Multi-objective selection for collecting cluster alternatives

Grouping objects into different categories is a basic means of cognition. In the fields of machine learning and statistics, this subject is addressed by cluster analysis. Yet, it is still controversially discussed how to assess the reliability and quality of clusterings. In particular, it is hard to determine the optimal number of clusters inherent in the underlying data. Running different cluster algorithms and cluster validation methods usually yields different optimal clusterings. In fact, several clusterings with different numbers of clusters are plausible in many situations, as different methods are specialized on diverse structural properties. To account for the possibility of multiple plausible clusterings, we employ a multi-objective approach for collecting cluster alternatives (MOCCA) from a combination of cluster algorithms and validation measures. In an application to artificial data as well as microarray data sets, we demonstrate that exploring a Pareto set of optimal partitions rather than a single solution can identify alternative solutions that are overlooked by conventional clustering strategies. Competitive solutions are hereby ranked following an impartial criterion, while the ultimate judgement is left to the investigator.     

Novel nanofluids based on mesoporous silica for enhanced heat transfer

Nanofluids, which are liquids with engineered nanometer-sized particles suspensions, have drawn remarkable attraction from the researchers because of their enormous potential to enhance the efficiency in heat-transfer fluids. In the present study, water-based calcined mesoporous silica nanofluids were prepared and characterized. The commercial mesoporous silica (MPSiO₂) nanoparticles were dispersed in deionized water by means of pH adjustment and ultrasonic agitation. MPSiO₂ nanoparticles were observed to have an average particle size of 350 ± 100 nm by SEM analysis. The concentration of MPSiO₂ was varied between 1 and 6 wt%. The physicochemical properties of nanofluids were characterized using various techniques, such as particle size analyzer, zeta-potential meter, TEM, and FT-IR. The thermal conductivity was measured by Transient Plane Source (TPS) method, and nanofluids showed a higher thermal conductivity than the base liquid for all the tested concentrations.     

Isotope Effects in Organic Chemistry and Biochemistry

The present article shows the extent to which isotope effects are likely to be encountered in the use of isotope techniques and what problems are studied with primary and secondary isotope effects. By way of example, the results of studies on E2 reactions, particularly in the Hofmann degradation, are discussed, followed by a discussion of some “analytical isotope effects”. Finally, the problems encountered and the information that can be obtained from isotope effects studies on enzyme reactions, and the advantages and disadvantages of competitive and non-competitive techniques are described. In addition to a survey of isotope effects in dehydrogenase reactions, new isotope effects encountered in the dehydrogenation of T-labeled alcohols are reported.     

Competitive reactions among three monomers over a catalytic surface

We studied in this work a three-monomer reaction model on one- and two-dimensional lattices. We have taken different reactivity rates among pairs of monomers and the reaction between two selected monomers was forbidden. We have employed the mean field and the pair approximation to decouple the equations of motion for the densities of single and pairs of monomers. We found the stationary states and the phase diagram of the model. We have shown that, in two dimensions and within the pair approximation, there is a first-order transition line between active and poisoned steady states.