The preparation and properties of coprecipitated Cu-Zr-Y and Cu-Zr-La catalysts used for the steam reforming of methanol

A series of Cu-zirconia catalysts containing various additives (Y2O3, La2O3, Al2O3 and CeO2) have been prepared by coprecipitation and their activities and stabilities under operating conditions have been obtained for the steam reforming of methanol. It has been found that an yttria-promoted catalyst containing 30 mol% Cu and 20 mol% of Y2O3 is not only very active but is also very stable under reaction conditions. The yttria appears to stabilise a high copper surface area and may also have a slight promotional effect on the copper. The results obtained with this material compare very favourably with data for the best catalysts reported in the literature. (C) 2007 Published by Elsevier B.V.

NON-GAUSSIAN STATISTICS OF OIL PRICING TIME-SERIES: A CASE STUDY

The stochastic nature of oil price fluctuations is investigated over a twelve-year period, borrowing feedback from an existing database (USA Energy Information Administration database, available online). We evaluate the scaling exponents of the fluctuations by employing different statistical analysis methods, namely rescaled range analysis (R/S), scale windowed variance analysis (SWV) and the generalized Hurst exponent (GH) method. Relying on the scaling exponents obtained, we apply a rescaling procedure to investigate the complex characteristics of the probability density functions (PDFs) dominating oil price fluctuations. It is found that PDFs exhibit scale invariance, and in fact collapse onto a single curve when increments are measured over microscales (typically less than 30 days). The time evolution of the distributions is well fitted by a Levy-type stable distribution. The relevance of a Levy distribution is made plausible by a simple model of nonlinear transfer. Our results also exhibit a degree of multifractality as the PDFs change and converge toward to a Gaussian distribution at the macroscales.

Optimal Time Series Momentum

We develop a continuous-time asset price model to capture the timeseries momentum documented recently. The underlying stochastic delay differentialsystem facilitates the analysis of effects of different time horizons used bymomentum trading. By studying an optimal asset allocation problem, we find thatthe performance of time series momentum strategy can be significantly improvedby combining with market fundamentals and timing opportunity with respect tomarket trend and volatility. Furthermore, the results also hold for different timehorizons, the out-of-sample tests and with short-sale constraints. The outperformanceof the optimal strategy is immune to market states, investor sentiment andmarket volatility.

X=Y-ZH COMPOUNDS AS POTENTIAL 1,3-DIPOLES .43. METAL-ION CATALYZED ASYMMETRIC 1,3-DIPOLAR CYCLOADDITION REACTIONS OF IMINES AND MENTHYL ACRYLATE

Metallo-azomethine ylides, generated from imines by the action of amine bases in combination with LiBr or AgOAc, undergo cycloaddition with both 1R, 2S, 5R- and 1S, 2R, 5S-menthyl acrylate at room temperature to give homochiral pyrrolidines in excellent yield. The stronger the base the faster the cycloaddition and the greater the yield with: 2-t-butyl-1,1,3,3-tetramethylguanidine > DBU > NEt(3) X-Ray crystal structures of representative cycloadducts establish that the absolute configuration of the newly established pyrrolidine stereocentres is independent of the metal salt and the size of the pyrrolidineC(2)-substituent for a series of aryl and aliphatic imines.

An ultra-short-term wind power prediction method using “offline classification and optimization, online model matching” based on time series features

The applicability of ultra-short-term wind power prediction (USTWPP) models is reviewed. The USTWPP method proposed extracts featrues from historical data of wind power time series (WPTS), and classifies every short WPTS into one of several different subsets well defined by stationary patterns. All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern. Every above WPTS subset needs a USTWPP model specially optimized for it offline. For on-line application, the pattern of the last short WPTS is recognized, then the corresponding prediction model is called for USTWPP. The validity of the proposed method is verified by simulations.