The availability of snack food displays that may trigger impulse purchases in Melbourne supermarkets

Background

Supermarkets play a major role in influencing the food purchasing behaviours of most households. Snack food exposures within these stores may contribute to higher levels of consumption and ultimately to increasing levels of obesity, particularly within socioeconomically disadvantaged neighbourhoods. We aimed to examine the availability of snack food displays at checkouts, end-of-aisle displays and island displays in major supermarket chains in the least and most socioeconomically disadvantaged neighbourhoods of Melbourne.
Methods

Within-store audits of 35 Melbourne supermarkets. Supermarkets were sampled from the least and most socioeconomically disadvantaged suburbs within 30 km of the Melbourne CBD. We measured the availability of crisps, chocolate, confectionery, and soft drinks (diet and regular) at the checkouts, in end-of-aisle displays, and in island bin displays.
Results

Snack food displays were most prominent at checkouts with only five stores not having snack foods at 100% of their checkouts. Snack foods were also present at a number of end-of-aisle displays (at both the front (median 38%) and back (median 33%) of store), and in island bin displays (median number of island displays: 7; median total circumference of island displays: 19.4 metres). Chocolate items were the most common snack food item on display. There was no difference in the availability of these snack food displays by neighbourhood disadvantage.
Conclusions

As a result of the high availability of snack food displays, exposure to snack foods is almost unavoidable in Melbourne supermarkets, regardless of levels of neighbourhood socioeconomic disadvantage. Results of this study could promote awareness of the prominence of unhealthy food items in chain-brand supermarkets outlets.

Visual effects in great bowerbird sexual displays and their implications for signal design

It is often assumed that the primary purpose of a male's sexual display is to provide information about quality, or to strongly stimulate prospective mates, but other functions of courtship displays have been relatively neglected. Male great bowerbirds (Ptilonorhynchus nuchalis) construct bowers that exploit the female's predictable field of view (FOV) during courtship displays by creating forced perspective illusions, and the quality of illusion is a good predictor of mating success. Here, we present and discuss two additional components of male courtship displays that use the female's predetermined viewpoint: (i) the rapid and diverse flashing of coloured objects within her FOV and (ii) chromatic adaptation of the female's eyes that alters her perception of the colour of the displayed objects. Neither is directly related to mating success, but both are likely to increase signal efficacy, and may also be associated with attracting and holding the female's attention. Signal efficacy is constrained by trade-offs between the signal components; there are both positive and negative interactions within multicomponent signals. Important signal components may have a threshold effect on fitness rather than the often assumed linear relationship.

Bayesian nonparametric extraction of hidden contexts from pervasive honest signals

Hidden patterns and contexts play an important part in intelligent pervasive systems. Most of the existing works have focused on simple forms of contexts derived directly from raw signals. High-level constructs and patterns have been largely neglected or remained under-explored in pervasive computing, mainly due to the growing complexity over time and the lack of efficient principal methods to extract them. Traditional parametric modeling approaches from machine learning find it difficult to discover new, unseen patterns and contexts arising from continuous growth of data streams due to its practice of training-then-prediction paradigm. In this work, we propose to apply Bayesian nonparametric models as a systematic and rigorous paradigm to continuously learn hidden patterns and contexts from raw social signals to provide basic building blocks for context-aware applications. Bayesian nonparametric models allow the model complexity to grow with data, fitting naturally to several problems encountered in pervasive computing. Under this framework, we use nonparametric prior distributions to model the data generative process, which helps towards learning the number of latent patterns automatically, adapting to changes in data and discovering never-seen-before patterns, contexts and activities. The proposed methods are agnostic to data types, however our work shall demonstrate to two types of signals: accelerometer activity data and Bluetooth proximal data. © 2014 IEEE.

Seeking attention: An eye tracking study of in-store merchandise displays

Purpose – The purpose of this paper is to elucidate the role that visual measures of attention to product and information and price display signage have on purchase intention. The authors assessed the effect of visual attention to the product, information or price sign on purchase intention, as measured by likelihood to buy. Design/methodology/approach – The authors used eye–tracking technology to collect data from Australian and US garden centre customers, who viewed eight plant displays in which the signs had been altered to show either price or supplemental information (16 images total). The authors compared the role of visual attention to price and information sign, and the role of visual attention to the product when either sign was present on likelihood to buy. Findings – Overall, providing product information on a sign without price elicited higher likelihood to buy than providing a sign with price. The authors found a positive relationship between visual attention to price on the display sign and likelihood to buy, but an inverse relationship between visual attention to information and likelihood to buy. Research limitations/implications – An understanding of the attention–capturing power of merchandise display elements, especially signs, has practical significance. The findings will assist retailers in creating more effective and efficient display signage content, for example, featuring the product information more prominently than the price. The study was conducted on a minimally packaged product, live plants, which may reduce the ability to generalize findings to other product types. Practical implications – The findings will assist retailers in creating more effective and efficient display signage content. The study used only one product category (plants) which may reduce the ability to generalize findings to other product types. Originality/value – The study is one of the first to use eye–tracking in a macro–level, holistic investigation of the attention–capturing value of display signage information and its relationship to likelihood to buy. Researchers, for the first time, now have the ability to empirically test the degree to which attention and decision–making are linked.

A shadow-like task migration model based on context semantics for mobile and pervasive environments

Pervasive computing is a user-centric mobile computing paradigm, in which tasks should be migrated over different platforms in a shadow-like way when users move around. In this paper, we propose a context-sensitive task migration model that recovers program states and rebinds resources for task migrations based on context semantics through inserting resource description and state description sections in source programs. Based on our model, we design and develop a task migration framework xMozart which extends the Mozart platform in terms of context awareness. Our approach can recover task states and rebind resources in the context-aware way, as well as support multi- modality I/O interactions. The extensive experiments demonstrate that our approach can migrate tasks by resuming them from the last broken points like shadows moving along with the users.

SECC: simultaneous extraction of context and community from pervasive signals

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain the human dynamics or behaviors and then use them as the way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows nested structure to be built to explain data at multiple levels. We demonstrate our framework on three public datasets where the advantages of the proposed approach are validated.

Bayesian nonparametric learning of contexts and activities from pervasive signals

This thesis develops machine learning techniques to discover activities and contexts from pervasive sensor data. These techniques are especially suitable for streaming sensor data as they can infer the context space automatically. They are applicable in many real world applications such as activity monitoring or organization management.

Innovation practice using pervasive mobile technology solutions to improve population health management: a pilot study of gestational diabetes patient care in Australia

Healthcare service delivery is moving forward from individual care to population health management, because of the fast growth of health records. However, to improve population health performance, it is necessary to leverage relevant data and information using new technology solutions, such as pervasive diabetes mobile technology solution of Inet International Inc., which offers the potential to facilitate patient empowerment with gestational diabetic care. Hence, this article examines the pilot study outcomes of a small clinical trial focusing on pregnant patients affected by gestational diabetes mellitus, in an Australian not for profit healthcare context. The aims include establishing proof of concept and also assessing the usability, acceptability, and functionality of this mobile solution and thereby generate hypotheses to be tested in a large-scale confirmatory clinical trial.

Doxorubicin conjugated to immunomodulatory anticancer lactoferrin displays improved cytotoxicity overcoming prostate cancer chemo resistance and Inhibits tumour development in TRAMP mice

Advanced, metastatic, castration resistant and chemo-resistant prostate cancer has triggered change in the drug development landscape against prostate cancer. Bovine lactoferrin (bLf) is currently attracting attention in clinics for its anti-cancer properties and proven safety profile. bLf internalises into cancer cells via receptor mediated endocytosis, boosts immunity and complements chemotherapy. We employed bLf as an excellent functional carrier protein for delivering doxorubicin (Dox) into DU145 cells, CD44+/EpCAM+ double positive enriched DU145 3D prostaspheres and drug resistant ADR1000-DU145 cells, thus circumventing Dox efflux, to overcome chemo-resistance. Successful bLf-Dox conjugation with iron free or iron saturated bLf forms did not affect the integrity and functionality of bLf and Dox. bLf-Dox internalised into DU145 cells within 6 h, enhanced nuclear Dox retention up to 24 h, and proved significantly effective (p < 0.001) in reducing LC50 value of Dox from 5.3 μM to 1.3 μM (4 fold). Orally fed iron saturated bLf-Dox inhibited tumour development, prolonged survival, reduced Dox induced general toxicity, cardiotoxicity, neurotoxicity in TRAMP mice and upregulated serum levels of anti-cancer molecules TNF-α, IFN-γ, CCL4 and CCL17. The study identifies promising potential of a novel and safer bLf-Dox conjugate containing a conventional cytotoxic drug along with bLf protein to target drug resistance.