Solving a concrete sleepers production scheduling by genetic algorithms

PRECON S.A is a manufacturing company dedicated to produce prefabricatedconcrete parts to several industries as rail transportation andagricultural industries.Recently, PRECON signed a contract with RENFE,the Spanish Nnational Rail Transportation Company to manufacturepre-stressed concrete sleepers for siding of the new railways of the highspeed train AVE. The scheduling problem associated with the manufacturingprocess of the sleepers is very complex since it involves severalconstraints and objectives. The constraints are related with productioncapacity, the quantity of available moulds, satisfying demand and otheroperational constraints. The two main objectives are related withmaximizing the usage of the manufacturing resources and minimizing themoulds movements. We developed a deterministic crowding genetic algorithmfor this multiobjective problem. The algorithm has proved to be a powerfuland flexible tool to solve the large-scale instance of this complex realscheduling problem.

Comparing Random-based and k-Anonymity-Based Algorithms for Graph Anonymization

Recently, several anonymization algorithms have appeared for privacy preservation on graphs. Some of them are based on random-ization techniques and on k-anonymity concepts. We can use both of them to obtain an anonymized graph with a given k-anonymity value. In this paper we compare algorithms based on both techniques in orderto obtain an anonymized graph with a desired k-anonymity value. We want to analyze the complexity of these methods to generate anonymized graphs and the quality of the resulting graphs.

Socio-economic benefits of using space technologies to monitor and respond to earthquakes

Earthquakes represent a major hazard for populations around the world, causing frequent loss of life,human suffering and enormous damage to homes, other buildings and infrastructure. The Technology Resources forEarthquake Monitoring and Response (TREMOR) Team of 36 space professionals analysed this problem over thecourse of the International Space University Summer Session Program and published their recommendations in the formof a report. The TREMOR Team proposes a series of space- and ground-based systems to provide improved capabilityto manage earthquakes. The first proposed system is a prototype earthquake early-warning system that improves theexisting knowledge of earthquake precursors and addresses the potential of these phenomena. Thus, the system willat first enable the definitive assessment of whether reliable earthquake early warning is possible through precursormonitoring. Should the answer be affirmative, the system itself would then form the basis of an operational earlywarningsystem. To achieve these goals, the authors propose a multi-variable approach in which the system will combine,integrate and process precursor data from space- and ground-based seismic monitoring systems (already existing andnew proposed systems) and data from a variety of related sources (e.g. historical databases, space weather data, faultmaps). The second proposed system, the prototype earthquake simulation and response system, coordinates the maincomponents of the response phase to reduce the time delays of response operations, increase the level of precisionin the data collected, facilitate communication amongst teams, enhance rescue and aid capabilities and so forth. It isbased in part on an earthquake simulator that will provide pre-event (if early warning is proven feasible) and post-eventdamage assessment and detailed data of the affected areas to corresponding disaster management actors by means of ageographic information system (GIS) interface. This is coupled with proposed mobile satellite communication hubs toprovide links between response teams. Business- and policy-based implementation strategies for these proposals, suchas the establishment of a non-governmental organisation to develop and operate the systems, are included.

Surface topography and mixed-pixel effects on the simulated L-band brightness temperatures.

The impact of topography and mixed pixels on L-band radiometric observations over land needs to be quantified to improve the accuracy of soil moisture retrievals. For this purpose, a series of simulations has been performed with an improved version of the soil moisture and ocean salinity (SMOS) end-to-end performance simulator (SEPS). The brightness temperature generator of SEPS has been modified to include a 100-m-resolution land cover map and a 30-m-resolution digital elevation map of Catalonia (northeast of Spain). This high-resolution generator allows the assessment of the errors in soil moisture retrieval algorithms due to limited spatial resolution and provides a basis for the development of pixel disaggregation techniques. Variation of the local incidence angle, shadowing, and atmospheric effects (up- and downwelling radiation) due to surface topography has been analyzed. Results are compared to brightness temperatures that are computed under the assumption of an ellipsoidal Earth.

Defective TNF-alpha-mediated hepatocellular apoptosis and liver damage in acidic sphingomyelinase knockout mice

This study addressed the contribution of acidic sphingomyelinase (ASMase) in TNF-alpha-mediated hepatocellular apoptosis. Cultured hepatocytes depleted of mitochondrial glutathione (mGSH) became sensitive to TNF-alpha, undergoing a time-dependent apoptotic cell death preceded by mitochondrial membrane depolarization, cytochrome c release, and caspase activation. Cyclosporin A treatment rescued mGSH-depleted hepatocytes from TNF-alpha-induced cell death. In contrast, mGSH-depleted hepatocytes deficient in ASMase were resistant to TNF-alpha-mediated cell death but sensitive to exogenous ASMase. Furthermore, although in vivo administration of TNF-alpha or LPS to galactosamine-pretreated ASMase(+/+) mice caused liver damage, ASMase(-/-) mice exhibited minimal hepatocellular injury. To analyze the requirement of ASMase, we assessed the effect of glucosylceramide synthetase inhibition on TNF-alpha-mediated apoptosis. This approach, which blunted glycosphingolipid generation by TNF-alpha, protected mGSH-depleted ASMase(+/+) hepatocytes from TNF-alpha despite enhancement of TNF-alpha-stimulated ceramide formation. To further test the involvement of glycosphingolipids, we focused on ganglioside GD3 (GD3) because of its emerging role in apoptosis through interaction with mitochondria. Analysis of the cellular redistribution of GD3 by laser scanning confocal microscopy revealed the targeting of GD3 to mitochondria in ASMase(+/+) but not in ASMase(-/-) hepatocytes. However, treatment of ASMase(-/-) hepatocytes with exogenous ASMase induced the colocalization of GD3 and mitochondria. Thus, ASMase contributes to TNF-alpha-induced hepatocellular apoptosis by promoting the mitochondrial targeting of glycosphingolipids.

Damage spreading transition in glasses: A probe for the ruggedness of the configurational landscape

We consider damage spreading transitions in the framework of mode-coupling theory. This theory describes relaxation processes in glasses in the mean-field approximation which are known to be characterized by the presence of an exponentially large number of metastable states. For systems evolving under identical but arbitrarily correlated noises, we demonstrate that there exists a critical temperature T0 which separates two different dynamical regimes depending on whether damage spreads or not in the asymptotic long-time limit. This transition exists for generic noise correlations such that the zero damage solution is stable at high temperatures, being minimal for maximal noise correlations. Although this dynamical transition depends on the type of noise correlations, we show that the asymptotic damage has the good properties of a dynamical order parameter, such as (i) independence of the initial damage; (ii) independence of the class of initial condition; and (iii) stability of the transition in the presence of asymmetric interactions which violate detailed balance. For maximally correlated noises we suggest that damage spreading occurs due to the presence of a divergent number of saddle points (as well as metastable states) in the thermodynamic limit consequence of the ruggedness of the free-energy landscape which characterizes the glassy state. These results are then compared to extensive numerical simulations of a mean-field glass model (the Bernasconi model) with Monte Carlo heat-bath dynamics. The freedom of choosing arbitrary noise correlations for Langevin dynamics makes damage spreading an interesting tool to probe the ruggedness of the configurational landscape.

Neuronal nicotinic receptors as new targets foramphetamine-induced oxidative damage and neurotoxicity

Amphetamine derivatives such as methamphetamine (METH) and 3,4-methylenedioxymethamphetamine (MDMA, ecstasy) are drugs widely abused in a recreational context. This has led to concern because of the evidence that they are neurotoxic in animal models and cognitive impairments have been described in heavy abusers. The main targets of these drugs are plasmalemmal and vesicular monoamine transporters, leading to reverse transport and increased monoamine efflux to the synapse. As far as neurotoxicity is concerned, increased reactive oxygen species (ROS) production seems to be one of the main causes. Recent research has demonstrated that blockade of 7 nicotinic acetylcholine receptors (nAChR) inhibits METH- and MDMA-induced ROS production in striatal synaptosomes which is dependent on calcium and on NO-synthase activation. Moreover, 7 nAChR antagonists (methyllycaconitine and memantine) attenuated in vivo the neurotoxicity induced by METH and MDMA, and memantine prevented the cognitive impairment induced by these drugs. Radioligand binding experiments demonstrated that both drugs have affinity to 7 and heteromeric nAChR, with MDMA showing lower Ki values, while fluorescence calcium experiments indicated that MDMA behaves as a partial agonist on 7 and as an antagonist on heteromeric nAChR. Sustained Ca increase led to calpain and caspase-3 activation. In addition, modulatory effects of MDMA on 7 and heteromeric nAChR populations have been found.

Selenium uptake by edible plants from enriched peat.

As a constituent of selenoproteins, selenium (Se) is considered an essential element for human health.The main way that Se enters the body is via the consumption of vegetables, whose concentration of thiselement depends on soil Se content. We grew cabbage, lettuce, chard and parsley, in peat enriched in Seby means of the additive Selcote Ultra®and Na2SeO3and Na2SeO4. Total Se in plants was determinedby acidic digestion and Se speciation by an enzymatic extraction. Both were measured by ICP/MS. Theconcentration ranges were between 0.1 mg Se kg−1and 30 mg Se kg−1for plants grown in Selcote Ultra®media, and between 0.4 mg Se kg−1and 1606 mg Se kg−1for those grown in peat enriched with Se sodiumsalts. We found Se (IV), Se (VI) and SeMet in all the extracts. Peat fortified with Selcote Ultra®gave slightlyhigher Se concentration than natural content values. For plants grown with selenium sodium salts, Secontent increases with the Se added and part of the inorganic Se was converted mainly to SeMet. A highSe fortification can damage or inhibit plant growth. Cabbage showed the greatest tolerance to Se.

Empatía y tolerancia a la diversidad en un contexto educativo intercultural = Empathy and tolerance of diversity in an intercultural educative setting

Las relaciones entre empatía y conducta prosocial han estado ampliamente estudiadas desde hace años. Sin embargo, no existen estudios que utilicen estudiantes indígenas y mestizos de una universidad intercultural. El objetivo principal de la investigación fue analizar la tolerancia a la diversidad en relación a la empatía. La muestra estaba formada por 534 indígenas y mestizos, de edades comprendidas entre los 17 y los 22 años. Los resultados mostraron que los estudiantes con una alta capacidad empática eran también más tolerantes. Las chicas puntuaron significativamente superior en tolerancia y empatía que los chicos. Se encuentran diferencias entre indígenas y mestizos y entre universidad intercultural y universidad pública en relación a áreas específicas de la tolerancia a la diversidad

Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation

High-throughput prioritization of cancer-causing mutations (drivers) is a key challenge of cancer genome projects, due to the number of somatic variants detected in tumors. One important step in this task is to assess the functional impact of tumor somatic mutations. A number of computational methods have been employed for that purpose, although most were originally developed to distinguish disease-related nonsynonymous single nucleotide variants (nsSNVs) from polymorphisms. Our new method, transformed Functional Impact score for Cancer (transFIC), improves the assessment of the functional impact of tumor nsSNVs by taking into account the baseline tolerance of genes to functional variants.

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

Coherency and sharpness measures by using ICA algorithms. An investigation for Alzheimer’s disease discrimination

In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to investigate the possibility of early detection of Alzheimer’s disease (AD). We found that ICA algorithms can help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum number of selected components is investigated, in order to help decision processes for future works.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

Comparing Random-based and k-Anonymity-Based Algorithms for Graph Anonymization

Recently, several anonymization algorithms have appeared for privacy preservation on graphs. Some of them are based on random-ization techniques and on k-anonymity concepts. We can use both of them to obtain an anonymized graph with a given k-anonymity value. In this paper we compare algorithms based on both techniques in orderto obtain an anonymized graph with a desired k-anonymity value. We want to analyze the complexity of these methods to generate anonymized graphs and the quality of the resulting graphs.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen