J. M. Antón

Compromise programming calibration for financial analysis of firms of a common sector of business, case study for a set of Spanish banks in 1995

To perform a Financial Analysis (FA) procedure on a set of different firms of the same business sector and region or stock market (maybe with a definite management goal) a set from 7 to 20 balance ratios is chosen as representative of the firms situation, and a benchmark set of good firms of the same kind is selected to calibrate the ratios as attribute variables using Compromise Programming (CP) comparison procedures. For that using the ratios of the benchmark set a reference CP ideal point is obtained, and with it for each ratio a reduced Ratio Quality Index (RQI) is obtained containing a relative CP quality evaluation. From these RQI, a CP Global Utility Index (GUI) of each firm is then proposed, using a common set of chosen CP weights to incorporate the effect of each ratio on the quality of the firms for the intended FA study, with indication of some special techniques including possible use of utility-like functions. An illustrative case study follows with normalized balance sheets data of some main Spanish banks in 1995, showing that some of the real features of this suggestive example are put in evidence by this method, that quality classification depends on various factors, and that external wide information is necessary and gets incorporated with the proposed method. A discussion follows concerning the case, the method and possible future developments.

MCDM Methods for Territorial Services Planning in an Andine Rural Area

For election of a system of disposal of urban residues in small andine cities of the Provincia de Salta, Argentina, some discrete MCDM methods were found adequate and were used choosing between three alternatives {direct filling, sanitary filling+compost and transport out of the city}, after a local team defined and elicited a comprehensive set of criteria. ELECTRE-I and PROMETHEE methods were performed, requiring elicitation of weights and some adaptations. A.H.P. was locally performed. The second alternative was preferred with the three methods.

Variation in Spectral and Mass Dimension on Three-Dimensional Soil Image Processing

Abstract A quantitative characterization of the three-dimensional soil pore architecture is important for understanding soil processes as it relates to the control of biological, chemical, and physical processes across scales. Recent advances in nondestructive imaging, such as X-ray computed tomography (CT), provide an opportunity to analyze pore space features from direct visualization of soil structure. At the same time that these techniques provide new opportunities, they also introduce new processing steps on which the final results depend. Fractal formalism has been shown to be a useful tool in cases where highly complex and heterogeneous media are studied. One of these quantifications is mass dimension (Dm) and spectral dimension (d) applied for water and gas diffusion in soil. In this work, intact soil samples were collected from four horizons of a Brazilian soil, and three-dimensional images, of 45.1-&mgr;m resolution (256 × 256 × 256 voxels), were obtained. Four different threshold criteria were used to transform CT grayscale imagery in binary imagery (pore/solid), based on the frequency of CT units. We calculated the sensitivity of a geometrical parameter (the mass fractal dimension, Dm), a topological parameter (the spectral dimension, d), and the ratio of the two (Dm), which relates to the scaling property of dynamic processes in soil such as diffusion. Each threshold criterion had a direct influence on the measured porosity and on the value of Dm, showing a clear logarithmic increase in Dm with porosity. Meanwhile, d increased faster, that is, linearly with measured porosity. In all cases, the detailed dependence on porosity was different for each horizon. In contrast, the ratio for each horizon was less sensitive to the thresholding criteria applied to the image. Thus, the results based on our soil samples suggest that thresholding has a strong influence on parameters that relate to geometrical and topological properties of structure but may have a less important impact on parameters relevant to dynamic processes such as diffusion.

Sustainable agriculture using an intelligent mechatronic system

The goal of the Project group created by U.P.M. in collaboration with foreign universities, research institutions and companies is the development of an intelligent mechatronic system for the use of precision and sustainable agriculture. The project as a whole includes the following components: photographing and decoding of the soil surface; fertility determination and formation of the fertility map; generation of the controlling signal for mechatronic dosing device; intelligent dosing of fertilizers; simulation, prototype and testing; human-machine interaction and training preparation.

Fractals as Pre-Processing Tool for Computational Intelligence Application

Preprocessing is the process of adapting the input of our Computational Intelligence (CI) problem to the CI technique applied. Images are inputs of many problems, and Fractal processing of the images to extract relevant geometry characteristics is a very important tool. This chapter is dedicated to Fractal Preprocessing. In Pedology, fractal models were fitted to match the structure of soils and techniques of multifractal analysis of soil images were developed as is described in a state-of-the-art panorama. A box-counting method and a gliding box method are presented, both obtaining from images sets of dimension parameters, and are evaluated in a discussed case study from images of samples, and the second seems preferable. Finally, a comprehensive list of references is given

Mathematical Models to Elaborate Plans for Adaptation of Rural Communities to Climate Change

Climate change is on the policy agenda at the global level, with the aim of understanding and reducing its causes and to mitigate its consequences. In most countries and international organizations, the efforts and debates have been directed to know the possible causes, to predict the future evolution of some variable conditioners, trying to make studies to fight against the effects or to delay its negative evolution. This article presents the results from a global model that can help to choose the best alternative among the feasible ones, by taking into consideration environment and population as the main criteria.

Mathematical decision theory applied to land capability: a case study in the community of madrid.

In land evaluation science, a standard data set is obtained for each land unit to determine the land capability class for various uses, such as different farming systems, forestry, or the conservation or suitability of a specific crop. In this study, we used mathematical decision theory (MDT) methods to address this task. Mathematical decision theory has been used in areas such as management, finance, industrial design, rural development, the environment, and projects for future welfare to study quality and aptness problems using several criteria. We also review MDT applications in soil science and discuss the suitability of MDT methods for dealing simultaneously with a number of problems. The aim of the work was to show how MDT can be used to obtain a valid land quality index and to compare this with a traditional land capability method. Therefore, an additive classification method was applied to obtain a land quality index for 122 land units that were compiled for a case study of the Community of Madrid, Spain, and the results were compared with a previously assigned land capability class using traditional methods based on the minimum requirements for land attributes.

Application of Decision Theory methods for a soil classification in the Community of Madrid (Spain)

A land classification method was designed for the Community of Madrid (CM), which has lands suitable for either agriculture use or natural spaces. The process started from an extensive previous CM study that contains sets of land attributes with data for 122 types and a minimum-requirements method providing a land quality classification (SQ) for each land. Borrowing some tools from Operations Research (OR) and from Decision Science, that SQ has been complemented by an additive valuation method that involves a more restricted set of 13 representative attributes analysed using Attribute Valuation Functions to obtain a quality index, QI, and by an original composite method that uses a fuzzy set procedure to obtain a combined quality index, CQI, that contains relevant information from both the SQ and the QI methods.