3,422 research outputs found

    Toward Equations of Galactic Structure

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    We find that all classes of galaxies, ranging from disks to spheroids and from dwarf spheroidals to brightest cluster galaxies, lie on a two dimensional surface within the space defined by the logarithms of the half-light radius, r_e, mean surface brightness within r_e, I_e, and internal velocity, V^2 = (1/2)v_c^2 + sigma^2, where v_c is the rotational velocity and sigma is the velocity dispersion. If these quantities are expressed in terms of kpc, L_solar/pc^2, and km/s, then log r_e - log V^2 + log I_e + log Upsilon_e + 0.8 = 0, where we provide a fitting function for Upsilon_e, the mass-to-light ratio within r_e in units of M_solar/L_solar, that depends only on V and I_e. The scatter about this surface for our heterogeneous sample of 1925 galaxies is small (< 0.1 dex) and could be as low as ~ 0.05 dex, or 10%. This small scatter has three possible implications for how gross galactic structure is affected by internal factors, such as stellar orbital structure, and by external factors, such as environment. These factors either 1) play no role beyond generating some of the observed scatter, 2) move galaxies along the surface, or 3) balance each other to maintain this surface as the locus of galactic structure equilibria. We cast the behavior of Upsilon_e in terms of the fraction of baryons converted to stars, eta, and the concentration of those stars within the dark matter halo, xi = R_{200}/r_e. We derive eta = 1.9 x 10^{-5} (L/L^*) Upsilon_* V^{-3} and xi = 1.4 V/r_e. Finally, we present and discuss the distributions of eta and xi for the full range of galaxies. For systems with internal velocities comparable to that of the Milky Way (149 < V < 163 km/s), eta = 0.14 +- 0.05, and xi is, on average, ~ 5 times greater for spheroids than for disks. (Abridged)Comment: submitted to Ap

    The Effectiveness of Augmented Reality as a Facilitator of Information Acquisition in Aviation Maintenance Applications

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    Until recently, in the field of Augmented Reality (AR) little research attention has been paid to the cognitive benefits of this emerging technology. AR, the synthesis of computer images and text in the real world, affords a supplement to normal information acquisition that has yet to be fully explored and exploited. AR achieves a more smooth and seamless interface by complementing human cognitive networks, and aiding information integration through multimodal sensory elaboration (visual, verbal, proprioceptive, and tactile memory) while the user is performing real world tasks. AR also incorporates visuo-spatial ability, which involves the representations of spatial information in memory. The use of this type of information is an extremely powerful form of elaboration. This study examined four learning paradigms: print (printed material) mode, observe (video tape) mode, interact (text annotations activated by mouse interaction) mode, and select (AR) mode. The results of the experiment indicated that the select (AR) mode resulted in better learning and recall when compared to the other three conventional learning modes

    Activities of daily life recognition using process representation modelling to support intention analysis

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    Purpose – This paper aims to focus on applying a range of traditional classification- and semantic reasoning-based techniques to recognise activities of daily life (ADLs). ADL recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer’s disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge. Design/methodology/approach – This paper presents an ADL recognition approach, which uses a hierarchical structure for the representation and modelling of the activities, its associated tasks and their relationships. This study describes an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer’s patients. Findings – A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches. Originality/value – The work presented in this paper is novel, as the developed ADL recognition approach takes into account all relationships and dependencies within the modelled ADLs. This is very useful when conducting activity recognition with very limited features
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