The Effect of Psychiatric Rehabilitation on the Activity and Participation Level of Clients with Long-Term Psychiatric Disabilities

During the last decades of the 20th century, many psychiatric hospitals changed the living environments of their clients with long-term psychiatric disabilities. We investigated the effect of this environmental psychiatric rehabilitation and normalization process on the activity and participation level of such clients residing in one Dutch psychiatric hospital. The seven years of panel research demonstrated that more normal living environments have a positive effect on clients’ activity and participation level. This is controlled for the fact that younger clients, and clients with a relative high activity and participation level were selected for these normal living environments.

Fixing the Pole in the Pyramid

We revisit the problem of the hidden sector Landau pole in the Pyramid Scheme. There is a fixed line in the plane of hidden sector gauge coupling and a Yukawa coupling between the trianon fields. We postulate that the couplings flow to this line, at a point where the hidden sector gauge coupling is close to the strong coupling edge of its perturbative regime. Below the masses of the heavier trianons, the model quickly flows to a confining N_F=N_C=3 supersymmetric gauge theory, as required by phenomenological considerations. We study possible discrete R-symmetries, which guarantee, among other things, that the basin of attraction of the fixed line has full co-dimension in the space of R-allowed couplings. The Yukawa couplings required to get the fixed line violate the pyrma-baryon symmetries we invoked in previous work to find a dark matter candidate. Omitting one of them, we have a dark matter candidate, and an acceptable RG flow down from the unification scale, if the confinement scale of the hidden sector group is lowered from 5 to 2 TeV.Comment: 14 pages, 3 table

An adaptive sampling method for global sensitivity analysis based on least-squares support vector regression

In the field of engineering, surrogate models are commonly used for approximating the behavior of a physical phenomenon in order to reduce the computational costs. Generally, a surrogate model is created based on a set of training data, where a typical method for the statistical design is the Latin hypercube sampling (LHS). Even though a space filling distribution of the training data is reached, the sampling process takes no information on the underlying behavior of the physical phenomenon into account and new data cannot be sampled in the same distribution if the approximation quality is not sufficient. Therefore, in this study we present a novel adaptive sampling method based on a specific surrogate model, the least-squares support vector regresson. The adaptive sampling method generates training data based on the uncertainty in local prognosis capabilities of the surrogate model - areas of higher uncertainty require more sample data. The approach offers a cost efficient calculation due to the properties of the least-squares support vector regression. The opportunities of the adaptive sampling method are proven in comparison with the LHS on different analytical examples. Furthermore, the adaptive sampling method is applied to the calculation of global sensitivity values according to Sobol, where it shows faster convergence than the LHS method. With the applications in this paper it is shown that the presented adaptive sampling method improves the estimation of global sensitivity values, hence reducing the overall computational costs visibly

Ontology-assisted database integration to support natural language processing and biomedical data-mining

Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for the purposes of making computers understand medical natural language

Transmission of near-resonant light through a dense slab of cold atoms

The optical properties of randomly positioned, resonant scatterers is a fundamentally difficult problem to address across a wide range of densities and geometries. We investigate it experimentally using a dense cloud of rubidium atoms probed with near-resonant light. The atoms are confined in a slab geometry with a sub-wavelength thickness. We probe the optical response of the cloud as its density and hence the strength of the light-induced dipole-dipole interactions are increased. We also describe a theoretical study based on a coupled dipole simulation which is further complemented by a perturbative approach. This model reproduces qualitatively the experimental observation of a saturation of the optical depth, a broadening of the transition and a blue shift of the resonance