Abstract
A methodology for estimating the destination of passenger journeys from automated fare collection (AFC) system data is described. It proposes new spatial validation features to increase the accuracy of destination inference results and to verify key assumptions present in previous origin-destination estimation literature. The methodology applies to entry-only system configurations combined with distance-based fare structures, and it aims to enhance raw AFC system data with the destination of individual journeys. This paper describes an algorithm developed to implement the methodology and the results from its application to bus service data from Porto. The data relate to an AFC system integrated with an automatic vehicle location system that records a transaction for each passenger boarding a bus, containing attributes regarding the route, the vehicle, and the travel card used, along with the time and the location where the journey began. Some of these are recorded for the purpose of allowing onboard ticket inspection but additionally enable innovative spatial validation features introduced by the methodology. The results led to the conclusion that the methodology is effective for estimating journey destinations at the disaggregate level and identifies false positives reliably.

Abstract
In this paper, a multi-objective framework is proposed for the daily operation of a Smart Grid (SG) with high penetration of sensitive loads. The Virtual Power Player (VPP) manages the day-ahead energy resource scheduling in the smart grid, considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G), while maintaining a highly reliable power for the sensitive loads. This work considers high penetration of sensitive loads, i.e. loads such as some industrial processes that require high power quality, high reliability and few interruptions. The weighted-sum approach is used with the distributed and parallel computing techniques to efficiently solve the multi-objective problem. A two-stage optimization method is proposed using a Particle Swarm Optimization (PSO) and a deterministic technique based on Mixed-Integer Linear Programming (MILP). A realistic mathematical formulation considering the electric network constraints for the day-ahead scheduling model is described. The execution time of the large-scale problem can be reduced by using a parallel and distributed computing platform. A Pareto front algorithm is applied to determine the set of non-dominated solutions. The maximization of the minimum available reserve is incorporated in the mathematical formulation in addition to the cost minimization, to take into account the reliability requirements of sensitive and vulnerable loads. A case study with a 180-bus distribution network and a fleet of 1000 gridable Electric Vehicles (EVs) is used to illustrate the performance of the proposed method. The execution time to solve the optimization problem is reduced by using distributed computing.

Abstract
Background: Malaria is a leading cause of death and disease in many developing countries, where young children and pregnant women are the most affected groups. In 2012, there were an estimated 207 million cases of malaria, which caused approximately 627 000 malaria deaths. Around 80% of malaria cases occur in Africa, where the lack of access to malaria diagnosis is largely due to a shortage of expertise, being the shortage of equipment the secondary factor. This lack of expertise for malaria diagnosis frequently results on the increase of false positives, since prescription of medication is based only on symptoms. Thus, there is an urgent need of new tools that can facilitate the rapid and easy diagnosis of malaria, especially in areas with limited access to quality healthcare services. Methods: Various image processing and analysis approaches already proposed on the literature for the detection and segmentation of malaria parasites in blood smear microscopic images were collected and reviewed. This timely review aims to support the increasing interest in the development of low cost tools that can facilitate the rapid and easy diagnosis of malaria, especially in areas with limited access to quality healthcare services. Results: Malaria parasites detection and segmentation techniques in microscopic images are, in general, still in need of improvement and further testing. Most of the methodologies reviewed in this work were tested with a limited number of images, and more studies with significantly larger datasets for the evaluation of the proposed approaches are needed. Despite promising results reported during the past years, the great majority of the computer-aided methods found on the literature for malaria diagnosis are based on images acquired under well controlled conditions and with proper microscopic equipment. However, one should take into account that 80% of malaria cases occur in Africa, where this type of equipment is scarce or even nonexistent in common healthcare facilities. Conclusion: This work collects and reviews various image processing and analysis approaches already proposed on the literature for the detection and segmentation of malaria parasites in blood smear microscopic images. This timely review aims to support the increasing interest in the development of image processing-based systems to be used in rural areas of developing countries, which might be the next future trend in malaria computer-aided diagnosis. © 2016 Bentham Science Publishers.

Abstract
Modeling Heart Rate Variability (HRV) data has become important for clinical applications and as a research tool. These data exhibit long memory and time-varying conditional variance (volatility). In HRV, volatility is traditionally estimated by recursive least squares combined with short memory AutoRegressive (AR) models. This work considers a parametric approach based on long memory Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with heteroscedastic errors. To model the heteroscedasticity nonlinear Generalized Autoregressive Conditionally Heteroscedastic (GARCH) and Exponential Generalized Autoregressive Conditionally Heteroscedastic (EGARCH) models are considered. The latter are necessary to model empirical characteristics of conditional volatility such as clustering and asymmetry in the response, usually called leverage in time series literature. The ARFIMA-EGARCH models are used to capture and remove long memory and characterize conditional volatility in 24 hour HRV recordings from the Noltisalis database.

Abstract
Digital photogrammetry and GIS-based mapping are increasingly recognised as powerful tools in littoral issues. This paper considers the interoperability framework for high-resolution imagery acquisition and the development of coastal geoscience maps. The layered system architecture of the cartographic methodology is also explained. Moreover, it highlights a new approach to assessing heterogeneous geologic, geomorphological and maritime environments. The main goal of the present study was to test a new concept for photogrammetric images in order to assist modelling techniques, spatial analysis and coastal conceptual models. This approach proposes a methodological approach to coastal zone monitoring and to maritime forcing conditions evaluating. This approach will allow: (1) the acquisition of a large archive of high-resolution imagery; (2) the development of a coastal database including the entire data field and in situ assessments; (3) the study of coastal dynamics and shoreline evolution; (4) the assessment of the rock platforms and hydraulic structures; (5) the production of coastal geosciences maps. An integrated coastal geoscience and engineering methodology was outlined in NW of Iberian Peninsula (South Galicia and North/Central Portugal regions). This paper reports on the increased knowledge of the studied regions, providing essential data concerning coastal geo-morphodynamics. The overall assessment revealed additional evidence of erosion issues, which contributes to a better understanding of the hydraulic conditions. The main results are presented in regional coastal geoscience maps and local approach-outputs that could help government, local authorities and stakeholders to develop coastal management plans and to recommend strategies.

Abstract
Simulation is a computer-based experimentation tool suitable to determine the efficacy of a previously untried decision. In this paper, we present a model of climate change. The goal behind this project is to provide a test-bed to evaluate theories related to the Earth system so as to test and evaluate metrics such as greenhouse gases and climate change in general. The proposed approach is based on a multi-agent model which has as input a representation of nature and as output the changes that will occur on Earth within a given instant of time. Most views about climate change do not take into account the real severity of the subject matter; however, the present perspective is given in a way so as to make non-experts aware of the risks that are threatening life on Earth. Just recently, the general population has developed considerable sensitivity to these issues. One important contribution of this work is to use agent-based modeling and simulation as an instructional tool that will allow people to easily understand all aspects involved in the preservation of the environment in a more aware and responsible way.