Fermentation profile and microbial population in soybean silages with inoculant and powdered molasses

Fermentation profile and microbial population were assessed in soybean silages without any additive (control), with inoculant (I), with I + powdered molasses (I+M), and with powdered molasses only (M). Soybean plants were harvested at the R6 stage and ensiled in 2kg-capacity laboratory silos. The additives were added to the natural matter base of silages. The assessed fermentation periods were 1, 3, 7, 14, 28, and 56 days. A 4×6 factorial arrangement (4 additives × 6 fermentation periods) in a completely randomized design with 3 replicates was used. Lactic, acetic, and butyric acids concentrations were influenced by additives and periods (P< 0.05). It was observed higher lactic acid values to control silages, on the 56 th day. Lower average values of acetic and butyric acids were observed to I+M and M silages. It was observed quadratic effect to pH values with a reduction estimated of 0.5504, 0.5358, 0.6312 and 0.6680 units to pH values to control, I, I+M, and M silages in the first 10 days. A maximum lactic acid bacteria population was observed at the 28 th day of fermentation in silages with inoculant. The inoculant and powdered molasses improve the fermentation profile of soybean silages

Bulk viscosity driving the acceleration of the Universe

The possibility that the present acceleration of the universe is driven by a kind of viscous fluid is exploited. At background level this model is similar to the generalized Chaplygin gas model (GCGM). But, at perturbative level, the viscous fluid exhibits interesting properties. In particular the oscillations in the power spectrum that plagues the GCGM are not present. Possible fundamental descriptions for this viscous dark energy are discussed.Comment: Latex file, 8 pages, 3 eps figure

Using Datamining Techniques to Help Metaheuristics: A Short Survey

International audienceHybridizing metaheuristic approaches becomes a common way to improve the efficiency of optimization methods. Many hybridizations deal with the combination of several optimization methods. In this paper we are interested in another type of hybridization, where datamining approaches are combined within an optimization process. Hence, we propose to study the interest of combining metaheuristics and datamining through a short survey that enumerates the different opportunities of such combinations based on literature examples