The time-dependent Ginzburg-Landau model of superconductivity is examined in the high-x, high magnetic field setting. This work generalizes the previous result for the steady-state model with a constant applied magnetic field. The significance of this generalization lies in the ability to incorporate the effects of both the applied magnetic field and applied current or voltage. Thus, it is possible to use the simplified setting obtained in this paper to study the motion and "pinning" of vortices in the presence of an applied current and a variable applied field. The results within are obtained via a formal asymptotic expansion of the Ginzburg-Landau equations in terms of , which yields a simplified system of leading-order equations. The formal asymptotic expansion is then justified by showing the solution to the full time-dependent Ginzburg-Landau equations converges to the solution of the leading-order equations as c. Computational results are also given that show the simplified leading-order model is indeed an accurate approximation to the solution of the full system of equations, even for moderate values of .

We present results of molecular dynamics (MD) computer simulations of hexane (C6H14) adlayers physisorbed onto a graphite substrate for coverages in the range 0.5 < or = rho < or = 1 monolayers. The hexane molecules are simulated with explicit hydrogens, and the graphite substrate is modeled as an all-atom structure having six graphene layers. At coverages above about rho congruent with 0.9 the low-temperature herringbone solid loses its orientational order at T(1) = 140 +/- 3 K. At rho = 0.878, the system presents vacancy patches and T(1) decreases to ca. 100 K. As coverage decreases further, the vacancy patches become larger and by rho = 0.614 the solid is a connected network of randomly oriented islands and there is no global herringbone order-disorder transition. In all cases we observe a weak nematic mespohase. The melting temperature for our explicit-hydrogen model is T(2) = 160 +/- 3 K and falls to ca. 145 K by rho = 0.614 (somewhat lower than seen in experiment). The dynamics seen in the fully atomistic model agree well with experiment, as the molecules remain overall flat on the substrate in the solid phase and do not show anomalous tilting behavior at any phase transition observed in earlier simulations in the unified atom (UA) approximation. Energetics and structural parameters also are more reasonable and, collectively, the results from the simulations in this work demonstrate that the explicit-hydrogen model of hexane is substantially more realistic than the UA approximation.

Pnictine derivatives can behave as both 2e(-) donors (Lewis bases) and 2e(-) acceptors (Lewis acids). As prototypical ligands in the coordination chemistry of transition metals, amines and phosphines also form complexes with p-block Lewis acids, including a variety of pnictogen-centered acceptors. The inherent Lewis acidity of pnictogen centers can be enhanced by the introduction of a cationic charge, and this feature has been exploited in recent years in the development of compounds resulting from coordinate Pn-Pn and Pn-Pn' interactions. These compounds offer the unusual opportunity for homoatomic coordinate bonding and the development of complexes that possess a lone pair of electrons at the acceptor center. This Review presents new directions in the systematic extension of coordination chemistry from the transition series into the p-block.

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.

Heterogeneous Adaptable Reconfigurable Networked SystemS (HARNESS) is an experimental metacomputing system [L. Smarr, C.E. Catlett, Communications of the ACM 35 (6) (1992) 45-52] built around the services of a highly customizable and reconfigurable Distributed Virtual Machine (DVM). The successful experience of the HARNESS design team with the Parallel Virtual Machine (PVM) project has taught us both the features which make the DVM model so valuable to parallel programmers and the limitations imposed by the PVM design. HARNESS seeks to remove some of those limitations by taking a totally different approach to creating and modifying a DVM.