The Blue Stragglers of the Old Open Cluster NGC 188

The old (7 Gyr) open cluster NGC 188 has yielded a wealth of astrophysical insight into its rich blue straggler population. Specifically, the NGC 188 blue stragglers are characterized by: A binary frequency of 80% for orbital periods less than $10^4$ days;Typical orbital periods around 1000 days;Typical secondary star masses of 0.5 M$_{\odot}$; At least some white dwarf companion stars; Modestly rapid rotation; A bimodal radial spatial distribution; Dynamical masses greater than standard stellar evolution masses (based on short-period binaries); Under-luminosity for dynamical masses (short-period binaries). Extensive $N$-body modeling of NGC 188 with empirical initial conditions reproduces the properties of the cluster, and in particular the main-sequence solar-type binary population. The current models also reproduce well the binary orbital properties of the blue stragglers, but fall well short of producing the observed number of blue stragglers. This deficit could be resolved by reducing the frequency of common-envelope evolution during Roche lobe overflow. Both the observations and the $N$-body models strongly indicate that the long-period blue-straggler binaries - which dominate the NGC 188 blue straggler population - are formed by asymptotic-giant (primarily) and red-giant mass transfer onto main sequence stars. The models suggest that the few non-velocity-variable blue stragglers formed from mergers or collisions. Several remarkable short-period double-lined binaries point to the importance of subsequent dynamical exchange encounters, and provide at least one example of a likely collisional origin for a blue straggler.Comment: Chapter 3, in Ecology of Blue Straggler Stars, H.M.J. Boffin, G. Carraro & G. Beccari (Eds), Astrophysics and Space Science Library, Springe

Imitating accelerated expansion of the Universe by matter inhomogeneities - corrections of some misunderstandings

A number of misunderstandings about modeling the apparent accelerated expansion of the Universe, and about the `weak singularity' are clarified: 1. Of the five definitions of the deceleration parameter given by Hirata and Seljak (HS), only $q_1$ is a correct invariant measure of acceleration/deceleration of expansion. The $q_3$ and $q_4$ are unrelated to acceleration in an inhomogeneous model. 2. The averaging over directions involved in the definition of $q_4$ does not correspond to what is done in observational astronomy. 3. HS's equation (38) connecting $q_4$ to the flow invariants gives self-contradictory results when applied at the centre of symmetry of the Lema\^{\i}tre-Tolman (L-T) model. The intermediate equation (31) that determines $q_{3'}$ is correct, but approximate, so it cannot be used for determining the sign of the deceleration parameter. Even so, at the centre of symmetry of the L-T model, it puts no limitation on the sign of $q_{3'}(0)$. 4. The `weak singularity' of Vanderveld {\it et al.} is a conical profile of mass density at the centre - a perfectly acceptable configuration. 5. The so-called `critical point' in the equations of the `inverse problem' for a central observer in an L-T model is a manifestation of the apparent horizon - a common property of the past light cones in zero-lambda L-T models, perfectly manageable if the equations are correctly integrated.Comment: 15 pages. Completely rewritten to match the published version. We added discussion of 2 key papers cited by VFW and identified more clearly the assumptions, approximations and mistakes that led to certain misconceptions

Relative mobility determines the efficacy of MPAs in a two species mixed fishery with conflicting management objectives

Marine Protected Areas (MPAs) have been used to protect species in need of conservation and as a fisheries management tool. It has been suggested MPAs can benefit mobile stocks by protecting spawning grounds whilst also allowing yields to be maintained as mature fish move out of the protected areas. However, the robustness of this claim in mixed species fisheries has yet to be established. We use a simulation model to explore the efficacy of spatial closures and effort regulation when other forms of fishery control (e.g., Total Allowable Catches) are absent or non-enforced as ways of addressing management objectives that are difficult to reconcile due to the contrasting life-histories of a target and a bycatch, conservation species in a two-species fishery. The mobility of each stock in such a fishery affects the benefits conferred by an MPA. The differing management objectives of the two species can be partially met by effort regulations or closures when the species exhibit similar mobility. However, a more mobile conservation species prevents both sets of aims being met by either management tool. We use simulations to explore how spatial closures and effort regulation can be used to seek compromise between stakeholders when the mobility of one stock prevents conflicting management objectives to be fully met. Our results demonstrate that stock mobility is a key factor in considering whether an MPA can meet conflicting aims in a multispecies fishery compromised of stocks with differing life histories and mobilities

Regional variations in walking for different purposes - The South East Queensland quality of life study

Where people are located can influence behavioral choices and health outcomes through the effects of place on health. Walking is the most commonly reported form of nonoccupational and nonhousehold physical activity for adults. It is a behavior of particular interest to those in the transportation, urban planning, and public health fields. Researchers have examined patterns of walking from both an individual perspective (psychological and social factors) and from a broader community focus (location and built environment factors). The majority of studies have examined walking in the context of urban environments. Variations within regions (urban, periurban, and rural, for example) in walking have not been previously described. We use data from a regionally based quality of life survey to examine subregional variations in walking for particular purposes. Both the social and contextual variations that may underlie these differences are considered. This is useful in helping identify particular factors that may be further investigated in disaggregated analyses using GIS methods to identify specific differences in objective attributes between subregions that may influence peoples' choices to walk, such as walking infrastructure and the availability of destinations