Epistemic irrelevance on sets of desirable gambles

This paper studies graphoid properties for epistemic irrelevance in sets of desirable gambles. For that aim, the basic operations of conditioning and marginalization are expressed in terms of variables. Then, it is shown that epistemic irrelevance is an asymmetric graphoid. The intersection property is verified in probability theory when the global probability distribution is positive in all the values. Here it is always verified due to the handling of zero probabilities in sets of gambles. An asymmetrical D-separation principle is also presented, by which this type of independence relationships can be represented in directed acyclic graphs.     

New skeleton-based approaches for Bayesian structure learning of Bayesian networks

Automatically learning the graph structure of a single Bayesian network (BN) which accurately represents the underlying multivariate probability distribution of a collection of random variables is a challenging task. But obtaining a Bayesian solution to this problem based on computing the posterior probability of the presence of any edge or any directed path between two variables or any other structural feature is a much more involved problem, since it requires averaging over all the possible graph structures. For the former problem, recent advances have shown that search + score approaches find much more accurate structures if the search is constrained by a previously inferred skeleton (i.e. a relaxed structure with undirected edges which can be inferred using local search based methods). Based on similar ideas, we propose two novel skeleton-based approaches to approximate a Bayesian solution to the BN learning problem: a new stochastic search which tries to find directed acyclic graph (DAG) structures with a non-negligible score; and a new Markov chain Monte Carlo method over the DAG space. These two approaches are based on the same idea. In a first step, both employ a previously given skeleton and build a Bayesian solution constrained by this skeleton. In a second step, using the preliminary solution, they try to obtain a new Bayesian approximation but this time in an unconstrained graph space, which is the final outcome of the methods. As shown in the experimental evaluation, this new approach strongly boosts the performance of these two standard techniques proving that the idea of employing a skeleton to constrain the model space is also a successful strategy for performing Bayesian structure learning of BNs.     

Alumina Region of the Lithium Aluminosilicate System: A New Window for Temperature Ultrastable Materials Design

Revisiting classic phase diagrams and chemical phase relations in the solid state of a very well‐studied oxide system, such as the lithium aluminosilicate (LAS) system, can open a new window for the design of new advanced materials with improved properties. Crystal chemistry and phase equilibria are used to demonstrate the ability to design materials with particular desired properties in the alumina‐rich corner of the LAS phase diagram. The experimental results demonstrate the alumina and β‐eucryptite solid‐state compatibility.     

Shallow foundations on expansive soils: a case study of the El Viso Geotechnical Unit, Salamanca, Spain

Construction on expansive soils can generally be resolved using piles. Where shallow foundations are used in these materials, the swelling pressure should be determined and the foundations designed to provide a greater pressure to counteract this. Clearly, it will also be necessary to determine the capacity of the ground to take this load. The paper discusses the bearing capacity of unsaturated expansive soils for a given suction value and notes the bearing capacity was increased by 26% when the effect of suction was taken into account. However, an increase in the water content would cause a decrease in suction and hence a decrease in the bearing capacity.     

Analysis of electricity consumption profiles in public buildings with dimensionality reduction techniques

The analysis of the daily electricity consumption profile of a building and its correlation with environmental factors makes it possible to examine and estimate its electricity demand. As an alternative to the traditional correlation analysis, a new approach is proposed to provide a detailed and visual analysis of the correlations between consumption and environmental variables. Since consumption profiles can be characterized by many components, the input space is high dimensional. For that reason, it is necessary to apply dimensionality reduction techniques that enable a projection of these data onto an easily interpretable 2D space. In this paper, several dimensionality reduction techniques are tested in order to determine the most appropriate one for the stated purpose. Later, the proposed approach uses the chosen algorithm to analyze the influence of the environmental variables on the electricity consumption in public buildings located at the University of León. Finally, electricity profiles from all buildings are compared with regard to two aspects, the magnitude and dynamics of the consumption.     

Partitioning CO2 effluxes from an Atlantic pine forest soil between endogenous soil organic matter and recently incorporated 13C-enriched plant material

Soil CO2 effluxes from recently added 13C-labeled phytomass versus endogenous soil organic matter (SOM) were studied in an acid soil from Atlantic pine forests (NW Spain). After several cultures to incorporate fresh 13C-enriched Lolium perenne to a Humic Cambisol with predominance of humus--Al over humus--Fe complexes, potential soil C mineralization was determined by laboratory aerobic incubation (84 days). Isotopic 13C analyses of SOM fractions were assessed to know in which organic compartments the 13C was preferentially incorporated. Although in the 13C-labeled soil the C mineralization coefficient totalized less than 3% of soil C, the 13C mineralization coefficient exceeded 14%, indicating a greater lability of the newly incorporated organic matter. Organic compounds coming from added phytomass showed a higher lability and contributed considerably to the total soil CO2 effluxes (52% of total soil CO2 evolved during the first decomposition stages and 27% at the end), even though added-C comprised less than 4% of total soil C. Good determination coefficients, when values of CO2--C released were fitted to a first-order double exponential kinetic model, support the existence of two C pools of different lability. Kinetic parameters obtained with this model indicated that phytomass addition augmented the biodegradability of the labile pool (instantaneous mineralization rate k increased from 0.07 d(-1) to 0.12 d(-1)) but diminished that of the recalcitrant pool (instantaneous mineralization rate h decreased from 2.7 x 10(-4) d(-1) to 1.6 x 10(-4) d(-1)). Consequently, the differentiation between both SOM pools increased, showing the importance of SOM quality on CO2 emissions from this kind of soil to the atmosphere.     

Mathematical optimization of a supply chain for the production of fuel pellets from residual biomass

One of the main concerns of humankind in the last years is the availability of energy sources. Research has been focused on finding clean and renewable ways to satisfy the energy demand worldwide. In the particular case of the state of Guanajuato, Mexico, clay industry burns each year about 15,000 m³ of fuel oil and residual oils, and 96,000 t of wood derivatives. As a way to reduce the environmental impact of clay industry, the use of solid fuel pellets, obtained from vegetable residual material, is proposed. The raw material for the pellets is obtained from agribusiness and from the cities of the state. The solid biofuel has high density, low content of humidity, a homogeneous shape and high energy density. Nevertheless, special care must be taken about the location of the production facility and hubs, in order to make the production of the biofuel economically feasible. Furthermore, to have an environmentally friendly fuel, the supply chain and the production process must minimize the global environmental impact. In this work, a mathematical programming model is proposed to determinate the optimal location of the production facilities, the hubs, and the best distribution logistics. The problem is modelled using a general disjunctive programming approach, and then relaxed into a mixed-integer non-linear programming (MINLP) problem. It has been determined that the main plant should be located in the city of Irapuato, while secondary plants must be established in the cities of León, Irapuato, Abasolo and Salamanca. Moreover, it has been estimated that, when the residual biomass is converted into pellets, about 72,548 t/year of equivalent CO₂ are avoided in the main plant, together with 24,182 of equivalent CO₂ avoided per secondary facility.     

The Development of Bioactive Glass‐Ceramic Substrates with Biocide Activity

The biocide efficiency of bioactive 45S5 Bioglass® derived glass‐ceramic substrates against common Gram positive and Gram negative bacteria, and also against yeast, is investigated in this study. The results confirm the high bactericidal capacity of this material. Inorganic coatings containing silver nanoparticles on glass‐ceramic substrates are developed in order to ascertain whether the presence of silver enhances the biocide activity. It is found that a coating made of soda‐glass containing silver nanoparticles does not improve the antifungal properties of the 45S5 Bioglass® derived glass‐ceramic substrates.     

Estimating mixtures of truncated exponentials in hybrid bayesian networks

The MTE (Mixture of Truncated Exponentials) model allows to deal with Bayesian networks containing discrete and continuous variables simultaneously. This model offers an alternative to discretisation, since standard algorithms to compute the posterior probabilities in the network, in principle designed for discrete variables, can be directly applied over MTE models. In this paper, we study the problem of estimating these models from data. We propose an iterative algorithm based on least squares approximation. The performance of the algorithm is tested both with artificial and actual data. This work has been supported by the Spanish Ministry of Education and Science and the European Funds for Regional Development, through projects TIC2001-2973-C05-01,02 and by UAL-CAJAMAR, project E-729/01