Tracking of dietary intakes in early childhood : the Melbourne InFANT program

Background/Objectives: The objectives of the present study were to describe food and nutrient intakes in children aged 9 and 18 months, and to assess tracking of intakes between these two ages.

Subjects/Methods: Participants were 177 children of first-time mothers from the control arm of the Melbourne Infant Feeding Activity and Nutrition Trial (InFANT) Program. Dietary intake was collected at 9 and 18 months using three 24 h diet recalls. Tracking was assessed for food and nutrient intakes using logistic regression analysis and estimating partial correlation coefficients, respectively.

Results: Although overall nutrient intakes estimated in this study did not indicate a particular risk of nutrient deficiency, our findings suggest that consumption of energy-dense, nutrient-poor foods occurred as early as 9 months of age, with some of these foods tracking highly over the weaning period. Intakes of healthier foods such as fruits, vegetables, dairy products, eggs, fish and water were also relatively stable over this transition from infancy to toddlerhood, along with moderate tracking for riboflavin, iodine, fibre, calcium and iron. Tracking was low but close to ρ=0.3 for zinc, magnesium and potassium intakes.

Conclusions: The tracking of energy-dense, nutrient-poor foods has important implications for public health, given the development of early eating behaviours is likely to be modifiable. At this stage of life, dietary intakes are largely influenced by the foods parents provide, parental feeding practices and modelling. This study supports the importance of promoting healthy dietary trajectories from infancy.

Quantification of tackling demands in professional Australian football using integrated wearable athlete tracking technology

Objectives
To describe and quantify the frequency, velocity and acceleration at impact during tackling in Australian football using a combination of video and athlete tracking technology.

Design
Quasi-experimental.

Methods
Data was collected from twenty professional Australian Football League players during four in-season matches. All tackles made by the player and those against the player were video-coded and time stamped at the point of contact and then subjectively categorised into low, medium and high intensity impact groups. Peak GPS and acceleration data were identified at the point of contact. Two-way analysis of variance was used to assess differences (p < 0.05) between tackle type (made and against) and tackle intensity.

Results
A total of 173 tackles made and 179 tackles against were recorded. Significant differences were found between all tackle intensity groups. Peak velocity was significantly greater in high (19.5 ± 6.1 km h−1) compared to medium (13.4 ± 5.8 km h−1) and low intensity (11.3 ± 5.0 km h−1) tackles. Peak Player Load™, a modified vector magnitude of tri-axial acceleration, was significantly greater in high (7.5 ± 1.7 a.u.) compared to medium (4.9 ± 1.5 a.u.) and low intensity (4.0 ± 1.3 a.u.) tackles.

Conclusions
High intensity tackles, although less frequent, are significantly greater in speed of movement immediately prior to contact and in the resultant impact acceleration compared to tackles of lower intensity. Differences in accelerometer data between tackles observed to be progressively greater in intensity suggest a level of ecological validity and provide preliminary support for the use of accelerometers to assess impact forces in contact invasion sports.

Satellite tracking reveals unusual diving characteristics for a marine reptile, the olive ridley turtle : Lepidochelys olivacea

The movements, diving behaviour and thermal environment occupied by 4 adult female olive ridley turtles Lepidochelys olivacea in northern Australia were determined through satellite telemetry. Patterns of behaviour recorded were rather unusual compared to other sea turtles in that dives were mainly deep, largely benthic and exceptionally long (>2 h) in some cases, characteristics typical of over-wintering turtles in colder environments. One individual occupied shallow coastal foraging zones, while the others foraged far from land (probably on the seabed) in relatively deep water (>100 m). Individuals performed long dives (frequently >100 min), but from the short post-dive intervals we suggest that these dives were mainly aerobic. Maximum dive depth recorded was 200 ± 20 m (mean maximum depths ranged from 20.1 to 46.7 m across individuals; n = 17328 dives in total; depths ≥3 m were considered ‘dives’) and the maximum duration was 200 ± 20 min (mean durations ranged from 24.5 to 48.0 min across individuals). Temperature profiles indicate that turtles experienced temperatures ranging from 23 to 29°C at the surface, with the lowest temperature recorded (18.7°C) at a depth of 98 m. Only 6.9% of the dives were in water <20°C. From time-allocation at depth (TAD) scores, we demonstrated that many dives reaching the known or inferred sea bottom were U-shaped, but there was no apparent diel signal in dive depth. This suggests that many benthic dives were not associated exclusively with resting behaviour and likely had a foraging component as well. The ability to perform long benthic dives allows this species to exploit deeper benthic environments in addition to the shallow coastal areas more generally occupied by adult hard-shelled sea turtles (e.g. green and hawksbill turtles). Deep benthic dives also occur in certain marine mammals (e.g. narwhals) and sea birds (e.g. rockhopper penguins) and therefore seem to be a general foraging strategy exploited by animals that can perform long dives.

Multiple-object tracking in cluttered and crowded public spaces

This paper addresses the problem of tracking moving objects of variable appearance in challenging scenes rich with features and texture. Reliable tracking is of pivotal importance in surveillance applications. It is made particularly difficult by the nature of objects encountered in such scenes: these too change in appearance and scale, and are often articulated (e.g. humans). We propose a method which uses fast motion detection and segmentation as a constraint for both building appearance models and their robust propagation (matching) in time. The appearance model is based on sets of local appearances automatically clustered using spatio-kinetic similarity, and is updated with each new appearance seen. This integration of all seen appearances of a tracked object makes it extremely resilient to errors caused by occlusion and the lack of permanence of due to low data quality, appearance change or background clutter. These theoretical strengths of our algorithm are empirically demonstrated on two hour long video footage of a busy city marketplace.

The good news about television: attitudes aren't getting worse: tracking public attitudes toward TV advertising

Periodically tracking public sentiment toward television advertising (TVA) is an important barometer for the advertising industry and its myriad stakeholders. To date, however, most studies of consumers’ attitudes to TVA have been cross-sectional. This study, alternatively, provides a quasi-longitudinal examination of Australian attitudes toward TVA across four time points (2002, 2005, 2008, and 2010). Findings suggest that although attitudes toward TVA are generally negative, in fact they have not deteriorated over time. Considerable scope consequently exists for improving consumer attitudes toward TVA.

Finite element model updating using estimation of distribution algorithm

Finite Element (FE) model updating has been attracting research attentions in structural engineering fields for over 20 years. Its immense importance to the design, construction and maintenance of civil and mechanical structures has been highly recognised. However, many sources of uncertainties may affect the updating results. These uncertainties may be caused by FE modelling errors, measurement noises, signal processing techniques, and so on. Therefore, research efforts on model updating have been focusing on tackling with uncertainties for a long time. Recently, a new type of evolutionary algorithms has been developed to address uncertainty problems, known as Estimation of Distribution Algorithms (EDAs). EDAs are evolutionary algorithms based on estimation and sampling from probabilistic models and able to overcome some of the drawbacks exhibited by traditional genetic algorithms (GAs). In this paper, a numerical steel simple beam is constructed in commercial software ANSYS. The various damage scenarios are simulated and EDAs are employed to identify damages via FE model updating process. The results show that the performances of EDAs for model updating are efficient and reliable.

Wireless sensor networks for heritage object deformation detection and tracking algorithm

Deformation is the direct cause of heritage object collapse. It is significant to monitor and signal the early warnings of the deformation of heritage objects. However, traditional heritage object monitoring methods only roughly monitor a simple-shaped heritage object as a whole, but cannot monitor complicated heritage objects, which may have a large number of surfaces inside and outside. Wireless sensor networks, comprising many small-sized, low-cost, low-power intelligent sensor nodes, are more useful to detect the deformation of every small part of the heritage objects. Wireless sensor networks need an effective mechanism to reduce both the communication costs and energy consumption in order to monitor the heritage objects in real time. In this paper, we provide an effective heritage object deformation detection and tracking method using wireless sensor networks (EffeHDDT). In EffeHDDT, we discover a connected core set of sensor nodes to reduce the communication cost for transmitting and collecting the data of the sensor networks. Particularly, we propose a heritage object boundary detecting and tracking mechanism. Both theoretical analysis and experimental results demonstrate that our EffeHDDT method outperforms the existing methods in terms of network traffic and the precision of the deformation detection.

Time-in-area represents foraging activity in a wide-ranging pelagic forager

Successful Marine Spatial Planning depends upon the identification of areas with high importance for particular species, ecosystems or processes. For seabirds, advancements in biologging devices have enabled us to identify these areas through the detailed study of at-sea behaviour. However, in many cases, only positional data are available and the presence of local biological productivity and hence seabird foraging behaviour is inferred from these data alone, under the untested assumption that foraging activity is more likely to occur in areas where seabirds spend more time. We fitted GPS devices and accelerometers to northern gannets Morus bassanus and categorised the behaviour of individuals outside the breeding colony as plunge diving, surface foraging, floating and flying. We then used the locations of foraging events to test the efficiency of 2 approaches: time-in-area and kernel density (KD) analyses, which are widely employed to detect highly-used areas and interpret foraging behaviour from positional data. For KD analyses, the smoothing parameter (h) was calculated using the ad hoc method (KDad hoc), and KDh=9.1, where h = 9.1 km, to designate core foraging areas from location data. A high proportion of foraging events occurred in core foraging areas designated using KDad hoc, KDh=9.1, and time-in-area. Our findings demonstrate that foraging activity occurs in areas where seabirds spend more time, and that both KD analysis and the time-in-area approach are equally efficient methods for this type of analysis. However, the time-in-area approach is advantageous in its simplicity, and in its ability to provide the shapes commonly used in planning. Therefore, the time-in-area approach can be used as a simple way of using seabirds to identify ecologically important locations from both tracking and survey data.

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.

Simulation and hardware implementation of new maximum power point tracking technique for partially shaded PV system using hybrid DEPSO method

In photovoltaic (PV) power generation, partial shading is an unavoidable complication that significantly reduces the efficiency of the overall system. Under this condition, the PV system produces a multiple-peak function in its output power characteristic. Thus, a reliable technique is required to track the global maximum power point (GMPP) within an appropriate time. This study aims to employ a hybrid evolutionary algorithm called the DEPSO technique, a combination of the differential evolutionary (DE) algorithm and particle swarm optimization (PSO), to detect the maximum power point under partial shading conditions. The paper starts with a brief description about the behavior of PV systems under partial shading conditions. Then, the DEPSO technique along with its implementation in maximum power point tracking (MPPT) is explained in detail. Finally, Simulation and experimental results are presented to verify the performance of the proposed technique under different partial shading conditions. Results prove the advantages of the proposed method, such as its reliability, system-independence, and accuracy in tracking the GMPP under partial shading conditions.

Classification of team sport activities using a single wearable tracking device

Wearable tracking devices incorporating accelerometers and gyroscopes are increasingly being used for activity analysis in sports. However, minimal research exists relating to their ability to classify common activities. The purpose of this study was to determine whether data obtained from a single wearable tracking device can be used to classify team sport-related activities. Seventy-six non-elite sporting participants were tested during a simulated team sport circuit (involving stationary, walking, jogging, running, changing direction, counter-movement jumping, jumping for distance and tackling activities) in a laboratory setting. A MinimaxX S4 wearable tracking device was worn below the neck, in-line and dorsal to the first to fifth thoracic vertebrae of the spine, with tri-axial accelerometer and gyroscope data collected at 100Hz. Multiple time domain, frequency domain and custom features were extracted from each sensor using 0.5, 1.0, and 1.5s movement capture durations. Features were further screened using a combination of ANOVA and Lasso methods. Relevant features were used to classify the eight activities performed using the Random Forest (RF), Support Vector Machine (SVM) and Logistic Model Tree (LMT) algorithms. The LMT (79-92% classification accuracy) outperformed RF (32-43%) and SVM algorithms (27-40%), obtaining strongest performance using the full model (accelerometer and gyroscope inputs). Processing time can be reduced through feature selection methods (range 1.5-30.2%), however a trade-off exists between classification accuracy and processing time. Movement capture duration also had little impact on classification accuracy or processing time. In sporting scenarios where wearable tracking devices are employed, it is both possible and feasible to accurately classify team sport-related activities.

Hierarchical dirichlet process for tracking complex topical structure evolution and its application to autism research literature

In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension. In contrast to previous work our model does not impose a prior on the rate at which documents are added to the corpus nor does it adopt the Markovian assumption which overly restricts the type of changes that the model can capture. Our key technical contribution is a framework based on (i) discretization of time into epochs, (ii) epoch-wise topic discovery using a hierarchical Dirichlet process-based model, and (iii) a temporal similarity graph which allows for the modelling of complex topic changes: emergence and disappearance, evolution, splitting and merging. The power of the proposed framework is demonstrated on the medical literature corpus concerned with the autism spectrum disorder (ASD) - an increasingly important research subject of significant social and healthcare importance. In addition to the collected ASD literature corpus which we made freely available, our contributions also include two free online tools we built as aids to ASD researchers. These can be used for semantically meaningful navigation and searching, as well as knowledge discovery from this large and rapidly growing corpus of literature.

Dynamical analysis of neural networks with time-varying delays using the LMI approach

This study is concerned with the delay-range-dependent stability analysis for neural networks with time-varying delay and Markovian jumping parameters. The time-varying delay is assumed to lie in an interval of lower and upper bounds. The Markovian jumping parameters are introduced in delayed neural networks, which are modeled in a continuous-time along with finite-state Markov chain. Moreover, the sufficient condition is derived in terms of linear matrix inequalities based on appropriate Lyapunov-Krasovskii functionals and stochastic stability theory, which guarantees the globally asymptotic stable condition in the mean square. Finally, a numerical example is provided to validate the effectiveness of the proposed conditions.

Metabolic power method underestimates energy expenditure in field sport movements using a GPS tracking system

The purpose of this study was to assess the validity of a GPS tracking system to estimate energy expenditure (EE) during exercise and field sport locomotor movements. Twenty-seven participants each completed one 90 minute exercise session on an outdoor synthetic futsal pitch. During the exercise session participants wore a 5 Hz GPS unit interpolated to 15 Hz (SPI HPU, GPSports Pty Ltd, Australia) and a portable gas analyser (Metamax® 3B, Cortex Pty Ltd, Germany) which acted as the criterion measure of EE. The exercise session was comprised of alternating five minute exercise bouts of randomised walking, jogging, running or a field sport circuit (x3) followed by 10 minutes of recovery. One-way ANOVA showed significant (p<0.01) and very large underestimations between GPS metabolic power derived EE and VO2 derived EE for all field sport circuits (% difference ≈ -44%). No differences in EE were observed for the jog (7.8%) and run (4.8%) while very large overestimations were found for the walk (43.0%). The GPS metabolic power EE over the entire 90 minute session was significantly lower (p<0.01) than the VO2 EE, resulting in a moderate underestimation overall (-19%). The results of this study suggest that a GPS tracking system using the metabolic power model of EE does not accurately estimate EE in field sport movements or over an exercise session consisting of mixed locomotor activities interspersed with recovery periods; however is able to provide a reasonably accurate estimation of EE during continuous jogging and running.

Indexing stock portfolios in Brazil: tracking the IBOVESPA and the FGV 100

Indexing is a passive investment strategy in which the investor weights bis portfolio to match the performance of a broad-based indexo Since severaI studies showed that indexed portfolios have consistently outperformed active management strategies over the last decades, an increasing number of investors has become interested in indexing portfolios IateIy. Brazilian financiaI institutions do not offer indexed portfolios to their clients at this point in time. In this work we propose the use of indexed portfolios to track the performance oftwo ofthe most important Brazilian stock indexes: the mOVESPA and the FGVIOO. We test the tracking performance of our modeI by a historical simulation. We applied several statistical tests to the data to verify how many stocks should be used to controI the portfolio tracking error within user specified bounds.