Care staff intentions to support adults with an intellectual disability to engage in physical activity: An application of the Theory of Planned Behaviour

Researchers suggest that people with an intellectual disability (ID) undertake less physical activity than the general population and many rely, to some extent, on others to help them to access activities. The Theory of Planned Behaviour (TPB) model was previously found to significantly predict the intention of care staff to facilitate a healthy diet in those they supported. The present study examined whether the TPB was useful in predicting the intentions of 78 Scottish care staff to support people with ID to engage in physical activity. Regression analyses indicated that perceived behavioural control was the most significant predictor of both care staff intention to facilitate physical activity and reported physical activity levels of the people they supported. Attitudes significantly predicted care staff intention to support physical activity, but this intention was not itself significantly predictive of reported activity levels. Increasing carers' sense of control over their ability to support clients' physical activity may be more effective in increasing physical activity than changing their attitudes towards promoting activit

Polchinski equation, reparameterization invariance and the derivative expansion

The connection between the anomalous dimension and some invariance properties of the fixed point actions within exact RG is explored. As an application, Polchinski equation at next-to-leading order in the derivative expansion is studied. For the Wilson fixed point of the one-component scalar theory in three dimensions we obtain the critical exponents \eta=0.042, \nu=0.622 and \omega=0.754.Comment: 28 pages, LaTeX with psfig, 12 encapsulated PostScript figures. A number wrongly quoted in the abstract correcte

Cascade Dynamics of Multiplex Propagation

Random links between otherwise distant nodes can greatly facilitate the propagation of disease or information, provided contagion can be transmitted by a single active node. However we show that when the propagation requires simultaneous exposure to multiple sources of activation, called multiplex propagation, the effect of random links is just the opposite: it makes the propagation more difficult to achieve. We calculate analytical and numerically critical points for a threshold model in several classes of complex networks, including an empirical social network.Comment: 4 pages, 5 figures, for similar work visit http://hsd.soc.cornell.edu and http://www.imedea.uib.es/physdep

Digital Painting Course Prepares Students for Pre-Medical Illustration

Anna Morris is an undergraduate student in the School of Biological Sciences at Louisiana Tech University. Jamie Newman is an Associate Professor in the School of Biological Sciences at Louisiana Tech University. Nicholas Bustamante is an Associate Professor in the School of Design at Louisiana Tech University

A spatial model for social networks

We study spatial embeddings of random graphs in which nodes are randomly distributed in geographical space. We let the edge probability between any two nodes to be dependent on the spatial distance between them and demonstrate that this model captures many generic properties of social networks, including the ``small-world'' properties, skewed degree distribution, and most distinctively the existence of community structures.Comment: To be published in Physica A (2005

Line graphs as social networks

The line graphs are clustered and assortative. They share these topological features with some social networks. We argue that this similarity reveals the cliquey character of the social networks. In the model proposed here, a social network is the line graph of an initial network of families, communities, interest groups, school classes and small companies. These groups play the role of nodes, and individuals are represented by links between these nodes. The picture is supported by the data on the LiveJournal network of about 8 x 10^6 people. In particular, sharp maxima of the observed data of the degree dependence of the clustering coefficient C(k) are associated with cliques in the social network.Comment: 11 pages, 4 figure

Epsilon Expansion for Multicritical Fixed Points and Exact Renormalisation Group Equations

The Polchinski version of the exact renormalisation group equations is applied to multicritical fixed points, which are present for dimensions between two and four, for scalar theories using both the local potential approximation and its extension, the derivative expansion. The results are compared with the epsilon expansion by showing that the non linear differential equations may be linearised at each multicritical point and the epsilon expansion treated as a perturbative expansion. The results for critical exponents are compared with corresponding epsilon expansion results from standard perturbation theory. The results provide a test for the validity of the local potential approximation and also the derivative expansion. An alternative truncation of the exact RG equation leads to equations which are similar to those found in the derivative expansion but which gives correct results for critical exponents to order $\epsilon$ and also for the field anomalous dimension to order $\epsilon^2$. An exact marginal operator for the full RG equations is also constructed.Comment: 40 pages, 12 figures version2: small corrections, extra references, final appendix rewritten, version3: some corrections to perturbative calculation

Exact Renormalization Group Equations. An Introductory Review

We critically review the use of the exact renormalization group equations (ERGE) in the framework of the scalar theory. We lay emphasis on the existence of different versions of the ERGE and on an approximation method to solve it: the derivative expansion. The leading order of this expansion appears as an excellent textbook example to underline the nonperturbative features of the Wilson renormalization group theory. We limit ourselves to the consideration of the scalar field (this is why it is an introductory review) but the reader will find (at the end of the review) a set of references to existing studies on more complex systems.Comment: Final version to appear in Phys. Rep.; Many references added, section 4.2 added, minor corrections. 65 pages, 6 fig

Assortative mixing in networks

A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. We define a measure of assortative mixing for networks and use it to show that social networks are often assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortative network, which we study both analytically and numerically. Within the framework of this model we find that assortative networks tend to percolate more easily than their disassortative counterparts and that they are also more robust to vertex removal.Comment: 5 pages, 1 table, 1 figur