Probabilistic Wind Farms Generation Model for Reliability Studies Applied to Brazilian Sites

This paper presents a computer model for the probabilistic representation of wind farms generation for reliability studies, which can provide an annual estimation of energy production and calculate several performance indexes. The model combines the stochastic characteristics of wind speed with the operational information of the turbines, such as the failure and repair rates, representing the wind farm by a Markov process. The simulations are made with real time series of wind speed of several Brazilian regions and actual turbine models. The influence of some wind farm and installation site characteristics on the results are evaluated, such as the wind speed statistical clustering technique, the number and type of the turbines, and the failure and repair rates. The results obtained reproduce successfully the behavior of the components considered in the model     

A multiscale directional operator and morphological tools for reconnecting broken ridges in fingerprint images

In this paper, we introduce a multiscale operator which, together with some morphological tools, can be used to reconnect broken components of fingerprint images. This operator, which can be applied to both binary and gray-scale pictures, extracts the orientation field of an image by taking into account information parameters of sets of pixels in a given neighborhood and located in different directions. It has proved to be very robust to noise and outperforms the gradient- and directional mask-based methods, generally used for orientation field definition. Although we illustrate its application in the fingerprint domain, the approach described here can be easily extended to images whose components exhibit well-defined directional information.     

Hand gesture recognition from depth and infrared Kinect data for CAVE applications interaction

This paper presents a real-time framework that combines depth data and infrared laser speckle pattern (ILSP) images, captured from a Kinect device, for static hand gesture recognition to interact with CAVE applications. At the startup of the system, background removal and hand position detection are performed using only the depth map. After that, tracking is started using the hand positions of the previous frames in order to seek for the hand centroid of the current one. The obtained point is used as a seed for a region growing algorithm to perform hand segmentation in the depth map. The result is a mask that will be used for hand segmentation in the ILSP frame sequence. Next, we apply motion restrictions for gesture spotting in order to mark each image as a ‘Gesture’ or ‘Non-Gesture’. The ILSP counterparts of the frames labeled as “Gesture” are enhanced by using mask subtraction, contrast stretching, median filter, and histogram equalization. The result is used as the input for the feature extraction using a scale invariant feature transform algorithm (SIFT), bag-of-visual-words construction and classification through a multi-class support vector machine (SVM) classifier. Finally, we build a grammar based on the hand gesture classes to convert the classification results in control commands for the CAVE application. The performed tests and comparisons show that the implemented plugin is an efficient solution. We achieve state-of-the-art recognition accuracy as well as efficient object manipulation in a virtual scene visualized in the CAVE.     

3D porosity prediction from seismic inversion and neural networks

In this work, we address the problem of transforming seismic reflection data into an intrinsic rock property model. Specifically, we present an application of a methodology that allows interpreters to obtain effective porosity 3D maps from post-stack 3D seismic amplitude data, using measured density and sonic well log data as constraints. In this methodology, a 3D acoustic impedance model is calculated from seismic reflection amplitudes by applying an L₁-norm sparse-spike inversion algorithm in the time domain, followed by a recursive inversion performed in the frequency domain. A 3D low-frequency impedance model is estimated by kriging interpolation of impedance values calculated from well log data. This low-frequency model is added to the inversion result which otherwise provides only a relative numerical scale. To convert acoustic impedance into a single reservoir property, a feed-forward Neural Network (NN) is trained, validated and tested using gamma-ray and acoustic impedance values observed at the well log positions as input and effective porosity values as target. The trained NN is then applied for the whole reservoir volume in order to obtain a 3D effective porosity model. While the particular conclusions drawn from the results obtained in this work cannot be generalized, such results suggest that this workflow can be applied successfully as an aid in reservoir characterization, especially when there is a strong non-linear relationship between effective porosity and acoustic impedance.     

A Framework for Integrating Non-Functional Requirements into Conceptual Models

The development of complex information systems calls for conceptual models that describe aspects beyond entities and activities. In particular, recent research has pointed out that conceptual models need to model goals, in order to capture the intentions which underlie complex situations within an organisational context. This paper focuses on one class of goals, namely non-functional requirements (NFR), which need to be captured and analysed from the very early phases of the software development process. The paper presents a framework for integrating NFRs into the ER and OO models. This framework has been validated by two case studies, one of which is very large. The results of the case studies suggest that goal modelling during early phases can lead to a more productive and complete modelling activity.       

Spin Polarization by Tilted Magnetic Field in Wide Ga1?xAlxAs Parabolic Quantum Wells

Wide parabolic wells can be created by properly controlling the Al content during the growth of successive Ga_1–xAl_xAs thin layers. Under a tilted magnetic field these systems present interesting transport properties, which are associated to their composition dependent g-factor. We present an exact solution for the eigenstates of an electron gas inside such a quantum well. We calculate the renormalized cyclotron frequencies as functions of the angle of tilt, as well as the density of states, and the Fermi level. We discuss the conditions for the existence of spin-polarized charge.     

Effects of Low-Intensity and Long-Term Aerobic Exercise on the Psoas Muscle of mdx Mice: An Experimental Model of Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a muscle disease characterized by the absence of the protein dystrophin, which causes a loss of sarcolemma integrity, determining recurrent muscle injuries, decrease in muscle function, and progressive degeneration. Currently, there is a need for therapeutic treatments to improve the quality of life of DMD patients. Here, we investigated the effects of a low-intensity aerobic training (37 sessions) on satellite cells, peroxisome proliferator-activated receptor-gamma coactivator (PGC)-1α protein (PGC-1α), and different types of fibers of the psoas muscle from mdx mice (DMD experimental model). Wildtype and mdx mice were randomly divided into sedentary and trained groups (n = 24). Trained animals were subjected to 37 sessions of low-intensity running on a motorized treadmill. Subsequently, the psoas muscle was excised and analyzed by immunofluorescence for dystrophin, satellite cells, myosin heavy chain (MHC), and PGC-1α content. The minimal Feret’s diameters of the fibers were measured, and light microscopy was applied to observe general morphological features of the muscles. The training (37 sessions) improved morphological features in muscles from mdx mice and caused an increase in the number of quiescent/activated satellite cells. It also increased the content of PGC-1α in the mdx group. We concluded that low-intensity aerobic exercise (37 sessions) was able to reverse deleterious changes determined by DMD.     

Forms of reasoning used by prospective physical sciences teachers when explaining and predicting natural phenomena: The case of air pressure

Abstract

The co‐ordination between theory and evidence is an outstanding characteristic of scientific thinking. Research indicates that students often have difficulty in explaining natural phenomena because they use their own theories to explain phenomena or they are unable to build a bridge between theory and evidence. Science teachers must teach students to collect and select evidence and to use theory to explain it. The objective of this study was to investigate the forms of reasoning used by prospective physical sciences teachers when they build up explanations and make predictions about natural phenomena. Thirty‐eight prospective teachers answered a questionnaire structured around three problems focusing on phenomena that can be explained through air‐pressure variation. The results seem to indicate a variation in the forms of reasoning used, depending on the problem and the type of request. The number of prospective teachers who consistently use a certain form of reasoning is higher within problems than across problems.