Analysis of the Importance of Oxides and Clays in Cd, Cr, Cu, Ni, Pb and Zn Adsorption and Retention with Regression Trees.

This study determines the influence of the different soil components and of the cation-exchange capacity on the adsorption and retention of different heavy metals: cadmium, chromium, copper, nickel, lead and zinc. In order to do so, regression models were created through decision trees and the importance of soil components was assessed. Used variables were: humified organic matter, specific cation-exchange capacity, percentages of sand and silt, proportions of Mn, Fe and Al oxides and hematite, and the proportion of quartz, plagioclase and mica, and the proportions of the different clays: kaolinite, vermiculite, gibbsite and chlorite. The most important components in the obtained models were vermiculite and gibbsite, especially for the adsorption of cadmium and zinc, while clays were less relevant. Oxides are less important than clays, especially for the adsorption of chromium and lead and the retention of chromium, copper and lead.

Application of a hybrid 3D-2D laser scanning system to the characterization of slate slabs.

Dimensional control based on 3D laser scanning techniques is widely used in practice. We describe the application of a hybrid 3D-2D laser scanning system to the characterization of slate slabs with structural defects that are difficult for the human eye to characterize objectively. Our study is based on automating the process using a 3D laser scanner and a 2D camera. Our results demonstrate that the application of this hybrid system optimally characterizes slate slabs in terms of the defects described by the Spanish UNE-EN 12326-1 standard.

Learning machines applied to potential forest distribution.

The clearing of forests to obtain land for pasture and agriculture and the replacement of autochthonous species by other faster-growing varieties of trees for timber have both led to the loss of vast areas of forest worldwide. At present, many developed countries are attempting to reverse these effects, establishing policies for the restoration of older woodland systems. Reforestation is a complex matter, planned and carried out by experts who need objective information regarding the type of forest that can be sustained in each area. This information is obtained by drawing up feasibility models constructed using statistical methods that make use of the information provided by morphological and environmental variables (height, gradient, rainfall, etc.) that partially condition the presence or absence of a specific kind of forestation in an area. The aim of this work is to construct a set of feasibility models for woodland located in the basin of the River Liébana (NW Spain), to serve as a support tool for the experts entrusted with carrying out the reforestation project. The techniques used are multilayer perceptron neural networks and support vector machines. Their results will be compared to the results obtained by traditional techniques (such as discriminant analysis and logistic regression) by measuring the degree of fit between each model and the existing distribution of woodlands. The interpretation and problems of the feasibility models are commented on in the Discussion section.

Síndrome de Kallmann: hallazgos por resonancia magnética / Kallmann's syndrome: magnetic resonance findings

El síndrome de Kallmann es una entidad poco frecuente, que se caracteriza por hipogonadismo hipogonadotropo asociado a anosmia o hiposmia. Morfológicamente, se caracteriza por un desarrollo defectuoso del sistema olfatorio, con ausencia o extrema hipoplasia de los bulbos, cintillas y surcos olfatorios. Presentamos un caso de síndrome de Kallmann en el que la resonancia magnética aportó datos distintivos de la entidad (AU)