Abstract

Abstract
This paper presents a study of a uniform dry sand compacted by the pluviation method and submitted to different isotropic and anisotropic stress paths. The stress states were performed in two distinct triaxial cells using specimens of 100 mm and 150 mm diameter in order to assess also the scale-effects. The small strain stiffness for each stress state was evaluated in both triaxial cells by bender elements which were assembled vertically and horizontally for the comparison of the shear wave velocity in the two directions. In the specimens of 100mm, additionally to the bender elements in the vertical direction, accelerometers were used for the improvement of the results accuracy. All tests results were analysed in order to assess the initial fabric (inherent) anisotropy and the induced anisotropy by the different anisotropic stress paths.

Abstract
This paper intends to propose a platform dedicated to a community for inclusive fashion. This platform can innovate in the awareness creation process by involving a wide range of stakeholders through a novel combination of visually-centered networking/collaboration together with a powerful recommendation system based on data analytics. This solution can potentially impact positively the well-being of more than 50 million of motor/physical disabled citizens as consumers of fashion products. This can be achieved by bringing together three different networks: (i) fashion consumers, emphasizing consumers with disabilities; (ii) companies from fashion/garment/footwear industry; (iii) social and research organizations supporting disabled people. This will allow to create individual and collective awareness about sustainability at the social level demanding the engagement of groups of citizens and industries in sharing information and knowledge and developing ideas and solutions for people with disabilities facing problems related to fashion, garment and footwear products and services.

Abstract
The purpose of this paper is to present the main results of the ongoing analysis of applying dynamic network access tariffs in Portugal. For the 2015-2017 regulatory period, the Portuguese National Regulatory Authority, ERSE, required the three main Portuguese DSOs to submit, until the end of June 2016, plans for the implementation of network dynamic tariff schemes targeting Medium, High and Extra High Voltage customers, as well as the respective cost-benefit analysis. EDP Distribuição, the main Portuguese DSO, is preparing a report regarding the implementation of pilot projects on a sample of these segments of customers, which are due to be on the field during 2017. These pilots should help electrical energy stakeholders understand how the Electric System can benefit from the use of dynamic tariffs focused on networks, allowing for the quantification of benefits in a more accurate way. The level of demand response that results from price signals is a key issue that both the regulator and EDP Distribuição will quantify. Other important issue to assess in this study is the efficiency of cost recovery under a dynamic tariff scheme. In conclusion, this paper will present some results obtained from the cost-benefit analysis regarding the implementation of a Critical Peak Pricing scheme, as well as the key learnings supporting the introduction of dynamic schemes in the future, not only for EHV, HV and MV customers but also eventually extending it to LV ones.

Abstract
Current technological and market challenges increase the need for development of intelligent systems to support decision making, allowing managers to concentrate on high-level tasks while improving decision response and effectiveness. A Racing based learning module is proposed to increase the effectiveness and efficiency of a Multi-Agent System used to model the decision-making process on scheduling problems. A computational study is put forward showing that the proposed Racing learning module is an important enhancement to the developed Multi-Agent Scheduling System since it can provide more effective and efficient recommendations in most cases.

Abstract
The increasing penetration of renewable generation brings a significant escalation of intermittency to the power and energy system. This variability requires a new degree of flexibility from the whole system. The active participation of small and medium players becomes essential in this context. This is only possible by using adequate forecasting techniques applied both to the consumption and to generation. However, the large number of incontrollable factors, such as the presence of consumers in the building, the luminosity, or external temperature, makes the forecasting of energy consumption an arduous task. This paper addresses the electrical energy consumption forecasting problem, by studying the correlation between the solar radiation and the electrical consumption of lights. This study is performed by means of three forecasting methods, namely a multi-layer perceptron artificial neural network, a support vector regression method, and a linear regression method. The performed studies are analyzed using data gathered from a real installation - campus of the Polytechnic of Porto, in real time.