994 resultados para Old person


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During the early years, parents have a major influence on their children’s diets, food choices and development of eating habits. However, research concerning the influence of parental feeding practices on young children’s diets is limited. This paper presents a systematic review of intervention studies with parents of preschool children. The aim was to investigate the effectiveness of interventions that target parent nutrition knowledge and/or parenting practices with parents of young children aged two to five years in the development of healthy dietary habits. Seventeen studies were identified. Findings highlight the limited number of good quality studies in this age group. Limitations include design inconsistency and a lack of longitudinal data to evaluate sustainability. Research on parental understanding of healthy diets and specific parenting styles and feeding practices is lacking. Further insights into how parents can positively influence children’s diets will come from quality longitudinal research examining both parent feeding practices and nutrition knowledge in this age group.

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The current generation of young children has been described as “digital natives”, having been born into a ubiquitous digital media environment. They are envisaged as educationally independent of the guided interaction provided by “digital immigrants”: parents and teachers. This paper uses data from the Longitudinal Study of Australian Children (LSAC) to study children’s (aged 0-8 years) development of vocabulary and traditional literacy; access to digital devices; parental mediation practices; the child’s use of digital devices as recorded in time-diaries and, finally, the association between patterns of media use and family contexts on children’s learning. The analysis shows the importance of the parental context framing media use in acquisition of vocabulary, and suggests that computer (but not games) use is associated with more developed language skills. Independently of these factors raw exposure to television is not harmful to learning.


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A shortage of specialised facilities means that general practitioners have a prominent role in community care of the elderly with mental illness.

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Personal identity theory has become increasingly sensitive to the importance of the first-person perspective. However, certain ways of speaking about that perspective do not allow the full temporal aspects of first-person perspectives on the self to come into view. In this paper I consider two recent phenomenologically-informed discussions of personal identity that end up yielding metaphysically divergent views of the self: those of Barry Dainton and Galen Strawson. I argue that when we take a properly temporally indexical view of the first-person perspective, and thereby resist the assumption that phenomenally figured and theoretically-figured identity claims must have a common object, the metaphysically awkward accommodations each of these authors is compelled to make cease to be necessary.

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The k-means algorithm is a partitional clustering method. Over 60 years old, it has been successfully used for a variety of problems. The popularity of k-means is in large part a consequence of its simplicity and efficiency. In this paper we are inspired by these appealing properties of k-means in the development of a clustering algorithm which accepts the notion of "positively" and "negatively" labelled data. The goal is to discover the cluster structure of both positive and negative data in a manner which allows for the discrimination between the two sets. The usefulness of this idea is demonstrated practically on the problem of face recognition, where the task of learning the scope of a person's appearance should be done in a manner which allows this face to be differentiated from others.

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In this chapter we focus on face appearance-based biometrics. The cheap and readily available hardware used to acquire data, their non-invasiveness and the ease of employing them from a distance and without the awareness of the user, are just some of the reasons why these continue to be of great practical interest. However, a number of research challenges remain. Specifically, face biometrics have traditionally focused on images acquired in the visible light spectrum and these are greatly affected by such extrinsic factors such as the illumination, camera angle (or, equivalently, head pose) and occlusion. In practice, the effects of changing pose are usually least problematic and can oftentimes be overcome by acquiring data over a time period, e.g., by tracking a face in a surveillance video. Consequently, image sequence or image set matching has recently gained a lot of attention in the literature [137–139] and is the paradigm adopted in this chapter as well. In other words, we assume that the training image set for each individual contains some variability in pose, but is not obtained in scripted conditions or in controlled illumination. In contrast, illumination is much more difficult to deal with: the illumination setup is in most cases not practical to control and its physics is difficult to accurately model. Thermal spectrum imagery is useful in this regard as it is virtually insensitive to illumination changes, as illustrated in Fig. 6.1. On the other hand, it lacks much of the individual, discriminating facial detail contained in visual images. In this sense, the two modalities can be seen as complementing each other. The key idea behind the system presented in this chapter is that robustness to extreme illumination changes can be achieved by fusing the two. This paradigm will further prove useful when we consider the difficulty of recognition in the presence of occlusion caused by prescription glasses.

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Recognition algorithms that use data obtained by imaging faces in the thermal spectrum are promising in achieving invariance to extreme illumination changes that are often present in practice. In this paper we analyze the performance of a recently proposed face recognition algorithm that combines visual and thermal modalities by decision level fusion. We examine (i) the effects of the proposed data preprocessing in each domain, (ii) the contribution to improved recognition of different types of features, (iii) the importance of prescription glasses detection, in the context of both 1-to-N and 1-to-1 matching (recognition vs. verification performance). Finally, we discuss the significance of our results and, in particular, identify a number of limitations of the current state-of-the-art and propose promising directions for future research.

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The purposes of this study were to provide data on the peak Vo 2 of 12-18-year-old boys from Hong Kong, a densely populated urban environment; to compare these data with those for other similarly aged populations; and to examine the correlations between peak Vo 2 and various anthropometric parameters of this group. A stratified, random sample of 86 ethnic Chinese boys had their peak Vo 2 determined using an on-line gas analysis system during incremental, treadmill running. The mean peak 17o2 of the boys was 2.7 SD 0.44 1- rain - 1 or, when expressed in relation to body mass, 52.0 SD 5-8 ml- kg- 1. min - 1. Peak Vo 2 (1. min - 1) was significantly correlated with body mass (r = 0.72, p < 0.001, age (r = 0.49, p < 0.001) and height (r = 0.71, p = 0-001). Peak Vo 2 (ml 'kg- 1. min- 1) showed no correlation with age or height. These data suggest that this population group has peak Vo 2 values very similar to those observed in boys from most other population groups.