Jesús Rodríguez Morales

In some small-scale societies, a sexual division of labor is common. For subadult hunter-gatherers, the onset of this division dates to middle childhood and the start of puberty; however, there is apparently no physiological explanation for this timing. The present study uses an experimental approach to evaluate possible energetic differences by sex in gathering-related activities. The energetic cost of gathering-related activities was measured in a sample of 42 subjects of both sexes aged between 8 and 14 years. Body mass and other anthropometric variables were also recorded. Our results show that the energetic differences in the simulated gathering activities depend only on body mass. Both sexes expend a similar amount of energy during locomotion activities related to gathering. Discarding the energetic factor, the sexual division of tasks may be explained as an adaptation to acquire the skills needed to undertake the complex activities required during adulthood as early as possible. Carrying out gathering activities during childhood and adolescence could be favored by the growth and development cycles of Homo sapiens. Moreover, if most of the energetic costs of gathering activities depend on body mass, the delayed growth in humans relative to other primates allows subadults to practice these tasks for longer periods, and to become better at performing them. In fact, this strategy could enable them to acquire adults’ complex skills at a low energetic cost that can be easily subsidized by other members of the group.

Extractive foraging tasks, such as digging, are broadly practiced among hunter-gatherer populations in different ecological conditions. Despite tuber-gathering tasks being widely practiced by children and adolescents, little research has focused on the physical traits associated with digging ability. Here, we assess how age and energetic expenditure affect the performance of this extractive task. Using an experimental approach, the energetic cost of digging to extract simulated tubers is evaluated in a sample of 40 urban children and adolescents of both sexes to measure the intensity of the physical effort and the influence of several anatomical variables. Digging is a moderately vigorous activity for inexperienced girls and boys from 8 to 14 years old, and it requires significant physical effort depending on strength and body size. However, extracting subterranean resources is a task that may be performed effectively without previous training. Sex-specific and age-specific differences in the net energy expenditure of digging were detected, even though both sexes exhibited similar proficiency levels when performing the task. Our results highlight that both boys and girls spend considerable energy while digging, with differences largely driven by body size and age. Other factors beyond ability and experience, such as strength and body size, may influence the proficiency of juveniles in performing certain physically intensive foraging tasks, such as gathering tubers.

Adaptive optics are techniques used for processing the spatial resolution of astronomical images taken from large ground-based telescopes. In this work, computational results are presented for a modified curvature sensor, the tomographic pupil image wavefront sensor, which measures the turbulence of the atmosphere, expressed in terms of an expansion over Zernike polynomials. Convolutional neural networks are presented as an alternative to the TPI-WFS reconstruction. This technique is a machine learning model of the family of artificial neural networks, which are widely known for its performance as modeling and prediction technique in complex systems. Results obtained from the reconstruction of the networks are compared with the TPI-WFS reconstruction by estimating errors and optical measurements. The reconstructed wavefronts from both techniques are compared for wavefronts of 153 Zernike modes. For this case, a detailed comparison and grid search to find the most suitable neural network is performed, searching between multi-layer perceptron, CNN and recurrent networks topologies. In general, the best network was a CNN trained for TPI-WFS reconstruction, achieving better performance than the reconstruction software from TPI-WFS in most of the turbulent profiles, but the most significant improvements were found for higher turbulent profiles that have the lowest r0 values.