Influence of the dispersion of Nanoclays on the cellular structure of foams based on polystyrene

In the present work blends of polystyrene (PS) with sepiolites have been produced using a melt extrusion process. The dispersion degree of the sepiolites in the PS has been analyzed by dynamic shear rheology and X-ray micro-computed tomography. Sepiolites treated with quaternary ammonium salts (O-QASEP) are better dispersed in the PS matrix than natural sepiolites (N-SEP) or sepiolites organo-modified with silane groups (O-SGSEP). A percolated network is obtained when using 6.0 wt% of O-QASEP, 8.0 wt% of N-SEP and 10.0 wt% of O-SGSEP. It has been shown that multiple extrusion processes have a negative effect on the polymer architecture. They produce a reduction in the length of the polymeric chains, and they do not lead to a better dispersion of the particles in the polymer matrix. Foams have been produced using a gas dissolution foaming process, where a strong effect of the dispersion degree on the cellular structure of the different foams was found. The effects on the cellular structure obtained by using different types of sepiolites, different contents of sepiolites and different extrusion conditions have been analyzed. The foams produced with the formulations containing O-QASEP present the lowest cell size and the most homogeneous cellular structures.     

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.     

Speciation of the semivolatile hydrocarbon engine emissions from sunflower biodiesel

Biodiesel fuel is a very attractive alternative to conventional fuel and its effects in the engine performance and regulated emissions have been widely studied. However, the impact on unregulated individual compounds or chemical characteristics of exhaust emissions is not fully characterized. In this paper, the semivolatile fractions of the exhaust emissions of biodiesel blends and conventional fuel have been speciated. The results show great differences between the compounds emitted, especially regarding oxygenated and aromatic species. Likewise, there is a clear relationship between speciated hydrocarbons and other important emissions such as total hydrocarbons (HC), particulate matter (PM), volatile organic fraction (VOF) and mean particle diameter (Dm) of PM.     

Immunohistochemical expression of p53 and c-erbB-2 in breast carcinoma: relation with epidemiologic factors, histologic features and prognosis

We have performed immunohistochemical staining for p53 and c-erbB-2 on formaldehyde-fixed, paraffin-embedded primary invasive ductal carcinomas from 112 patients, with a minimal follow-up time of 60 months. All of them had received postoperative chemoradiation therapy. We have analyzed the association of these factors with epidemiologic risk factors, histopathologic features and hormonal receptor status and the influence on prognosis. Our results indicate that the expression of c-erbB-2 protein defines a group of node-negative patients with poor prognosis. The overexpression of c-erbB-2 has shown a significant association with estrogen receptor status (those tumors expressing c-erbB-2 are usually estrogen receptor negative), presence of fibrosis and lymphoplasmacytoid infiltrates. P53 expression has shown no relation either with prognosis or with any other histopathologic or clinical feature. The only factors with prognostic influence in our series have been tumor size, the presence of node metastases, TNM stage and the prognostic morphometric index (Baak's index), apart from c-erbB-2 in node-negative patients. However, only the TNM stage showed an independent association with prognosis after a multivariate analysis. In summary, in our experience the expression of p53 protein has no prognostic influence on breast carcinoma, and TNM stage remains to be as the most powerful prognostic factor in these patients.     

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.     

Mechanical and thermal behaviour of an acrylic bone cement modified with a triblock copolymer

The basic formulation of an acrylic bone cement has been modified by the addition of a block copolymer, Nanostrength^® (NS), in order to augment the mechanical properties and particularly the fracture toughness of the bone cement. Two grades of NS at different levels of loading, between 1 and 10 wt.%, have been used. Mechanical tests were conducted to study the behaviour of the modified cements; specific tests measured the bend, compression and fracture toughness properties. The failure mode of the fracture test specimens was analysed using scanning electron microscopy (SEM). The effect of NS addition on the thermal properties was also determined, and the polymerisation reaction using differential scanning calorimetry. It was observed that the addition of NS produced an improvement in the fracture toughness and ductility of the cement, which could have a positive contribution by reducing the premature fracture of the cement mantle. The residual monomer content was reduced when the NS was added. However this also produced an increase in the maximum temperature and the heat delivered during the polymerisation of the cement.     

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.