Peramorphosis, an evolutionary developmental mechanism in neotropical bat skull diversity

Background The neotropical leaf‐nosed bats (Chiroptera, Phyllostomidae) are an ecologically diverse group of mammals with distinctive morphological adaptations associated with specialized modes of feeding. The dramatic skull shape changes between related species result from changes in the craniofacial development process, which brings into focus the nature of the underlying evolutionary developmental processes. Results In this study, we use three‐dimensional geometric morphometrics to describe, quantify, and compare morphological modifications unfolding during evolution and development of phyllostomid bats. We examine how changes in development of the cranium may contribute to the evolution of the bat craniofacial skeleton. Comparisons of ontogenetic trajectories to evolutionary trajectories reveal two separate evolutionary developmental growth processes contributing to modifications in skull morphogenesis: acceleration and hypermorphosis. Conclusion These findings are consistent with a role for peramorphosis, a form of heterochrony, in the evolution of bat dietary specialists

Acceleration Profiles and Processing Methods for Parabolic Flight

Parabolic flights provide cost-effective, time-limited access to "weightless" or reduced gravity conditions experienced in space or on planetary surfaces, e.g. the Moon or Mars. These flights facilitate fundamental research - from materials science to space biology - and testing/validation activities that support and complement infrequent and costly access to space. While parabolic flights have been conducted for decades, reference acceleration profiles and processing methods are not widely available - yet are critical for assessing the results of these activities. Here we present a method for collecting, analyzing, and classifying the altered gravity environments experienced during a parabolic flight. We validated this method using a commercially available accelerometer during a Boeing 727-200F flight with $20$ parabolas. All data and analysis code are freely available. Our solution can be easily integrated with a variety of experimental designs, does not depend upon accelerometer orientation, and allows for unsupervised and repeatable classification of all phases of flight, providing a consistent and open-source approach to quantifying gravito-intertial accelerations (GIA), or $g$ levels. As academic, governmental, and commercial use of space increases, data availability and validated processing methods will enable better planning, execution, and analysis of parabolic flight experiments, and thus, facilitate future space activities.Comment: Correspondence to C.E. Carr ([email protected]). 15 pages, 4 figures, 3 supplemental figures. Code: https://github.com/CarrCE/zerog, Dataset: https://osf.io/nk2w4

Stock market crashes, Precursors and Replicas

We present an analysis of the time behavior of the $S\&P500$ (Standard and Poors) New York stock exchange index before and after the October 1987 market crash and identify precursory patterns as well as aftershock signatures and characteristic oscillations of relaxation. Combined, they all suggest a picture of a kind of dynamical critical point, with characteristic log-periodic signatures, similar to what has been found recently for earthquakes. These observations are confirmed on other smaller crashes, and strengthen the view of the stockmarket as an example of a self-organizing cooperative system.Comment: accepted for publication in J.Phys.I Franc

Driving Cars by Means of Genetic Algorithms

Proceedings of: 10th International Conference on Parallel Problem Solving From Nature, PPSN 2008. Dortmund, Germany, September 13-17, 2008The techniques and the technologies supporting Automatic Vehicle Guidance are an important issue. Automobile manufacturers view automatic driving as a very interesting product with motivating key features which allow improvement of the safety of the car, reducing emission or fuel consumption or optimizing driver comfort during long journeys. Car racing is an active research field where new advances in aerodynamics, consumption and engine power are critical each season. Our proposal is to research how evolutionary computation techniques can help in this field. As a first goal we want to automatically learn to drive, by means of genetic algorithms, optimizing lap times while driving through three different circuits.Publicad