A Fluorometric Enzymatic Assay for Quantification of Steryl Glucosides in Biodiesel

Steryl glucosides (SG) are common contaminants in biodiesel that form precipitates, which form and cause problems due to fouling during transport and storage. Therefore, their quantification is necessary to assess the quality of this fuel. The methods currently available for SG analysis require expensive instrumentation, need a previous concentration step by solid‐phase extraction (SPE) or are of limited use for the quantitative assessment. We developed an enzymatic method for SG quantification in biodiesel samples based on the hydrolysis of the glucoside catalyzed by a broadly specific beta glucosidase and the subsequent determination of the glucose released by the reaction. The method is non‐expensive, sensitive and was adapted to 96‐well format fluorescence plate reader, making it useful for the parallel assay of multiple samples. The enzymatic assay presented here represent a valuable tool for both quality control and the development of improved biodiesel production and purification procedures.     

MDMA ('Ecstasy') enhances 5-HT1A receptor density and 8-OH-DPAT-induced hypothermia: blockade by drugs preventing 5-hydroxytryptamine depletion

One week after a single administration of 3,4-methylenedioxymethamphetamine (MDMA HCI, 30 mg/kg i.p.), 5-HT1A receptor density was significantly increased by approximately 25-30% in the frontal cortex and hypothalamus of rats. The increased density correlated with the potentiation of the hypothermic response to the 5-HT1A receptor agonist 8-hydroxy-2-(di-n-propylamino)tetralin (8-OH-DPAT, 1 mg/kg s.c.). Hypothalamic 5-HT7 receptors, which also bind 8-OH-DPAT, were not changed, however, by MDMA. Fluoxetine (5 mg/kg s.c.), ketanserin (5 mg/kg s.c.) or haloperidol (2 mg/kg i.p.), given 15 min prior to MDMA, prevented the depletion of 5-hydroxytryptamine (5-HT) induced by MDMA and also blocked the effects of this neurotoxin on 5-HT1A receptor density and on 8-OH-DPAT-induced hypothermia. The protection afforded by drugs against 5-HT loss did not correlate, however, with the antagonism of the acute hyperthermic effect of MDMA. The present results indicate that drugs able to prevent or to attenuate MDMA-induced 5-HT loss also prevent the changes in 5-HT1A receptor density as well as the enhanced hypothermic response to the 5-HT1A receptor agonist 8-OH-DPAT in MDMA-treated rats.     

HEAT TRANSFER IN PARTIALLY MISCIBLE TERNARY SYSTEMS

In the study of mass transfer between partially miscible liquids, temperature perturbations have been found to have an important effect in the generation of interfacial activity. Micro- and macro-generated interfacial convection usually occurs in combination with one another even in fairly simple heat or mass transfer events. These combinations are responsible for the enhancement of mass transfer rates. In an effort to acquire a better understanding of the heat transfer effects in ternary liquid-liquid systems, temperature difference profiles were measured when contacting two partially miscible phases. A vertical and a rotational transfer cell were designed to contact the partially miscible phases without inducing external disturbances. Five thermocouples were immersed in the bottom liquid phase at predetermined positions below the interface. In order to understand the influence of the convection generated, the vertical cell was designed to hinder convection in one phase, while the rotational cell permits convection in both phases. The experimental results showed larger temperature differences in the rotational cell after the contact of the phases. Also, systems which were initially unsaturated presented larger heat effects than those in which the partially miscible solvents were initially saturated. Several parameters indicating the importance of heat effects in the ternary systems studied are reported.     

Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems

In this work, we analyze variable space diversity of Pareto optimal solutions (POS) and study the effectiveness of crossover and mutation operators in evolutionary many-objective optimization. First we examine the diversity of variables in the true POS on many-objective 0/1 knapsack problems with up to 20 items (bits), showing that variables in POS become noticeably diverse as we increase the number of objectives. We also verify the effectiveness of conventional two-point and uniform crossovers, Local Recombination that selects mating parents based on proximity in objective space, and two-point and uniform crossover operators which Controls the maximum number of Crossed Genes (CCG). We use NSGA-II, SPEA2, IBEA _??+? and MSOPS, which adopt different selection methods, and many-objective 0/1 knapsack problems with $n=\{100,250,500,750,\mbox{1,000}\}$ items (bits) and m?=?{2,4,6,8,10} objectives to verify the search performance of each crossover operator. Simulation results reveal that Local Recombination and CCG operators significantly improve search performance especially for NSGA-II and MSOPS, which have high diversity of genes in the population. Also, results show that CCG operators achieve higher search performance than Local Recombination for m?≥?4 objectives and that their effectiveness becomes larger as the number of objectives m increases. In addition, the contribution of CCG and mutation operators for the solutions search is analyzed and discussed.     

The use of coevolution and the artificial immune system for ensemble learning

This paper presents two new approaches for constructing an ensemble of neural networks (NN) using coevolution and the artificial immune system (AIS). These approaches are extensions of the CLONal Selection Algorithm for building ENSembles (CLONENS) algorithm. An explicit diversity promotion technique was added to CLONENS and a novel coevolutionary approach to build neural ensembles is introduced, whereby two populations representing the gates and the individual NN are coevolved. The former population is responsible for defining the ensemble size and selecting the members of the ensemble. This population is evolved using the differential evolution algorithm. The latter population supplies the best individuals for building the ensemble, which is evolved by AIS. Results show that it is possible to automatically define the ensemble size being also possible to find smaller ensembles with good generalization performance on the tested benchmark regression problems. More interestingly, the use of the diversity measure during the evolutionary process did not necessarily improve generalization. In this case, diverse ensembles may be found using only implicit diversity promotion techniques.     

Random 3-SAT: The Plot Thickens

This paper presents an experimental investigation of the following questions: how does the average-case complexity of random 3-SAT, understood as a function of the order (number of variables) for fixed density (ratio of number of clauses to order) instances, depend on the density? Is there a phase transition in which the complexity shifts from polynomial to exponential in the order? Is the transition dependent or independent of the solver? Our experiment design uses three complete SAT solvers embodying different algorithms: GRASP, CPLEX, and CUDD. We observe new phase transitions for all three solvers, where the median running time shifts from polynomial in the order to exponential. The location of the phase transition appears to be solver-dependent. GRASP shifts from polynomial to exponential complexity near the density of 3.8, CPLEX shifts near density 3, while CUDD exhibits this transition between densities of 0.1 and 0.5. This experimental result underscores the dependence between the solver and the complexity phase transition, and challenges the widely held belief that random 3-SAT exhibits a phase transition in computational complexity very close to the crossover point.