A formal consensus process integrating evidence and expert opinion based on the ICF framework and classification led to the definition of ICF Core Sets for chronic widespread pain. Both the Comprehensive ICF Core Set and the Brief ICF Core Set were defined.

We study the time-dependent cross-correlations of stock returns, i.e., we measure the correlation as the function of the time shift between pairs of stock return time series using tick-by-tick data. We find a weak but significant effect showing that in many cases the maximum correlation appears at nonzero time shift, indicating directions of influence between the companies. Due to the weakness of this effect and the shortness of the characteristic time (of the order of a few minutes), our findings are compatible with market efficiency. The interaction of companies defines a directed network of influence.

The clustering of companies within a specific stock market index is studied by means of superparamagnetic transitions of an appropriate q-state Potts model where the spins correspond to companies and the interactions are functions of the correlation coefficients determined from the time dependence of the companies' individual stock prices. The method is a generalization of the clustering algorithm by Domany et. al. to the case of anti-ferromagnetic interactions corresponding to anti-correlations. For the Dow Jones Industrial Average where no anti-correlations were observed in the investigated time period, the previous results obtained by different tools were well reproduced. For the Standard & Poor's 500, where anti-correlations occur, repulsion between stocks modify the cluster structure.Stock market indices, like the Dow Jones (DJ) or Standard & Poor's 500 (S&P 500) are used as indicators of the status of the markets. They are averaged values of a different number of selected companies indicative of the economy of a given market. It is of both theoretical and practical importance to analyze how individual contributions to the average behave. The customary approach in the financial literature focuses on the investigation of the properties of the covariance matrix. Here we take a different approach aiming to identify the presence of a hierarchical structure inside the set of stocks simultaneously traded in a market. The identification of the hierarchy of clusters is of central importance both from the point of view of understanding the dynamics of the stock index and for portfolio optimization [1,2]. As far as we know this question was first analyzed by Mantegna by means of the minimal spanning tree method [3][4][5], see also [6]. Here we analyze the problem of clustering of companies in the S&P 500 and the DJ indices by a different method based on the q-state Potts model which turns out to be particularly suitable to handle anti-correlations.The idea to use the super-paramagnetic (SPM) ordering of a q-state Potts model for cluster identification is due to Domany et. al [7][8][9]. They start from a set of points which lie in a metric space where the mutual distances of the points are known. By introducing a distance dependent ferromagnetic (FM) interaction between Potts spins assigned to the points at appropriately chosen temperatures the close points within a cluster feel strong interaction and align while far clusters point into different "Potts directions". The functional dependence of the interaction on the distance should be chosen in an appropriate way. For a given interaction the possible hierarchic clustering shows up in a series of SPM transitions.We have generalized this method by dropping the condition of the metric and allowing negative (antiferromagnetic, AFM) couplings. The coupling between the pair of Potts spins (i.e. companies) is in our case the explicit function of the correlation coefficient and it is FM for positive correlations and AFM for anti-correlations (the latter are present in the S&P...

Teamwork is the cornerstone of rehabilitation medicine. Rehabilitation workers in European countries are well educated in their own disciplines and attain appropriate professional knowledge; however, they lack educational opportunities for acquiring skills and attitudes necessary for effective teamwork, mainly communication, cooperation, and leadership. Consequently, teamwork is compromised and rehabilitation effectiveness reduced. Therefore, training in these components of professional competence needs scaling up in order to increase their impact on rehabilitation care.