Using Discrete and Continuous Models to Solve Nanoporous Flow Optimization Problems

We consider using a discrete network model in combination with continuous nonlinear optimization models to solve the problem of optimizing channels in nanoporous materials. The problem and the hierarchical optimization algorithm are described in [2]. A key feature of the model is the fact that we use the edges of the finite element grid as the locations of the channels. The focus here is on the use of the discrete model within that algorithm. We develop several approximations to the relevant flow and a greedy algorithm for quickly generating a "good" tree connecting all of the nodes in the finite-element mesh to a designated root node. We also consider Metropolis-Hastings (MH) improvements to the greedy result. We consider both a regular triangulation and a Delaunay triangulation of the region, and present some numerical results.     

The K−p charge exchange and elastic scattering reactions at 4.2 GeV/c

In this letter results are presented on the reactions $\text{K}{}^{\mathrm{?}}\text{p →}\text{K}{}^0\text{n and K}{}^{\mathrm{?}}\text{p → K}{}^{\mathrm{?}}\text{p}$ from a high statistics CERN 2-metre hydrogen bubble chamber exposure at 4.15 GeV/c. The behaviour of the differential cross section as a function of four-momentum transfer shows remarkable similarities between the two reactions studied. From a comparison of our data with K⁺p elastic scattering at 4.27 GeV/c we draw some conclusions concerning the magnitude of the contributing amplitudes.     

Improving protein identification from peptide mass fingerprinting through a parameterized multi-level scoring algorithm and an optimized peak detection

We have developed a new algorithm to identify proteins by means of peptide mass fingerprinting. Starting from the matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) spectra and environmental data such as species, isoelectric point and molecular weight, as well as chemical modifications or number of missed cleavages of a protein, the program performs a fully automated identification of the protein. The first step is a peak detection algorithm, which allows precise and fast determination of peptide masses, even if the peaks are of low intensity or they overlap. In the second step the masses and environmental data are used by the identification algorithm to search in protein sequence databases (SWISS-PROT and/or TrEMBL) for protein entries that match the input data. Consequently, a list of candidate proteins is selected from the database, and a score calculation provides a ranking according to the quality of the match. To define the most discriminating scoring calculation we analyzed the respective role of each parameter in two directions. The first one is based on filtering and exploratory effects, while the second direction focuses on the levels where the parameters intervene in the identification process. Thus, according to our analysis, all input parameters contribute to the score, however with different weights. Since it is difficult to estimate the weights in advance, they have been computed with a generic algorithm, using a training set of 91 protein spectra with their environmental data. We tested the resulting scoring calculation on a test set of ten proteins and compared the identification results with those of other peptide mass fingerprinting programs.     

Product of hyperfunctions on the circle

Let φ and ψ be two hyperfunctions on the circle which have disjoint support. We interpret in terms of Fourier coefficients the fact that their product, defined in the sense of sheaf theory, vanishes.     

Dehalogenation of α -haloketones and vic-dibromides with nickel boride

α-Haloketones and $\text{vic}$-dibromides are converted to the corresponding ketones and alkenes respectively with nickel boride generated $\text{in situ}$ from sodium borohydride and nickel chloride.