper, we commence the study of the so called supplementarity measures. They are introduced axiomatically and are then related to incompatibility measures by antonyms. To do this, we have to establish what we mean by antonymous measure. We then prove that, under certain conditions, supplementarity and incompatibility measuresare antonymous. Besides, with the aim of constructing antonymous measures, we introduce the concept of involution on the set made up of all the ordered pairs of fuzzy sets. Finally, we obtain some antonymous supplementarity measures from incompatibility measures by means of involutions.

A first approach to an axiomatic model of multi-measures

We establish an axiomatic model of multi-measures, capturing some classes of measures studied in the fuzzy sets literature, where they are applied to only one or two arguments.

Time series distances measures to analyze in-air signatures to authenticate users on mobile phones

Improving the security of mobile phones is one of the crucial points required to assure the personal information and the operations that can be performed from them. This article presents an authentication procedure consisting of verifying the identity of people by making a signature in the air while holding the mobile phone. Different temporal distance algorithms have been proposed and evaluated through a database of 50 people making their signatures in the air and 6 people trying to forge each of them by studying their records. Approaches based on DTW have obtained better EER results than those based on LCS (2.80% against 3.34%). Besides, different signal normalization methods have been evaluated not finding any with better EER results that when no normalization has carried out.