Spatially Adaptive Stochastic Methods for Fluid-Structure Interactions Subject to Thermal Fluctuations in Domains with Complex Geometries

We develop stochastic mixed finite element methods for spatially adaptive simulations of fluid-structure interactions when subject to thermal fluctuations. To account for thermal fluctuations, we introduce a discrete fluctuation-dissipation balance condition to develop compatible stochastic driving fields for our discretization. We perform analysis that shows our condition is sufficient to ensure results consistent with statistical mechanics. We show the Gibbs-Boltzmann distribution is invariant under the stochastic dynamics of the semi-discretization. To generate efficiently the required stochastic driving fields, we develop a Gibbs sampler based on iterative methods and multigrid to generate fields with $O(N)$ computational complexity. Our stochastic methods provide an alternative to uniform discretizations on periodic domains that rely on Fast Fourier Transforms. To demonstrate in practice our stochastic computational methods, we investigate within channel geometries having internal obstacles and no-slip walls how the mobility/diffusivity of particles depends on location. Our methods extend the applicability of fluctuating hydrodynamic approaches by allowing for spatially adaptive resolution of the mechanics and for domains that have complex geometries relevant in many applications

Statistical analysis of variability properties of the Kepler blazar W2R 1926+42

We analyzed Kepler light curves of the blazar W2R 1926+42 that provided nearly continuous coverage from quarter 11 through quarter 17 (589 days between 2011 and 2013) and examined some of their flux variability properties. We investigate the possibility that the light curve is dominated by a large number of individual flares and adopt exponential rise and decay models to investigate the symmetry properties of flares. We found that those variations of W2R 1926+42 are predominantly asymmetric with weak tendencies toward positive asymmetry (rapid rise and slow decay). The durations (D) and the amplitudes (F0) of flares can be fit with log-normal distributions. The energy (E) of each flare is also estimated for the first time. There are positive correlations between logD and logE with a slope of 1.36, and between logF0 and logE with a slope of 1.12. Lomb-Scargle periodograms are used to estimate the power spectral density (PSD) shape. It is well described by a power law with an index ranging between -1.1 and -1.5. The sizes of the emission regions, R, are estimated to be in the range of 1.1*10^15 cm - 6.6*10^16 cm. The flare asymmetry is difficult to explain by a light travel time effect but may be caused by differences between the timescales for acceleration and dissipation of high-energy particles in the relativistic jet. A jet-in-jet model also could produce the observed log-normal distributions

Software Agents for Automated Transaction Negotiations: Implementation and Evaluation

Software agents have the potential to serve as effective surrogates of humans in conducting business transactions in an electronic commerce environment. The reported proceeding research aims to evaluate the performance of software agents in automated transaction negotiations. As part of this research, agents are being built using IBM aglets, and their performance evaluation within various experimental settings is currently underway

Predicting Medication Prescription Rankings with Medication Relation Network

Medication prescription rankings and demands prediction could benefit both medication consumers and pharmaceutical companies from various aspects. Our study predicts the medication prescription rankings focusing on patients’ medication switch and combination behavior, which is an innovative genre of medication knowledge that could be learned from unstructured patient generated contents. We first construct two supervised machine learning systems for medication references identification and medication relations classification from unstructured patient’s reviews. We further map the medication switch and combination relations into directed and undirected networks respectively. An adjusted transition in and out (ATIO) system is proposed for medication prescription rankings prediction. The proposed system demonstrates the highest positive correlation with actual medication prescription amounts comparing to other network-based measures. In order to predict the prescription demand changes, we compare four predictive regression models. The model incorporated the network-based measure from ATIO system achieve the lowest mean square errors