Abstract

Abstract
The ever-increasing penetration of wind energy generation (WEG) and electric vehicle (EV) batteries in power systems could bring two significant challenges to day-ahead energy balancing markets. First challenge, the uncertainty of WEG and random behavior of the EV batteries can raise energy imbalance in energy markets. Second challenge, the intermittent nature of WEG and uncontrolled EV batteries charging, bring high congestion costs and new congestion patterns in transmission system. In this condition, there is no guarantee that the WEG is deliverable throughout the power system. The hourly coordination of WEG units with EV batteries can play important roles in addressing the first challenge. Similarly, to addressing the second challenge, a transmission impedance adjustment (TIA) device is taken as a promising way to reduce transmission congestion and promote the integration of large-scale WEG and EV batteries through controlling the power flows. However, utilization of TIA device is limited nowadays due to the complexity of this device in the optimization problem of day-ahead energy market clearing with the DC approximation of the power flow network. Consequently, a computationally effi-cient methodology, which is compatible with existing customary solvers, is proposed to adjust the TIA device set point with minimal modification efforts. An adaptive robust optimization formulation is adapted in the proposed problem to handle the WEG uncertainty. Finally, the simulation results on six and IEEE 118 bus test systems suggest that: 1) substantial economic savings can be achieved through utilization of WEG and storage capability of EV batteries, beyond the independent capabilities of TIA technology; 2) the TIA device plays a critical role in removing transmission congestion; and 3) the storage capability of EV batteries could relieve the uncertainty of WEG and increase its dispatchability.

Abstract
Virtual Reality applications have the goal of transporting their users to a given virtual environment (VE). Thus, Presence is a consensual metric for evaluating the VEs' effectiveness. The present study adapts the Presence Questionnaire (PQ) for the Portuguese-speaking population, maintaining the validity of the contents and concepts, to ascertain the psychometric properties of the instrument.The adaptation to Portuguese was achieved through the standard adaptation process of translation and back-translation process. The sample consisted of 451 individuals (268 males and 183 females). Factor reliability ranged from 0.63 to 0.86. Confirmatory factor analysis produced a theoretical model of 21 items distributed among seven factors, where the covariance between some residual item errors was established. The fit indices obtained were , GFI , CFI , RMSEA , P [RMSEA ], MECVI . Results obtained allowed us to consider that the adapted Portuguese version of the PQ, with 21 items, forms a robust and valid questionnaire whose use is recommended to evaluate Presence in virtual reality research programmes, provided that they use samples of the Portuguese language (Europe).

Abstract
Information and communication technologies provide their users with permanent global connectivity through a wide range of devices and with the Internet as their main support. Currently assuming a very significant economic relevance for a considerable group of countries, the Tourism sector is in the mindset of a digital transformation, in the scope of which a good part of its services is evolving towards ICT and Internet supported versions. In this context, the assurance of universal access to websites (Web accessibility) that support the businesses of organizations in this sector gains special relevance. This article presents an analysis at the level of Web accessibility of 240 websites directly related to the tourism sector, using TAW - Test de Acessibilidad Web evaluation tool. Despite the growing concern to provide more and more inclusive means, the overwhelming majority of websites did not reach the minimum level of accessibility, thus not assuring properly functional access for those who have a disability or limitation.

Abstract

Abstract
Cities are becoming increasingly complex to manage, as they increase in size and must provide higher living standards for their populations. New technology-based solutions must be developed towards attending this growth and ensuring that it is socially sustainable. This paper puts forward the notion that these solutions must share some properties: they should be anthropocentric, holistic, horizontal, multi-dimensional, multi-modal, and predictive. We propose an architecture in which streaming data sources that characterize the city context are used to feed a real-time graph of the city's assets and states, as well as to train predictive models that hint into near future states of the city. This allows human decision-makers and automated services to take decisions, both for the present and for the future. To achieve this, multiple data sources about a city were gradually connected to a message broker, that enables increasingly rich decision-support. Results show that it is possible to predict future states of a city, in aspects such as traffic, air pollution, and other ambient variables. The key innovative aspect of this work is that, as opposed to the majority of existing approaches which focus on a real-time view of the city, we also provide insights into the near-future state of the city, thus allowing city services to plan ahead and adapt accordingly. The main goal is to optimize decision-making by anticipating future states of the city and make decisions accordingly.