M. C. Bueso

A state-space model approach to optimum spatial sampling design based on entropy

We consider the spatial sampling design problem for a random field X. This random field is in general assumed not to be directly observable, but sample information from a related variable Y is available. Our purpose in this paper is to present a state-space model approach to network design based on Shannons definition of entropy, and describe its main points with regard to some of the most common practical problems in spatial sampling design. For applications, an adaptation of Ko et al.s (1995) algorithm for maximum entropy sampling in this context is provided. We illustrate the methodology using piezometric data from the Velez aquifer (Malaga, Spain).

Optimal Spatial Sampling Design in a Multivariate Framework

The problem of spatial sampling design for estimating a multivariate random field from information obtained by sampling related variables is considered. A formulation assigning different degrees of importance to the variables and locations involved is introduced. Adopting an entropy-based approach, an objective function is defined as a linear combination in terms of the amount of information on the variables and/or the locations of interest contained in the data. In the multivariate Gaussian case, the objective function is obtained as a geometric mean of conditional covariance matrices. The effect of varying the degrees of importance for the variables and/or the locations of interest is illustrated in some numerical examples.

A study on sampling design for optimal prediction of space–time stochastic processes

Abstract. Optimal selection of sampling strategies is considered for the prediction of spatio-temporal processes in a state-space-model framework. General conditions are assumed in relation to the basic elements of the problem: modelling space-time interaction, formulating prediction objectives, defining the type and structure of sampling configurations, and formulating optimality criteria. An empirical study, involving a diversity of cases selected within two different examples, is carried out with the aim of illustrating some aspects of interest inherent to the problem considered, with special emphasis on highlighting the important effect of the space-time interaction structure on the ratios of information associated with different possible sampling configurations.

Probabilistic model-based methodology for the conformational study of cyclic systems: application to copper complexes double-bridged by phosphate and related ligands

A methodology for the conformational study of cyclic systems through the statistical analysis of torsion angles is presented. It relies on a combination of different methods based on a probabilistic model which takes into account the topological symmetry of the structures. This methodology is applied to copper complexes double-bridged by phosphate and related ligands. Structures from the Cambridge Structural Database (CSD) are analyzed and the chair, boat-chair and boat conformations are identified as the most frequent conformations. The output of the methodology also provides information about distortions from the ideal conformations, the most frequent being: chair <--> twist-chair, chair <--> twist-boat-chair and boat <--> twist-boat. Molecular mechanics calculations identify these distortions as energetically accessible pathways.

Modeling Aluminum Smelter Plants Using Sliced Inverse Regression With a View Towards Load Flexibility

This paper describes the application of sliced inverse regression to model the electrochemical process of aluminum smelter plants. Real data measurements obtained during several years in a Spanish industrial environment are used to illustrate the main dependencies between parameters. Nonlinear relations between the output variables and relevant linear combinations of input variables are deduced. An exploratory statistical analysis is also presented, checking for correlations and possible linear dependencies. The developed model is used to analyze the range of electrical power demand variations as a consequence of modifications in chemical and electrical parameters. An example is described maintaining constant the aluminum production. The results can be considered for future load flexibility programs, which might include aluminum smelter plants as a flexible industrial customer.

Aroma volatiles obtained at harvest by HS-SPME/GC-MS and INDEX/MS-E-nose fingerprint discriminate climacteric behaviour in melon fruit.

BACKGROUND Melon aroma volatiles were extracted at harvest from juice of a climacteric near-isogenic line (NIL) SC3-5-1 with two quantitative trait loci (QTLs) introgressed which produced climacteric behaviour and its non-climacteric parental (PS) using two methodologies of analysis: static headspace solid phase micro-extraction (HS-SPME) by gas chromatography-mass spectrometry (GC-MS) and inside needle dynamic extraction (INDEX) by MS-based electronic nose (MS-E-nose). RESULTS Of the 137 volatiles compounds identified, most were found at significantly higher concentrations in SC3-5-1 than in PS in both seasons. These volatiles were mostly esters, alcohols, sulfur-derived esters and even some aldehydes and others. The number of variables with high correlation values was reduced by using correlation network analysis. Partial least squares-discriminant analysis (PLS-DA) achieved the correct classification of PS and SC3-5-1. The ions m/z 74, 91, 104, 105, 106 and 108, mainly volatile derivatives precursor phenylalanine, were the most discriminant in SC3-5-1 and PS. As many as 104 QTLs were mapped in season 1 and at least 78 QTLs in each season with an effect above the PS mean. CONCLUSION GC-MS gave better discrimination than E-nose. Most of the QTLs that mapped in both seasons enhanced aroma volatiles associated with climacteric behaviour.

Stochastic complexity and model selection from incomplete data

The principle of minimum description length (MDL) provides an approach for selecting the model class with the smallest stochastic complexity of the data among a set of model classes. However, when only incomplete data are available the stochastic complexity for the complete data cannot be numerically computed. In this paper, this problem is solved by introducing a notion of expected stochastic complexity for the complete data conditional on the observed data, which can be computed by the EM algorithm. Based on this notion, model selection from incomplete data can also be performed by the MDL principle. A simulation study is presented for illustration of the methodology.

A New Solar Module Modeling for PV Applications Based on a Symmetrized and Shifted Gompertz Model

This paper focuses on a new approach to model photovoltaic (PV) solar modules based on symmetric-shifted Gompertz functions. This solution significantly reduces the number of fitting parameters, convergence problems, and computational costs in comparison with previous modeling. The entire I-V curve is provided, as well as relevant nonlinear relations between environmental and electrical variables. A comparison with previous approaches has also been included in this paper, discussing the relevant advantages of the proposed technique with respect to previously published solutions. Real data from CdTe thin-film and Si PV power plants have been used to assess this new nonlinear model. The proposed solution can also be applied to check the active power generated by PV modules based on the expected datasheet values provided by the manufacturers. This alternative process avoids the necessity of removing and analyzing the solar modules in a laboratory environment.

Estimation and smoothing from incomplete data for a class of lattice processes

Multidimensional discrete-parameter processes with factorable covariance structure are of great importance for applications and approximations to certain continuous parameter processes. In practical situations, usually only incomplete data are available, so state-space schemes are normally used for modelling and prediction. In this work we describe maximum-likelihood estimation and smoothing for doubly geometric lattice processes using incomplete data. The procedure proposed is based on an application of the EM algorithm, and is inspired by its use in time-series analysis. Minimum mean-square-error prediction is also described. Extension to more general models is commented on. Some examples using simulated data are provided.

A study on sensitivity of spatial sampling designs to a priori discretization schemes

In a previous paper (Environ. Ecol. Stat. 5 (1998) 29.) we presented an entropy-based approach to spatial sampling design in a state-space model framework. We now address the problem of sensitivity of optimal designs with respect to the configuration of the set of potential observation sites considered, as well as to the model specifications. The latter involve both the spatial dependence structure of the variable of interest and its relationship with the observable variable. To analyze several aspects related to this problem, we have developed an extensive empirical study, from which we conclude the critical influence that the a priori selection of candidate observation sites can have on the final sampling designs for different situations. 2004 Elsevier Ltd. All rights reserved.