Recovery of Bacillus sphaericus spores by flocculation/sedimentation and flotation
The aim of this work was use flocculation/sedimentation and flotation for recovery of spores of the Bacillus sphaericus. Microorganism was produced batchwise using culture medium based skimmed milk, corn steep liquor and mineral salts. The best results of flocculation were obtained using CaCl2.2H2O, FeCl3.6H2O, Al2(SO4)3 and tannin as flocculating agents, with optimal flocculation concentrations of 1,500, 3,000, 2,000 and 1,700ppm, respectively. Flocculent suspensions were characterized based on floc diameter and density. Settling tests were performed in batch at different concentrations of the cellular suspensions and revealed high recovery of the solids in suspension in all cases. Flotation tests were accomplished using a mechanical agitated flotation cell and the process was favoured by the increase of the system agitation and for the presence of a cationic collector.
Binding of the 51- and 42-kDa individual components from the Bacillus sphaericus crystal toxin to mosquito larval midgut membranes from Culex and Anopheles sp. (Diptera: Culicidae).
Individual components (P51 and P42) from the crystal toxin (Bin) of Bacillus sphaericus were used for in vitro binding competition experiments with brush border membranes (BBMFs) from Culex pipiens and Anopheles gambiae larval midguts. P51 competed for the Bin binding site with a similar affinity to the Bin toxin, on both BBMFs. For C. pipiens, P42 bound non-specifically until P51 was added with maximum binding of P42 at a molar ratio of each component. The binding of P42 was much greater on A. gambiae BBMFs and the presence of either P51 or P42 enhanced the binding of the other component, with highest binding when a mole ratio of each protein was supplied.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.