Adsorption of CO on a Platinum (111) surface - a study within a four-component relativistic density functional approach

We report on results of a theoretical study of the adsorption process of a single carbon oxide molecule on a Platinum (111) surface. A four-component relativistic density functional method was applied to account for a proper description of the strong relativistic effects. A limited number of atoms in the framework of a cluster approach is used to describe the surface. Different adsorption sites are investigated. We found that CO is preferably adsorbed at the top position.Comment: 23 Pages with 4 figure

Patients' experiences and perceived causes of persisting discomfort following day surgery

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to describe patients' experiences and perceived causes of persisting discomfort following day surgery. Earlier research has mainly covered symptoms and signs during a recovery period of up to one month, and not dealt with patients' perceptions of what causes persisting, longer-term discomfort.</p> <p>Methods</p> <p>This study is a part from a study carried out during the period May 2006 to May 2007 with a total of 298 day surgery patients. Answers were completed by 118 patients at 48 hours, 110 at seven days and 46 at three months to one open-ended question related to discomfort after day surgery constructed as follows: <it>If you are still experiencing discomfort related to the surgery, what is the reason, in your opinion</it>? Data was processed, quantitatively and qualitatively. Descriptive, inferential, correlation and content analyses were performed.</p> <p>Results</p> <p>The results suggest that patients suffer from remaining discomfort e.g. pain and wound problem, with effects on daily life following day surgery up to three months. Among patients' perceptions of <it>factors leading to discomfort </it>may be <it>wrongful or suboptimal treatment</it>, <it>type of surgery </it>or <it>insufficient access to provider/information.</it></p> <p>Conclusions</p> <p>The results have important implications for preventing and managing discomfort at home following day surgery, and for nursing interventions to help patients handle the recovery period better.</p

Hand use predicts the structure of representations in sensorimotor cortex.

Fine finger movements are controlled by the population activity of neurons in the hand area of primary motor cortex. Experiments using microstimulation and single-neuron electrophysiology suggest that this area represents coordinated multi-joint, rather than single-finger movements. However, the principle by which these representations are organized remains unclear. We analyzed activity patterns during individuated finger movements using functional magnetic resonance imaging (fMRI). Although the spatial layout of finger-specific activity patterns was variable across participants, the relative similarity between any pair of activity patterns was well preserved. This invariant organization was better explained by the correlation structure of everyday hand movements than by correlated muscle activity. This also generalized to an experiment using complex multi-finger movements. Finally, the organizational structure correlated with patterns of involuntary co-contracted finger movements for high-force presses. Together, our results suggest that hand use shapes the relative arrangement of finger-specific activity patterns in sensory-motor cortex

Viewpoints of pedestrians with and without cognitive impairment on shared zones and zebra crossings

Background: Shared zones are characterised by an absence of traditional markers that segregate the road and footpath. Negotiation of a shared zone relies on an individual’s ability to perceive, assess and respond to environmental cues. This ability may be impacted by impairments in cognitive processing, which may lead to individuals experiencing increased anxiety when negotiating a shared zone. Method: Q method was used in order to identify and explore the viewpoints of pedestrians, with and without cognitive impairments as they pertain to shared zones. Results: Two viewpoints were revealed. Viewpoint one was defined by “confident users” while viewpoint two was defined by users who “know what [they] are doing but drivers might not”. Discussion: Overall, participants in the study would not avoid shared zones. Pedestrians with intellectual disability were, however, not well represented by either viewpoint, suggesting that shared zones may pose a potential barrier to participation for this group

DICE: Exploiting All Bivariate Dependencies in Binary and Multary Search Spaces

Although some of the earliest Estimation of Distribution Algorithms (EDAs) utilized bivariate marginal distribution models, up to now, all discrete bivariate EDAs had one serious limitation: they were constrained to exploiting only a limited O(d) subset out of all possible O(d2) bivariate dependencies. As a first we present a family of discrete bivariate EDAs that can learn and exploit all O(d2) dependencies between variables, and yet have the same run-time complexity as their more limited counterparts. This family of algorithms, which we label DICE (DIscrete Correlated Estimation of distribution algorithms), is rigorously based on sound statistical principles, and particularly on a modelling technique from statistical physics: dichotomised multivariate Gaussian distributions. Initially (Lane et al. in European Conference on the Applications of Evolutionary Computation, Springer, 1999), DICE was trialled on a suite of combinatorial optimization problems over binary search spaces. Our proposed dichotomised Gaussian (DG) model in DICE significantly outperformed existing discrete bivariate EDAs; crucially, the performance gap increasingly widened as dimensionality of the problems increased. In this comprehensive treatment, we generalise DICE by successfully extending it to multary search spaces that also allow for categorical variables. Because correlation is not wholly meaningful for categorical variables, interactions between such variables cannot be fully modelled by correlation-based approaches such as in the original formulation of DICE. Therefore, here we extend our original DG model to deal with such situations. We test DICE on a challenging test suite of combinatorial optimization problems, which are defined mostly on multary search spaces. While the two versions of DICE outperform each other on different problem instances, they both outperform all the state-of-the-art bivariate EDAs on almost all of the problem instances. This further illustrates that these innovative DICE methods constitute a significant step change in the domain of discrete bivariate EDAs