Long-term energy scenarios for Mexico: Policy options for carbon savings and main barriers

This paper shows that possibilities exist for maintaining current energy-related carbon emissions per capita in Mexico at almost constant levels in the long term. It is argued, however, that the identified carbon emissions reductions will not be achieved easily or rapidly, as they require a restructuring of Mexico's current technological base and an unusual degree of coordination among the government, lending institutions, equipment manufacturers, and final users. Such changes also will entail surmounting major institutional and financial barriers and creating a better international environment for technology transfer and capital lending.     

Modelling the rate of secondary succession after farmland abandonment in a Mediterranean mountain area

Secondary succession after farmland abandonment has become a common process in north Mediterranean countries, especially in mountain areas. In this paper, a methodology is tested which combines Markov chains and logistic multivariate regression to model secondary succession after farmland abandonment in environments where abiotic constraints play a major role, like mountain areas. In such landscapes, a decay in the succession rate with time is usually found, as the best locations are progressively occupied. This is frequently addressed using non-stationary Markov chains. Here, we test if the combination of logistic multivariate regression with Markov chains, however, allows for spatially distributed transitions probabilities based on abiotic factors and therefore, it is able to reproduce the preferential colonization of the most favourable locations. The model is tested in the Ijuez Valley in the Spanish Pyrenees, which underwent generalized land abandonment during the 1950s. Results confirm a substantial improvement in the prediction success of the Markov-logistic model when compared to the standard Markov chain approach. As a result, the decay in the succession rate can be successfully modelled. The specific results for our study area are discussed further in an ecological context. The methodology proposed is applicable to any landscape where vegetation dynamics are constrained by environmental factors. However, the inclusion of land use as an explanatory factor would be necessary in human-managed landscapes.     

Biodegradability and improved mechanical performance of polyhydroxyalkanoates/agave fiber biocomposites compatibilized by different strategies

In this work, biocomposites made of polyhydroxyalkanoates (PHA) with natural fibers were produced via compression molding. In particular, polyhydroxybutyrate (PHB) and polyhydroxybutyrate-co-hydroxyvalerate (PHBV) were reinforced with 20 wt% of agave fibers. Different compatibilization strategies were investigated to improve the fiber-matrix interaction: fiber surface treatment in PHA solution, fiber surface treatment in maleated PHA solution, fiber propionylation, and extrusion with maleated PHA. The biocomposites were characterized in terms of morphology, mechanical properties, water absorption, and biodegradability by CO₂ production tracking. In general, fiber propionylation was the best strategy for mechanical properties enhancement and water uptake decreasing. Biocomposites with propionylated fibers showed improved flexural strength (170% for PHB and 84% for PHBV). The flexural modulus was also enhanced with propionylated fibers up to 19% and 18% compared to uncompatibilized biocomposites (PHB and PHBV, respectively). Tensile strength increased by 16% (PHB) and 14% (PHBV), and the water absorption was reduced using propionylated fibers going from 6.6% to 4.4% compared with biocomposites with untreated fibers. Most importantly, the impact strength was also improved for all biocomposites by up to 96% compared with the neat PHA matrices. Finally, it was found that the compatibilization did not negatively modify the PHA biodegradability.     

A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems

Recommender systems arose with the goal of helping users search in overloaded information domains (like e-commerce, e-learning or Digital TV). These tools automatically select items (commercial products, educational courses, TV programs, etc.) that may be appealing to each user taking into account his/her personal preferences. The personalization strategies used to compare these preferences with the available items suffer from well-known deficiencies that reduce the quality of the recommendations. Most of the limitations arise from using syntactic matching techniques because they miss a lot of useful knowledge during the recommendation process. In this paper, we propose a personalization strategy that overcomes these drawbacks by applying inference techniques borrowed from the Semantic Web. Our approach reasons about the semantics of items and user preferences to discover complex associations between them. These semantic associations provide additional knowledge about the user preferences, and permit the recommender system to compare them with the available items in a more effective way. The proposed strategy is flexible enough to be applied in many recommender systems, regardless of their application domain. Here, we illustrate its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV.     

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Recommender systems fight information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based recommenders suggest items similar to those the user liked in the past, using syntactic matching mechanisms. The rigid nature of such mechanisms leads to recommending only items that bear strong resemblance to those the user already knows. Traditional collaborative approaches face up to overspecialization by considering the preferences of other users, which causes other severe limitations. In this paper, we avoid the intrinsic pitfalls of collaborative solutions and diversify the recommendations by reasoning about the semantics of the user’s preferences. Specifically, we present a novel content-based recommendation strategy that resorts to semantic reasoning mechanisms adopted in the Semantic Web, such as Spreading Activation techniques and semantic associations. We have adopted these mechanisms to fulfill the personalization requirements of recommender systems, enabling to discover extra knowledge about the user’s preferences and leading to more accurate and diverse suggestions. Our approach is generic enough to be used in a wide variety of domains and recommender systems. The proposal has been preliminary evaluated by statistics-driven tests involving real users in the recommendation of Digital TV contents. The results reveal the users’ satisfaction regarding the accuracy and diversity of the reasoning-driven content-based recommendations.     

A stabilized difference scheme for deformable porous media and its numerical resolution by multigrid methods

This paper deals with the 2D system of incompressible poroelasticity equations in which an artificial stabilization term has been added to the discretization on collocated grids. Two issues are discussed: It is proved and shown that the additional term indeed brings stability and does not spoil the second order accurate convergence. Secondly, various smoothers are examined in order to find an optimal multigrid method for the discrete system of equations. Numerical experiments confirm the stability and the second order accuracy, as well as fast multigrid convergence for a realistic poroelasticity experiment. This research has been partially supported by the INTAS project 03-50-4395, the Spanish project MEC/FEDER MTM 2004-019051, the Diputación General de Aragón and the Dutch program BSIK: knowledge and research capacity, in the ICT project BRICKS (http://www.bsik-bricks.nl), theme MSV1.     

Guidelines for the incremental identification of aspects in requirements specifications

The desired principle of separation of concerns in software development can be jeopardized by the so-called crosscutting concerns, which tend to be scattered over (and tangled with) the functionality of the modular units of a system. The correct identification of such concerns (and their encapsulation into separate artifacts) is thereby considered a way to improve software understanding and evolution. Pursuing a proper management of concerns from the requirements engineering stage can greatly benefit the entire software life-cycle. In this paper, we propose conceptual guidelines on how to perform the identification of crosscutting concerns in the process of building requirements specifications. We argue that the identification must be carried out in an incremental way, to encapsulate apart the crosscutting concerns even if they have not emerged completely yet.

Complementary Tasks to Measure Working Memory in Distinct Prefrontal Cortex Subregions in Rats.

Acquisition of odor-guided or visually-guided delayed win-shift behavior was evaluated in rats after lidocaine-induced inactivation within the agranular insular area of the prefrontal cortex (PFC) or the prelimbic area of the PFC. Additional sites and tasks were used to control for neuroanatomical and behavioral specificity of lidocaine inactivation of the agranular insular and prelimbic areas. Results showed that acquisition of the odor-guided delayed win-shift task was dependent on the agranular insular area, whereas acquisition of the visually-guided version was dependent on the prelimbic area. This dissociation suggests that the stimulus modality used is critical for revealing working memory functions of different PFC subregions. The described methods provide a complementary means to study working memory in PFC subregions using a radial-arm maze. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A semantic framework for automatic generation of computational workflows using distributed data and component catalogues

Computational workflows are a powerful paradigm to represent and manage complex applications, particularly in large-scale distributed scientific data analysis. Workflows represent application components that result in individual computations as well as their interdependences in terms of dataflow. Workflow systems use these representations to manage various aspects of workflow creation and execution for users, such as the automatic assignment of execution resources. This article describes an approach to automating a new aspect of the process: the selection of application components and data sources. We present a novel approach that enables users to specify varying degrees of detail and amount of constraints in a workflow request, including the specification of constraints on input, intermediate or output data in the workflow, abstract workflow component classes rather than specific component implementations, and generic reusable workflow templates that express a pre-defined combination of components. The algorithm elaborates the user request into a set of fully ground workflows with specific choices of data sources and codes to be used so that they can be submitted for mapping and execution. The algorithm searches through the space of possible candidate workflows by creating increasingly more specialized versions of the original template and eliminating candidates that violate constraints cumulated in the candidate workflow as components and data sources are selected. A novel feature of our approach is that it assumes a distributed architecture where data and component catalogues are separate from the workflow system. The algorithm explicitly poses queries to external catalogues, and therefore any reasoning regarding data or component properties is not assumed to occur within the workflow system. We describe our implementation of this approach in the Wings workflow system. This implementation uses the W3C Web Ontology Language and associated reasoners to implement the workflow system as well as the data and component catalogues. This research demonstrates the use of artificial intelligence techniques to support the kinds of automation envisioned by the scientific community for large-scale distributed scientific data analysis.