Nanostructured core-shell Ni deposition on SiC particles by alkaline electroless coating

In this study, core-shell nanostructured nickel formation on silicon carbide (SiC) ceramic powders was achieved through the electroless deposition method using alkaline solutions. To produce a nano core-shell Ni deposition on the SiC surfaces, process parameters such as pH values, the type of reducer material, deposition temperature, stirring rate and activation procedure among others were determined. Full coverage of core-shell nickel structures on SiC surfaces was achieved with a grain size of between 100 and 300 nm, which was approximately the same deposition thickness on the SiC surfaces. The surface morphology of the coated SiC particles showed a homogenous distribution of nanostructured nickel grains characterized by scanning electron microscopy and X-ray diffraction techniques. The nanostructures of the crystalline Ni coatings were observed to be attractive for achieving both good bonding and dense structure. The thin core shell-structure of Ni on the SiC surfaces was assessed as a beneficial reinforcement for possible metal matrix composite manufacturing.     

Compressed images for affinity prediction (CIFAP): a study on predicting binding affinities for checkpoint kinase 1 protein inhibitors

Analyses of known protein–ligand interactions play an important role in designing novel and efficient drugs, contributing to drug discovery and development. Recently, machine learning methods have proven useful in the design of novel drugs, which utilize intelligent techniques to predict the outcome of unknown protein–ligand interactions by learning from the physical and geometrical properties of known protein–ligand interactions. The aim of this study is to work through a specific example of a novel computational method, namely compressed images for affinity prediction (CIFAP), in which binding affinities for structurally related ligands in complexes with human checkpoint kinase 1 (CHK1) are predicted. The CIFAP algorithm presented here relates published pIC ₅₀ values of 57 compounds, derived from a thienopyridine pharmacophore, in complexes with CHK1 to their two‐dimensional (2D) electrostatic potential images compressed in orthogonal dimensions. Patterns obtained from the 2D images are then used as inputs in regression and learning algorithms such as support vector regression (SVR) and adaptive neuro‐fuzzy inference system (ANFIS) methods to validate the experimental pIC ₅₀ values. This study revealed that the 2D image pixels in the vicinity of bound ligand surfaces provide more relevant information to make correlations with the empirical pIC ₅₀ values. As compared with ANFIS, SVR gave rise to the lowest root mean square errors and the greatest correlations, suggesting that SVR could be a plausible choice of machine learning methods in predicting binding affinities by CIFAP. Copyright © 2013 John Wiley & Sons, Ltd.     

BV Estimates in Optimal Transportation and Applications

In this paper we study the BV regularity for solutions of certain variational problems in Optimal Transportation. We prove that the Wasserstein projection of a measure with BV density on the set of measures with density bounded by a given BV function f is of bounded variation as well and we also provide a precise estimate of its BV norm. Of particular interest is the case f = 1, corresponding to a projection onto a set of densities with an L ^∞ bound, where we prove that the total variation decreases by projection. This estimate and, in particular, its iterations have a natural application to some evolutionary PDEs as, for example, the ones describing a crowd motion. In fact, as an application of our results, we obtain BV estimates for solutions of some non-linear parabolic PDE by means of optimal transportation techniques. We also establish some properties of the Wasserstein projection which are interesting in their own right, and allow, for instance, for the proof of the uniqueness of such a projection in a very general framework.     

Integrated capacity and inventory management with capacity acquisition lead times

We model a make-to-stock production system that utilizes permanent and contingent capacity to meet non-stationary stochastic demand, where a constant lead time is associated with the acquisition of contingent capacity. We determine the structure of the optimal solution concerning both the operational decisions of integrated inventory and flexible capacity management, and the tactical decision of determining the optimal permanent capacity level. Furthermore, we show that the inventory (either before or after production), the pipeline contingent capacity, the contingent capacity to be ordered, and the permanent capacity are economic substitutes. We also show that the stochastic demand variable and the optimal contingent capacity acquisition decisions are economic complements. Finally, we perform numerical experiments to evaluate the value of utilizing contingent capacity and to study the effects of capacity acquisition lead time, providing useful managerial insights.     

On nonlinear cross-diffusion systems: an optimal transport approach

We study a nonlinear, degenerate cross-diffusion model which involves two densities with two different drift velocities. A general framework is introduced based on its gradient flow structure in Wasserstein space to derive a notion of discrete-time solutions. Its continuum limit, due to the possible mixing of the densities, only solves a weaker version of the original system. In one space dimension, we find a stable initial configuration which allows the densities to be segregated. This leads to the evolution of a stable interface between the two densities, and to a stronger convergence result to the continuum limit. In particular derivation of a standard weak solution to the system is available. We also study the incompressible limit of the system, which addresses transport under a height constraint on the total density. In one space dimension we show that the problem leads to a two-phase Hele-Shaw type flow.     

Photophysical and Complexation Properties of Benzenesulfonamide Derivatives with Different Donor and Acceptor Moieties

Pyrrolobenzosulfonamide, indolobenzosulfonamide and carbazolobenzosulfonamide derivatives with different acceptor groups were synthesized and their photophysical properties were compared. The electron donor linking sites are found to influence the emission characteristics of these compounds while acceptor linking sites have no noticeable effects on the spectral properties. P2-A5 which is a C-C linked pyrrole derivative exhibited different spectral properties from the C-N linked pyrrole derivatives. The complexation properties of the molecules were also investigated employing Na (I), Ca (II), Li (I), Mg (II), Zn (II) and Cu (II) ions.     

An Efficient Genetic Algorithm for the p-Median Problem

We propose a new genetic algorithm for a well-known facility location problem. The algorithm is relatively simple and it generates good solutions quickly. Evolution is facilitated by a greedy heuristic. Computational tests with a total of 80 problems from four different sources with 100 to 1,000 nodes indicate that the best solution generated by the algorithm is within 0.1% of the optimum for 85% of the problems. The coding effort and the computational effort required are minimal, making the algorithm a good choice for practical applications requiring quick solutions, or for upper-bound generation to speed up optimal algorithms.     

Evidence for an instability near twice the fermi wave vector in the low electronic density liquid metal Li(NH3)4

We report high-resolution inelastic x-ray scattering measurements in the metallic liquid Li(NH3)4, which to a good approximation can be treated as a dilute alkali metal. We see a well-defined excitation out to large momentum transfers. This excitation shows a strong softening at wave vectors near the first peak in the structure factor, which occurs near twice the Fermi momentum.