Abstract
Switched reluctance machines (SRM) are simple, robust and fault tolerant machines, usually operating under strong nonlinear characteristics. Therefore, accurately modeling this machine is a demanding task. Several models have been proposed, where magnetic saturation is often addressed, without considering hysteresis effect. In the proposed model, the SRM magnetization characteristics are generated through the Jiles-Atherton (J-A) hysteresis model. Thus, instead of a post-processing inclusion, static hysteresis is considered in simulation. This is the model main attribute, which is discussed in detail. This may contribute for a better understanding of hysteresis impact over the SRM operation, limited to static modes. Simulation results are also presented and discussed.

Abstract
Processes shaping urban ecosystems reflect and influence the cultural context in which they emerge, bearing implications for ecosystem services (ES) planning and management. Investigating the perception of benefits and losses / costs delivered by a specific service providing unit (SPU) can generate objective orientations suitable for urban planning and management deeply embedded in the social-ecological systems where they occur, because the realization of ES into benefits and losses / costs is mediated by specific beneficiaries and reflects their characteristics, information and use of ecosystems. Street trees are a particularly relevant SPU in many densely built Southern-European cities due to the difficulty in implementing new sizeable green areas. In this study, a questionnaire was developed and applied in Porto to investigate how benefits (cultural, regulating and economic) and losses / costs caused by street trees are perceived by citizens and influenced by a set of socioeconomic variables (N = 819 people aged 18 years or older), and parametric statistical tests were used to analyze the effect of gender, age and school level. Results evidenced that people in Porto valued more environmental benefits (particularly air quality improvement) than cultural ones. School level was the variable accounting for more differences, underlining a tendency in people with lower level of academic education to value less the benefits provided by street trees in Porto and attribute more importance to losses and damages, compared to people who attended university or had higher academic degree. Age also held considerable differences in mean responses, with older people showing more concern towards losses and costs, while gender influenced perception of cultural benefits, which were more important for women than for men. The findings of the research are discussed concerning implications for environmental justice, planning and management of urban ecosystems.

Abstract
The autonomous robotic system accurate localization is a challenging step in robot navigation field once the mobile device should avoid dangerous situations, such as unsafe conditions and collisions. In this context, the present paper proposes a localization method using the Extended Kalman Filter (EKF) to fuse the information coming from two different sensors (i.e. odometry and computer vision). The localization results present with known and unknown starting points and are tested in a simulated environment. © 2018 IEEE.

Abstract

Abstract
Missing data consists in the lack of information in a dataset and since it directly influences classification performance, neglecting it is not a valid option. Over the years, several studies presented alternative imputation strategies to deal with the three missing data mechanisms, Missing Completely At Random, Missing At Random and Missing Not At Random. However, there are no studies regarding the influence of all these three mechanisms on the latest high-performance Artificial Intelligence techniques, such as Deep Learning. The goal of this work is to perform a comparison study between state-of-the-art imputation techniques and a Stacked Denoising Autoencoders approach. To that end, the missing data mechanisms were synthetically generated in 6 different ways; 8 different imputation techniques were implemented; and finally, 33 complete datasets from different open source repositories were selected. The obtained results showed that Support Vector Machines imputation ensures the best classification performance while Multiple Imputation by Chained Equations performs better in terms of imputation quality. © Springer Nature Switzerland AG 2018.

Abstract
Resumo O abandono escolar é um problema complexo que afeta a maioria dos programas de graduação pós-secundária, em todo o mundo. O curso de engenharia industrial do Instituto ISVOUGA, localizado em Santa Maria da Feira, Portugal, não é exceção. Este estudo usou um conjunto de dados contendo informações gerais dos estudantes e suas notas para as unidades curriculares já avaliadas. A partir deste conjunto de dados, foram selecionados dezessete preditores potenciais: cinco intrínsecos (gênero, estado civil, situação profissional, idade e regime de dedicação aos estudos – integral ou parcial) e doze extrínsecos (as notas em todas as doze unidades curriculares ministradas durante os dois primeiros semestres do curso). O objetivo principal desta investigação foi prever a probabilidade de um estudante abandonar o curso com base nos referidos preditores. Foi usada uma regressão logística binária para classificar os estudantes como tendo uma probabilidade alta ou baixa de não se reinscreverem no curso. Para validar se a metodologia utilizada é apropriada para o estudo em causa, a precisão obtida com o modelo de regressão logística foi comparada, por via de uma validação cruzada com cinco partições, com a precisão obtida pela utilização de três métodos muito utilizados em data mining: One R, K Nearest Neighbors e Naive Bayes. O modelo de regressão logística identificou quatro variáveis significativas na previsão do abandono escolar (as classificações nas unidades curriculares de ciência dos materiais, eletricidade, cálculo 1 e química). Os dois preditores mais influentes do abandono dos estudantes são não conseguir aprovação nas unidades curriculares menos exigentes: ciência dos materiais e eletricidade. Ao contrário do que seria de supor antes desta investigação, descobrimos que a não aprovação em unidades curriculares mais exigentes, como física ou estatística, não tem influência significativa no abandono escolar.