Reported consumption of takeaway food and its contribution to socioeconomic inequalities in body mass index

The aim of this study was to examine whether takeaway food consumption mediated (explained) the association between socioeconomic position and body mass index (BMI). A postal-survey was conducted among 1500 randomly selected adults aged between 25 and 64 years in Brisbane, Australia during 2009 (response rate 63.7%, N=903). BMI was calculated using self-reported weight and height. Participants reported usual takeaway food consumption, and these takeaway items were categorised into "healthy" and "less healthy" choices. Socioeconomic position was ascertained by education, household income, and occupation. The mean BMI was 27.1kg/m(2) for men and 25.7kg/m(2) for women. Among men, none of the socioeconomic measures were associated with BMI. In contrast, women with diploma/vocational education (β=2.12) and high school only (β=2.60), and those who were white-collar (β=1.55) and blue-collar employees (β=2.83) had significantly greater BMI compared with their more advantaged counterparts. However, household income was not associated with BMI. Among women, the consumption of "less healthy" takeaway food mediated BMI differences between the least and most educated, and between those employed in blue collar occupations and their higher status counterparts. Decreasing the consumption of "less healthy" takeaway options may reduce socioeconomic inequalities in overweight and obesity among women but not men.

Professionalizing Business Process Management : towards a Body of Knowledge for BPM

Business Process Management (BPM) is rapidly evolving as an established discipline. There are a number of efforts underway to formalize the various aspects of BPM practice; creating a formal Body of Knowledge (BoK) is one such effort. Bodies of knowledge are artifacts that have a proven track record for accelerating the professionalization of various disciplines. In order for this to succeed in BPM, it is vital to involve the broader business process community and derive a BoK that has essential characteristics that addresses the discipline’s needs. We argue for the necessity of a comprehensive BoK for the BPM domain, and present a core list of essential features to consider when developing a BoK based on preliminary empirical evidence. The paper identifies and critiques existing Bodies of Knowledge related to BPM, and firmly calls for an effort to develop a more accurate and sustainable BoK for BPM. An approach for this effort is presented with preliminary outcomes.

A preliminary study on assessing body burden of persistent organic pollutants (POPs) in infants through analysis of faeces

Most persistent organic pollutants (POPs) like polychlorinated biphenyls (PCBs), a range of polybrominated diphenyl ethers (PBDEs) and organochlorine pesticides (OCPs) are readily absorbed (via the ingestion and inhalation) and accumulate in fatty tissue, including adipose tissue and human milk [1]. Health effects related to exposure to these chemicals may include neurological effects, altered functioning of the nervous system and/or endocrine disruption [2-4]. The burden of environmental disease is recognized as much higher for children than adults, especially in young children under 5 years of age worldwide [5]. There is increased concern regarding the environmental impact on the health of children who have been disproportionately affected by environmental problems. For example they may be subjected to relatively higher exposure, have greater physiological susceptibility and/or suffer more extreme consequences due to growth [6-9]. It is therefore worthwhile to assess the correlation between burden of disease and exposure to xenobiotic chemical pollutants like POPs. Such assessment may provide guidance for legislative changes regarding chemical bans and give reliable advice to parents including lactating mothers.

Alcohol ingestion impairs maximal post-exercise rates of myofibrillar protein synthesis following a single bout of concurrent training

Introduction The culture in many team sports involves consumption of large amounts of alcohol after training/competition. The effect of such a practice on recovery processes underlying protein turnover in human skeletal muscle are unknown. We determined the effect of alcohol intake on rates of myofibrillar protein synthesis (MPS) following strenuous exercise with carbohydrate (CHO) or protein ingestion. Methods In a randomized cross-over design, 8 physically active males completed three experimental trials comprising resistance exercise (8×5 reps leg extension, 80% 1 repetition maximum) followed by continuous (30 min, 63% peak power output (PPO)) and high intensity interval (10×30 s, 110% PPO) cycling. Immediately, and 4 h post-exercise, subjects consumed either 500 mL of whey protein (25 g; PRO), alcohol (1.5 g·kg body mass−1, 12±2 standard drinks) co-ingested with protein (ALC-PRO), or an energy-matched quantity of carbohydrate also with alcohol (25 g maltodextrin; ALC-CHO). Subjects also consumed a CHO meal (1.5 g CHO·kg body mass−1) 2 h post-exercise. Muscle biopsies were taken at rest, 2 and 8 h post-exercise. Results Blood alcohol concentration was elevated above baseline with ALC-CHO and ALC-PRO throughout recovery (P<0.05). Phosphorylation of mTORSer2448 2 h after exercise was higher with PRO compared to ALC-PRO and ALC-CHO (P<0.05), while p70S6K phosphorylation was higher 2 h post-exercise with ALC-PRO and PRO compared to ALC-CHO (P<0.05). Rates of MPS increased above rest for all conditions (~29–109%, P<0.05). However, compared to PRO, there was a hierarchical reduction in MPS with ALC-PRO (24%, P<0.05) and with ALC-CHO (37%, P<0.05). Conclusion We provide novel data demonstrating that alcohol consumption reduces rates of MPS following a bout of concurrent exercise, even when co-ingested with protein. We conclude that alcohol ingestion suppresses the anabolic response in skeletal muscle and may therefore impair recovery and adaptation to training and/or subsequent performance.

Selective combination of visual and thermal imaging for resilient localization in adverse conditions : day and night, smoke and fire

Long-term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and low-visibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g., operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual-SLAM algorithm to achieve localization. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low-quality data, i.e., data most likely to induce large localization errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low-light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using five different variations of data sources and data combination strategies in the localization method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without preselection. We show that the proposed framework allows for camera-based localization resilient to a large range of low-visibility conditions.

Laser-to-radar sensing redundancy for resilient perception in adverse environmental conditions

This paper presents an approach to promote the integrity of perception systems for outdoor unmanned ground vehicles (UGV) operating in challenging environmental conditions (presence of dust or smoke). The proposed technique automatically evaluates the consistency of the data provided by two sensing modalities: a 2D laser range finder and a millimetre-wave radar, allowing for perceptual failure mitigation. Experimental results, obtained with a UGV operating in rural environments, and an error analysis validate the approach.

Perception Quality Evaluation with Visual and Infrared Cameras in Challenging Environmental Conditions

This work aims to contribute to the reliability and integrity of perceptual systems of unmanned ground vehicles (UGV). A method is proposed to evaluate the quality of sensor data prior to its use in a perception system by utilising a quality metric applied to heterogeneous sensor data such as visual and infrared camera images. The concept is illustrated specifically with sensor data that is evaluated prior to the use of the data in a standard SIFT feature extraction and matching technique. The method is then evaluated using various experimental data sets that were collected from a UGV in challenging environmental conditions, represented by the presence of airborne dust and smoke. In the first series of experiments, a motionless vehicle is observing a ’reference’ scene, then the method is extended to the case of a moving vehicle by compensating for its motion. This paper shows that it is possible to anticipate degradation of a perception algorithm by evaluating the input data prior to any actual execution of the algorithm.

Towards discrimination of challenging conditions for UGVs with visual and infrared sensors

This work aims to contribute to reliability and integrity in perceptual systems of autonomous ground vehicles. Information theoretic based metrics to evaluate the quality of sensor data are proposed and applied to visual and infrared camera images. The contribution of the proposed metrics to the discrimination of challenging conditions is discussed and illustrated with the presence of airborne dust and smoke.

Datasets for the evaluation of multi-sensor perception in natural environments with challenging conditions

This paper presents large, accurately calibrated and time-synchronised datasets, gathered outdoors in controlled environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. It discusses how the data collection process was designed, the conditions in which these datasets have been gathered, and some possible outcomes of their exploitation, in particular for the evaluation of performance of sensors and perception algorithms for UGVs.

The Marulan data sets: multi-sensor perception in a natural environment with challenging conditions

In this paper we present large, accurately calibrated and time-synchronized data sets, gathered outdoors in controlled and variable environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. These include four 2D laser scanners, a radar scanner, a color camera and an infrared camera. It provides a full description of the system used for data collection and the types of environments and conditions in which these data sets have been gathered, which include the presence of airborne dust, smoke and rain.

Towards reliable perception for unmanned ground vehicles in challenging conditions

This work aims to promote reliability and integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicle (UGV) autonomy. For this purpose, a comprehensive UGV system, comprising many different exteroceptive and proprioceptive sensors has been built. The first contribution of this work is a large, accurately calibrated and synchronised, multi-modal data-set, gathered in controlled environmental conditions, including the presence of dust, smoke and rain. The data have then been used to analyse the effects of such challenging conditions on perception and to identify common perceptual failures. The second contribution is a presentation of methods for mitigating these failures to promote perceptual integrity in adverse environmental conditions.

Physiological tolerance times while wearing explosive ordnance disposal protective clothing in simulated environmental extremes

Explosive ordnance disposal (EOD) technicians are required to wear protective clothing to protect themselves from the threat of overpressure, fragmentation, impact and heat. The engineering requirements to minimise these threats results in an extremely heavy and cumbersome clothing ensemble that increases the internal heat generation of the wearer, while the clothing’s thermal properties reduce heat dissipation. This study aimed to evaluate the heat strain encountered wearing EOD protective clothing in simulated environmental extremes across a range of differing work intensities. Eight healthy males [age 25±6 years (mean ± sd), height 180±7 cm, body mass 79±9 kg, V˙O2max 57±6 ml.kg−1.min−1] undertook nine trials while wearing an EOD9 suit (weighing 33.4 kg). The trials involved walking on a treadmill at 2.5, 4 and 5.5 km⋅h−1 at each of the following environmental conditions, 21, 30 and 37°C wet bulb globe temperature (WBGT) in a randomised controlled crossover design. The trials were ceased if the participants’ core temperature reached 39°C, if heart rate exceeded 90% of maximum, if walking time reached 60 minutes or due to fatigue/nausea. Tolerance times ranged from 10–60 minutes and were significantly reduced in the higher walking speeds and environmental conditions. In a total of 15 trials (21%) participants completed 60 minutes of walking; however, this was predominantly at the slower walking speeds in the 21°C WBGT environment. Of the remaining 57 trials, 50 were ceased, due to attainment of 90% maximal heart rate. These near maximal heart rates resulted in moderate-high levels of physiological strain in all trials, despite core temperature only reaching 39°C in one of the 72 trials.

Development and validation of anthropometric prediction equations for estimation of body fat in Indonesian men

Body composition of 292 males aged between 18 and 65 years was measured using the deuterium oxide dilution technique. Participants were divided into development (n=146) and cross-validation (n=146) groups. Stature, body weight, skinfold thickness at eight sites, girth at five sites, and bone breadth at four sites were measured and body mass index (BMI), waist-to-hip ratio (WHR), and waist-to-stature ratio (WSR) calculated. Equations were developed using multiple regression analyses with skinfolds, breadth and girth measures, BMI, and other indices as independent variables and percentage body fat (%BF) determined from deuterium dilution technique as the reference. All equations were then tested in the cross-validation group. Results from the reference method were also compared with existing prediction equations by Durnin and Womersley (1974), Davidson et al (2011), and Gurrici et al (1998). The proposed prediction equations were valid in our cross-validation samples with r=0.77- 0.86, bias 0.2-0.5%, and pure error 2.8-3.6%. The strongest was generated from skinfolds with r=0.83, SEE 3.7%, and AIC 377.2. The Durnin and Womersley (1974) and Davidson et al (2011) equations significantly (p<0.001) underestimated %BF by 1.0 and 6.9% respectively, whereas the Gurrici et al (1998) equation significantly (p<0.001) overestimated %BF by 3.3% in our cross-validation samples compared to the reference. Results suggest that the proposed prediction equations are useful in the estimation of %BF in Indonesian men.

Brief guided imagery and body scanning interventions reduce food cravings

Elaborated Intrusion (EI) Theory proposes that cravings occur when involuntary thoughts about food are elaborated; a key part of elaboration is affectively-charged imagery. Craving can be weakened by working memory tasks that block imagery. EI Theory predicts that cravings should also be reduced by preventing involuntary thoughts being elaborated in the first place. Research has found that imagery techniques such as body scanning and guided imagery can reduce the occurrence of food thoughts. This study tested the prediction that these techniques also reduce craving. We asked participants to abstain from food overnight, and then to carry out 10 min of body scanning, guided imagery, or a control mind wandering task. They rated their craving at 10 points during the task on a single item measure, and before and after the task using the Craving Experience Questionnaire. While craving rose during the task for the mind wandering group, neither the guided imagery nor body scanning group showed an increase. These effects were not detected by the CEQ, suggesting that they are only present during the competing task. As they require no devices or materials and are unobtrusive, brief guided imagery strategies might form useful components of weight loss programmes that attempt to address cravings.