Abstract

Abstract
Ozone (O3) is a secondary pollutant mainly produced in the troposphere by photochemical reactions with high impact on human health. In this work hourly average O3 concentrations from the Portuguese air quality network collected during 24 years (1988–2011) were analysed. The background time series were assessed by environment type (urban, suburban and rural) and considering several criteria: the annual mean O3 concentration, the Directive 2008/50/EC O3 long-term objective and the 2005 WHO Guideline for protection of human health and, finally, the hourly information threshold exceedances. The trend of annual mean O3 concentration data and the maximum daily eight-hour mean ozone concentration data were both annually compared using an one-way ANOVA followed by a Tamhane post-hoc comparison test. In both series, although of an instable trend, a tendency for an increase of the mean O3 concentrations was found. On the other hand, after 2006, for all stations types, the normalized number of exceedances for public O3 information are significantly decreasing (p<0.05). In this paper, the O3 trend will be analyzed and discussed, contributing to improve knowledge of long time series of O3 concentrations in Portugal. © 2014 WIT Press.

Abstract
The unit commitment (UC) problem is a well-known combinatorial optimization problem arising in operations planning of power systems. It involves deciding both the scheduling of power units, when each unit should be turned on or off, and the economic dispatch problem, how much power each of the on units should produce, in order to meet power demand at minimum cost while satisfying a set of operational and technological constraints. This problem is typically formulated as nonlinear mixed-integer programming problem and has been solved in the literature by a huge variety of optimization methods, ranging from exact methods (such as dynamic programming and branch-and-bound) to heuristic methods (genetic algorithms, simulated annealing, and particle swarm). Here, we discuss how the UC problem can be formulated with an optimal control model, describe previous discrete-time optimal control models, and propose a continuous-time optimal control model. The continuous-time optimal control formulation proposed has the advantage of involving only real-valued decision variables (controls) and enables extra degrees of freedom as well as more accuracy, since it allows to consider sets of demand data that are not sampled hourly.

Abstract
The increasing wind power penetration in power systems represents a techno-economic challenge for power producers and system operators. Because of the variability and uncertainty of wind power, system operators require new solutions to increase the controllability of wind farm output. On the other hand, producers that include wind farms in their portfolio need to find new ways to boost their profits in electricity markets. This can be done by optimizing the combination of wind farms and storage so as to make larger profits when selling power (trading) and reduce penalties from imbalances in the operation. The present work describes a new integrated approach for analysing wind-storage solutions that make use of probabilistic forecasts and optimization techniques to aid decision making on operating such systems. The approach includes a set of three complementary functions suitable for use in current systems. A real-life system is studied, comprising two wind farms and a large hydro station with pumping capacity. Economic profits and better operational features can be obtained from the proposed cooperation between the wind farms and storage. The revenues are function of the type of hydro storage used and the market characteristics, and several options are compared in this study. The results show that the use of a storage device can lead to a significant increase in revenue, up to 11% (2010 data, Iberian market). Also, the coordinated action improves the operational features of the integrated system. Copyright (c) 2013 John Wiley & Sons, Ltd.

Abstract
This paper presents a comparison between listwise and pointwise approaches for instance ranking using Multiple Linear Models. A theoretical review of both approaches is performed, including the evaluation methods. Experiments done in seven datasets from 4 different problems show that the pointwise approach is slightly better or similar than the listwise approach. However the models obtained with the listwise approach are more interpretable because they have in average fewer features than the models obtained with the pointwise approach. The obtained results are important for problems where interpretable ranking models are necessary.

Abstract
Background: Transmission models can aid understanding of disease dynamics and are useful in testing the efficiency of control measures. The aim of this study was to formulate an appropriate stochastic Susceptible-Infectious-Resistant/ Carrier (SIR) model for Salmonella Typhimurium in pigs and thus estimate the transmission parameters between states. Results: The transmission parameters were estimated using data from a longitudinal study of three Danish farrow-to-finish pig herds known to be infected. A Bayesian model framework was proposed, which comprised Binomial components for the transition from susceptible to infectious and from infectious to carrier; and a Poisson component for carrier to infectious. Cohort random effects were incorporated into these models to allow for unobserved cohort-specific variables as well as unobserved sources of transmission, thus enabling a more realistic estimation of the transmission parameters. In the case of the transition from susceptible to infectious, the cohort random effects were also time varying. The number of infectious pigs not detected by the parallel testing was treated as unknown, and the probability of non-detection was estimated using information about the sensitivity and specificity of the bacteriological and serological tests. The estimate of the transmission rate from susceptible to infectious was 0.33 [0.06, 1.52], from infectious to carrier was 0.18 [0.14, 0.23] and from carrier to infectious was 0.01 [0.0001, 0.04]. The estimate for the basic reproduction ration (R-0) was 1.91 [0.78, 5.24]. The probability of non-detection was estimated to be 0.18 [0.12, 0.25]. Conclusions: The proposed framework for stochastic SIR models was successfully implemented to estimate transmission rate parameters for Salmonella Typhimurium in swine field data. R0 was 1.91, implying that there was dissemination of the infection within pigs of the same cohort. There was significant temporal-cohort variability, especially at the susceptible to infectious stage. The model adequately fitted the data, allowing for both observed and unobserved sources of uncertainty (cohort effects, diagnostic test sensitivity), so leading to more reliable estimates of transmission parameters.