Coalescence and fragmentation of equal and unequal liquid-drop pairs are studied using a new experimental technique in which mercury drops collide while sliding on a horizontal glass surface. The limits for coalescence measured as a function of the incident relative velocity and impact parameter are found to be similar to what has been reported for free-moving drops of other liquids, while new correlations are found to occur among the number, size, speed and angular distribution of fragmentation residues. The predictions of various models, including a dynamic theory originally developed for nuclear reactions, and specifically modified by us for macroscopic applications, are compared with the observations.

Stressful conditions can affect resource allocation among different life-history traits. The effect of dietary restriction (DR) on longevity and reproduction has been studied in many species, but we know little about its effects on energetics, especially in flying animals that have high energy demand. We assessed the effects of DR on metabolic rate throughout the entire adult life span in two butterfly species, Colias eurytheme and Speyeria mormonia. We cut the food intake of adult females in half and measured resting metabolic rate (RMR) and flight metabolic rate (FMR) together with body mass repeatedly throughout life. In both species, DR reduced body mass, but mass-corrected FMR was not affected, indicating that flight capacity was retained. DR lowered RMR and reduced fecundity but had no effect on life span. FMR declined with age, but the rate of senescence was not affected by DR. In contrast, aging had a strong negative effect on RMR only in control females, whereas food-restricted females had more stable RMR throughout their lives. The results suggest that flight capacity is conserved during nutritional stress but that investment in flight and survival may negatively affect other important physiological processes when resources are limited.

How does the brain represent musical properties? Even with our growing understanding of the cognitive neuroscience of music, the answer to this question remains unclear. One method for conceiving possible representations is to use artificial neural networks, which can provide biologically plausible models of cognition. One could train networks to solve musical problems, and then study how these networks encode musical properties. However, researchers rarely examine network structure in detail because networks are difficult to interpret, and because many assume that networks capture informal or subsymbolic properties. Here we report very high correlations between network connection weights and discrete Fourier phase spaces used to represent musical sets. This is remarkable because there is no clear mathematical relationship between network learning rules and discrete Fourier analysis. That networks discover Fourier phase spaces indicates that these spaces have an important role to play outside of formal music theory. Finding phase spaces in networks raises the strong possibility that Fourier components are possible codes for musical cognition.

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This work is focused in studying the inclusion of chemical speciation in the characterization of mining tailings in Mexican regulation with a case study in Parral, Chihuahua, Mexico. In this site, high concentration of lead and arsenic in tailings located in the surroundings represent a high concern to the local population. The total concentration of Pb in the samples ranged from 78.03 ± 2.67 to 5748 ± 263.63 [mg kg^-1] and from 5.49 ± 0.43 to 509.84 ± 40.18 [mg kg^-1] for As. Chemical speciation was tested for samples that exceeded the limits of the Mexican Regulation using sequential extractions proposed by the Bureau Community of Reference (BCR) to obtain the distribution of lead and arsenic in four different fractions. The set of extractions consisted in the extractable/exchangeable fraction (F1), the reducible fraction (F2), the metals bound to organic matter and sulfides (F3) those under oxidizing conditions, and the residual fraction (RF). The results show that 70% of lead is found in F1 and F2 fractions while 20% is found in F3 fraction. In case of arsenic, 60% is found in residual fraction, 25% in fraction F3 and less than 15% in fractions F1 and F2.