How much is '5-a-day'? A qualitative investigation into consumer understanding of fruit and vegetable intake guidelines

BACKGROUND: Despite the known health benefits of fruit and vegetables (FV), population intakes remain low. One potential contributing factor may be a lack of understanding surrounding recommended intakes. The present study aimed to explore the understanding of FV intake guidelines among a sample of low FV consumers.

METHODS: Six semi-structured focus groups were held with low FV consumers (n = 28, age range 19-55 years). Focus groups were recorded digitally, transcribed verbatim and analysed thematically using nvivo (QSR International, Melbourne, Australia) to manage the coded data. Participants also completed a short questionnaire assessing knowledge on FV intake guidelines. Descriptive statistics were used to analyse responses.

RESULTS: The discussions highlighted that, although participants were aware of FV intake guidelines, they lacked clarity with regard to the meaning of the '5-a-day' message, including what foods are included in the guideline, as well as what constitutes a portion of FV. There was also a sense of confusion surrounding the concept of achieving variety with regard to FV intake. The sample highlighted a lack of previous education on FV portion sizes and put forward suggestions for improving knowledge, including increased information on food packaging and through health campaigns. Questionnaire findings were generally congruent with the qualitative findings, showing high awareness of the '5-a-day' message but a lack of knowledge surrounding FV portion sizes.

CONCLUSIONS: Future public health campaigns should consider how best to address the gaps in knowledge identified in the present study, and incorporate evaluations that will allow the impact of future initiatives on knowledge, and ultimately behaviour, to be investigated.

A simple approach to the supernova progenitor–explosion connection

We present a new approach to understand the landscape of supernova explosion energies, ejected nickel masses, and neutron star birth masses. In contrast to other recent parametric approaches, our model predicts the properties of neutrino-driven explosions based on the pre-collapse stellar structure without the need for hydrodynamic simulations. The model is based on physically motivated scaling laws and simple differential equations describing the shock propagation, the contraction of the neutron star, the neutrino emission, the heating conditions, and the explosion energetics. Using model parameters compatible with multi-D simulations and a fine grid of thousands of supernova progenitors, we obtain a variegated landscape of neutron star and black hole formation similar to other parametrized approaches and find good agreement with semi-empirical measures for the ‘explodability’ of massive stars. Our predicted explosion properties largely conform to observed correlations between the nickel mass and explosion energy. Accounting for the coexistence of outflows and downflows during the explosion phase, we naturally obtain a positive correlation between explosion energy and ejecta mass. These correlations are relatively robust against parameter variations, but our results suggest that there is considerable leeway in parametric models to widen or narrow the mass ranges for black hole and neutron star formation and to scale explosion energies up or down. Our model is currently limited to an all-or-nothing treatment of fallback and there remain some minor discrepancies between model predictions and observational constraints.

Clustering-based Query Routing in Cooperative Semi-structured Peer to Peer Networks

We consider the problem of resource selection in clustered Peer-to-Peer Information Retrieval (P2P IR) networks with cooperative peers. The clustered P2P IR framework presents a significant departure from general P2P IR architectures by employing clustering to ensure content coherence between resources at the resource selection layer, without disturbing document allocation. We propose that such a property could be leveraged in resource selection by adapting well-studied and popular inverted lists for centralized document retrieval. Accordingly, we propose the Inverted PeerCluster Index (IPI), an approach that adapts the inverted lists, in a straightforward manner, for resource selection in clustered P2P IR. IPI also encompasses a strikingly simple peer-specific scoring mechanism that exploits the said index for resource selection. Through an extensive empirical analysis on P2P IR testbeds, we establish that IPI competes well with the sophisticated state-of-the-art methods in virtually every parameter of interest for the resource selection task, in the context of clustered P2P IR.