A metaheuristic-based downlink power allocation for LTE/LTE-A cellular deployments

The explosive growth of cellular networks makes their deployment and maintenance more and more complex, time consuming, and expensive. Self-Organizing Networks have been recognized as a promising way to alleviate this problem by minimizing human intervention in such processes. This paper introduces a novel multiobjective framework, based on evolutionary optimization, aiming at improving network performance and users Quality of Service. By tuning the transmitted power at each cell, average intercell interference levels are minimized. The design of the proposed scheme is feasible for distributed implementations in Long Term Evolution (LTE) and LTE-Advanced networks and its operation is compatible with current specifications. The framework is able to provide effective network-specific optimization and obtained results show that gains in terms of network capacity and cell edge performance are 5 and 10 %, respectively. Energy savings always accompanied such enhancements with reductions up to 35 %.     

The ALTER-NATIVA knowledge management approach

Nowadays, it is commonly known that information systems need an agile capability of handling knowledge. To accomplish this, systems have to have a formal knowledge representation ability supported by specific and advanced reasoning features. This research work proposes a knowledge management approach with the purpose to gather, model and consume community knowledge for specific recommendation commitments. Such approach is accomplished by a semantic lexicon alignment between the various community knowledge assets, to facilitate collaborations establishment between people and systems in an interoperable fashion. Thus, a knowledge base supported by a thesaurus able to represent all the metadata needed to represent and characterize the various community stakeholders’ resources is proposed. The thesaurus represents the lexicon in the domain, which in the ALTER-NATIVA systems is mostly used to support the various e-Learning elements (e.g. courses) and users categorization, sustained by synchronization features to facilitate a constant update of its information. A set of services designed to recommend specific resources in relation to a determined profile of user is provided. Additionally, a discussion about how the ALTER-NATIVA knowledge management approach can be applied to industrial environments is presented.     

Glass and Jamming Rheology in Soft Particles Made of PNIPAM and Polyacrylic Acid

The phase behaviour of soft colloids has attracted great attention due to the large variety of new phenomenologies emerging from their ability to pack at very high volume fractions. Here we report rheological measurements on interpenetrated polymer network microgels composed of poly(N-isopropylacrylamide) (PNIPAM) and polyacrylic acid (PAAc) at fixed PAAc content as a function of weight concentration. We found three different rheological regimes characteristic of three different states: a Newtonian shear-thinning fluid, an attractive glass characterized by a yield stress, and a jamming state. We discuss the possible molecular mechanisms driving the formation of these states.     

Towards concept anchoring for cognitive robots

We present a model for anchoring categorical conceptual information which originates from physical perception and the web. The model is an extension of the anchoring framework which is used to create and maintain over time semantically grounded sensor information. Using the augmented anchoring framework that employs complex symbolic knowledge from a commonsense knowledge base, we attempt to ground and integrate symbolic and perceptual data that are available on the web. We introduce conceptual anchors which are representations of general, concrete conceptual terms. We show in an example scenario how conceptual anchors can be coherently integrated with perceptual anchors and commonsense information for the acquisition of novel concepts.     

Dietary Apigenin Exerts Immune-Regulatory Activity in Vivo by Reducing NF-κB Activity,Halting Leukocyte Infiltration and Restoring Normal Metabolic Function

The increasing prevalence of inflammatory diseases and the adverse effects associated with the long-term use of current anti-inflammatory therapies prompt the identification of alternative approaches to reestablish immune balance. Apigenin, an abundant dietary flavonoid, is emerging as a potential regulator of inflammation. Here, we show that apigenin has immune-regulatory activity in vivo. Apigenin conferred survival to mice treated with a lethal dose of Lipopolysaccharide (LPS) restoring normal cardiac function and heart mitochondrial Complex I activity. Despite the adverse effects associated with high levels of splenocyte apoptosis in septic models, apigenin had no effect on reducing cell death. However, we found that apigenin decreased LPS-induced apoptosis in lungs, infiltration of inflammatory cells and chemotactic factors’ accumulation, re-establishing normal lung architecture. Using NF-κB luciferase transgenic mice, we found that apigenin effectively modulated NF-κB activity in the lungs, suggesting the ability of dietary compounds to exert immune-regulatory activity in an organ-specific manner. Collectively, these findings provide novel insights into the underlying immune-regulatory mechanisms of dietary nutraceuticals in vivo.     

Adaptive Quadrature for Maximum Likelihood Estimation of a Class of Dynamic Latent Variable Models

Maximum likelihood estimation of models based on continuous latent variables generally requires to solve integrals that are not analytically tractable. Numerical approximations represent a possible solution to this problem. We propose to use the adaptive Gaussian–Hermite (AGH) numerical quadrature approximation for a particular class of continuous latent variable models for time-series and longitudinal data. These dynamic models are based on time-varying latent variables that follow an autoregressive process of order 1, AR(1). Two examples are the stochastic volatility models for the analysis of financial time series and the limited dependent variable models for the analysis of panel data. A comparison between the performance of AGH methods and alternative approximation methods proposed in the literature is carried out by simulation. Empirical examples are also used to illustrate the proposed approach.     

Too Low to Kill: Concentration of the Secondary Metabolite Ranunculin in Buttercup Pollen does not Affect Bee Larval Survival

Growing evidence suggests that the freely accessible pollen of some plants is chemically protected against pollen-feeding flower visitors. For example, a diet of pollen from buttercup plants (Ranunculus) recently was shown to have a deleterious effect on developing larvae of several bee species not specialized on Ranunculus. Numerous Ranunculus species contain ranunculin, the glucosyl hydrate form of the highly reactive and toxic lactone protoanemonin, that causes the toxicity of these plants. We tested whether the presence of ranunculin is responsible for the lethal effects of R. acris pollen on the larvae of two bee species that are not Ranunculus specialists. To investigate the effect on bee larval development, we added ranunculin to the pollen provisions of the Campanula specialist bee Chelostoma rapunculi and the Asteraceae specialist bee Heriades truncorum, and allowed the larvae to feed on these provisions. We quantified ranunculin in pollen of R. acris and in brood cell provisions collected by the Ranunculus specialist bee Chelostoma florisomne. We demonstrated that although ranunculin was lethal to both tested bee species in high concentrations, the concentration in the pollen of R. acris was at least fourfold lower than that tolerated by the larvae of C. rapunculi and H. truncorum in the feeding experiments. Ranunculin concentration in the brood cells of C. florisomne was on average even twentyfold lower than that in Ranunculus pollen, suggesting that a mechanism different from ranunculin intoxication accounts for the larval mortality reported for bees not specialized on Ranunculus pollen.     

Geographical Authentication of Tequila According to its Mineral Content by Means of Support Vector Machines

The elemental profile of tequila samples from the three main production areas of the State of Jalisco, namely Amatitlan, Guadalajara, and Tequila, was used to carry out their geographical characterization. With this aim, the concentration of Al, Ba, Ca, Cu, Fe, K, Mg, Mn, Na, S, Sr, and Zn was determined by inductively coupled plasma atomic emission spectroscopy. Principal component analysis was addressed to visualize data trends. The number of input variables was reduced by means of backward stepwise linear discriminant analysis and support vector machines were used to construct an adequate classification model. The best classification performance was obtained by a linear support vector machine model with 100% of prediction ability.