Coupled Response for Rigid Foundations

A matrix formulation to determine the coupled response of rigid foundations where soil properties are modeled by springs and dashpots is presented. Location and orientation of springs and dashpots can be arbitrary, thus a general solution is determined. The response is given in terms of translational and rotational displacements considering coupled effects. Physics of the problem presented here has been extensively studied and a broad range of useful formulae to determine springs and dashpots properties for soil-structure interaction is available. These published formulae for springs and dashpots properties are an input to the approach presented. Thus, the novelty of this approach is a matrix manipulation that leads to a simple expression allowing coupling all degrees of freedom even when springs and dashpots are not orthogonally oriented. For validation purposes, finite element solutions are compared with the approach presented.     

Water,sanitation and hygiene and indigenous peoples: a review of the literature

The levels of sanitation and water services coverage as well as health attainment are low among indigenous peoples. This exclusion from basic service has not been sufficiently studied. The present review has analyzed 185 articles dealing with indigenous peoples and the water, sanitation and hygiene complex. The literature is dramatically skewed towards water resources, and overwhelmingly focused on conflicts, at the expense of basic sanitation and hygiene. More initiatives towards the acknowledgement of indigenous peoples’ world-views and institutions in all aspects of the water management cycle are needed. To this end, the development of effective intercultural dialogue mechanisms is crucial.     

Failure analysis of the standby liquid control system for a boiling water reactor with fuzzy cognitive maps

A fuzzy cognitive maps (FCM) application is proposed as a simple method to determine failure modes and effects of the standby liquid control system (SLC) during anticipated transient without scram (ATWS) in a boiling water reactor (BWR). The SLC has an important contribution to the total core damage frequency in a BWR. This is the first step in the development of an expert system that could involve many emergency systems of a BWR to simulate accident sequences, through the knowledge representation and reasoning with FCM designs in order to automate the decision making process. A simplified model of the SLC is analyzed with the fault tree analysis technique in order to compare this results with those obtained with the FCM and show consistency with the results, in order to see that both techniques show similar results even if the approaches are different.     

Bootstrap prediction intervals in state–space models

Abstract.  Prediction intervals in state–space models can be obtained by assuming Gaussian innovations and using the prediction equations of the Kalman filter, with the true parameters substituted by consistent estimates. This approach has two limitations. First, it does not incorporate the uncertainty caused by parameter estimation. Second, the Gaussianity of future innovations assumption may be inaccurate. To overcome these drawbacks, Wall and Stoffer [ Journal of Time Series Analysis (2002) Vol. 23, pp. 733–751] propose a bootstrap procedure for evaluating conditional forecast errors that requires the backward representation of the model. Obtaining this representation increases the complexity of the procedure and limits its implementation to models for which it exists. In this article, we propose a bootstrap procedure for constructing prediction intervals directly for the observations, which does not need the backward representation of the model. Consequently, its application is much simpler, without losing the good behaviour of bootstrap prediction intervals. We study its finite-sample properties and compare them with those of the standard and the Wall and Stoffer procedures for the local level model. Finally, we illustrate the results by implementing the new procedure to obtain prediction intervals for future values of a real time series.     

Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling

A novel method is introduced for the generation of landmarks for three-dimensional (3-D) shapes and the construction of the corresponding 3-D statistical shape models. Automatic landmarking of a set of manual segmentations from a class of shapes is achieved by 1) construction of an atlas of the class, 2) automatic extraction of the landmarks from the atlas, and 3) subsequent propagation of these landmarks to each example shape via a volumetric nonrigid registration technique using multiresolution B-spline deformations. This approach presents some advantages over previously published methods: it can treat multiple-part structures and requires less restrictive assumptions on the structure's topology. In this paper, we address the problem of building a 3-D statistical shape model of the left and right ventricle of the heart from 3-D magnetic resonance images. The average accuracy in landmark propagation is shown to be below 2.2 mm. This application demonstrates the robustness and accuracy of the method in the presence of large shape variability and multiple objects.     

Multivariable predictive control of a pressurized tank using neural networks

The behavior of a multivariable predictive control scheme based on neural networks applied to a model of a nonlinear multivariable real process, consisting of a pressurized tank is investigated in this paper. The neural scheme consists of three neural networks; the first is meant for the identification of plant parameters (identifier), the second one is for the prediction of future control errors (predictor) and the third one, based on the two previous, compute the control input to be applied to the plant (controller). The weights of the neural networks are updated on-line, using standard and dynamic backpropagation. The model of the nonlinear process is driven to an operation point and it is then controlled with the proposed neural control scheme, analyzing the maximum range over the neural control works properly.     

Pseudopterosin Content Variability of the Purple Sea Whip Pseudopterogorgia elisabethae at the Islands of San Andres and Providencia (Sw Caribbean)

To determine pseudopterosin composition and concentration in colonies of Pseudopterogorgia elisabethae from the islands of San Andres and Providencia, we collected fragments of individual colonies at various sites and depth ranges around the islands. Chromatographic profiles of the polar fraction, particularly those obtained by HPLC-MS analyses, allowed us to recognize two different chemotypes. Chemotype 1 characterized samples from Providencia whereas chemotype 2 characterized samples from San Andres. A complex pseudopterosin mixture (compounds 1-13) characterized chemotype 1. These compounds were isolated by a combination of chromatographic methods and identified by spectroscopic methods (MS, UV, 1H, and 13C NMR). We identified the known pseudopterosins G and K and seco-pseudopterosin A. We also isolated and identified seven new compounds, pseudopterosins P-V, isomers of known pseudopterosins. Pseudopterosins G and K were found at concentrations ranging between 1 and 3% of the animal dry mass. Pseudopterosins Q and U were the major compounds reaching up to 6% of the animal dry mass at some locations. Major metabolites in chemotype 2 had a molecular weight and fragmentation pattern different from that observed in the pseudopterosins, as determined by HPLC-MS. Total pseudopterosin concentration in this chemotype was below 3% dry mass at all sites. Total pseudopterosin concentration was significantly higher in chemotype 1, with concentrations ranging between 4 and 20% dry mass. At most locations on Providencia, however, total pseudopterosin concentration ranged between 11 and 15% dry mass. Concentrations exceed reports from other locations in the Caribbean. Furthermore, pseudopterosin composition in our samples is quite different from those in specimens of P. elisabethae from the Bahamas and Bermuda. Pseudopterosins G, K, and P-V are characteristic of P. elisabethae colonies from the island of Providencia, while pseudopterosins A-D are characteristic of colonies of P. elisabethae from the Bahamas islands, and pseudopterosins E-L have been isolated from P. elisabethae from the Bahamas and Bermuda. The overall morphology of P. elisabethae can be variable, and chemical differences are not correlated to specific morphs. We confirmed the species identity of each colony by morphological and sclerite analysis and found no significant differences in sclerite dimensions among different colonies and chemotypes.     

Two-dimensional PCA: a new approach to appearance-based face representation and recognition

In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.     

A new approach to the extraction of ANN rules and to their generalization capacity through GP

Various techniques for the extraction of ANN rules have been used, but most of them have focused on certain types of networks and their training. There are very few methods that deal with ANN rule extraction as systems that are independent of their architecture, training, and internal distribution of weights, connections, and activation functions. This article proposes a methodology for the extraction of ANN rules, regardless of their architecture, and based on genetic programming. The strategy is based on the previous algorithm and aims at achieving the generalization capacity that is characteristic of ANNs by means of symbolic rules that are understandable to human beings.