Resonant Raman characterisation of ultra-thin nano-protective carbon layers for magnetic storage devices

Carbon thin films are very important as protective coatings for a wide range of applications such as magnetic storage devices. The key parameter of interest is the sp3 fraction, since it controls the mechanical properties of the film. Visible Raman spectroscopy is a very popular technique to determine the carbon bonding. However, the visible Raman spectra mainly depend on the configuration and clustering of the sp2 sites. This can result in the Raman spectra of different samples looking similar albeit having a different structure. Thus, visible Raman alone cannot be used to derive the sp3 content. Here we monitor the carbon bonding by using a combined study of Raman spectra taken at two wavelengths (514 and 244 nm). We show how the G peak dispersion is a very useful parameter to investigate the carbon samples and we endorse it as a production-line characterisation tool. The dispersion is proportional to the degree of disorder, thus making it possible to distinguish between graphitic and diamond-like carbon. © 2003 Elsevier B.V. All rights reserved.

Structure learning in a sensorimotor association task.

Learning is often understood as an organism's gradual acquisition of the association between a given sensory stimulus and the correct motor response. Mathematically, this corresponds to regressing a mapping between the set of observations and the set of actions. Recently, however, it has been shown both in cognitive and motor neuroscience that humans are not only able to learn particular stimulus-response mappings, but are also able to extract abstract structural invariants that facilitate generalization to novel tasks. Here we show how such structure learning can enhance facilitation in a sensorimotor association task performed by human subjects. Using regression and reinforcement learning models we show that the observed facilitation cannot be explained by these basic models of learning stimulus-response associations. We show, however, that the observed data can be explained by a hierarchical Bayesian model that performs structure learning. In line with previous results from cognitive tasks, this suggests that hierarchical Bayesian inference might provide a common framework to explain both the learning of specific stimulus-response associations and the learning of abstract structures that are shared by different task environments.

Derivation of a vector model for a Brushless Doubly-Fed Machine with multiple loops per nest

The paper presents a vector model for a Brushless Doubly-Fed Machine (BDFM). The BDFM has 4 and 8 pole stator windings and a nested-loop rotor cage. The rotor cage has six nests equally spaced around the circumference and each nest comprises three loops. All the rotor loops are short circuited via a common end-ring at one end. The vector model is derived based on the electrical equations of the machine and appropriate vector transformations. In contrast to the stator, there is no three phase circuit in the rotor. Therefore, the vector transformations suitable for three phase circuits can not be utilised for the rotor circuit. A new vector transformation is employed for the rotor circuit quantities. The approach presented in this paper can be extended for a BDFM with any stator poles combination and any number of loops per nest. Simulation results from the model implemented in Simulink are presented. © 2008 IEEE.

Physicians in leadership: the association between medical director involvement and staff-to-patient ratios

In a hospital environment that demands a careful balance between commercial and clinical interests, the extent to which physicians are involved in hospital leadership varies greatly. This paper assesses the influence of the extent of this involvement on staff-to-patient ratios. Using data gathered from 604 hospitals across Germany, this study evidences the positive relationship between a full-time medical director (MD) or heavily involved part-time MD and a higher staff-to-patient ratio. The data allows us to control for a range of confounding variables, such as size, rural/urban location, ownership structure, and case-mix. The results contribute to the sparse body of empirical research on the effect of clinical leadership on organizational outcomes.