Abstract
The accuracy and precision of air quality models are usually associated with the emission inventories. Thus, in order to assess if there are any improvements on air quality regional simulations using detailed methodology of road traffic emission estimation, a regional air quality modelling system was applied. For this purpose, a combination of top-down and bottom-up approaches was used to build an emission inventory. To estimate the road traffic emissions, the bottom-up approach was applied using an instantaneous emission model (Vehicle Specific Power - VSP methodology), and an average emission model (CORINAIR methodology), while for the remaining activity sectors the top-down approach was used. Weather Research and Forecasting (WRF) and Comprehensive Air quality (CAMx) models were selected to assess two emission scenarios: (i) scenario 1, which includes the emissions from the top-down approach; and (ii) scenario 2, which includes the emissions resulting from integration of top-down and bottom-up approaches. The results show higher emission values for PM10, NOx and HC, for scenario 1, and an inverse behaviour to CO. The highest differences between these scenarios were observed for PM10 and HC, about 55% and 75% higher (respectively for each pollutant) than emissions provided by scenario 2. This scenario gives better results for PM10, CO and O-3. For NO2 concentrations better results were obtained with scenario 1. Thus, the results obtained suggest that with the combination of the top-down and bottom-up approaches to emission estimation several improvements in the air quality results can be achieved, mainly for PM10, CO and O-3.

Abstract
The future large-scale deployment of electric vehicles (EV) will not only have impact on load growth, but also create opportunities for the electricity sector. Generally, the current methods for security of supply long-term evaluation do not include this new type of load. While the electric components of the generating systems are usually modelled by the Markov process, this paper presents, as its major contribution, an EV model based on the Nonhomogeneous Poisson process, which has been developed in order to better represent the motorized citizen mobility and the EV opportunity to release spinning reserve to electric systems. The simulation procedure lies in combining both Poisson and Markov processes into a sequential Monte Carlo simulation (SMCS) to measure the impact of EV when evaluating the adequacy of generating systems. This evaluation is divided into two complementary concepts: static reserve (generating capacity reserve) and operating capacity reserve. The proposed models are analyzed using a modified version of the IEEE RTS-96 including renewable sources.

Abstract
The concerns about global warming (greenhouse-gas emissions), scarcity of fossil fuels reserves, and primary energy independence of regions or countries have led to a dramatic increase of renewable energy sources (RES) penetration in electric power systems, mainly wind and solar power. This created new challenges associated with the variability and uncertainty of these sources. Handling these two characteristics is a key issue that includes technological, regulatory, and computational aspects. Advanced tools for handling RES maximize the resultant benefits and keep the reliability indices at the required level. Recent advances in forecasting and management algorithms provided means to manage RES. Forecasts of renewable generation for the next hours/days play a crucial role in the management tools and protocols of the system operator. These forecasts are used as input for setting reserve requirements and performing the unit commitment (UC) and economic dispatch (ED) processes. Probabilistic forecasts are being included in the management tools, enabling a move from deterministic to stochastic methods, which conduct to robust solutions. On the technological side, advances to increase mid-merit and base-load generation flexibility should be a priority. The use of storage devices to mitigate uncertainty and variability is particularly valuable for isolated power system, whereas in interconnected systems, economic criteria might be a barrier to invest in new storage facilities. The possibility of sending active and reactive control set points to RES power plants offers more flexibility. Furthermore, the emergence of the smart grid concept and the increasing share of controllable loads contribute with flexibility to increase the RES penetration levels. (C) 2013 John Wiley & Sons, Ltd.

Abstract
This chapter surveys methodologies for the segmentation of carotid ultrasound images and describes a method for the semiautomatic detection of the lumen-intima and the media-adventitia interfaces of the near and far common carotid wall. The approach is based on feature extraction, fitting of cubic splines, dynamic programming, smooth intensity thresholding surfaces, and geometric snakes. A set of 47 B-mode images of the common carotid were used to assess the performance of the method. The detection errors are similar to the ones observed in manual segmentations for 95% of the far wall interfaces and 73% of the near wall interfaces. © 2014 Springer Science+Business Media, LLC. All rights are reserved.

Abstract
In Portugal the distribution of physicians is considered an appropriate proxy for the distribution of the actual hospital resources and additional information on hospital supply is mostly unavailable, while health care utilization data are also usually absent. A suitable method that can be used to analyze patients' access to hospital health care in countries with such characteristics is the two-step floating catchment area (2SFCA) method, since it requires only the number of physicians to represent supply and the population size to estimate demand. An improved version of the 2SFCA method is the kernel density 2SFCA (KD2SFCA) method. However, this method was not developed to analyze access to health care and it computes scores that express only the spatial access dimensions of proximity and availability. In this paper we present a new method, based on the KD2SFCA method, which improves health care access analysis and better identifies populations that are less empowered to use health care. We adapt the KD2SFCA method for the context of health care access analysis and extend it to capture additional access dimensions. We applied the extended method to the Portuguese hospital health care sector in a case study, and compared its results with those obtained with the KD2SFCA method. Our method was able to improve the identification of the less empowered populations and discovered that they represent 8.1% of the total population, instead of 4.6%, and reside in sixteen of the eighteen Portuguese districts, instead of in thirteen, as identified by the original KD2SFCA method. By improving the KD2SFCA method for the identification of the less empowered populations, our method can be a first step in an endeavor to identify opportunities to increase the health care supply or to redistribute supply resources, with the objective of increasing the access of those deprived populations.

Abstract
The main problem in integration of renewable power sources to the electricity grid is the uncertainty introduced by the power forecasting process in the optimal scheduling problem, which can considerably increase the generation cost. This problem has been widely analyzed using scenario generation/reduction methodologies. However, the consideration of a reduced number of scenarios can limit the capabilities of these methodologies. As an alternative, in this manuscript the dynamic economic dispatch problem has been solved by estimating the probability density function of energy surplus, the energy not supplied and the power production considering the forecasting error and system reliability. The incorporation of the system reliability and the forecasting error as probability distribution functions can avoid the use of scenario generation and reduction processes, which are time consuming tasks. The proposed model was illustrated by analyzing a typical insular power system under different conditions of load and uncertainty, concluding that the hardware failure can introduce a relevant increment in the generation costs, due to their relationship with the value of lost load. Moreover, the scalability of the proposed model was studied by analyzing several power systems between 10 and 150 units, which have been solved in an acceptable computational time.