Direction of the association between body fatness and self-reported screen time in Dutch adolescents

<p>Abstract</p> <p>Background</p> <p>Screen time has been associated with pediatric overweight. However, it is unclear whether overweight predicts or is predicted by excessive amounts of screen time. The aim of this study was to examine the direction of the association between screen time and body fatness in Dutch adolescents.</p> <p>Methods</p> <p>Longitudinal data of 465 Dutch adolescents (mean age at baseline 13 years, 53% boys) was used. Body fatness (objectively measured BMI, four skin folds and waist- and hip circumference), self-reported time spent watching TV and computer use, and aerobic fitness (shuttle run test) were assessed in all participants at three time points during 12 months. Multi-level linear autoregressive analyses was used to examine whether screen time predicted body fatness in the following time period and whether body fatness predicted screen time. Analyses were performed for boys and girls separately and adjusted for ethnicity and aerobic fitness.</p> <p>Results</p> <p>Time spent TV viewing did predict changes in BMI and hip circumference in boys, but not in girls, in the subsequent period. Computer time significantly predicted increases in skinfolds in boys and girls and increases in BMI in girls. Body fatness did not predict any changes in screen time.</p> <p>Conclusion</p> <p>The present study only partly supports the widely posited hypothesis that higher levels of screen time cause increases in body fatness. In addition, this study demonstrates that high levels of body fatness do not predict increases in screen time.</p

Wavepacket Dynamics in Yang-Mills Theory

We discuss the results of numerical simulations of colliding wavepackets in $SU(2)$ Yang--Mills theory. We investigate their behavior as a function of amplitude and momentum distribution. We find regions in our parameter space in which initial wave packets scatter into final configurations with dramatically different momentum distributions. These results constitute new classical trajectories with multiparticle boundary conditions. We explain their relevance for the calculation of scattering amplitudes in the semiclassical approximation. Finally, we give directions for future work.Comment: 11 pgs. text, 11 optional figs using PiCTeX and epsf, new version contains improved discussion of scaling properties of results and one additional figure

Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records

Background: Electronic health records (EHRs) provide enormous potential for health research but also present data governance challenges. Ensuring de-identification is a pre-requisite for use of EHR data without prior consent. The South London and Maudsley NHS Trust (SLaM), one of the largest secondary mental healthcare providers in Europe, has developed, from its EHRs, a de-identified psychiatric case register, the Clinical Record Interactive Search (CRIS), for secondary research. Methods: We describe development, implementation and evaluation of a bespoke de-identification algorithm used to create the register. It is designed to create dictionaries using patient identifiers (PIs) entered into dedicated source fields and then identify, match and mask them (with ZZZZZ) when they appear in medical texts. We deemed this approach would be effective, given high coverage of PI in the dedicated fields and the effectiveness of the masking combined with elements of a security model. We conducted two separate performance tests i) to test performance of the algorithm in masking individual true PIs entered in dedicated fields and then found in text (using 500 patient notes) and ii) to compare the performance of the CRIS pattern matching algorithm with a machine learning algorithm, called the MITRE Identification Scrubber Toolkit – MIST (using 70 patient notes – 50 notes to train, 20 notes to test on). We also report any incidences of potential breaches, defined by occurrences of 3 or more true or apparent PIs in the same patient’s notes (and in an additional set of longitudinal notes for 50 patients); and we consider the possibility of inferring information despite de-identification. Results: True PIs were masked with 98.8% precision and 97.6% recall. As anticipated, potential PIs did appear, owing to misspellings entered within the EHRs. We found one potential breach. In a separate performance test, with a different set of notes, CRIS yielded 100% precision and 88.5% recall, while MIST yielded a 95.1% and 78.1%, respectively. We discuss how we overcome the realistic possibility – albeit of low probability – of potential breaches through implementation of the security model. Conclusion: CRIS is a de-identified psychiatric database sourced from EHRs, which protects patient anonymity and maximises data available for research. CRIS demonstrates the advantage of combining an effective de-identification algorithm with a carefully designed security model. The paper advances much needed discussion of EHR de-identification – particularly in relation to criteria to assess de-identification, and considering the contexts of de-identified research databases when assessing the risk of breaches of confidential patient information

How control systems influence product innovation processes: examining the role of entrepreneurial orientation

This paper yields insights into the channels through which Management Accounting and Control Systems (MACS) exert an influence on product innovation by examining the extent to which different forms of control (i.e. value systems, diagnostic control systems, interactive control systems) are directly associated with the distinct phases of innovation processes. Using survey data collected from 118 medium and large Spanish companies, we find that: (1) value systems and interactive control systems have significant main effects on the creativity, coordination and knowledge integration, and filtering (sub-)phases of innovation processes; and (2) the significance and direction of these influences vary depending on the Entrepreneurial Orientation (EO) of firms. By highlighting the relevance of EO in shaping the influence of MACS on product innovation processes, this study calls for caution in generalising the expected effects of MACS on innovation

Multilayered feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India

In the present research, possibility of predicting average summer-monsoon rainfall over India has been analyzed through Artificial Neural Network models. In formulating the Artificial Neural Network based predictive model, three layered networks have been constructed with sigmoid non-linearity. The models under study are different in the number of hidden neurons. After a thorough training and test procedure, neural net with three nodes in the hidden layer is found to be the best predictive model.Comment: 19 pages, 1 table, 3 figure

Bose-Einstein condensation in multilayers

The critical BEC temperature $T_{c}$ of a non interacting boson gas in a layered structure like those of cuprate superconductors is shown to have a minimum $T_{c,m}$, at a characteristic separation between planes $a_{m}$. It is shown that for $a<a_{m}$, $T_{c}$ increases monotonically back up to the ideal Bose gas $T_{0}$ suggesting that a reduction in the separation between planes, as happens when one increases the pressure in a cuprate, leads to an increase in the critical temperature. For finite plane separation and penetrability the specific heat as a function of temperature shows two novel crests connected by a ridge in addition to the well-known BEC peak at $T_{c}$ associated with the 3D behavior of the gas. For completely impenetrable planes the model reduces to many disconnected infinite slabs for which just one hump survives becoming a peak only when the slab widths are infinite.Comment: Four pages, four figure

Tradable Credits Scheme on Urban Travel Demand: A Linear Expenditure System Approach and Simulation in Beijing

Using a linear expenditure system (LES) approach, we investigate the influences of a new mobility management measure, a tradable credits scheme (TCS), on the pattern of daily trips measured in kilometres. Generally, we assume that an individuals’ travel consists of a car mode and a non-car mode. The effects of the TCS are discussed from a microeconomic perspective and using a scenario simulation study for the municipality of Beijing. Whilst other research has shown that travellers trade their credits and are generally inclined to non-car mode, the implementation of the tradable credits scheme demonstrated here is that travellers are likely to restrain their use of both car and non-car travel modes. Furthermore, both car and non-car mode trips are shown to be price inelastic, whilst the cross-price elasticity for different districts demonstrates a complementary relationship between car and bus modes