O futurismo russo: Maiakovsky ou a "Nuvem de calças"

Este breve ensaio sobre o poeta e escritor russo Vladimir Mayakovsky é uma modesta tentativa de contribuir para um (re)descobrimento ou para uma simples revisitação da produção literária russa do período soviético, que, por razões muitas vezes apenas de ordem política e não estética, caiu no esquecimento, não sendo traduzida na sua totalidade, como é o caso de Mayakovsky, ou, pura e simplesmente, sendo votada ao “ostracismo”, porque rotulada, a priori ou ab initio, como obras escritas dentro das paredes estreitas do “realismo socialista”, padecendo de falta de criatividade e norteandose, aberta ou dissimuladamente, pelos princípios rígidos e imutáveis de um totalitarismo inflexível. Contudo, nem toda a literatura soviética pode ser enquadrada no realismo socialista, valendo a pena conhecê-la e analisá-la.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

Exploiting sensor redundancy for the calculation of fractional derivatives in the presence of noise

In this paper a new method for the calculation of the fractional expressions in the presence of sensor redundancy and noise, is presented. An algorithm, taking advantage of the signal characteristics and the sensor redundancy, is tuned and optimized through genetic algorithms. The results demonstrate the good performance for different types of expressions and distinct levels of noise.

Calculation of fractional derivatives of noisy data with genetic algorithms

This paper addresses the calculation of derivatives of fractional order for non-smooth data. The noise is avoided by adopting an optimization formulation using genetic algorithms (GA). Given the flexibility of the evolutionary schemes, a hierarchical GA composed by a series of two GAs, each one with a distinct fitness function, is established.

Numerical calculation of the left and right fractional derivatives

The calculation of fractional derivatives is an important topic in scientific research. While formal definitions are clear from the mathematical point of view, they pose limitations in applied sciences that have not been yet tackled. This paper addresses the problem of obtaining left and right side derivatives when adopting numerical approximations. The results reveal the relationship between the resulting distinct values for different fractional orders and types of signals.

Competitive paths for methanol decomposition on ruthenium: a DFT study

Methanol decomposition is one of the key reactions in direct methanol fuel cell (DMFC) state-of-the-art technology, research, and development. However, its mechanism still presents many uncertainties, which, if answered, would permit us to refine the manufacture of DMFCs. The mechanism of methanol decomposition on ruthenium surfaces was investigated using density functional theory and a periodic supercell approach. The possible pathways, involving either initial C−H, C−O or O−H scission, were defined from experimental evidence regarding the methanol decomposition on ruthenium and other metallic surfaces. The study yielded the O−H scission pathway as having both the most favorable energetics and kinetics. The computational data, which present a remarkable closeness with the experimental results, also indicate methanol adsorption, the starting point in all possible pathways, to be of weak nature, implying a considerable rate of methanol desorption from the ruthenium, compromising the reaction.