Adolescents’ engagement with social media

Social media plays an increasingly important role in the daily lives of adolescents. Yet evidence of its effects are mixed, and the field lacks underlying theory to guide more nuanced research. This study explored the psychosocial processes underpinning adolescent engagement with social media. Adolescents (n = 28) were interviewed regarding their experiences of social media, and interview transcripts were analysed using grounded theory methodology. The emergent theory describes a cyclical process of evaluating the risks vs rewards of social media use, experimenting, learning from experiences, and re-calibrating one’s stance towards social media. Two styles of use, active and passive, became apparent, each maintained and defended by numerous strategies employed consciously and unconsciously, with the overarching goal of maintaining a sense of safety regarding their sense of self and status within their social hierarchy. This study depicts a complex, nuanced picture of adolescent engagement with SM, one that encompasses both positive and negative experiences. The model points to the importance of identity and social identity theories, and raises important questions about identity development in this evolving context

Temporal Bayesian classifiers for modelling muscular dystrophy expression data

The analysis of microarray data from time-series experiments requires specialised algorithms, which take the temporal ordering of the data into account. In this paper we explore a new architecture of Bayesian classifier that can be used to understand how biological mechanisms differ with respect to time. We show that this classifier improves the classification of microarray data and at the same time ensures that the models can easily be analysed by biologists by incorporating time transparently. In this paper we focus on data that has been generated to explore different types of muscular dystrophy

Design and testing of a contra-rotating tidal current turbine

A contra-rotating marine current turbine has a number of attractive features: nearzero reactive torque on the support structure, near-zero swirl in the wake, and high relative inter-rotor rotational speeds. Modified blade element modelling theory has been used to design and predict the characteristics of such a turbine, and a model turbine and test rig have been constructed. Tests in a towing tank demonstrated the feasibility of the concept. Power coefficients were high for such a small model and in excellent agreement with predictions, confirming the accuracy of the computational modelling procedures. High-frequency blade loading data were obtained in the course of the experiments. These show the anticipated dynamic components for a contra-rotating machine. Flow visualization of the wake verified the lack of swirl behind the turbine. A larger machine is presently under construction for sea trials

Development of a contra-rotating tidal current turbine and analysis of performance

A contra-rotating marine current turbine has a number of attractive features: nearzero reactive torque on the support structure, near-zero swirl in the wake, and high relative inter-rotor rotational speeds. Modified blade element modelling theory has been used to design and predict the characteristics of such a turbine, and a model turbine and test rig have been constructed. Tests in a towing tank demonstrated the feasibility of the concept. Power coefficients were high for such a small model and in excellent agreement with predictions, confirming the accuracy of the computational modelling procedures. Highfrequency blade loading data were obtained in the course of the experiments. These show the anticipated dynamic components for a contra-rotating machine. Flow visualization of the wake verified the lack of swirl behind the turbine. A larger machine is presently under construction for sea trials

A method for Bayesian regression modelling of composition data

Many scientific and industrial processes produce data that is best analysed as vectors of relative values, often called compositions or proportions. The Dirichlet distribution is a natural distribution to use for composition or proportion data. It has the advantage of a low number of parameters, making it the parsimonious choice in many cases. In this paper we consider the case where the outcome of a process is Dirichlet, dependent on one or more explanatory variables in a regression setting. We explore some existing approaches to this problem, and then introduce a new simulation approach to fitting such models, based on the Bayesian framework. We illustrate the advantages of the new approach through simulated examples and an application in sport science. These advantages include: increased accuracy of fit, increased power for inference, and the ability to introduce random effects without additional complexity in the analysis.Comment: 10 pages, 1 figure, 2 table

Bayesian P-Splines to investigate the impact of covariates on Multiple Sclerosis clinical course

This paper aims at proposing suitable statistical tools to address heterogeneity in repeated measures, within a Multiple Sclerosis (MS) longitudinal study. Indeed, due to unobservable sources of heterogeneity, modelling the effect of covariates on MS severity evolves as a very difficult feature. Bayesian P-Splines are suggested for modelling non linear smooth effects of covariates within generalized additive models. Thus, based on a pooled MS data set, we show how extending Bayesian P-splines to mixed effects models (Lang and Brezger, 2001), represents an attractive statistical approach to investigate the role of prognostic factors in affecting individual change in disability

Non-Gaussian dynamic Bayesian modelling for panel data

A first order autoregressive non-Gaussian model for analysing panel data is proposed. The main feature is that the model is able to accommodate fat tails and also skewness, thus allowing for outliers and asymmetries. The modelling approach is to gain sufficient flexibility, without sacrificing interpretability and computational ease. The model incorporates individual effects and we pay specific attention to the elicitation of the prior. As the prior structure chosen is not proper, we derive conditions for the existence of the posterior. By considering a model with individual dynamic parameters we are also able to formally test whether the dynamic behaviour is common to all units in the panel. The methodology is illustrated with two applications involving earnings data and one on growth of countries.autoregressive modelling; growth convergence; individual effects; labour earnings; prior elicitation; posterior existence; skewed distributions

Bayesian model comparison for compartmental models with applications in positron emission tomography

We develop strategies for Bayesian modelling as well as model comparison, averaging and selection for compartmental models with particular emphasis on those that occur in the analysis of positron emission tomography (PET) data. Both modelling and computational issues are considered. Biophysically inspired informative priors are developed for the problem at hand, and by comparison with default vague priors it is shown that the proposed modelling is not overly sensitive to prior specification. It is also shown that an additive normal error structure does not describe measured PET data well, despite being very widely used, and that within a simple Bayesian framework simultaneous parameter estimation and model comparison can be performed with a more general noise model. The proposed approach is compared with standard techniques using both simulated and real data. In addition to good, robust estimation performance, the proposed technique provides, automatically, a characterisation of the uncertainty in the resulting estimates which can be considerable in applications such as PET