Abstract
Air quality traffic-related measures have been implemented worldwide to control the pollution levels of urban areas. Although some of those measures are claiming environmental improvements, few studies have checked their real impact. In fact, quantitative estimates are often focused on reducing emissions, rather than on evaluating the actual measures' effect on air quality. Even when air quality studies are conducted, results are frequently unclear. In order to properly assess the real impact on air quality of traffic-related measures, a statistical method is proposed. The method compares the pollutant concentration levels observed after the implementation of a measure with the concentration values of the previous year. Short- and long-term impact is assessed considering not only their influence on the average pollutant concentration, but also on its maximum level. To control the effect of the main confounding factors, only the days with similar environmental conditions are analysed. The changeability of the key meteorological variables that affect the transport and dispersion of the pollutant studied are used to identify and group the days categorized as similar. Resemblance of the pollutants' concentration of the previous day is also taken into account. The impact of the road traffic measures on the air pollutants' concentration is then checked for those similar days using specific statistical functions. To evaluate the proposed method, the impact on PM2.5 concentrations of two air quality traffic-related measures (M1 and M2) implemented in the city of Beijing are taken into consideration: M1 was implemented in 2009, restricting the circulation of yellow-labelled vehicles, while M2 was implemented in 2014, restricting the circulation of heavy-duty vehicles. To compare the results of each measure, a time-period when these measures were not applied is used as case-control.

Abstract
The content of this paper aims to assist in the development and implementation of microgrids by addressing the challenges and possible solutions for their protection systems. Therefore, an overview of some protection methods available in the literature that can be implemented to ensure a safe and reliable microgrid operation is presented, including the most common protection devices and earthing schemes that can be adopted in low voltage distribution systems. In addition, this paper also presents a brief fault analysis of internal faults at three different locations in an industrial microgrid with centralized and decentralized deployment of energy sources, as well as a short-circuit analysis of symmetric and asymmetric faults at these faulty locations. An approximate method based on the calculation of the equivalent impedance seen from the fault location is used to determine the fault currents. This study is made to observe how microgrids with different configurations perform in the event of internal faults. It is demonstrated in this work that setting a specific protection strategy to allow the microgrid to operate effectively during both operation modes can be problematic and expensive in most situations. With this in mind, additional effort is necessary to engineer and implement new protection approaches that can overcome the limitations of protection systems in future microgrids.

Abstract
Recent studies have shown that the early detection of neurodegenerative diseases (such as Parkinson) can significantly improve the effectiveness of treatments that increase quality of life, reducing the costs associated with the disease. In this paper, the proposed methodology consists in detecting early signs of Parkinson's disease through speech, with the presence of background noise. The approach uses machine learning algorithms and signal processing techniques to correctly distinguish between healthy controls and Parkinson's disease patients. In order to detect early signs of the disease, a database with patients at different stages of the Parkinson's disease is used. The learning algorithms were optimized for generalization and accuracy. An analysis of the results obtained from the proposed methodology show potential uses of machine learning algorithms in biomedical applications to detect early signs of Parkinson's disease.

Abstract

Abstract
This paper draws on an innovative methodological framework to assess the energy performance of a set of urban development alternatives, using the city of Porto (Portugal) as a case study. The methodology combines the advantages of a spatially-explicit analysis with the prediction accuracy of neural networks to estimate the energy demand (for space heating, space cooling and mobility) resulting from the physical configuration of urban areas. The urban alternatives under assessment reflect a number of development strategies taking place in different locations within the city. These correspond to well-known urban development approaches (infill development, consolidated development, modern development, multi-family housing, transit-oriented development and green infrastructure). The results for the city of Porto show that the transit-oriented development, the urban infill and the consolidated development are the urban alternatives yielding the most relevant energy savings, especially regarding mobility needs. This study makes evident that planning for more efficient urban forms potentially brings about more efficient urban settings and reinforces the relevance of ex ante appraisals of urban projects and plans.

Abstract
In this paper, the unrelated parallel machine scheduling problem considering machine-dependent and job sequence-dependent setup times is addressed. This problem involves the scheduling of n jobs on m unrelated machines with setup times in order to minimize the makespan. The Simulated Annealing algorithm is used to solve four sets of small scheduling problems, from the literature, on two unrelated machines: the first one has six jobs, the second has seven jobs and the third and fourth has eight and nine jobs, respectively. The results seem promising when compared with other methods referred in literature.