A methodology to empower citizens towards a low-carbon economy. The potential of schools and sustainability indicators.

Environmental empowering to control resource consumption and environmental impacts is critical to engage citizens to adopt more sustainable habits. This study demonstrates the potential benefits of innovative approaches based on sustainability indicators towards a low-carbon economy. A methodology to measure and promote sustainability in schools has been proposed and evaluated, aiming at showing the environmental performance and informing of potential environmental savings. The methodology, titled ClimACT, has two main purposes: measuring the environmental performance of schools through a school sustainability index based on measurable indicators in the areas of transport, procurement, green spaces, indoor air quality, energy, water and waste; and encouraging students, teachers and families towards an energy-efficient and low-carbon pathway through a structural procedure based on roles, activities and progress evaluation. The approach, applied to 39 pilot schools from Portugal, Spain, France and Gibraltar, achieved promising and encouraging results. All schools deployed the methodology successfully, achieving measurable environmental benefits in 95% of cases, with an average improvement of 10% in the global performance of schools after one year. Moreover, the 5112 surveys applied to school communities, before and after the methodology implementation, highlighted how the sustainable indicators had a significant influence on the daily lives of families, leading to improvements of their behaviour, with an average increase of 20% in indicators regarding good practices in transport, energy, water, waste and citizenship. The environmental empowering through measurable indicators is a step forward a low-carbon economy. This methodology is open and adaptable to all sectors and requirements.

Perceptual Generalization and Context in a Network Memory Inspired Long-Term Memory for Artificial Cognition.

In the framework of open-ended learning cognitive architectures for robots, this paper deals with the design of a Long-Term Memory (LTM) structure that can accommodate the progressive acquisition of experience-based decision capabilities, or what different authors call "automation" of what is learnt, as a complementary system to more common prospective functions. The LTM proposed here provides for a relational storage of knowledge nuggets given the form of artificial neural networks (ANNs) that is representative of the contexts in which they are relevant in a configural associative structure. It also addresses the problem of continuous perceptual spaces and the task- and context-related generalization or categorization of perceptions in an autonomous manner within the embodied sensorimotor apparatus of the robot. These issues are analyzed and a solution is proposed through the introduction of two new types of knowledge nuggets: P-nodes representing perceptual classes and C-nodes representing contexts. The approach is studied and its performance evaluated through its implementation and application to a real robotic experiment.

Performance of biomorphic Silicon Carbide as particulate filter in diesel boilers.

Biomorphic Silicon Carbide (bioSiC) is a novel porous ceramic material with excellent mechanical and thermal properties. Previous studies have demonstrated that it may be a good candidate for its use as particle filter media of exhaust gases at medium or high temperature. In order to determine the filtration efficiency of biomorphic Silicon Carbide, and its adequacy as substrate for diesel particulate filters, different bioSiC-samples have been tested in the flue gases of a diesel boiler. For this purpose, an experimental facility to extract a fraction of the boiler exhaust flow and filter it under controlled conditions has been designed and built. Several filter samples with different microstructures, obtained from different precursors, have been tested in this bench. The experimental campaign was focused on the measurement of the number and size of particles before and after placing the samples. Results show that the initial efficiency of filters made from natural precursors is severely determined by the cutting direction and associated microstructure. In biomorphic Silicon Carbide derived from radially cut wood, the initial efficiency of the filter is higher than 95%. Nevertheless, when the cut of the wood is axial, the efficiency depends on the pore size and the permeability, reaching in some cases values in the range 70-90%. In this case, the presence of macropores in some of the samples reduces their efficiency as particle traps. In continuous operation, the accumulation of particles within the porous media leads to the formation of a soot cake, which improves the efficiency except in the case when extra-large pores exist. For all the samples, after a few operation cycles, capture efficiency was higher than 95%. These experimental results show the potential for developing filters for diesel boilers based on biomorphic Silicon Carbide.

A Framework for the Development of Context-Adaptable User Interfaces for Ubiquitous Computing Systems.

This paper addresses the problem of developing user interfaces for Ubiquitous Computing (UC) and Ambient Intelligence (AmI) systems. These kind of systems are expected to provide a natural user experience, considering interaction modalities adapted to the user abilities and preferences and using whatever interaction devices are present in the environment. These interaction devices are not necessarily known at design time. The task is quite complicated due to the variety of devices and technologies, and the diversity of scenarios, and it usually burdens the developer with the need to create many different UIs in order to consider the foreseeable user-environment combinations. Here, we propose an UI abstraction framework for UC and AmI systems that effectively improves the portability of those systems between different environments and for different users. It allows developers to design and implement a single UI capable of being deployed with different devices and modalities regardless the physical location.

Variation in phenolic compounds of Ugni molinae populations and their potential use as antioxidant supplement

In the present work we carried out a comparative study of total phenolic contents and antioxidant capacity of aqueous leaf extracts of Ugni molinae Turcz., Myrtaceae (infusion and Soxhlet extracted) prepared from continent and Juan Fernández Island samples. The results revealed that total phenol content (TPC), tannins (TTC) and flavonoids (TFC) for U. molinae extracts (infusion and Soxhlet extracts) from island leaves were 38.5, 56.7 and 37.5% higher than those obtained with leaves from the continent, respectively. Also, HPLC profiles showed important differences between U. molinae populations. In vitro antioxidant capacity (scavenging of DPPH radical) for 1% infusion and aqueous extract (Soxhlet method) of U. molinae from island samples, was 15% greater than from continent samples. Further, in vivo impact of U. molinae intake (1% infusion) was studied in plasma samples obtained from healthy volunteers. Participants that consumed tea prepared with leaves from island population showed higher TBARS reduction and plasma antioxidant capacity (TEAC-CUPRAC) than those who consumed tea prepared with leaves from continental population. The conditions of the territory in which U. molinae populations growth could explain the differences in their composition and activity. According to results, island U. molinae populations could be an important source of study for the development of an antioxidant supplement, and thereby contribute to the use of this species that has becoming an ecological problem in the island.