Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

Psychometric Properties of the Portuguese Version of the Posttraumatic Growth Inventory Short Form Among Divorced Adults

The aim of this study was to develop and validate a Portuguese version of the Short Form of the Posttraumatic Growth Inventory (PTGI-SF). Using an online convenience sample of Portuguese divorced adults (N = 482), we confirmed the oblique five-factor structure of the PTGI-SF by confirmatory factor analysis. The results demonstrated the measurement invariance across divorce initiator status groups. Total score and factors of PTGI-SF showed good internal consistency, with the exception of the New Possibilities factor, which revealed an acceptable reliability. The Portuguese PTGI-SF showed a satisfactory convergent validity. In terms of discriminant validity, posttraumatic growth assessed by the Portuguese PTGI-SF was a distinct factor from posttraumatic psychological adjustment. These preliminary findings suggest the cultural adaptation and also psychometric properties of the present Portuguese PTGI-SF to measure posttraumatic growth after personal crisis.

Frailty as a predictor of short-term adverse outcomes

The objectives of this study were to compare how different frailty measures (Frailty Phenotype/FP, Groningen Frailty Indicator/GFI and Tilburg Frailty Indicator/TFI) predict short-term adverse outcomes. Secondarily, adopting a multidimensional approach to frailty (integral conceptual model–TFI), this study aims to compare how physical, psychological and social frailty predict the outcomes. A longitudinal study was carried out with 95 community-dwelling elderly. Participants were assessed at baseline for frailty, determinants of frailty, and adverse outcomes (healthcare utilization, quality of life, disability in basic and instrumental activities of daily living/ADL and IADL). Ten months later the outcomes were assessed again. Frailty was associated with specific healthcare utilization indicators: the FP with a greater utilization of informal care; GFI with an increased contact with healthcare professionals; and TFI with a higher amount of contacts with a general practitioner. After controlling for the effect of life-course determinants, comorbidity and adverse outcome at baseline, GFI predicted IADL disability and TFI predicted quality of life. The effect of the FP on the outcomes was not significant, when compared with the other measures. However, when comparing TFI’s domains, the physical domain was the most significant predictor of the outcomes, even explaining part of the variance of ADL disability. Frailty at baseline was associated with adverse outcomes at follow-up. However, the relationship of each frailty measure (FP, GFI and TFI) with the outcomes was different. In spite of the role of psychological frailty, TFI’s physical domain was the determinant factor for predicting disability and most of the quality of life.

Short sea como alternativa ao transporte terrestre

O Short Sea Shipping (SSS) é um conceito de transporte marítimo de difícil definição. No entanto, considera-se neste trabalho, que corresponde ao movimento de carga e passageiros por mar entre portos situados geográficamente na Europa ou entre outros portos que não estejam situados na Europa, mas que partilham costa com mares que banham a Europa. Esta definição é sem dúvida aquela que melhor se adequa à inclusão do Short SSS como elo de uma cadeia multimodal de transporte de mercadorias no espaço Europeu, e como alternativa ao transporte de mercadorias apenas por estrada. Este trabalho aborda alguns conceitos importantes subjacentes ao transporte marítimo de forma a permitir uma contextualização que possibilite uma melhor compreensão da abordagem feita ao SSS. A sua promoção e implementação, como alternativa viável para o transporte de mercadorias dentro do espaço Europeu, são objetivos da política de transportes da União Europeia (UE). Este trabalho reflete sobre algumas das políticas estratégicas de promoção da UE para impulsionar o SSS como alternativa ao transporte rodoviário. O objetivo deste trabalho é analisar se o SSS é uma alternativa ao transporte terrestre, no contexto de uma distribuição no Reino Unido de produtos de uma Industria localizada nos arredores da cidade do Porto. Neste trabalho analisou-se as vantagens e desvantagens do SSS, foi efetuada uma descrição do Porto de Leixões como porto escolhido para as expedições, assim como uma análise descritiva dos serviços de Short Sea disponiveis neste porto para as zonas de distribuição desta empresa. Foram descritos os fatores chave da mudança de transporte terrestre para SSS, efectuada uma caracterização da distribuição desta empresa e da solução que esta adotou para distribuir os seus produtos no Reino Unido. A anállise económica da operação, dos tempos de trânsito são dois temas fundamentais para a viabilidade da solução como alternativa ao transporte terrestre.

CANDi: context-aware node discovery for short-range cooperation

Multi-standard mobile devices are allowing users to enjoy higher data rates with ubiquitous connectivity. However, the benefits gained from multiple interfaces come at an expense—that being higher energy consumption in an era where mobile devices need to be energy compliant. One promising solution is the usage of short-range cooperative communication as an overlay for infrastructure-based networks taking advantage of its context information. However, the node discovery mechanism, which is pivotal to the bearer establishment process, still represents a major burden in terms of the total energy budget. In this paper, we propose a technology agnostic approach towards enhancing the MAC energy ratings by presenting a context-aware node discovery (CANDi) algorithm, which provides a priori knowledge towards the node discovery mechanism by allowing it to search nodes in the near vicinity at the ‘right time and at the right place’. We describe the different beacons required for establishing the cooperation, as well as the context information required, including battery level, modes, location and so on. CANDi uses the long-range network (WiMAX and WiFi) to distribute the context information about cooperative clusters (Ultra-wideband-based) in the vicinity. The searching nodes can use this context in locating the cooperative clusters/nodes, which facilitates the establishing of short-range connections. Analytical and simulation results are obtained, and the energy saving gains are further demonstrated in the laboratory using a customised testbed. CANDi saves up to 50% energy during the node discovery process, while the demonstrative testbed shows up to 75% savings in the total energy budget, thus validating the algorithm, as well as providing viable evidence to support the usage of short-range cooperative communications for energy savings.

Using data mining with time series data in short-term stocks prediction

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on short- time stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.

Short-Circuit Impedance of Power Transformers: the Fractional Order Calculus Approach

First IFAC Workshop on Fractional Differentiation and Its Application - 19-21 July 2004, Enseirb, Bordeaux, France - FDA'04

New Concepts Towards the Synthesis of Digital Circuits Through Genetic Algorithms

This paper analyses the performance of a Genetic Algorithm using two new concepts, namely a static fitness function including a discontinuity measure and a fractional-order dynamic fitness function, for the synthesis of combinational logic circuits. In both cases, experiments reveal superior results in terms of speed and convergence to achieve a solution.

On the Estimation of the Short-Circuit Impedance of Power Transformers Using Fractional Order Calculus

This paper reports investigation on the estimation of the short circuit impedance of power transformers, using fractional order calculus to analytically study the influence of the diffusion phenomena in the windings. The aim is to better characterize the medium frequency range behavior of leakage inductances of power transformer models, which include terms to represent the magnetic field diffusion process in the windings. Comparisons between calculated and measured values are shown and discussed.

Challenges in the creation and development of a mathematics MOOC

The fast development of distance learning tools such as Open Educational Resources (OER) and Massive Open Online Courses (MOOC or MOOCs) are indicators of a shift in the way in which digital teaching and learning are understood. MOOC are a new style of online classes that allow any person with web access, anywhere, usually free of charge, to participate through video lectures, computer graded tests and discussion forums. They have been capturing the attention of many higher education institutions around the world. This paper will give us an overview of the “Introduction to Differential Calculus” a MOOC Project, created by an engaged volunteer team of Mathematics lecturers from four schools of the Polytechnic Institute of Oporto (IPP). The MOOC theories and their popularity are presented and complemented by a discussion of some MOOC definitions and their inherent advantages and disadvantages. It will also explore what MOOC mean for Mathematics education. The Project development is revealed by focusing on used MOOC structure, as well as the quite a lot of types of course materials produced. It ends with a presentation of a short discussion about problems and challenges met throughout the development of the project. It is also our goal to contribute for a change in the way teaching and learning Mathematics is seen and practiced nowadays, trying to make education more accessible to as many people as possible and increase our institution (IPP) recognition.

System Modeling and Control Through Fractional-Order Algorithms

The theory of fractional calculus goes back to the beginning of thr throry of differential calculus but its inherent complexity postponed the applications of the associated concepts. In the last decade the progress in the areas of chaos and fractals revealed subtle relationships with the fractional calculus leading to an increasing interest in the development of the new paradigm. In the area of automaticcontrol preliminary work has already been carried out but the proposed algorithms are restricted to the frequency domain. The paper discusses the design of fractional-order discrete-time controllers. The algorithms studied adopt the time domein, which makes them suited for z-transform analusis and discrete-time implementation. The performance of discrete-time fractional-order controllers with linear and non-linear systems is also investigated.

Use of a fluorescence-based approach to assess short-term responses of the alga Pseudokirchneriella subcapitata to metal stress

This work explores the use of fluorescent probes to evaluate the responses of the green alga Pseudokirchneriella subcapitata to the action of three nominal concentrations of Cd(II), Cr(VI), Cu(II) and Zn(II) for a short time (6 h). The toxic effect of the metals on algal cells was monitored using the fluorochromes SYTOX Green (SG, membrane integrity), fluorescein diacetate (FDA, esterase activity) and rhodamine 123 (Rh123, mitochondrial membrane potential). The impact of metals on chlorophyll a (Chl a) autofluorescence was also evaluated. Esterase activity was the most sensitive parameter. At the concentrations studied, all metals induced the loss of esterase activity. SG could be used to effectively detect the loss of membrane integrity in algal cells exposed to 0.32 or 1.3 μmol L−1 Cu(II). Rh123 revealed a decrease in the mitochondrial membrane potential of algal cells exposed to 0.32 and 1.3 μmol L−1 Cu(II), indicating that mitochondrial activity was compromised. Chl a autofluorescence was also affected by the presence of Cr(VI) and Cu(II), suggesting perturbation of photosynthesis. In conclusion, the fluorescence-based approach was useful for detecting the disturbance of specific cellular characteristics. Fluorescent probes are a useful diagnostic tool for the assessment of the impact of toxicants on specific targets of P. subcapitata algal cells.

Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains

The vision of the Internet of Things (IoT) includes large and dense deployment of interconnected smart sensing and monitoring devices. This vast deployment necessitates collection and processing of large volume of measurement data. However, collecting all the measured data from individual devices on such a scale may be impractical and time consuming. Moreover, processing these measurements requires complex algorithms to extract useful information. Thus, it becomes imperative to devise distributed information processing mechanisms that identify application-specific features in a timely manner and with a low overhead. In this article, we present a feature extraction mechanism for dense networks that takes advantage of dominance-based medium access control (MAC) protocols to (i) efficiently obtain global extrema of the sensed quantities, (ii) extract local extrema, and (iii) detect the boundaries of events, by using simple transforms that nodes employ on their local data. We extend our results for a large dense network with multiple broadcast domains (MBD). We discuss and compare two approaches for addressing the challenges with MBD and we show through extensive evaluations that our proposed distributed MBD approach is fast and efficient at retrieving the most valuable measurements, independent of the number sensor nodes in the network.