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Gebhardt C.G.,Fraunhofer Institute for Wind Energy and Energy System Technology | Roccia B.A.,National University of Rio Cuarto | Roccia B.A.,National Scientific and Technical Research Council
Renewable Energy | Year: 2014

In this work, we present an aeroelastic model intended for three-blade large-scale horizontal-axis wind turbines. This model results from the coupling of an existing aerodynamic model and a structural model based on a segregated formulation derived in an index-based notation that enables combining very different descriptions such as rigid-body dynamics, assumed-modes techniques and finite element methods. The developed structural model comprises a supporting tower, a nacelle, which contains the electrical generator, power electronics and control systems, a hub in which the blades are connected to a rotating shaft, and three blades, which extract energy from the wind. Flexible blades are discretized into beam finite elements and the flexible tower is discretized into assumed modes. The nacelle and hub are considered rigid. To illustrate the flexibility of the structural modeling, the tower, nacelle and hub are modeled as a single kinematic chain and each blade is modeled separately. To establish the blade-hub attachments, we use constraint equations. Thus, the resulting equations are differential algebraic. We also expose a general procedure for connecting the non-matching structural and aerodynamic meshes. Finally, we present results, some of them are validations, which prove that our new approach is reliable and does have capability to capture non-linear phenomena such as centrifugal stiffening, flutter and large yaw errors, and the remaining ones correspond to the aeroelastic response of a wind turbine during a start-up maneuvering. © 2014 Elsevier Ltd. Source

Bardach A.E.,Institute for Clinical Effectiveness and Health Policy | Ciapponi A.,Institute for Clinical Effectiveness and Health Policy | Soto N.,Institute for Clinical Effectiveness and Health Policy | Chaparro M.R.,Institute for Clinical Effectiveness and Health Policy | And 3 more authors.
Science of the Total Environment | Year: 2015

Four million people in Argentina are exposed to arsenic contamination from drinking waters of several center-northern provinces. A systematic review to examine the geographical distribution of arsenic-related diseases in Argentina was conducted, searching electronic databases and gray literature up to November 2013. Key informants were also contacted. Of the 430 references identified, 47 (mostly cross-sectional and ecological designs) referred to arsenic concentration in water and its relationship with the incidence and mortality of cancer, dermatological diseases and genetic disorders. A high percentage of the water samples had arsenic concentrations above the WHO threshold value of 10. μg/L, especially in the province of Buenos Aires. The median prevalence of arsenicosis was 2.6% in exposed areas. The proportion of skin cancer in patients with arsenicosis reached 88% in case-series from the Buenos Aires province. We found higher incidence rate ratios per 100. μg/L increment in inorganic arsenic concentration for colorectal, lung, breast, prostate and skin cancer, for both genders. Liver and skin cancer mortality risk ratios were higher in regions with medium/high concentrations than in those with low concentrations. The relative risk of mortality by skin cancer associated to arsenic exposure in the province of Buenos Aires ranged from 2.5 to 5.2. In the north of this province, high levels of arsenic in drinking water were reported; however, removal interventions were scarcely documented. Arsenic contamination in Argentina is associated with an increased risk of serious chronic diseases, including cancer, showing the need for adequate and timely actions. © 2015 Elsevier B.V. Source

Mikulan E.P.,University of Buenos Aires | Mikulan E.P.,Diego Portales University | Reynaldo L.,University of Buenos Aires | Ibanez A.,University of Buenos Aires | And 3 more authors.
Frontiers in Human Neuroscience | Year: 2014

Emerging theories on embodied cognition have caused high expectations, ambitious promises, and strong controversies. Several criticisms have been explained elsewhere (Mahon and Caramazza, 2008; Cardona et al., 2014) and will not be discussed further here. In this paper, we will focus on a specific explanatory strategy frequently assessed by the radical embodied cognition approaches: the use of homuncular explanations for the explicit (or implicit) attribution of causal roles in the comprehension of language understanding. We first present this criticism regarding a prototypical example: the mirror neuron system (MNS) (Rizzolatti and Craighero, 2004; Iacoboni and Dapretto, 2006) in the field of language understanding and then extend our conclusions to other programs of embodied cognition. Here we discuss the radical claims that propose the MNS as the putative mechanism for multiple cognitive and social psychology constructs (e.g., Gallese, 2008; Cattaneo and Rizzolatti, 2009; Iacoboni, 2009) and the critical role of the MNS in language understanding (Heyes, 2010a; Hickok, 2013). © 2014 Mikulan, Reynaldo and Ibáñez. Source

Cevasco J.,National Scientific and Technical Research Council | van Den Broek P.,Leiden University
Cognitive Processing | Year: 2016

The purpose of this study was to examine the effect of filled pauses (uh) on the verification of words and the establishment of causal connections during the comprehension of spoken expository discourse. With this aim, we asked Spanish-speaking students to listen to excerpts of interviews with writers, and to perform a word-verification task and a question-answering task on causal connectivity. There were two versions of the excerpts: filled pause present and filled pause absent. Results indicated that filled pauses increased verification times for words that preceded them, but did not make a difference on response times to questions on causal connectivity. The results suggest that, as signals of delay, filled pauses create a break with surface information, but they do not have the same effect on the establishment of meaningful connections. © 2016 Marta Olivetti Belardinelli and Springer-Verlag Berlin Heidelberg Source

Stegmayer G.,National Scientific and Technical Research Council | Gerard M.,Research Center for Signals | Milone D.,Research Center for Signals
IEEE Computational Intelligence Magazine | Year: 2012

Biology is in the middle of a data explosion. The technical advances achieved by the genomics, metabolomics, transcriptomics and proteomics technologies in recent years have significantly increased the amount of data that are available for biologists to analyze different aspects of an organism. However, *omics data sets have several additional problems: they have inherent biological complexity and may have significant amounts of noise as well as measurement artifacts. The need to extract information from such databases has once again become a challenge. This requires novel computational techniques and models to automatically perform data mining tasks such as integration of different data types, clustering and knowledge discovery, among others. In this article, we will present a novel integrated computational intelligence approach for biological data mining that involves neural networks and evolutionary computation. We propose the use of self-organizing maps for the identification of coordinated patterns variations; a new training algorithm that can include a priori biological information to obtain more biological meaningful clusters; a validation measure that can assess the biological significance of the clusters found; and finally, an evolutionary algorithm for the inference of unknown metabolic pathways involving the selected clusters. © 2005-2012 IEEE. Source

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