Gyrotactic suppression and emergence of chaotic trajectories of swimming particles in three-dimensional flows

We study the effects of imposed {three-dimensional flows} on the trajectories and mixing of gyrotactic swimming micro-organisms, and identify new phenomena not seen in flows restricted to two dimensions. Through numerical simulation of Taylor--Green and ABC flows, we explore the role that the flow and the cell shape play in determining the long-term configuration of the cells' trajectories, which often take the form of multiple sinuous and helical `plume-like' structures, even in the chaotic ABC flow. This gyrotactic suppression of Lagrangian chaos persists even in the presence of random noise. Analytical solutions for a number of cases reveal the how plumes form and the nature of the competition between torques acting on individual cells. \note{Furthermore, studies of Lyapunov exponents reveal that as the ratio of cell swimming speed relative to the flow speed increases from zero, the initial chaotic trajectories are first suppressed and then give way to a second unexpected window of chaotic trajectories at speeds greater than unity, before suppression of chaos at high relative swimming speeds

Properties of the Interstellar Medium and the Propagation of Cosmic Rays in the Galaxy

The problem of the origin of cosmic rays in the shocks produced by supernova explosions at energies below the so called 'knee' (at ~3*10$^6$ GeV) in the energy spectrum is addressed, with special attention to the propagation of the particles through the inhomogenious interstellar medium and the need to explain recent anisotropy results, [1]. It is shown that the fractal character of the matter density and magnetic field distribution leads to the likelihood of a substantial increase of spatial fluctuations in the cosmic ray energy spectra. While the spatial distribution of cosmic rays in the vicinity of their sources (eg. inside the Galactic disk) does not depend much on the character of propagation and is largely determined by the distribution of their sources, the distribution at large distances from the Galactic disk depends strongly on the character of the propagation. In particular, the fractal character of the ISM leads to what is known as 'anomalous diffusion' and such diffusion helps us to understand the formation of Cosmic Ray Halo. Anomalous diffusion allows an explanation of the recent important result from the Chacaltaya extensive air shower experiment [1], viz. a Galactic Plane Enhancement of cosmic ray intensity in the Outer Galaxy, which is otherwise absent for the case of the so-called 'normal' diffusion. All these effects are for just one reason: anomalous diffusion emphasizes the role of local phenomena in the formation of cosmic ray characteristics in our Galaxy and elsewhere.Comment: 18 pages, 5 figures, accepted by Astropartoicle Physic

A comparison of pedigree, genetic and genomic estimates of relatedness for informing pairing decisions in two critically endangered birds: Implications for conservation breeding programmes worldwide

Conservation management strategies for many highly threatened species include conservation breeding to prevent extinction and enhance recovery. Pairing decisions for these conservation breeding programmes can be informed by pedigree data to minimize relatedness between individuals in an effort to avoid inbreeding, maximize diversity and maintain evolutionary potential. However, conservation breeding programmes struggle to use this approach when pedigrees are shallow or incomplete. While genetic data (i.e., microsatellites) can be used to estimate relatedness to inform pairing decisions, emerging evidence indicates this approach may lack precision in genetically depauperate species, and more effective estimates will likely be obtained from genomic data (i.e., thousands of genome-wide single nucleotide polymorphisms, or SNPs). Here, we compare relatedness estimates and subsequent pairing decisions using pedigrees, microsatellites and SNPs from whole-genome resequencing approaches in two critically endangered birds endemic to New Zealand: kakī/ black stilt (Himantopus novaezelandiae) and kākāriki karaka/orange-fronted parakeet (Cyanoramphus malherbi). Our findings indicate that SNPs provide more precise estimates of relatedness than microsatellites when assessing empirical parent–offspring and full sibling relationships. Further, our results show that relatedness estimates and subsequent pairing recommendations using PMx are most similar between pedigree and SNP-based approaches. These combined results indicate that in lieu of robust pedigrees, SNPs are an effective tool for informing pairing decisions, which has important implications for many poorly pedigreed conservation breeding programmes worldwide

Adjusting to Retirement from Sport: Narratives of Former Competitive Rhythmic Gymnasts

This study used narrative inquiry to understand the retirement experiences of rhythmic gymnasts. Eight female former competitive gymnasts (M age = 24.5, SD = 8.33) each participated in four life-history interviews. Following dialogical narrative analysis, three narrative typologies were outlined: Entangled Narrative, Going Forward Narrative, and Making Sense Narrative. The entangled narrative shows an individual with a monological athletic identity, who is unable to develop a new identity following her retirement to the detriment of her well-being, and wishes to return to being a gymnast. The going-forward narrative describes those former gymnasts who were able to develop multiple identities during their gymnastics career, and are now flourishing in their life post-retirement. The making-sense narrative is an emergent narrative, which transcends the previous two narratives. Findings expand narrative research by providing new narrative resources to understand the experience of retirement from gymnastics. These narrative resources might assist gymnasts to expand their narrative repertoire by raising awareness of different narratives available in their culture

Local field factors in a polarized two-dimensional electron gas

We derive approximate expressions for the static local field factors of a spin polarized two-dimensional electron gas which smoothly interpolate between their small- and large-wavevector asymptotic limits. For the unpolarized electron gas, the proposed analytical expressions reproduce recent diffusion Monte Carlo data. We find that the degree of spin polarization produces important modifications to the local factors of the minority spins, while the local field functions of the majority spins are less affected.Comment: 8 pages, 10 figure

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

The use of sewage treatment works as foraging sites by insectivorous bats

Sewage treatment works with percolating filter beds are known to provide profitable foraging areas for insectivorous birds due to their association with high macroinvertebrate densities. Fly larvae developing on filter beds at sewage treatment works may similarly provide a valuable resource for foraging bats. Over the last two decades, however, there has been a decline in filter beds towards a system of “activated sludge”. Insects and bat activity were surveyed at 30 sites in Scotland employing these two different types of sewage treatment in order to assess the possible implications of these changes for foraging bats. Bat activity (number of passes) recorded from broad-band bat detectors was quantified at three points within each site. The biomass of aerial insects, sampled over the same period as the detector surveys, was measured using a suction trap. The biomass of insects and activity of Pipistrellus spp. was significantly higher at filter beds than at activated sludge sites. In addition, whilst foraging activity of Pipistrellus spp. at filter beds was comparable to that of adjacent “good” foraging habitat, foraging at activated sludge sites was considerably lower. This study indicates the high potential value of an anthropogenic process to foraging bats, particularly in a landscape where their insect prey has undergone a marked decline, and suggests that the current preference for activated sludge systems is likely to reduce the value of treatment works as foraging sites for bats

A critical reassessment of particle Dark Matter limits from dwarf satellites

Dwarf satellite galaxies are ideal laboratories for identifying particle Dark Matter signals. When setting limits on particle Dark Matter properties from null searches, it becomes however crucial the level at which the Dark Matter density profile within these systems is constrained by observations. In the limit in which the spherical Jeans equation is assumed to be valid for a given tracer stellar population, we study the solution of this equation having the Dark Matter mass profile as an output rather than as a trial parametric input. Within our new formulation, we address to what level dwarf spheroidal galaxies feature a reliable mass estimator. We assess then possible extrapolation of the density profiles in the inner regions and -- keeping explicit the dependence on the orbital anisotropy profile of the tracer population -- we derive general trends on the line-of-sight integral of the density profile squared, a quantity commonly dubbed J-factor and crucial to estimate fluxes from prompt Dark Matter pair annihilations. Taking Ursa Minor as a study case among Milky Way satellites, we perform Bayesian inference using the available kinematical data for this galaxy. Contrary to all previous studies, we avoid marginalization over quantities poorly constrained by observations or by theoretical arguments. We find minimal J-factors to be about 2 to 4 times smaller than commonly quoted estimates, approximately relaxing by the same amount the limit on Dark Matter pair annihilation cross section from gamma-ray surveys of Ursa Minor. At the same time, if one goes back to a fixed trial parametric form for the density, e.g. using a NFW or Burkert profile, we show that the minimal J can hardly be reduced by more than a factor of 1.5. \ua9 2016 IOP Publishing Ltd and Sissa Medialab srl