Wichita State University

About: Wichita State University is a education organization based out in Wichita, Kansas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 4988 authors who have published 9563 publications receiving 253824 citations. The organization is also known as: WSU & Fairmount College.

Depression is not more common in rheumatoid arthritis: a 10-year longitudinal study of 6,153 patients with rheumatic disease.

Abstract: OBJECTIVE To determine if depression scores and depressive symptoms are higher in patients with rheumatoid arthritis (RA) than among those with other rheumatic disorders; and to describe norms for the Arthritis Impact Measurement Scale (AIMS) depression scale. METHODS A 100% sample of all clinic visits of 6,153 consecutive patients with rheumatic disease seen in an outpatient rheumatic disease clinic during a 10-year period. 19,122 AIMS depression scores were utilized. For each patient an average depression score was calculated. Covariates included age, sex, education level, ethnic origin, and number of clinic visits. RESULTS RA depressive symptoms and depression scores did not differ from all other clinic patients (taken as a whole). Patients with fibromyalgia had significantly more abnormal scores. CONCLUSION Depression scores are not higher or depressive symptoms more common in patients with RA compared with other clinic patients. By every measure depression is increased in fibromyalgia. The notion that patients with RA have increased depression or are somehow more susceptible to depression is not supported by the data and should be abandoned.

Molecular reorientational dynamics of the neat ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate by measurement of 13C nuclear magnetic relaxation data.

Abstract: The reorientational dynamics of the ionic liquid 1-butyl-3-methyl-imidazolium hexafluorophosphate ([BMIM]PF 6 ) were studied over a wide range of temperatures by measurement of 13 C spin-lattice relaxation rates and NOE factors. The reorientational dynamics were evaluated by performing fits to the experimental relaxation data. Thus, the overall reorientational motion was described by a Cole - Davidson spectral density with a Vogel - Fulcher - Tammann temperature dependence of the correlation times. The reorientational motion of the butyl chain was modelled by a combination of the latter model for the overall motion with a Bloembergen - Purcell - Pound spectral density and an Arrhenius temperature dependence for the internal motion. Except for C2 in the aromatic ring, an additional reduction of the spectral density by the Lipari -Szabo model had to be employed. This reduction is a consequence of fast molecular motions before the rotational diffusion process becomes effective. The C2 atom did not exhibit this reduction, because the librational motion of the corresponding C2-H vector is severely hindered due to hydrogen bonding with the hexafluorophosphate anion. The observed dynamic features of the [BMIM] - cation confirm quantum-chemical structures obtained in a former study.

Simulations of quantum neural networks

Abstract: We explore by simulation ways in which an array of quantum dot molecules could serve as a quantum neural computer. First, we show that a single quantum dot molecule evolving in real time can act as a recurrent temporal quantum neural network. Inputs are prepared by fixing the initial states of a quantum dot molecule, and outputs determined by reading its value at a given time T later. The nodes of the network are the instantaneous states of the molecule at successive time slices. The nodes interact indirectly through their mutual interaction with local and phononic modes of the substrate. These modes can be preferentially excited optically, and, therefore, controlled externally. The number of excitations can thus be used as trainable “weight” parameters for a neural network. This network is shown to perform classical logic gates. By preparing the input state as a superposition state, multiple inputs can be encoded as a single initial state. Second, we simulate the possibility of a spatial, rather than temporal, design, as a Hopfield net. The network consists of a regular array of quantum dot molecules on a suitable substrate. The molecules interact indirectly as before, and, now, with each other directly through Coulombic interactions. Both of the quantum networks have none of the “wiring problems” of traditional neural nets: the necessary connections are supplied by the physical system itself. Computation is performed by the intrinsic physics of the physical system. The long range character of the phononic interactions takes the net beyond traditional local connectionist structures. The hypothesized increase in complexity and power, in going to the quantum regime, is demonstrated. We train the quantum Hopfield net using simultaneous recurrent backpropagation.

A Maximum Confidence Enhancement Based Sequential Sampling Scheme for Simulation-Based Design

Abstract: This paper presents a maximum confidence enhancement based sequential sampling approach for simulation-based design under uncertainty. In the proposed approach, the ordinary Kriging method is adopted to construct surrogate models for all constraints and thus Monte Carlo simulation (MCS) is able to be used to estimate reliability and its sensitivity with respect to design variables. A cumulative confidence level is defined to quantify the accuracy of reliability estimation using MCS based on the Kriging models. To improve the efficiency of proposed approach, a maximum confidence enhancement based sequential sampling scheme is developed to update the Kriging models based on the maximum improvement of the defined cumulative confidence level, in which a sample that produces the largest improvement of the cumulative confidence level is selected to update the surrogate models. Moreover, a new design sensitivity estimation approach based upon constructed Kriging models is developed to estimate the reliability sensitivity information with respect to design variables without incurring any extra function evaluations. This enables to compute smooth sensitivity values and thus greatly enhances the efficiency and robustness of the design optimization process. Two case studies are used to demonstrate the proposed methodology.Copyright © 2013 by ASME