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Chemical composition, fermentation characteristics, in vitro digestibility and aerobic stability were evaluated in corn silage inoculated with microbial additives in two different experiments. Inoculant treatments (untreated, Bacillus subtilis and B. subtilis combined with Lactobacillus plantarum) were applied to fresh forages. Chopped corn plants (2B655 Hx) were ensiled in laboratory silos for periods of 7, 14, 21 and 63 days to evaluate the fermentation parameters. The experimental silos were weighed to determine gas losses. After the ensiling period, the silage was sampled to determine chemical composition and in vitro organic matter digestibility. To evaluate aerobic stability, chopped corn plants (AG‐1051) were ensiled in laboratory silos that were opened after 96 days of ensiling. The silage was placed in different buckets containing data loggers. The silage was sampled after 0, 4, 8 and 12 days of exposure to air to evaluate the microbial populations and pH. The data were analysed as a completely randomised design using a mixed repeated-measures model in the MIXED procedure of SAS. To evaluate each treatment relative to the fermentation times, a regression analysis using the PROC REG procedure of SAS was applied. A significance level of P < 0.05 was used. Inoculation with both strains increased lactic acid concentration, whereas the use of B. subtilis alone or combined with L. plantarum improved in vitro apparent organic matter digestibility. In the B. subtilis and B. subtilis combined with L. plantarum silages, moulds and yeasts decreased, and aerobic stability was improved. Inoculation with B. subtilis alone or combined with L. plantarum improved the nutritional value and aerobic stability of corn silage.

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a b s t r a c tThe phenol quantification using boron-doped nanocrystalline diamond (BDND) from electroanalytical technique of square wave voltammetry (SWV) is reported. BDND depositions were performed using Hot Filament Chemical Vapor Deposition reactor, where it was possible to grow films with statics substrate-holder (sample S1) or with spinning substrate-holder (sample S2). The variation of this growth parameter induced significant changes on the electrode properties. For example, the electrode S2 presented smoothness surface with low roughness in relation to that for electrode S1. Besides, Raman spectra showed different features for both electrodes that could be related to boron incorporation. Electrochemical measurements also presented differences between electrodes, showing the advantages of electrode S2, such as, rapid charge transfer, large electrochemical area and excellent phenol detection limit $0.1 mg L À1 . The phenol standard sample of 8.0 mg L À1 was used to validate the application of this electrode as a nanosensor. Its concentration calculated from SWV using electrode S2 was 8.2 ± 0.2 mg L À1 while from Ion Chromatograph it was 7.9 ± 0.1 mg L À1 . These results demonstrated the high potential of BDND electrodes for electroanalytical applications.

Experimental studies have shown that hatching rate depends, among other factors, on the main physical characteristics of the eggs. The physical parameters used in our work were egg weight, eggshell thickness, egg sphericity, and yolk per albumen ratio. The relationships of these parameters in the incubation process were modeled by Fuzzy logic. The rules of the Fuzzy modeling were based on the analysis of the physical characteristics of the hatching eggs and the respective hatching rate using a commercial hatchery by applying a trapezoidal membership function into the modeling process. The implementations were per-formed in software. Aiming to compare the Fuzzy with a statistical modeling, the same data obtained in the commercial hatchery were analyzed using multiple linear regression. The estimated parameters of multiple linear regressions were based on a backward selection procedure. The results showed that the determination coefficient and the mean square error were higher using the Fuzzy method when compared with the statistical modeling. Furthermore, the predicted hatchability rates by Fuzzy Logic agreed with hatching rates obtained in the commercial hatchery.

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